System

Abstract

To provide a system that generates AI with a personality based on past data and responds to a user.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, and an interface unit. The collection unit collects past data. The generation unit analyzes the data collected by the collection unit and generates AI with a past personality. The interface unit causes the AI generated by the generation unit to respond to a user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem that it was not able to adequately generate AI with a personality based on past data and respond to the user.

[0005] The system of the embodiment aims to generate an AI with a personality based on past data and respond to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an interface unit. The collection unit collects past data. The generation unit analyzes the data collected by the collection unit and generates an AI with a past personality. The interface unit causes the AI ​​generated by the generation unit to respond to the user. Effect of the Invention

[0007] The system of the embodiment can generate an AI with a personality based on past data and respond to the user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for generating an AI with a personality based on past data. This system collects past data, and the generation AI analyzes the data to generate an AI with a personality from the past. The generated AI has knowledge and values ​​from past eras, and can respond to modern users from a past perspective. For example, the system inputs a large amount of past data such as history books and historical backgrounds. At this time, data focusing on a specific era or region is collected and input to the generation AI. For example, data related to Japan in the Edo period is collected and input to the generation AI. Next, the generation AI analyzes the input data and generates an AI with a personality from the past. The generation AI forms a personality with knowledge and values ​​from that era based on the past data. For example, an AI with knowledge and values ​​from the Edo period is generated based on data related to Japan in the Edo period. The generated AI can respond to modern users from a past perspective. For example, when a user asks a question about life in the Edo period, the generated AI responds based on the knowledge and values ​​of the Edo period. This allows the user to learn about the knowledge and values ​​of past eras. This mechanism allows an AI with knowledge and values ​​from past eras to be created, and a response from a past perspective to modern users. This allows users to learn about the knowledge and values ​​of past eras, which is expected to contribute to the promotion of historical education and cultural understanding. This allows the system to generate an AI with a personality based on past data and respond to the user from a past perspective.

[0029] The system according to the embodiment includes a collection unit, a generation unit, and an interface unit. The collection unit collects past data. The past data includes, for example, text data, audio data, video data, etc., but is not limited to such examples. The collection unit, for example, builds a database related to history books and historical backgrounds, and selects reliable data. For example, the collection unit evaluates reliability based on the author and publication year of the history book, and selects reliable data. The collection unit can also cross-reference multiple materials and select matching information as reliable data. Furthermore, the collection unit can also select reliable data with reference to reviews and evaluations by experts. The generation unit uses a generation AI to analyze the data collected by the collection unit, and generates an AI with a past personality. The generation unit, for example, uses natural language processing technology to analyze text data, and forms a personality with knowledge and values ​​of the past era. The generation unit can also use image analysis technology to analyze video data, and form a personality based on visual information of the past era. Furthermore, the generation unit can also use voice analysis technology to analyze voice data, and form a personality based on voice information of the past era. For example, the generation unit generates an AI with knowledge and values ​​of the Edo period based on data about Japan in the Edo period. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. The interface unit allows the AI ​​generated by the generation unit to respond to the user. The interface unit provides multiple formats, such as a chat format and a voice assistant format. For example, the interface unit interacts with the user through a text chat. The interface unit can also interact with the user through a voice assistant. Furthermore, the interface unit can also interact with the user through a video chat. As a result, the system according to the embodiment can generate an AI with a personality based on past data and respond to the user from a past perspective. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0030] The collection department collects past data. Examples of past data include, but are not limited to, text data, audio data, and video data. The collection department, for example, builds a database on history books and historical backgrounds and selects reliable data. Specifically, the collection department evaluates reliability based on the author and publication year of the history book and selects reliable data. For example, if the author of the history book is well-known in the field and the publication year is relatively recent, the data is determined to be reliable. The collection department can also cross-reference multiple materials and select matching information as reliable data. For example, if different authors describe the same event, the information is determined to be reliable. Furthermore, the collection department can select reliable data with reference to expert reviews and evaluations. Expert reviews are an important means of evaluating the accuracy and reliability of data, which allows the collection department to select higher quality data. The collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the generation department and interface department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The generation unit uses the generation AI to analyze the data collected by the collection unit and generate an AI with a past personality. The generation unit, for example, uses natural language processing technology to analyze text data and form a personality with knowledge and values ​​of past eras. Specifically, the generation unit uses natural language processing technology to extract language expressions, grammar, and vocabulary of past eras from text data and builds a language model of AI based on the extracted language expressions, grammar, and vocabulary. The generation unit can also analyze video data using image analysis technology and form a personality based on visual information of past eras. For example, visual information such as clothing, buildings, and scenery of the time is extracted from the video data and a visual model of AI is built based on the extracted visual information. The generation unit can also analyze audio data using voice analysis technology and form a personality based on audio information of past eras. For example, the generation unit extracts the pronunciation, intonation, and speaking characteristics of the time from the audio data and builds a voice model of AI based on the extracted visual information. The generation unit can generate an AI with a more realistic and reliable past personality by combining these technologies. For example, the generation unit generates an AI with knowledge and values ​​of the Edo period based on data about Japan in the Edo period. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. This allows the generation unit to generate a highly accurate AI based on past data and respond to the user from a past perspective.

[0032] In the interface unit, the AI ​​generated by the generation unit responds to the user. The interface unit provides multiple formats, such as a chat format and a voice assistant format. Specifically, the interface unit interacts with the user through text chat. The user asks the AI ​​a question through the text chat, and the AI ​​responds based on past data. The interface unit can also interact with the user through a voice assistant. The user asks the AI ​​a question through the voice assistant, and the AI ​​responds by voice. Furthermore, the interface unit can also interact with the user through video chat. In video chat, the AI ​​interacts with the user through video generated by the AI, providing a more realistic interaction experience. The interface unit can flexibly switch between these interaction formats and provide the optimal interaction format according to the user's needs. For example, if the user prefers text chat, the interface unit provides text chat, and if the user prefers voice assistant, the interface unit provides voice assistant. In addition, the interface unit can collect user feedback and continuously improve the accuracy and effectiveness of the interaction content. For example, the interface unit can review the AI's response content and interaction format based on user feedback to provide a better interaction experience. This enables the interface unit to provide the user with a prompt and appropriate response and effectively convey information from a past perspective.

[0033] The collection department can build a database of historical books and historical backgrounds and select reliable data. The collection department evaluates reliability based on, for example, the author and publication year of the historical book, and selects reliable data. For example, the collection department evaluates reliability based on whether the author of the historical book is an expert and whether the publication year is the latest. The collection department can also cross-reference multiple materials and select matching information as reliable data. For example, the collection department compares multiple historical books and documents, and selects matching information as reliable data. Furthermore, the collection department can also select reliable data with reference to reviews and evaluations by experts. For example, the collection department selects reliable data with reference to reviews by historians and experts. This improves the accuracy of the generated AI by collecting reliable data.

[0034] The generation unit can analyze past data and interpret it according to a modern context. The generation unit can, for example, analyze text data using natural language processing technology and interpret it according to a modern context. For example, the generation unit can analyze past data and interpret it according to a modern social situation and cultural background. The generation unit can also analyze video data using image analysis technology and interpret it according to a modern context. For example, the generation unit can analyze past video data and interpret it according to a modern technology trend and visual expression. The generation unit can also analyze voice data using voice analysis technology and interpret it according to a modern context. For example, the generation unit can analyze past voice data and interpret it according to a modern language and pronunciation. As a result, an appropriate response can be given to the user by interpreting past data according to a modern context.

[0035] The interface unit can provide at least one of a chat format or a voice assistant format. The interface unit interacts with the user through, for example, a text chat. For example, the interface unit responds to a user's question in text through the text chat. The interface unit can also interact with the user through a voice assistant. For example, the interface unit analyzes the user's voice using voice recognition technology and responds in voice. Furthermore, the interface unit can also interact with the user through a video chat. For example, the interface unit analyzes the user's facial expressions and gestures through the video chat and provides visual feedback. As a result, by providing multiple formats, the user can select an interface that is easy to use.

[0036] The collection department can evaluate the reliability of data related to a specific era or region at the time of collection and select highly reliable data. The collection department evaluates reliability based on, for example, the author and publication year of the history book and selects highly reliable data. For example, the collection department evaluates reliability based on whether the author of the history book is an expert and whether the publication year is the latest. The collection department can also cross-reference multiple materials and select matching information as highly reliable data. For example, the collection department compares multiple history books and documents and selects matching information as highly reliable data. Furthermore, the collection department can select highly reliable data with reference to reviews and evaluations by experts. For example, the collection department selects highly reliable data with reference to reviews by historians and experts. This selection of highly reliable data improves the accuracy of the generated AI.

[0037] The collection unit can automatically record the source of data when collecting the data, making it possible to refer to it later. For example, the collection unit automatically records source information (author, publication year, publisher, etc.) when collecting data. For example, the collection unit adds the source information as metadata when collecting data. Also, the collection unit can add the source information as metadata when saving the collected data in a database. Furthermore, the collection unit can make it possible to display the source information when a user refers to the data. For example, the collection unit provides a function to display the source information when a user searches for data. In this way, by recording the source of the data, it becomes easier to refer to it later.

[0038] The collection unit can customize the range of data collection based on the user's interests and concerns at the time of collection. For example, if the user is interested in a particular era, the collection unit preferentially collects data related to that era. For example, if the user is interested in the Edo period, the collection unit preferentially collects data related to the Edo period. In addition, if the user is interested in a particular region, the collection unit can also preferentially collect data related to that region. For example, if the user is interested in Europe, the collection unit preferentially collects data related to Europe. In addition, if the user is interested in a particular theme (e.g., science, art, politics), the collection unit can also preferentially collect data related to that theme. For example, if the user is interested in science, the collection unit preferentially collects data related to science. This allows more appropriate data to be collected by customizing the range of data collection based on the user's interests and concerns.

[0039] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects historical data related to the area where the user is currently located. For example, when the user is in Japan, the collection unit preferentially collects historical data related to Japan. In addition, when the user is traveling, the collection unit can also preferentially collect historical data related to the travel destination. For example, when the user is traveling in Europe, the collection unit preferentially collects historical data related to Europe. Furthermore, when the user is interested in a specific area, the collection unit can also preferentially collect data related to that area. For example, when the user is interested in Africa, the collection unit preferentially collects data related to Africa. In this way, by collecting data in consideration of the geographical location information of the user, more relevant data can be collected.

[0040] At the time of generation, the generation unit can analyze past data and generate an AI that reflects the values ​​of a particular era or region. For example, the generation unit generates an AI with the values ​​of the era based on data related to Japan in the Edo period. For example, the generation unit analyzes data related to Japan in the Edo period and generates an AI that reflects the cultural background and social norms of the era. The generation unit can also generate an AI with the values ​​of the era based on data related to Europe in the Renaissance period. For example, the generation unit analyzes data related to Europe in the Renaissance period and generates an AI that reflects the development of art and science in the era. Furthermore, the generation unit can generate an AI with the values ​​of the era based on data related to ancient Greece. For example, the generation unit analyzes data related to ancient Greece and generates an AI that reflects the philosophical and political ideas of the era. This allows for more appropriate responses by generating an AI that reflects the values ​​of a particular era or region.

[0041] The generation unit can adjust the AI's personality at the time of generation so that it can adapt to a modern context. For example, the generation unit adjusts the AI ​​generated based on past data so that it has knowledge of modern technology and culture. For example, the generation unit analyzes past data and adds information on modern technological trends and cultural backgrounds to adapt the AI's personality to a modern context. The generation unit can also adjust the AI ​​with past values ​​so that it can adapt to modern ethics and social norms. For example, the generation unit analyzes past data and adds information on modern ethics and social norms to adapt the AI's personality to a modern context. Furthermore, the generation unit can adjust the AI ​​with knowledge of past eras so that it can adapt to communication with modern users. For example, the generation unit analyzes past data and adds information on modern communication styles and languages ​​to adapt the AI's personality to a modern context. This allows the AI's personality to be adjusted to adapt to a modern context, making it possible to give an appropriate response to the user.

[0042] The generation unit can generate an AI with diverse perspectives by combining data from multiple eras and regions at the time of generation. For example, the generation unit can generate an AI with a composite personality by combining data from Japan in the Edo period and Europe in the Renaissance period. For example, the generation unit can analyze data on Japan in the Edo period and data on Europe in the Renaissance period to generate an AI with the knowledge and values ​​of each era. The generation unit can also generate an AI with a composite personality by combining data from ancient Greece and medieval China. For example, the generation unit can analyze data on ancient Greece and data on medieval China to generate an AI with the knowledge and values ​​of each era. Furthermore, the generation unit can generate an AI with a composite personality by combining data from modern America and ancient Egypt. For example, the generation unit can analyze data on modern America and data on ancient Egypt to generate an AI with the knowledge and values ​​of each era. In this way, by combining data from multiple eras and regions, an AI with a more diverse perspective can be generated.

[0043] The generation unit can generate a more personalized AI by referring to the user's past interaction history at the time of generation. The generation unit generates a personalized AI based on, for example, the era or region in which the user has shown interest in the past. For example, if the user has shown interest in the Edo period in the past, the generation unit generates an AI based on data related to the Edo period. The generation unit can also generate an AI that responds more appropriately based on the content of the user's past questions. For example, the generation unit analyzes the content of questions the user has asked in the past and adjusts the AI's response style based on the content. Furthermore, the generation unit can analyze the user's past interaction history and generate an AI with optimal personality characteristics. For example, the generation unit analyzes the user's past dialogue log and adjusts the AI's personality characteristics based on the user's preferences and interests. In this way, a more personalized AI can be generated by referring to the user's past interaction history.

[0044] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit preferentially displays an interface design that the user has used in the past. For example, the interface unit records an interface design that the user has used in the past and preferentially displays that design. The interface unit can also select the most efficient display method from the user's past operation history. For example, the interface unit analyzes the user's past operation log and selects the most efficient display method. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. For example, the interface unit preferentially displays functions that the user has used frequently in the past. In this way, a more appropriate display method can be selected by referring to the user's past operation history.

[0045] The interface unit can select an optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, the interface unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, the interface unit provides a display method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. For example, the interface unit provides a display method optimized for the small screen of the smartwatch. This allows a more appropriate display method to be selected by considering the device information of the user.

[0046] The interface unit can select an optimal display method in consideration of the geographical location information of the user when displaying the interface. The interface unit, for example, preferentially displays information related to the area where the user is currently located. For example, when the user is in Japan, the interface unit preferentially displays information related to Japan. Furthermore, when the user is traveling, the interface unit can also preferentially display information related to the travel destination. For example, when the user is traveling in Europe, the interface unit preferentially displays information related to Europe. Furthermore, when the user is interested in a specific area, the interface unit can also preferentially display information related to that area. For example, when the user is interested in Africa, the interface unit preferentially displays information related to Africa. In this way, a more appropriate display method can be selected by considering the geographical location information of the user.

[0047] The interface unit can analyze the user's social media activity and display related information when the interface is displayed. The interface unit displays related data based on, for example, information shared by the user on social media. For example, the interface unit analyzes the content of posts shared by the user on social media and displays related data. The interface unit can also analyze the user's social media activity history and display information that may be of interest to the user. For example, the interface unit displays related information based on an analysis of the user's past posts and followers. Furthermore, the interface unit can display information related to accounts the user follows on social media. For example, the interface unit analyzes the content of posts from accounts the user follows and displays related information. In this way, related information can be displayed by analyzing the user's social media activity.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can customize the range of data collection based on the user's interests and concerns. For example, if the user is interested in a particular era, the collection unit prioritizes collecting data related to that era. For example, if the user is interested in the Edo period, the collection unit prioritizes collecting data related to the Edo period. In addition, if the user is interested in a particular region, the collection unit can also prioritize collecting data related to that region. For example, if the user is interested in Europe, the collection unit prioritizes collecting data related to Europe. In addition, if the user is interested in a particular theme (e.g., science, art, politics), the collection unit can also prioritize collecting data related to that theme. For example, if the user is interested in science, the collection unit prioritizes collecting data related to science. This allows more appropriate data to be collected by customizing the range of data collection based on the user's interests and concerns.

[0050] The generation unit can generate an AI with diverse perspectives by combining data from multiple eras and regions at the time of generation. For example, data from Japan in the Edo period and Europe in the Renaissance period can be combined to generate an AI with a composite personality. For example, the generation unit analyzes data on Japan in the Edo period and data on Europe in the Renaissance period to generate an AI with the knowledge and values ​​of each era. The generation unit can also generate an AI with a composite personality by combining data from ancient Greece and medieval China. For example, the generation unit analyzes data on ancient Greece and data on medieval China to generate an AI with the knowledge and values ​​of each era. Furthermore, the generation unit can generate an AI with a composite personality by combining data from modern America and ancient Egypt. For example, the generation unit analyzes data on modern America and data on ancient Egypt to generate an AI with the knowledge and values ​​of each era. This allows AI with more diverse perspectives to be generated by combining data from multiple eras and regions.

[0051] At the time of generation, the generation unit can generate a more personalized AI by referring to the user's past interaction history. For example, the generation unit generates a personalized AI based on the era or region in which the user has shown interest in the past. For example, if the user has shown interest in the Edo period in the past, the generation unit generates an AI based on data related to the Edo period. The generation unit can also generate an AI that responds more appropriately based on the content of the user's past questions. For example, the generation unit analyzes the content of questions asked by the user in the past and adjusts the response style of the AI ​​based on the content. Furthermore, the generation unit can analyze the user's past interaction history and generate an AI with optimal personality characteristics. For example, the generation unit analyzes the user's past dialogue log and adjusts the personality characteristics of the AI ​​based on the user's preferences and interests. In this way, a more personalized AI can be generated by referring to the user's past interaction history.

[0052] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit preferentially displays an interface design that the user has used in the past. For example, the interface unit records an interface design that the user has used in the past and preferentially displays that design. The interface unit can also select the most efficient display method from the user's past operation history. For example, the interface unit analyzes the user's past operation log and selects the most efficient display method. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. For example, the interface unit preferentially displays functions that the user has used frequently in the past. In this way, a more appropriate display method can be selected by referring to the user's past operation history.

[0053] The interface unit can select an optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, the interface unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, the interface unit provides a display method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. For example, the interface unit provides a display method optimized for the small screen of the smartwatch. This makes it possible to select a more appropriate display method by considering the device information of the user.

[0054] The interface unit can select an optimal display method in consideration of the geographical location information of the user when displaying the interface. The interface unit, for example, preferentially displays information related to the area where the user is currently located. For example, when the user is in Japan, the interface unit preferentially displays information related to Japan. Furthermore, when the user is traveling, the interface unit can also preferentially display information related to the travel destination. For example, when the user is traveling in Europe, the interface unit preferentially displays information related to Europe. Furthermore, when the user is interested in a specific area, the interface unit can also preferentially display information related to that area. For example, when the user is interested in Africa, the interface unit preferentially displays information related to Africa. In this way, a more appropriate display method can be selected by considering the geographical location information of the user.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection department collects past data. Past data includes text data, audio data, video data, etc. The collection department builds a database on historical books and historical background, and selects reliable data. For example, reliability is evaluated based on the author and publication year of the historical book, and matching information is selected as reliable data by cross-referencing multiple sources. Reliable data can also be selected with reference to expert reviews and evaluations. Step 2: The generation unit analyzes the data collected by the collection unit and generates an AI with a personality from the past. The generation unit analyzes the text data using natural language processing technology and forms a personality with the knowledge and values ​​of past eras. It can also analyze video data using image analysis technology and form a personality based on visual information from past eras. It can also analyze audio data using audio analysis technology and form a personality based on audio information from past eras. For example, based on data about Japan in the Edo period, an AI with the knowledge and values ​​of the Edo period is generated. Step 3: The interface unit uses the AI ​​generated by the generation unit to respond to the user. The interface unit provides multiple formats, such as chat and voice assistant formats. For example, it interacts with the user through text chat. It can also interact with the user through a voice assistant. It can also interact with the user through video chat.

[0057] (Example 2) The system according to the embodiment of the present invention is a system for generating an AI with a personality based on past data. This system collects past data, and the generation AI analyzes the data to generate an AI with a personality from the past. The generated AI has knowledge and values ​​from past eras, and can respond to modern users from a past perspective. For example, the system inputs a large amount of past data such as history books and historical backgrounds. At this time, data focusing on a specific era or region is collected and input to the generation AI. For example, data related to Japan in the Edo period is collected and input to the generation AI. Next, the generation AI analyzes the input data and generates an AI with a personality from the past. The generation AI forms a personality with knowledge and values ​​from that era based on the past data. For example, an AI with knowledge and values ​​from the Edo period is generated based on data related to Japan in the Edo period. The generated AI can respond to modern users from a past perspective. For example, when a user asks a question about life in the Edo period, the generated AI responds based on the knowledge and values ​​of the Edo period. This allows the user to learn about the knowledge and values ​​of past eras. This mechanism allows an AI with knowledge and values ​​from past eras to be created, and a response from a past perspective to modern users. This allows users to learn about the knowledge and values ​​of past eras, which is expected to contribute to the promotion of historical education and cultural understanding. This allows the system to generate an AI with a personality based on past data and respond to the user from a past perspective.

[0058] The system according to the embodiment includes a collection unit, a generation unit, and an interface unit. The collection unit collects past data. The past data includes, for example, text data, audio data, video data, etc., but is not limited to such examples. The collection unit, for example, builds a database related to history books and historical backgrounds, and selects reliable data. For example, the collection unit evaluates reliability based on the author and publication year of the history book, and selects reliable data. The collection unit can also cross-reference multiple materials and select matching information as reliable data. Furthermore, the collection unit can also select reliable data with reference to reviews and evaluations by experts. The generation unit uses a generation AI to analyze the data collected by the collection unit, and generates an AI with a past personality. The generation unit, for example, uses natural language processing technology to analyze text data, and forms a personality with knowledge and values ​​of the past era. The generation unit can also use image analysis technology to analyze video data, and form a personality based on visual information of the past era. Furthermore, the generation unit can also use voice analysis technology to analyze voice data, and form a personality based on voice information of the past era. For example, the generation unit generates an AI with knowledge and values ​​of the Edo period based on data about Japan in the Edo period. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. The interface unit allows the AI ​​generated by the generation unit to respond to the user. The interface unit provides multiple formats, such as a chat format and a voice assistant format. For example, the interface unit interacts with the user through a text chat. The interface unit can also interact with the user through a voice assistant. Furthermore, the interface unit can also interact with the user through a video chat. As a result, the system according to the embodiment can generate an AI with a personality based on past data and respond to the user from a past perspective. A part or all of the above-mentioned processing in the interface unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0059] The collection department collects past data. Examples of past data include, but are not limited to, text data, audio data, and video data. The collection department, for example, builds a database on history books and historical backgrounds and selects reliable data. Specifically, the collection department evaluates reliability based on the author and publication year of the history book and selects reliable data. For example, if the author of the history book is well-known in the field and the publication year is relatively recent, the data is determined to be reliable. The collection department can also cross-reference multiple materials and select matching information as reliable data. For example, if different authors describe the same event, the information is determined to be reliable. Furthermore, the collection department can select reliable data with reference to expert reviews and evaluations. Expert reviews are an important means of evaluating the accuracy and reliability of data, which allows the collection department to select higher quality data. The collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the generation department and interface department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0060] The generation unit uses the generation AI to analyze the data collected by the collection unit and generate an AI with a past personality. The generation unit, for example, uses natural language processing technology to analyze text data and form a personality with knowledge and values ​​of past eras. Specifically, the generation unit uses natural language processing technology to extract language expressions, grammar, and vocabulary of past eras from text data and builds a language model of AI based on the extracted language expressions, grammar, and vocabulary. The generation unit can also analyze video data using image analysis technology and form a personality based on visual information of past eras. For example, visual information such as clothing, buildings, and scenery of the time is extracted from the video data and a visual model of AI is built based on the extracted visual information. The generation unit can also analyze audio data using voice analysis technology and form a personality based on audio information of past eras. For example, the generation unit extracts the pronunciation, intonation, and speaking characteristics of the time from the audio data and builds a voice model of AI based on the extracted visual information. The generation unit can generate an AI with a more realistic and reliable past personality by combining these technologies. For example, the generation unit generates an AI with knowledge and values ​​of the Edo period based on data about Japan in the Edo period. Some or all of the above-mentioned processes in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. This allows the generation unit to generate a highly accurate AI based on past data and respond to the user from a past perspective.

[0061] In the interface unit, the AI ​​generated by the generation unit responds to the user. The interface unit provides multiple formats, such as a chat format and a voice assistant format. Specifically, the interface unit interacts with the user through text chat. The user asks the AI ​​a question through the text chat, and the AI ​​responds based on past data. The interface unit can also interact with the user through a voice assistant. The user asks the AI ​​a question through the voice assistant, and the AI ​​responds by voice. Furthermore, the interface unit can also interact with the user through video chat. In video chat, the AI ​​interacts with the user through video generated by the AI, providing a more realistic interaction experience. The interface unit can flexibly switch between these interaction formats and provide the optimal interaction format according to the user's needs. For example, if the user prefers text chat, the interface unit provides text chat, and if the user prefers voice assistant, the interface unit provides voice assistant. In addition, the interface unit can collect user feedback and continuously improve the accuracy and effectiveness of the interaction content. For example, the interface unit can review the AI's response content and interaction format based on user feedback to provide a better interaction experience. This enables the interface unit to provide the user with a prompt and appropriate response and effectively convey information from a past perspective.

[0062] The collection department can build a database of historical books and historical backgrounds and select reliable data. The collection department evaluates reliability based on, for example, the author and publication year of the historical book, and selects reliable data. For example, the collection department evaluates reliability based on whether the author of the historical book is an expert and whether the publication year is the latest. The collection department can also cross-reference multiple materials and select matching information as reliable data. For example, the collection department compares multiple historical books and documents, and selects matching information as reliable data. Furthermore, the collection department can also select reliable data with reference to reviews and evaluations by experts. For example, the collection department selects reliable data with reference to reviews by historians and experts. This improves the accuracy of the generated AI by collecting reliable data.

[0063] The generation unit can analyze past data and interpret it according to a modern context. The generation unit can, for example, analyze text data using natural language processing technology and interpret it according to a modern context. For example, the generation unit can analyze past data and interpret it according to a modern social situation and cultural background. The generation unit can also analyze video data using image analysis technology and interpret it according to a modern context. For example, the generation unit can analyze past video data and interpret it according to a modern technology trend and visual expression. The generation unit can also analyze voice data using voice analysis technology and interpret it according to a modern context. For example, the generation unit can analyze past voice data and interpret it according to a modern language and pronunciation. As a result, an appropriate response can be given to the user by interpreting past data according to a modern context.

[0064] The interface unit can provide at least one of a chat format or a voice assistant format. The interface unit interacts with the user through, for example, a text chat. For example, the interface unit responds to a user's question in text through the text chat. The interface unit can also interact with the user through a voice assistant. For example, the interface unit analyzes the user's voice using voice recognition technology and responds in voice. Furthermore, the interface unit can also interact with the user through a video chat. For example, the interface unit analyzes the user's facial expressions and gestures through the video chat and provides visual feedback. As a result, by providing multiple formats, the user can select an interface that is easy to use.

[0065] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the collection unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the collection unit can also estimate the user's emotion using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotion. This allows the collection unit to adjust the type of data to be collected according to the user's emotion. For example, if the user is excited, the collection unit preferentially collects data related to turbulent times such as wars and revolutions. Also, if the user is relaxed, the collection unit can preferentially collect data related to culture and art. Furthermore, if the user is sad, the collection unit can preferentially collect data related to religion and philosophy that will provide comfort. This allows for more appropriate data to be collected by adjusting the type of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The collection department can evaluate the reliability of data related to a specific era or region at the time of collection and select highly reliable data. The collection department evaluates reliability based on, for example, the author and publication year of the history book and selects highly reliable data. For example, the collection department evaluates reliability based on whether the author of the history book is an expert and whether the publication year is the latest. The collection department can also cross-reference multiple materials and select matching information as highly reliable data. For example, the collection department compares multiple history books and documents and selects matching information as highly reliable data. Furthermore, the collection department can select highly reliable data with reference to reviews and evaluations by experts. For example, the collection department selects highly reliable data with reference to reviews by historians and experts. This selection of highly reliable data improves the accuracy of the generated AI.

[0067] The collection unit can automatically record the source of data when collecting the data, making it possible to refer to it later. For example, the collection unit automatically records source information (author, publication year, publisher, etc.) when collecting data. For example, the collection unit adds the source information as metadata when collecting data. Also, the collection unit can add the source information as metadata when saving the collected data in a database. Furthermore, the collection unit can make it possible to display the source information when a user refers to the data. For example, the collection unit provides a function to display the source information when a user searches for data. In this way, by recording the source of the data, it becomes easier to refer to it later.

[0068] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the collection unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the collection unit can also estimate the user's emotion using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotion. This allows the collection unit to determine the priority of data to be collected according to the user's emotion. For example, if the user is excited, the collection unit preferentially collects data related to turbulent times such as wars and revolutions. Also, if the user is relaxed, the collection unit can preferentially collect data related to culture and art. Furthermore, if the user is sad, the collection unit can preferentially collect data related to religion and philosophy that will provide comfort. This allows more appropriate data to be collected by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The collection unit can customize the range of data collection based on the user's interests and concerns at the time of collection. For example, if the user is interested in a particular era, the collection unit preferentially collects data related to that era. For example, if the user is interested in the Edo period, the collection unit preferentially collects data related to the Edo period. In addition, if the user is interested in a particular region, the collection unit can also preferentially collect data related to that region. For example, if the user is interested in Europe, the collection unit preferentially collects data related to Europe. In addition, if the user is interested in a particular theme (e.g., science, art, politics), the collection unit can also preferentially collect data related to that theme. For example, if the user is interested in science, the collection unit preferentially collects data related to science. This allows more appropriate data to be collected by customizing the range of data collection based on the user's interests and concerns.

[0070] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects historical data related to the area where the user is currently located. For example, when the user is in Japan, the collection unit preferentially collects historical data related to Japan. In addition, when the user is traveling, the collection unit can also preferentially collect historical data related to the travel destination. For example, when the user is traveling in Europe, the collection unit preferentially collects historical data related to Europe. Furthermore, when the user is interested in a specific area, the collection unit can also preferentially collect data related to that area. For example, when the user is interested in Africa, the collection unit preferentially collects data related to Africa. In this way, by collecting data in consideration of the geographical location information of the user, more relevant data can be collected.

[0071] The generation unit can estimate the user's emotions and adjust the personality characteristics of the AI ​​to be generated based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotions. This allows the generation unit to adjust the personality characteristics of the AI ​​to be generated according to the user's emotions. For example, when the user is relaxed, the generation unit generates an AI with a calm and friendly personality characteristic. Also, when the user is excited, the generation unit can generate an AI with an active and energetic personality characteristic. Furthermore, when the user is sad, the generation unit can generate an AI with a comforting and gentle personality characteristic. This allows the AI's personality traits to be adjusted according to the user's emotions, generating a more appropriate AI. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] At the time of generation, the generation unit can analyze past data and generate an AI that reflects the values ​​of a particular era or region. For example, the generation unit generates an AI with the values ​​of the era based on data related to Japan in the Edo period. For example, the generation unit analyzes data related to Japan in the Edo period and generates an AI that reflects the cultural background and social norms of the era. The generation unit can also generate an AI with the values ​​of the era based on data related to Europe in the Renaissance period. For example, the generation unit analyzes data related to Europe in the Renaissance period and generates an AI that reflects the development of art and science in the era. Furthermore, the generation unit can generate an AI with the values ​​of the era based on data related to ancient Greece. For example, the generation unit analyzes data related to ancient Greece and generates an AI that reflects the philosophical and political ideas of the era. This allows for more appropriate responses by generating an AI that reflects the values ​​of a particular era or region.

[0073] The generation unit can adjust the AI's personality at the time of generation so that it can adapt to a modern context. For example, the generation unit adjusts the AI ​​generated based on past data so that it has knowledge of modern technology and culture. For example, the generation unit analyzes past data and adds information on modern technological trends and cultural backgrounds to adapt the AI's personality to a modern context. The generation unit can also adjust the AI ​​with past values ​​so that it can adapt to modern ethics and social norms. For example, the generation unit analyzes past data and adds information on modern ethics and social norms to adapt the AI's personality to a modern context. Furthermore, the generation unit can adjust the AI ​​with knowledge of past eras so that it can adapt to communication with modern users. For example, the generation unit analyzes past data and adds information on modern communication styles and languages ​​to adapt the AI's personality to a modern context. This allows the AI's personality to be adjusted to adapt to a modern context, making it possible to give an appropriate response to the user.

[0074] The generation unit can estimate the user's emotions and adjust the response style of the AI ​​to be generated based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotions. This allows the generation unit to adjust the response style of the AI ​​to be generated according to the user's emotions. For example, when the user is relaxed, the generation unit generates an AI with a calm and friendly response style. Also, when the user is excited, the generation unit can generate an AI with an active and energetic response style. Furthermore, when the user is sad, the generation unit can generate an AI with a comforting and gentle response style. This allows the AI's response style to be adjusted according to the user's emotions, resulting in a more appropriate response. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The generation unit can generate an AI with diverse perspectives by combining data from multiple eras and regions at the time of generation. For example, the generation unit can generate an AI with a composite personality by combining data from Japan in the Edo period and Europe in the Renaissance period. For example, the generation unit can analyze data on Japan in the Edo period and data on Europe in the Renaissance period to generate an AI with the knowledge and values ​​of each era. The generation unit can also generate an AI with a composite personality by combining data from ancient Greece and medieval China. For example, the generation unit can analyze data on ancient Greece and data on medieval China to generate an AI with the knowledge and values ​​of each era. Furthermore, the generation unit can generate an AI with a composite personality by combining data from modern America and ancient Egypt. For example, the generation unit can analyze data on modern America and data on ancient Egypt to generate an AI with the knowledge and values ​​of each era. In this way, by combining data from multiple eras and regions, an AI with a more diverse perspective can be generated.

[0076] The generation unit can generate a more personalized AI by referring to the user's past interaction history at the time of generation. The generation unit generates a personalized AI based on, for example, the era or region in which the user has shown interest in the past. For example, if the user has shown interest in the Edo period in the past, the generation unit generates an AI based on data related to the Edo period. The generation unit can also generate an AI that responds more appropriately based on the content of the user's past questions. For example, the generation unit analyzes the content of questions the user has asked in the past and adjusts the AI's response style based on the content. Furthermore, the generation unit can analyze the user's past interaction history and generate an AI with optimal personality characteristics. For example, the generation unit analyzes the user's past dialogue log and adjusts the AI's personality characteristics based on the user's preferences and interests. In this way, a more personalized AI can be generated by referring to the user's past interaction history.

[0077] The interface unit can estimate the user's emotion and adjust the display method of the interface based on the estimated user's emotion. The interface unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the interface unit photographs the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The interface unit can also estimate the user's emotion using a voice analysis technology. For example, the interface unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the interface unit can also estimate the user's emotion using a text analysis technology. For example, the interface unit analyzes the text input by the user and estimates the emotion. This allows the interface unit to adjust the display method of the interface according to the user's emotion. For example, when the user is nervous, the interface unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the interface unit can provide an interface with a bright color scheme to make input work enjoyable. Furthermore, when the user is tired, the interface unit can provide a simple and highly visible interface to facilitate input work. This allows a more appropriate display to be performed by adjusting the display method of the interface according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit preferentially displays an interface design that the user has used in the past. For example, the interface unit records an interface design that the user has used in the past and preferentially displays that design. The interface unit can also select the most efficient display method from the user's past operation history. For example, the interface unit analyzes the user's past operation log and selects the most efficient display method. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. For example, the interface unit preferentially displays functions that the user has used frequently in the past. In this way, a more appropriate display method can be selected by referring to the user's past operation history.

[0079] The interface unit can select an optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, the interface unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, the interface unit provides a display method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. For example, the interface unit provides a display method optimized for the small screen of the smartwatch. This allows a more appropriate display method to be selected by considering the device information of the user.

[0080] The interface unit can estimate the user's emotion and adjust the operation procedure of the interface based on the estimated user's emotion. The interface unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the interface unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The interface unit can also estimate the user's emotion using a voice analysis technology. For example, the interface unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the interface unit can also estimate the user's emotion using a text analysis technology. For example, the interface unit analyzes text entered by the user and estimates the emotion. This allows the interface unit to adjust the operation procedure of the interface according to the user's emotion. For example, when the user is nervous, the interface unit provides a simple and intuitive operation procedure. Also, when the user is enjoying himself, the interface unit can provide detailed operation procedures and increase customizable options. Furthermore, when the user is tired, the interface unit can minimize the operation procedure and make it easy to operate. This allows more appropriate operation by adjusting the operation procedure according to the user's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The interface unit can select an optimal display method in consideration of the geographical location information of the user when displaying the interface. The interface unit, for example, preferentially displays information related to the area where the user is currently located. For example, when the user is in Japan, the interface unit preferentially displays information related to Japan. Furthermore, when the user is traveling, the interface unit can also preferentially display information related to the travel destination. For example, when the user is traveling in Europe, the interface unit preferentially displays information related to Europe. Furthermore, when the user is interested in a specific area, the interface unit can also preferentially display information related to that area. For example, when the user is interested in Africa, the interface unit preferentially displays information related to Africa. In this way, a more appropriate display method can be selected by considering the geographical location information of the user.

[0082] The interface unit can analyze the user's social media activity and display related information when the interface is displayed. The interface unit displays related data based on, for example, information shared by the user on social media. For example, the interface unit analyzes the content of posts shared by the user on social media and displays related data. The interface unit can also analyze the user's social media activity history and display information that may be of interest to the user. For example, the interface unit displays related information based on an analysis of the user's past posts and followers. Furthermore, the interface unit can display information related to accounts the user follows on social media. For example, the interface unit analyzes the content of posts from accounts the user follows and displays related information. In this way, related information can be displayed by analyzing the user's social media activity.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can customize the range of data collection based on the user's interests and concerns. For example, if the user is interested in a particular era, the collection unit prioritizes collecting data related to that era. For example, if the user is interested in the Edo period, the collection unit prioritizes collecting data related to the Edo period. In addition, if the user is interested in a particular region, the collection unit can also prioritize collecting data related to that region. For example, if the user is interested in Europe, the collection unit prioritizes collecting data related to Europe. In addition, if the user is interested in a particular theme (e.g., science, art, politics), the collection unit can also prioritize collecting data related to that theme. For example, if the user is interested in science, the collection unit prioritizes collecting data related to science. This allows more appropriate data to be collected by customizing the range of data collection based on the user's interests and concerns.

[0085] The generation unit can generate an AI with diverse perspectives by combining data from multiple eras and regions at the time of generation. For example, data from Japan in the Edo period and Europe in the Renaissance period can be combined to generate an AI with a composite personality. For example, the generation unit analyzes data on Japan in the Edo period and data on Europe in the Renaissance period to generate an AI with the knowledge and values ​​of each era. The generation unit can also generate an AI with a composite personality by combining data from ancient Greece and medieval China. For example, the generation unit analyzes data on ancient Greece and data on medieval China to generate an AI with the knowledge and values ​​of each era. Furthermore, the generation unit can generate an AI with a composite personality by combining data from modern America and ancient Egypt. For example, the generation unit analyzes data on modern America and data on ancient Egypt to generate an AI with the knowledge and values ​​of each era. This allows AI with more diverse perspectives to be generated by combining data from multiple eras and regions.

[0086] At the time of generation, the generation unit can generate a more personalized AI by referring to the user's past interaction history. For example, the generation unit generates a personalized AI based on the era or region in which the user has shown interest in the past. For example, if the user has shown interest in the Edo period in the past, the generation unit generates an AI based on data related to the Edo period. The generation unit can also generate an AI that responds more appropriately based on the content of the user's past questions. For example, the generation unit analyzes the content of questions asked by the user in the past and adjusts the response style of the AI ​​based on the content. Furthermore, the generation unit can analyze the user's past interaction history and generate an AI with optimal personality characteristics. For example, the generation unit analyzes the user's past dialogue log and adjusts the personality characteristics of the AI ​​based on the user's preferences and interests. In this way, a more personalized AI can be generated by referring to the user's past interaction history.

[0087] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit preferentially displays an interface design that the user has used in the past. For example, the interface unit records an interface design that the user has used in the past and preferentially displays that design. The interface unit can also select the most efficient display method from the user's past operation history. For example, the interface unit analyzes the user's past operation log and selects the most efficient display method. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. For example, the interface unit preferentially displays functions that the user has used frequently in the past. In this way, a more appropriate display method can be selected by referring to the user's past operation history.

[0088] The interface unit can select an optimal display method in consideration of the device information of the user when displaying the interface. For example, when the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, the interface unit provides a display method optimized for the screen size of the smartphone. In addition, when the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. For example, the interface unit provides a display method optimized for the screen size of the tablet. Furthermore, when the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. For example, the interface unit provides a display method optimized for the small screen of the smartwatch. This makes it possible to select a more appropriate display method by considering the device information of the user.

[0089] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the collection unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the collection unit can also estimate the user's emotion using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotion. This allows the collection unit to adjust the type of data to be collected according to the user's emotion. For example, if the user is excited, the collection unit preferentially collects data related to turbulent times such as wars and revolutions. Also, if the user is relaxed, the collection unit can preferentially collect data related to culture and art. Furthermore, if the user is sad, the collection unit can preferentially collect data related to religion and philosophy that will provide comfort. This allows for more appropriate data to be collected by adjusting the type of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The generation unit can estimate the user's emotions and adjust the personality characteristics of the AI ​​to be generated based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotions. This allows the generation unit to adjust the personality characteristics of the AI ​​to be generated according to the user's emotions. For example, when the user is relaxed, the generation unit generates an AI with a calm and friendly personality characteristic. Also, when the user is excited, the generation unit can generate an AI with an active and energetic personality characteristic. Furthermore, when the user is sad, the generation unit can generate an AI with a comforting and gentle personality characteristic. This allows the AI's personality traits to be adjusted according to the user's emotions, generating a more appropriate AI. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The generation unit can estimate the user's emotions and adjust the response style of the AI ​​to be generated based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. Furthermore, the generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotions. This allows the generation unit to adjust the response style of the AI ​​to be generated according to the user's emotions. For example, when the user is relaxed, the generation unit generates an AI with a calm and friendly response style. Also, when the user is excited, the generation unit can generate an AI with an active and energetic response style. Furthermore, when the user is sad, the generation unit can generate an AI with a comforting and gentle response style. This allows the AI's response style to be adjusted according to the user's emotions, resulting in a more appropriate response. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The interface unit can estimate the user's emotion and adjust the display method of the interface based on the estimated user's emotion. The interface unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the interface unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The interface unit can also estimate the user's emotion using voice analysis technology. For example, the interface unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. Furthermore, the interface unit can

[0093] The interface unit can select an optimal display method in consideration of the geographical location information of the user when displaying the interface. The interface unit, for example, preferentially displays information related to the area where the user is currently located. For example, when the user is in Japan, the interface unit preferentially displays information related to Japan. Furthermore, when the user is traveling, the interface unit can also preferentially display information related to the travel destination. For example, when the user is traveling in Europe, the interface unit preferentially displays information related to Europe. Furthermore, when the user is interested in a specific area, the interface unit can also preferentially display information related to that area. For example, when the user is interested in Africa, the interface unit preferentially displays information related to Africa. In this way, a more appropriate display method can be selected by considering the geographical location information of the user.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection department collects past data. Past data includes text data, audio data, video data, etc. The collection department builds a database on historical books and historical background, and selects reliable data. For example, reliability is evaluated based on the author and publication year of the historical book, and matching information is selected as reliable data by cross-referencing multiple sources. Reliable data can also be selected with reference to expert reviews and evaluations. Step 2: The generation unit analyzes the data collected by the collection unit and generates an AI with a personality from the past. The generation unit analyzes the text data using natural language processing technology and forms a personality with the knowledge and values ​​of past eras. It can also analyze video data using image analysis technology and form a personality based on visual information from past eras. It can also analyze audio data using audio analysis technology and form a personality based on audio information from past eras. For example, based on data about Japan in the Edo period, an AI with the knowledge and values ​​of the Edo period is generated. Step 3: The interface unit uses the AI ​​generated by the generation unit to respond to the user. The interface unit provides multiple formats, such as chat and voice assistant formats. For example, it interacts with the user through text chat. It can also interact with the user through a voice assistant. It can also interact with the user through video chat.

[0096] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0098] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements including the above-mentioned collection unit, generation unit, and interface unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects past data using the camera 42 and microphone 38B of the smart device 14, and selects highly reliable data by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an AI with a past personality. The interface unit is realized, for example, by the control unit 46A of the smart device 14, and the generated AI responds to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0107] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0110] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned collection unit, generation unit, and interface unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects past data using the camera 42 and microphone 238 of the smart glasses 214, and selects highly reliable data by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an AI having a past personality. The interface unit is realized, for example, by the control unit 46A of the smart glasses 214, and the generated AI responds to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned collection unit, generation unit, and interface unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects past data using the camera 42 and microphone 238 of the headset type terminal 314, and selects highly reliable data by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an AI with a past personality. The interface unit is realized, for example, by the control unit 46A of the headset type terminal 314, and the generated AI responds to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] Each of the multiple elements including the above-mentioned collection unit, generation unit, and interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects past data using the camera 42 and microphone 238 of the robot 414, and selects highly reliable data by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an AI with a past personality. The interface unit is realized, for example, by the control unit 46A of the robot 414, and the generated AI responds to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0150] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0154] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] In addition, 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 upon request from the data processing device 12.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0161] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0165] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0167] (Appendix 1) A collection unit that collects past data; A generation unit that analyzes the data collected by the collection unit and generates an AI having a past personality; and an interface unit in which the AI ​​generated by the generation unit responds to the user. A system characterized by: (Appendix 2) The collecting unit includes: Build a database of historical documents and historical background, and select reliable data. 2. The system of claim 1, (Appendix 3) The generation unit is Analyzing historical data and interpreting it in a modern context 2. The system of claim 1, (Appendix 4) The interface unit includes: Offer at least one of the following formats: chat or voice assistant 2. The system of claim 1, (Appendix 5) The collecting unit includes: Inferring user sentiment and adjusting the type of data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting data, evaluate the reliability of data for a particular time period or region and select the most reliable data. 2. The system of claim 1, (Appendix 7) The collecting unit includes: At the time of collection, the origin of the data is automatically recorded for future reference 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: At the time of collection, customize the scope of data collection based on your interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, the data is collected based on the user's geographic location. 2. The system of claim 1, (Appendix 11) The generation unit is Infer the user's emotions and adjust the personality traits of the generated AI based on the inferred user emotions. 2. The system of claim 1, (Appendix 12) The generation unit is When generating AI, past data is analyzed to generate AI that reflects the values ​​of a particular era or region. 2. The system of claim 1, (Appendix 13) The generation unit is At generation time, adjust the AI's personality to fit the modern context 2. The system of claim 1, (Appendix 14) The generation unit is Estimate the user's emotions and adjust the AI's response style based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The generation unit is When generating data, data from multiple eras and regions is combined to generate AI with diverse perspectives. 2. The system of claim 1, (Appendix 16) The generation unit is When generating the AI, the system references the user's past interaction history to generate a more personalized AI. 2. The system of claim 1, (Appendix 17) The interface unit includes: Estimating a user's emotions and adjusting the interface display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The interface unit includes: When displaying an interface, the optimal display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 19) The interface unit includes: When displaying the interface, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 20) The interface unit includes: Estimating a user's emotions and adjusting the operation procedure of an interface based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The interface unit includes: When displaying an interface, the optimal display method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 22) The interface unit includes: When the interface is displayed, the app analyzes the user's social media activity and displays relevant information. 2. The system of claim 1, [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects past data; A generation unit that analyzes the data collected by the collection unit and generates an AI having a past personality; An interface unit in which the AI ​​generated by the generation unit responds to the user. A system characterized by:

2. The collecting unit includes: Build a database of historical documents and historical background, and select reliable data.

2. The system of claim 1 .

3. The generation unit is Analyzing historical data and interpreting it in a modern context 2. The system of claim 1 .

4. The interface unit includes: Offer at least one of the following formats: chat or voice assistant 2. The system of claim 1 .

5. The collecting unit includes: Inferring user sentiment and adjusting the type of data to be collected based on the inferred user sentiment 2. The system of claim 1 .

6. The collecting unit includes: When collecting data, evaluate the reliability of data for a particular time period or region and select the most reliable data.

2. The system of claim 1 .

7. The collecting unit includes: At the time of collection, the origin of the data is automatically recorded for future reference 2. The system of claim 1 .

8. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

Images