System

Abstract

An object of a system according to an embodiment is to collect opinions from various viewpoints by utilizing personalization data of an individual and make a final decision.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, an opinion collection unit, and a decision unit. The collection unit collects personalized data. The generation unit generates a pseudo-self based on the data collected by the collection unit. The opinion collection unit collects the pseudo-own opinions generated by the generation unit. The decision unit makes a final decision based on the opinions collected by the opinion collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize individuals' personalized data to gather opinions from diverse perspectives and make final decisions, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize personalized data of individuals to collect opinions from a variety of perspectives and make a final decision. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an opinion collection unit, and a decision unit. The collection unit collects personalized data. The generation unit generates a pseudo self based on the data collected by the collection unit. The opinion collection unit collects opinions of the pseudo self generated by the generation unit. The decision unit makes a final decision based on the opinions collected by the opinion collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize personalization data of individuals to collect opinions from various perspectives and make a final decision. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tool according to an embodiment of the present invention generates multiple pseudo-selves with different personalities based on a user's personalized data, and uses their opinions when making various decisions, thereby broadening the range of life choices. The tool collects the user's personalized data, and a generation AI generates multiple pseudo-selves with different personalities. The generated pseudo-selves offer their own opinions when the user makes various decisions, such as buying clothes, furniture, or deciding where to go for fun. The user then refers to these opinions when making a final decision. For example, when a user buys new clothes, the generated pseudo-selves offer different opinions based on their personalities. One pseudo-selve might say, "casual clothes are good," while another might say, "formal clothes are good." The user can refer to these opinions to select the clothes that are best for them. The same is true when the user decides where to go for fun. The generated pseudo-selves offer different opinions based on their personalities. One pseudo-selve might say, "relaxing in nature is good," while another might say, "shopping in the city is good." The user can refer to these opinions to select the best place to go for fun. This allows the tool to allow users to consider options from a wide range of perspectives, without being limited by their own personality or hobbies.This allows the tool to allow users to consider options from a wide range of perspectives, without being limited by their own personality or hobbies, and thereby broaden the range of choices in life.

[0029] The tool according to the embodiment includes a collection unit, a generation unit, an opinion collection unit, and a decision unit. The collection unit collects personalized data of a user. The personalized data includes, but is not limited to, the user's personality, hobbies, and past behavioral history. The collection unit includes, for example, an interface through which the user inputs data via an app. The collection unit can also estimate the user's emotions and adjust the timing of collecting personalized data based on the estimated user emotions. For example, if the user is estimated to be relaxed, the timing of data collection can be selected to prevent the user from feeling stressed. The generation unit uses a generation AI to generate a pseudo-self based on the data collected by the collection unit. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates multiple pseudo-selfs with different personalities based on, for example, data on the user's personality, hobbies, and past behavioral history. The generation unit also includes an algorithm that allows the generation AI to generate the pseudo-selfs with different personalities. For example, the generation AI can generate the pseudo-selfs using a neural network or natural language processing technology. The opinion collection unit collects the pseudo opinions of the user generated by the generation unit. For example, when a user inputs a question, the generation AI generates pseudo opinions of the user in response to the question and presents them to the user. The opinion collection unit can also estimate the user's emotions and adjust the method of opinion collection based on the estimated user emotions. For example, if the user is relaxed, the system collects opinions in a calm tone. The decision unit makes a final decision based on the opinions collected by the opinion collection unit. The decision unit can also estimate the user's emotions and adjust the method of final decision based on the estimated user emotions. For example, if the user is relaxed, the system makes a final decision in a calm tone. In this way, the tool according to the embodiment generates multiple pseudo selves with different personalities based on the user's personalized data, and by referring to each of their opinions when making various decisions, the user can broaden the range of life choices.

[0030] The generation unit can generate multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. The generation unit generates multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. For example, the generation AI generates pseudo-selves based on personality traits such as the Big Five personality traits or MBTI. The generation unit can also generate pseudo-selves based on the user's hobbies. For example, the pseudo-selves are generated based on hobbies such as sports, music, and reading. The generation unit can also generate pseudo-selves based on the user's past behavioral history. For example, the pseudo-selves are generated based on website browsing history and purchase history. This allows for the generation of multiple pseudo-selves with different personalities based on diverse user data, thereby providing more realistic opinions.

[0031] The collection unit may include an interface through which a user inputs data through an app. The collection unit may include, for example, an interface through which a user inputs data through an app. Examples of the interface include, but are not limited to, a graphical user interface (GUI) or a voice interface. For example, the collection unit may provide a GUI through which a user inputs data through an app. The collection unit may also provide a voice interface through which a user inputs data by voice. This allows the collection unit to efficiently collect data by having the user input data through the app.

[0032] The collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to a question input by a user and present it to the user. For example, when the user inputs a question, the collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. Questions include, but are not limited to, open-ended questions and closed-ended questions. For example, when the user inputs an open-ended question, the collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. Furthermore, when the user inputs a closed-ended question, the collection unit can also cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. This allows the user to input a question, and the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user.

[0033] The generation unit can be equipped with an algorithm that allows the generation AI to generate versions of itself with different personalities. The generation unit, for example, is equipped with an algorithm that allows the generation AI to generate versions of itself with different personalities. The generation AI generates pseudo versions of itself using, for example, a neural network or natural language processing technology. Examples of algorithms include, but are not limited to, decision trees and random forests. For example, the generation AI generates versions of itself with different personalities using a neural network. The generation AI can also generate versions of itself with different personalities using natural language processing technology. As a result, by having the generation AI equipped with an algorithm that generates versions of itself with different personalities, a more diverse range of opinions can be provided.

[0034] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. The collection unit, for example, analyzes the user's past behavioral history and selects an appropriate data collection method. The past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the collection unit selects the optimal data collection method based on devices and apps that the user has frequently used in the past. The collection unit can also analyze the user's past behavioral patterns and optimize the timing and method of data collection. The collection unit can also customize the data collection method based on the interface that the user has preferred in the past. In this way, by analyzing the user's past behavioral history, the optimal data collection method can be selected, enabling efficient data collection.

[0035] The collection unit can filter the personalized data based on the user's current living situation and areas of interest when collecting the personalized data. For example, the collection unit filters the personalized data based on the user's current living situation and areas of interest when collecting the personalized data. Living situations include, but are not limited to, the user's occupation and home environment. For example, the collection unit collects only relevant data based on topics in which the user is currently interested. The collection unit can also filter necessary data based on the user's current living situation (work, home, hobbies, etc.). The collection unit can also limit the range of data to be collected based on the user's areas of current interest. This allows more relevant data to be collected by filtering data based on the user's current living situation and areas of interest.

[0036] The collection unit can select the optimal collection means depending on the user's input method when collecting personalized data. For example, when collecting personalized data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the collection unit collects data using voice recognition technology. Also, if the user prefers text input, the collection unit can collect data by providing a text-based interface. Also, if the user prefers image input, the collection unit can collect data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0037] When collecting personalized data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting personalized data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also prioritize collecting data related to places the user has visited in the past. The collection unit can also prioritize collecting data related to places the user plans to visit in the future. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting personalized data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting personalized data. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting personalized data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting personalized data. Past feedback includes, but is not limited to, survey results and user comments, for example. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected by reflecting the user's past feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the collection method can be optimized and efficient data collection is possible.

[0040] When generating a pseudo-self, the generation unit can improve the accuracy of the generation by referring to the user's past behavioral history. For example, when generating a pseudo-self, the generation unit improves the accuracy of the generation by referring to the user's past behavioral history. Past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the generation unit analyzes the user's past behavioral patterns and generates a pseudo-self based on the analysis. The generation unit can also generate a more realistic pseudo-self by referring to the user's past selection history. The generation unit can also generate multiple pseudo-selfs with different personalities based on the user's past behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of the generation can be improved and a more realistic pseudo-self can be generated.

[0041] When generating a pseudo-self, the generation unit can customize the generated content based on the user's current living situation and areas of interest. For example, when generating a pseudo-self, the generation unit customizes the generated content based on the user's current living situation and areas of interest. Living situations include, but are not limited to, the user's occupation and home environment. For example, the generation unit generates a pseudo-self based on topics in which the user is currently interested. The generation unit can also generate a pseudo-self based on the user's current living situation (work, home, hobbies, etc.). The generation unit can also generate a pseudo-self based on the user's areas of current interest. In this way, by customizing the generated content based on the user's current living situation and areas of interest, a more relevant pseudo-self can be generated.

[0042] The generation unit can improve the generation algorithm by reflecting user feedback when generating a pseudo-self. For example, the generation unit improves the generation algorithm by reflecting user feedback when generating a pseudo-self. Feedback includes, but is not limited to, survey results and user comments, for example. For example, the generation unit improves the generation algorithm based on feedback provided by the user. The generation unit can also improve the accuracy of the generated pseudo-self by reflecting user feedback. The generation unit can also optimize the generation algorithm by referring to user feedback. In this way, the generation algorithm can be improved by reflecting user feedback, and a more accurate pseudo-self can be generated.

[0043] When generating a pseudo-self, the generation unit can select an optimal generation method taking into account the user's geographical location information. For example, when generating a pseudo-self, the generation unit selects an optimal generation method taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the generation unit generates a pseudo-self with a personality related to the location where the user is currently located. The generation unit can also generate a pseudo-self with a personality related to places the user has visited in the past. The generation unit can also generate a pseudo-self with a personality related to places the user plans to visit in the future. In this way, by taking into account the user's geographical location information, a more relevant pseudo-self can be generated.

[0044] When generating a pseudo-self, the generation unit can analyze the user's social media activity and suggest content to be generated. For example, when generating a pseudo-self, the generation unit analyzes the user's social media activity and suggests content to be generated. Social media activity includes, for example, but is not limited to, posted content and the number of likes. For example, the generation unit generates a pseudo-self based on information shared by the user on social media. The generation unit can also analyze the user's social media activity history and generate a pseudo-self based on that. The generation unit can also generate a pseudo-self based on the activity of the user's friends on social media. In this way, a more realistic pseudo-self can be generated by analyzing the user's social media activity.

[0045] When generating a pseudo-self, the generation unit can customize the generation method by reflecting the user's past feedback. For example, when generating a pseudo-self, the generation unit customizes the generation method by reflecting the user's past feedback. Past feedback includes, for example, survey results and user comments, but is not limited to these examples. For example, the generation unit improves the generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the generated pseudo-self by reflecting the user's past feedback. The generation unit can also optimize the generation method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the generation method can be optimized and a more accurate pseudo-self can be generated.

[0046] The opinion collection unit can select the optimal collection method by analyzing the user's past behavioral history when collecting opinions. For example, the opinion collection unit can select the optimal collection method by analyzing the user's past behavioral history when collecting opinions. The past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the opinion collection unit analyzes the user's past behavioral patterns and selects the opinion collection method based on the analysis. The opinion collection unit can also suggest the optimal opinion collection method by referring to the user's past selection history. The opinion collection unit can also select the most effective opinion collection method based on the user's past behavioral history. As a result, the optimal opinion collection method can be selected by analyzing the user's past behavioral history, enabling efficient opinion collection.

[0047] The opinion collection unit can perform filtering based on the user's current living situation and areas of interest when collecting opinions. For example, the opinion collection unit performs filtering based on the user's current living situation and areas of interest when collecting opinions. Living situations include, but are not limited to, the user's occupation and home environment. For example, the opinion collection unit collects only relevant opinions based on topics in which the user is currently interested. The opinion collection unit can also filter necessary opinions based on the user's current living situation (work, home, hobbies, etc.). The opinion collection unit can also limit the range of opinions to be collected based on the user's areas of current interest. In this way, by filtering opinions based on the user's current living situation and areas of interest, more relevant opinions can be collected.

[0048] The opinion collection unit can select the optimal collection means depending on the user's input method when collecting opinions. For example, the opinion collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting opinions. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the opinion collection unit collects opinions using voice recognition technology. Also, if the user prefers text input, the opinion collection unit can collect opinions by providing a text-based interface. Also, if the user prefers image input, the opinion collection unit can collect opinions using image recognition technology. This enables efficient opinion collection by selecting the optimal collection means depending on the user's input method.

[0049] The opinion collection unit can prioritize collecting highly relevant opinions by taking into account the user's geographical location information when collecting opinions. For example, the opinion collection unit prioritizes collecting highly relevant opinions by taking into account the user's geographical location information when collecting opinions. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the opinion collection unit prioritizes collecting opinions related to the user's current location. The opinion collection unit can also prioritize collecting opinions related to places the user has visited in the past. The opinion collection unit can also prioritize collecting opinions related to places the user plans to visit in the future. In this way, highly relevant opinions can be prioritized by taking into account the user's geographical location information.

[0050] The opinion collection unit can analyze the user's social media activity and collect related opinions when collecting opinions. For example, the opinion collection unit analyzes the user's social media activity and collects related opinions when collecting opinions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the opinion collection unit collects related opinions based on information shared by the user on social media. The opinion collection unit can also analyze the user's social media activity history and collect related opinions. The opinion collection unit can also collect related opinions by referring to the activities of the user's friends on social media. In this way, related opinions can be efficiently collected by analyzing the user's social media activity.

[0051] The opinion collection unit can customize the collection method by reflecting the user's past feedback when collecting opinions. For example, the opinion collection unit customizes the collection method by reflecting the user's past feedback when collecting opinions. Past feedback includes, but is not limited to, for example, survey results and user comments. For example, the opinion collection unit improves the opinion collection method based on feedback provided by the user in the past. Furthermore, the opinion collection unit can adjust the types of opinions to be collected by reflecting the user's past feedback. Furthermore, the opinion collection unit can optimize the collection timing by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the collection method can be optimized and efficient opinion collection is possible.

[0052] When making a final decision, the decision unit can improve the accuracy of the decision by referring to the user's past behavioral history. For example, when making a final decision, the decision unit improves the accuracy of the decision by referring to the user's past behavioral history. Past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the decision unit analyzes the user's past behavioral patterns and makes the final decision based on the analysis. The decision unit can also make a more realistic final decision by referring to the user's past selection history. The decision unit can also make the most effective final decision based on the user's past behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of the decision can be improved and a more realistic decision can be made.

[0053] When making a final decision, the decision unit can customize the decision content based on the user's current living situation and areas of interest. For example, when making a final decision, the decision unit customizes the decision content based on the user's current living situation and areas of interest. Living situations include, but are not limited to, the user's occupation and home environment. For example, the decision unit makes the final decision based on topics in which the user is currently interested. The decision unit can also make the final decision based on the user's current living situation (work, home, hobbies, etc.). The decision unit can also make the final decision based on the user's areas of interest. In this way, by customizing the decision content based on the user's current living situation and areas of interest, a more relevant decision can be made.

[0054] The decision unit can improve the decision algorithm by reflecting user feedback when making a final decision. For example, the decision unit improves the decision algorithm by reflecting user feedback when making a final decision. Feedback includes, but is not limited to, survey results and user comments, for example. For example, the decision unit improves the decision algorithm based on feedback provided by the user. The decision unit can also improve the accuracy of the final decision by reflecting user feedback. The decision unit can also optimize the decision algorithm by referring to user feedback. In this way, the decision algorithm can be improved by reflecting user feedback, and more accurate decisions can be made.

[0055] When making a final decision, the decision unit can select an optimal decision-making method by taking into account the user's geographical location information. For example, when making a final decision, the decision unit selects an optimal decision-making method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the decision unit prioritizes decisions related to the user's current location. The decision unit can also prioritize decisions related to places the user has visited in the past. The decision unit can also prioritize decisions related to places the user plans to visit in the future. In this way, by taking into account the user's geographical location information, more relevant decisions can be made.

[0056] When making a final decision, the decision unit can analyze the user's social media activity and suggest a decision. For example, when making a final decision, the decision unit analyzes the user's social media activity and suggests a decision. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the decision unit makes a final decision based on information shared by the user on social media. The decision unit can also analyze the user's social media activity history and make a final decision based on that. The decision unit can also make a final decision by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more realistic decisions can be suggested.

[0057] The decision unit can customize the decision-making method by reflecting the user's past feedback when making a final decision. For example, the decision unit customizes the decision-making method by reflecting the user's past feedback when making a final decision. Past feedback includes, but is not limited to, survey results and user comments, for example. For example, the decision unit improves the decision-making method based on feedback provided by the user in the past. The decision unit can also improve the accuracy of the final decision by reflecting the user's past feedback. The decision unit can also optimize the decision-making method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the decision-making method can be optimized and more accurate decisions can be made.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The tool not only generates multiple pseudo-selves with different personalities based on the user's personalization data, but also collects the user's health data and provides recommendations based on their health status. For example, if a user uses a fitness app, that data is collected and the pseudo-selves suggest healthy options. One pseudo-selve might say, "Running is good," while another might say, "Yoga is good." The tool can also collect the user's dietary data and provide healthy meal suggestions. For example, one pseudo-selve might say, "Eat a salad," while another might say, "Drink a protein shake." This allows the tool to provide healthier options based on the user's health status.

[0060] The generator can generate multiple pseudo-selves with different personalities based on the user's personality, hobbies, and past behavioral history data, as well as generate pseudo-selves based on the user's future goals and plans. For example, if a user has set future career goals, the generator can generate pseudo-selves based on those goals. One pseudo-selve might say, "It would be good to take training to improve my leadership skills," while another might say, "It would be good to try a new project." Furthermore, if a user is planning a future trip, the generator can generate pseudo-selves based on those plans. For example, one pseudo-selve might say, "It would be good to go to a beach resort," while another might say, "It would be good to go mountain climbing." This allows the generator to provide more specific opinions based on the user's future goals and plans.

[0061] The collection unit may not only have an interface for the user to input data through an app, but also have a function for synchronizing data between the user's devices. For example, if the user uses a smartphone and a tablet, the collection unit synchronizes data between these devices to ensure consistent data collection. Furthermore, if the user uses a wearable device, the collection unit may collect the data and supplement the data based on health conditions and activity levels. For example, if the user uses a smartwatch, the collection unit may collect the data and analyze daily activity levels and sleep patterns. This allows the collection unit to collect data from multiple devices of the user and provide more comprehensive personalized data.

[0062] When a user inputs a question, the collection unit not only uses the generation AI to generate pseudo-opinions and present them to the user, but also automatically searches for and provides related information based on the user's question. For example, if a user asks, "I want to start a new hobby," the collection unit searches for information on the Internet and suggests related hobbies. Also, if a user asks, "Where should I go on my next vacation?" the collection unit can search information on travel sites and provide popular travel destinations and recommended activities. This allows the collection unit to provide more specific and useful information in response to the user's question.

[0063] The generation unit can be equipped not only with an algorithm that allows the generation AI to generate different versions of itself, but also with a function to evaluate the generated pseudo-self opinions and provide feedback. For example, the generation unit can evaluate the quality of the generated opinions based on feedback provided by the user and improve the algorithm. The generation unit can also provide an interface for users to rate opinions and improve the accuracy of the opinions based on the ratings. For example, if a user rates an opinion as "helpful," the generation method for that opinion can be used preferentially. Also, if a user rates an opinion as "unhelpful," the generation method for that opinion can be improved. This allows the generation unit to reflect user feedback and provide more accurate opinions.

[0064] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method, as well as adjust the frequency of data collection based on the user's behavioral patterns. For example, if the user frequently engages in a particular activity, data related to that activity can be collected periodically. Also, if the user is active during a particular time period, data collection can be performed during that time period. Furthermore, if the user participates in a particular event or activity, data related to that event or activity can be collected. This allows the collection unit to adjust the frequency of data collection based on the user's behavioral patterns, thereby achieving more efficient data collection.

[0065] When collecting personalized data, the collection unit can not only filter the data based on the user's current living situation and areas of interest, but also customize the content of data collection according to the user's life stage. For example, if the user is a student, data related to academics and career can be collected preferentially. If the user is newly married, data related to family life and partnership can be collected. Furthermore, if the user is retired, data related to hobbies and health can be collected. This allows the collection unit to customize the content of data collection according to the user's life stage and provide more relevant data.

[0066] When collecting personalized data, the collection unit can not only select the optimal collection means depending on the user's input method, but also adjust the interface based on the user's input speed and accuracy. For example, if the user uses voice input, noise canceling technology can be used to improve the accuracy of voice recognition. Also, if the user uses text input, the keyboard layout can be adjusted depending on the input speed. Furthermore, if the user uses image input, a high-resolution camera can be used to improve the accuracy of image recognition. This allows the collection unit to adjust the interface depending on the user's input method and achieve more efficient data collection.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects personalized data from the user. The personalized data includes the user's personality, hobbies, past behavioral history, etc. The collection unit has an interface through which the user inputs data through the app, and can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Step 2: The generation unit uses a generation AI to generate pseudo-selves based on the data collected by the collection unit. The generation AI may be a text generation AI or a multimodal generation AI, and generates multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. Step 3: The opinion collection unit collects the pseudo-opinions generated by the generation unit. When the user inputs a question, the generation AI generates pseudo-opinions for each question and presents them to the user. The opinion collection unit can also estimate the user's emotions and adjust the method of opinion collection based on the estimated emotions. Step 4: The decision unit makes a final decision based on the opinions collected by the opinion collection unit. The decision unit can also estimate the user's emotions and adjust the method of making the final decision based on the estimated emotions.

[0069] (Example 2) A tool according to an embodiment of the present invention generates multiple pseudo-selves with different personalities based on a user's personalized data, and uses their opinions when making various decisions, thereby broadening the range of life choices. The tool collects the user's personalized data, and a generation AI generates multiple pseudo-selves with different personalities. The generated pseudo-selves offer their own opinions when the user makes various decisions, such as buying clothes, furniture, or deciding where to go for fun. The user then refers to these opinions when making a final decision. For example, when a user buys new clothes, the generated pseudo-selves offer different opinions based on their personalities. One pseudo-selve might say, "casual clothes are good," while another might say, "formal clothes are good." The user can refer to these opinions to select the clothes that are best for them. The same is true when the user decides where to go for fun. The generated pseudo-selves offer different opinions based on their personalities. One pseudo-selve might say, "relaxing in nature is good," while another might say, "shopping in the city is good." The user can refer to these opinions to select the best place to go for fun. This allows the tool to allow users to consider options from a wide range of perspectives, without being limited by their own personality or hobbies.This allows the tool to allow users to consider options from a wide range of perspectives, without being limited by their own personality or hobbies, and thereby broaden the range of choices in life.

[0070] The tool according to the embodiment includes a collection unit, a generation unit, an opinion collection unit, and a decision unit. The collection unit collects personalized data of a user. The personalized data includes, but is not limited to, the user's personality, hobbies, and past behavioral history. The collection unit includes, for example, an interface through which the user inputs data via an app. The collection unit can also estimate the user's emotions and adjust the timing of collecting personalized data based on the estimated user emotions. For example, if the user is estimated to be relaxed, the timing of data collection can be selected to prevent the user from feeling stressed. The generation unit uses a generation AI to generate a pseudo-self based on the data collected by the collection unit. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates multiple pseudo-selfs with different personalities based on, for example, data on the user's personality, hobbies, and past behavioral history. The generation unit also includes an algorithm that allows the generation AI to generate the pseudo-selfs with different personalities. For example, the generation AI can generate the pseudo-selfs using a neural network or natural language processing technology. The opinion collection unit collects the pseudo opinions of the user generated by the generation unit. For example, when a user inputs a question, the generation AI generates pseudo opinions of the user in response to the question and presents them to the user. The opinion collection unit can also estimate the user's emotions and adjust the method of opinion collection based on the estimated user emotions. For example, if the user is relaxed, the system collects opinions in a calm tone. The decision unit makes a final decision based on the opinions collected by the opinion collection unit. The decision unit can also estimate the user's emotions and adjust the method of final decision based on the estimated user emotions. For example, if the user is relaxed, the system makes a final decision in a calm tone. In this way, the tool according to the embodiment generates multiple pseudo selves with different personalities based on the user's personalized data, and by referring to each of their opinions when making various decisions, the user can broaden the range of life choices.

[0071] The generation unit can generate multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. The generation unit generates multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. For example, the generation AI generates pseudo-selves based on personality traits such as the Big Five personality traits or MBTI. The generation unit can also generate pseudo-selves based on the user's hobbies. For example, the pseudo-selves are generated based on hobbies such as sports, music, and reading. The generation unit can also generate pseudo-selves based on the user's past behavioral history. For example, the pseudo-selves are generated based on website browsing history and purchase history. This allows for the generation of multiple pseudo-selves with different personalities based on diverse user data, thereby providing more realistic opinions.

[0072] The collection unit may include an interface through which a user inputs data through an app. The collection unit may include, for example, an interface through which a user inputs data through an app. Examples of the interface include, but are not limited to, a graphical user interface (GUI) or a voice interface. For example, the collection unit may provide a GUI through which a user inputs data through an app. The collection unit may also provide a voice interface through which a user inputs data by voice. This allows the collection unit to efficiently collect data by having the user input data through the app.

[0073] The collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to a question input by a user and present it to the user. For example, when the user inputs a question, the collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. Questions include, but are not limited to, open-ended questions and closed-ended questions. For example, when the user inputs an open-ended question, the collection unit can cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. Furthermore, when the user inputs a closed-ended question, the collection unit can also cause the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user. This allows the user to input a question, and the generation AI to generate each pseudo-owner's opinion in response to the question and present it to the user.

[0074] The generation unit can be equipped with an algorithm that allows the generation AI to generate versions of itself with different personalities. The generation unit, for example, is equipped with an algorithm that allows the generation AI to generate versions of itself with different personalities. The generation AI generates pseudo versions of itself using, for example, a neural network or natural language processing technology. Examples of algorithms include, but are not limited to, decision trees and random forests. For example, the generation AI generates versions of itself with different personalities using a neural network. The generation AI can also generate versions of itself with different personalities using natural language processing technology. As a result, by having the generation AI equipped with an algorithm that generates versions of itself with different personalities, a more diverse range of opinions can be provided.

[0075] The collection unit can estimate the user's emotions and determine the timing of collecting personalized data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the timing of collecting personalized data based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the collection unit estimates that the user is relaxed, it selects the timing of data collection so that the user does not feel stressed. Furthermore, if the collection unit estimates that the user is busy, it can postpone data collection and collect data when the user is calm. Furthermore, if the collection unit estimates that the user is excited, it can temporarily suspend data collection and resume it when the user has calmed down. In this way, adjusting the timing of data collection based on the user's emotions reduces user stress and enables efficient data collection.

[0076] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. The collection unit, for example, analyzes the user's past behavioral history and selects an appropriate data collection method. The past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the collection unit selects the optimal data collection method based on devices and apps that the user has frequently used in the past. The collection unit can also analyze the user's past behavioral patterns and optimize the timing and method of data collection. The collection unit can also customize the data collection method based on the interface that the user has preferred in the past. In this way, by analyzing the user's past behavioral history, the optimal data collection method can be selected, enabling efficient data collection.

[0077] The collection unit can filter the personalized data based on the user's current living situation and areas of interest when collecting the personalized data. For example, the collection unit filters the personalized data based on the user's current living situation and areas of interest when collecting the personalized data. Living situations include, but are not limited to, the user's occupation and home environment. For example, the collection unit collects only relevant data based on topics in which the user is currently interested. The collection unit can also filter necessary data based on the user's current living situation (work, home, hobbies, etc.). The collection unit can also limit the range of data to be collected based on the user's areas of current interest. This allows more relevant data to be collected by filtering data based on the user's current living situation and areas of interest.

[0078] The collection unit can select the optimal collection means depending on the user's input method when collecting personalized data. For example, when collecting personalized data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the collection unit collects data using voice recognition technology. Also, if the user prefers text input, the collection unit can collect data by providing a text-based interface. Also, if the user prefers image input, the collection unit can collect data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0079] 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 and determines the priority of data to be collected based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is excited, the collection unit can also prioritize collecting data that is interesting. Furthermore, if the user is tired, the collection unit can prioritize collecting data related to rest. In this way, by determining the priority of data based on the user's emotion, more appropriate data can be collected.

[0080] When collecting personalized data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting personalized data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also prioritize collecting data related to places the user has visited in the past. The collection unit can also prioritize collecting data related to places the user plans to visit in the future. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information.

[0081] The collection unit can analyze the user's social media activities and collect related data when collecting personalized data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting personalized data. Social media activities include, but are not limited to, the content of posts and the number of likes. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting personalized data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting personalized data. Past feedback includes, but is not limited to, survey results and user comments, for example. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected by reflecting the user's past feedback. The collection unit can also optimize the collection timing by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the collection method can be optimized and efficient data collection is possible.

[0083] The generation unit can estimate the user's emotions and adjust the method for generating the pseudo self based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the method for generating the pseudo self based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, when the user is relaxed, the generation unit can generate a pseudo self with a calm personality. Furthermore, when the user is excited, the generation unit can generate a pseudo self with an active personality. Furthermore, when the user is stressed, the generation unit can generate a pseudo self with a calm personality. In this way, by adjusting the generation method based on the user's emotions, a more appropriate pseudo self can be generated.

[0084] When generating a pseudo-self, the generation unit can improve the accuracy of the generation by referring to the user's past behavioral history. For example, when generating a pseudo-self, the generation unit improves the accuracy of the generation by referring to the user's past behavioral history. Past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the generation unit analyzes the user's past behavioral patterns and generates a pseudo-self based on the analysis. The generation unit can also generate a more realistic pseudo-self by referring to the user's past selection history. The generation unit can also generate multiple pseudo-selfs with different personalities based on the user's past behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of the generation can be improved and a more realistic pseudo-self can be generated.

[0085] When generating a pseudo-self, the generation unit can customize the generated content based on the user's current living situation and areas of interest. For example, when generating a pseudo-self, the generation unit customizes the generated content based on the user's current living situation and areas of interest. Living situations include, but are not limited to, the user's occupation and home environment. For example, the generation unit generates a pseudo-self based on topics in which the user is currently interested. The generation unit can also generate a pseudo-self based on the user's current living situation (work, home, hobbies, etc.). The generation unit can also generate a pseudo-self based on the user's areas of current interest. In this way, by customizing the generated content based on the user's current living situation and areas of interest, a more relevant pseudo-self can be generated.

[0086] The generation unit can improve the generation algorithm by reflecting user feedback when generating a pseudo-self. For example, the generation unit improves the generation algorithm by reflecting user feedback when generating a pseudo-self. Feedback includes, but is not limited to, survey results and user comments, for example. For example, the generation unit improves the generation algorithm based on feedback provided by the user. The generation unit can also improve the accuracy of the generated pseudo-self by reflecting user feedback. The generation unit can also optimize the generation algorithm by referring to user feedback. In this way, the generation algorithm can be improved by reflecting user feedback, and a more accurate pseudo-self can be generated.

[0087] The generation unit can estimate the user's emotions and determine the priority of the pseudo selves to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of the pseudo selves to be generated based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, when the user is relaxed, the generation unit can prioritize generating a pseudo selves with a calm personality. Furthermore, when the user is excited, the generation unit can prioritize generating a pseudo selves with an active personality. Furthermore, when the user is stressed, the generation unit can prioritize generating a pseudo selves with a calm personality. In this way, by determining the priority of the pseudo selves to be generated based on the user's emotions, a more appropriate pseudo selves can be generated.

[0088] When generating a pseudo-self, the generation unit can select an optimal generation method taking into account the user's geographical location information. For example, when generating a pseudo-self, the generation unit selects an optimal generation method taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the generation unit generates a pseudo-self with a personality related to the location where the user is currently located. The generation unit can also generate a pseudo-self with a personality related to places the user has visited in the past. The generation unit can also generate a pseudo-self with a personality related to places the user plans to visit in the future. In this way, by taking into account the user's geographical location information, a more relevant pseudo-self can be generated.

[0089] When generating a pseudo-self, the generation unit can analyze the user's social media activity and suggest content to be generated. For example, when generating a pseudo-self, the generation unit analyzes the user's social media activity and suggests content to be generated. Social media activity includes, for example, but is not limited to, posted content and the number of likes. For example, the generation unit generates a pseudo-self based on information shared by the user on social media. The generation unit can also analyze the user's social media activity history and generate a pseudo-self based on that. The generation unit can also generate a pseudo-self based on the activity of the user's friends on social media. In this way, a more realistic pseudo-self can be generated by analyzing the user's social media activity.

[0090] When generating a pseudo-self, the generation unit can customize the generation method by reflecting the user's past feedback. For example, when generating a pseudo-self, the generation unit customizes the generation method by reflecting the user's past feedback. Past feedback includes, for example, survey results and user comments, but is not limited to these examples. For example, the generation unit improves the generation method based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the generated pseudo-self by reflecting the user's past feedback. The generation unit can also optimize the generation method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the generation method can be optimized and a more accurate pseudo-self can be generated.

[0091] The opinion collection unit can estimate the user's emotions and adjust the opinion collection method based on the estimated user's emotions. The opinion collection unit, for example, estimates the user's emotions and adjusts the opinion collection method based on the estimated user's emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to these examples. For example, if the user is relaxed, the opinion collection unit can collect opinions in a calm tone. Also, if the user is excited, the opinion collection unit can collect opinions in a positive tone. Also, if the user is stressed, the opinion collection unit can collect opinions in a calm tone. In this way, by adjusting the opinion collection method based on the user's emotions, more appropriate opinions can be collected.

[0092] The opinion collection unit can select the optimal collection method by analyzing the user's past behavioral history when collecting opinions. For example, the opinion collection unit can select the optimal collection method by analyzing the user's past behavioral history when collecting opinions. The past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the opinion collection unit analyzes the user's past behavioral patterns and selects the opinion collection method based on the analysis. The opinion collection unit can also suggest the optimal opinion collection method by referring to the user's past selection history. The opinion collection unit can also select the most effective opinion collection method based on the user's past behavioral history. As a result, the optimal opinion collection method can be selected by analyzing the user's past behavioral history, enabling efficient opinion collection.

[0093] The opinion collection unit can perform filtering based on the user's current living situation and areas of interest when collecting opinions. For example, the opinion collection unit performs filtering based on the user's current living situation and areas of interest when collecting opinions. Living situations include, but are not limited to, the user's occupation and home environment. For example, the opinion collection unit collects only relevant opinions based on topics in which the user is currently interested. The opinion collection unit can also filter necessary opinions based on the user's current living situation (work, home, hobbies, etc.). The opinion collection unit can also limit the range of opinions to be collected based on the user's areas of current interest. In this way, by filtering opinions based on the user's current living situation and areas of interest, more relevant opinions can be collected.

[0094] The opinion collection unit can select the optimal collection means depending on the user's input method when collecting opinions. For example, the opinion collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting opinions. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, if the user prefers voice input, the opinion collection unit collects opinions using voice recognition technology. Also, if the user prefers text input, the opinion collection unit can collect opinions by providing a text-based interface. Also, if the user prefers image input, the opinion collection unit can collect opinions using image recognition technology. This enables efficient opinion collection by selecting the optimal collection means depending on the user's input method.

[0095] The opinion collection unit can estimate the user's emotions and determine the priority of opinions to be collected based on the estimated user's emotions. The opinion collection unit, for example, estimates the user's emotions and determines the priority of opinions to be collected based on the estimated user's emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to these examples. For example, when the user is relaxed, the opinion collection unit can preferentially collect calm opinions. Furthermore, when the user is excited, the opinion collection unit can preferentially collect positive opinions. Furthermore, when the user is stressed, the opinion collection unit can preferentially collect calm opinions. In this way, by determining the priority of opinions based on the user's emotions, more appropriate opinions can be collected.

[0096] The opinion collection unit can prioritize collecting highly relevant opinions by taking into account the user's geographical location information when collecting opinions. For example, the opinion collection unit prioritizes collecting highly relevant opinions by taking into account the user's geographical location information when collecting opinions. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the opinion collection unit prioritizes collecting opinions related to the user's current location. The opinion collection unit can also prioritize collecting opinions related to places the user has visited in the past. The opinion collection unit can also prioritize collecting opinions related to places the user plans to visit in the future. In this way, highly relevant opinions can be prioritized by taking into account the user's geographical location information.

[0097] The opinion collection unit can analyze the user's social media activity and collect related opinions when collecting opinions. For example, the opinion collection unit analyzes the user's social media activity and collects related opinions when collecting opinions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. For example, the opinion collection unit collects related opinions based on information shared by the user on social media. The opinion collection unit can also analyze the user's social media activity history and collect related opinions. The opinion collection unit can also collect related opinions by referring to the activities of the user's friends on social media. In this way, related opinions can be efficiently collected by analyzing the user's social media activity.

[0098] The opinion collection unit can customize the collection method by reflecting the user's past feedback when collecting opinions. For example, the opinion collection unit customizes the collection method by reflecting the user's past feedback when collecting opinions. Past feedback includes, but is not limited to, for example, survey results and user comments. For example, the opinion collection unit improves the opinion collection method based on feedback provided by the user in the past. Furthermore, the opinion collection unit can adjust the types of opinions to be collected by reflecting the user's past feedback. Furthermore, the opinion collection unit can optimize the collection timing by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the collection method can be optimized and efficient opinion collection is possible.

[0099] The decision unit can estimate the user's emotions and adjust the final decision-making method based on the estimated user's emotions. The decision unit, for example, estimates the user's emotions and adjusts the final decision-making method based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the decision unit can make the final decision in a calm tone if the user is relaxed. Furthermore, the decision unit can make the final decision in a positive tone if the user is excited. Furthermore, the decision unit can make the final decision in a calm tone if the user is stressed. In this way, by adjusting the final decision-making method based on the user's emotions, a more appropriate decision can be made.

[0100] When making a final decision, the decision unit can improve the accuracy of the decision by referring to the user's past behavioral history. For example, when making a final decision, the decision unit improves the accuracy of the decision by referring to the user's past behavioral history. Past behavioral history includes, but is not limited to, website browsing history and purchase history. For example, the decision unit analyzes the user's past behavioral patterns and makes the final decision based on the analysis. The decision unit can also make a more realistic final decision by referring to the user's past selection history. The decision unit can also make the most effective final decision based on the user's past behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of the decision can be improved and a more realistic decision can be made.

[0101] When making a final decision, the decision unit can customize the decision content based on the user's current living situation and areas of interest. For example, when making a final decision, the decision unit customizes the decision content based on the user's current living situation and areas of interest. Living situations include, but are not limited to, the user's occupation and home environment. For example, the decision unit makes the final decision based on topics in which the user is currently interested. The decision unit can also make the final decision based on the user's current living situation (work, home, hobbies, etc.). The decision unit can also make the final decision based on the user's areas of interest. In this way, by customizing the decision content based on the user's current living situation and areas of interest, a more relevant decision can be made.

[0102] The decision unit can improve the decision algorithm by reflecting user feedback when making a final decision. For example, the decision unit improves the decision algorithm by reflecting user feedback when making a final decision. Feedback includes, but is not limited to, survey results and user comments, for example. For example, the decision unit improves the decision algorithm based on feedback provided by the user. The decision unit can also improve the accuracy of the final decision by reflecting user feedback. The decision unit can also optimize the decision algorithm by referring to user feedback. In this way, the decision algorithm can be improved by reflecting user feedback, and more accurate decisions can be made.

[0103] The decision unit can estimate the user's emotions and determine the priority of decisions based on the estimated user's emotions. The decision unit, for example, estimates the user's emotions and determines the priority of decisions based on the estimated user's emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to these examples. For example, the decision unit can prioritize calm decisions when the user is relaxed. Furthermore, the decision unit can prioritize aggressive decisions when the user is excited. Furthermore, the decision unit can prioritize calm decisions when the user is stressed. In this way, by determining the priority of decisions based on the user's emotions, more appropriate decisions can be made.

[0104] When making a final decision, the decision unit can select an optimal decision-making method by taking into account the user's geographical location information. For example, when making a final decision, the decision unit selects an optimal decision-making method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the decision unit prioritizes decisions related to the user's current location. The decision unit can also prioritize decisions related to places the user has visited in the past. The decision unit can also prioritize decisions related to places the user plans to visit in the future. In this way, by taking into account the user's geographical location information, more relevant decisions can be made.

[0105] When making a final decision, the decision unit can analyze the user's social media activity and suggest a decision. For example, when making a final decision, the decision unit analyzes the user's social media activity and suggests a decision. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the decision unit makes a final decision based on information shared by the user on social media. The decision unit can also analyze the user's social media activity history and make a final decision based on that. The decision unit can also make a final decision by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more realistic decisions can be suggested.

[0106] The decision unit can customize the decision-making method by reflecting the user's past feedback when making a final decision. For example, the decision unit customizes the decision-making method by reflecting the user's past feedback when making a final decision. Past feedback includes, but is not limited to, survey results and user comments, for example. For example, the decision unit improves the decision-making method based on feedback provided by the user in the past. The decision unit can also improve the accuracy of the final decision by reflecting the user's past feedback. The decision unit can also optimize the decision-making method by referring to the user's past feedback. In this way, by reflecting the user's past feedback, the decision-making method can be optimized and more accurate decisions can be made. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, opinion collection unit, and decision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects personalized data of the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a pseudo version of oneself using a generation AI. The opinion collection unit is realized, for example, by the control unit 46A of the smart device 14 and collects opinions of the generated pseudo version of oneself. The decision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes a final decision based on the collected opinions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, opinion collection unit, and decision 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 is realized by the control unit 46A of the smart glasses 214 and collects personalized data of the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a pseudo version of oneself using a generation AI. The opinion collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects opinions of the generated pseudo version of oneself. The decision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes a final decision based on the collected opinions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, opinion collection unit, and decision 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 is realized by the control unit 46A of the headset type terminal 314 and collects personalized data of the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a pseudo version of oneself using a generation AI. The opinion collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects opinions of the generated pseudo version of oneself. The decision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes a final decision based on the collected opinions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, opinion collection unit, and decision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects personalized data of the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a pseudo version of oneself using a generation AI. The opinion collection unit is realized, for example, by the control unit 46A of the robot 414 and collects opinions of the generated pseudo version of oneself. The decision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes a final decision based on the collected opinions.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The tool not only generates multiple pseudo-selves with different personalities based on the user's personalization data, but also collects the user's health data and provides recommendations based on their health status. For example, if a user uses a fitness app, that data is collected and the pseudo-selves suggest healthy options. One pseudo-selve might say, "Running is good," while another might say, "Yoga is good." The tool can also collect the user's dietary data and provide healthy meal suggestions. For example, one pseudo-selve might say, "Eat a salad," while another might say, "Drink a protein shake." This allows the tool to provide healthier options based on the user's health status.

[0109] The generator can generate multiple pseudo-selves with different personalities based on the user's personality, hobbies, and past behavioral history data, as well as generate pseudo-selves based on the user's future goals and plans. For example, if a user has set future career goals, the generator can generate pseudo-selves based on those goals. One pseudo-selve might say, "It would be good to take training to improve my leadership skills," while another might say, "It would be good to try a new project." Furthermore, if a user is planning a future trip, the generator can generate pseudo-selves based on those plans. For example, one pseudo-selve might say, "It would be good to go to a beach resort," while another might say, "It would be good to go mountain climbing." This allows the generator to provide more specific opinions based on the user's future goals and plans.

[0110] The collection unit may not only have an interface for the user to input data through an app, but also have a function for synchronizing data between the user's devices. For example, if the user uses a smartphone and a tablet, the collection unit synchronizes data between these devices to ensure consistent data collection. Furthermore, if the user uses a wearable device, the collection unit may collect the data and supplement the data based on health conditions and activity levels. For example, if the user uses a smartwatch, the collection unit may collect the data and analyze daily activity levels and sleep patterns. This allows the collection unit to collect data from multiple devices of the user and provide more comprehensive personalized data.

[0111] When a user inputs a question, the collection unit not only uses the generation AI to generate pseudo-opinions and present them to the user, but also automatically searches for and provides related information based on the user's question. For example, if a user asks, "I want to start a new hobby," the collection unit searches for information on the Internet and suggests related hobbies. Also, if a user asks, "Where should I go on my next vacation?" the collection unit can search information on travel sites and provide popular travel destinations and recommended activities. This allows the collection unit to provide more specific and useful information in response to the user's question.

[0112] The generation unit can be equipped not only with an algorithm that allows the generation AI to generate different versions of itself, but also with a function to evaluate the generated pseudo-self opinions and provide feedback. For example, the generation unit can evaluate the quality of the generated opinions based on feedback provided by the user and improve the algorithm. The generation unit can also provide an interface for users to rate opinions and improve the accuracy of the opinions based on the ratings. For example, if a user rates an opinion as "helpful," the generation method for that opinion can be used preferentially. Also, if a user rates an opinion as "unhelpful," the generation method for that opinion can be improved. This allows the generation unit to reflect user feedback and provide more accurate opinions.

[0113] The collection unit can estimate the user's emotions and determine the timing of collecting personalized data based on the estimated user's emotions, as well as select the type of data based on the user's emotions. For example, if the user is estimated to be relaxed, the collection unit can prioritize collecting data related to the user's hobbies and interests. Also, if the user is estimated to be stressed, the collection unit can prioritize collecting data related to reducing the user's stress. Furthermore, if the user is estimated to be excited, the collection unit can prioritize collecting data that attracts the user's interest. This allows the collection unit to select the type of data based on the user's emotions and collect more appropriate data.

[0114] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method, as well as adjust the frequency of data collection based on the user's behavioral patterns. For example, if the user frequently engages in a particular activity, data related to that activity can be collected periodically. Also, if the user is active during a particular time period, data collection can be performed during that time period. Furthermore, if the user participates in a particular event or activity, data related to that event or activity can be collected. This allows the collection unit to adjust the frequency of data collection based on the user's behavioral patterns, thereby achieving more efficient data collection.

[0115] When collecting personalized data, the collection unit can not only filter the data based on the user's current living situation and areas of interest, but also customize the content of data collection according to the user's life stage. For example, if the user is a student, data related to academics and career can be collected preferentially. If the user is newly married, data related to family life and partnership can be collected. Furthermore, if the user is retired, data related to hobbies and health can be collected. This allows the collection unit to customize the content of data collection according to the user's life stage and provide more relevant data.

[0116] When collecting personalized data, the collection unit can not only select the optimal collection means depending on the user's input method, but also adjust the interface based on the user's input speed and accuracy. For example, if the user uses voice input, noise canceling technology can be used to improve the accuracy of voice recognition. Also, if the user uses text input, the keyboard layout can be adjusted depending on the input speed. Furthermore, if the user uses image input, a high-resolution camera can be used to improve the accuracy of image recognition. This allows the collection unit to adjust the interface depending on the user's input method and achieve more efficient data collection.

[0117] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions, as well as adjust the way data is displayed based on the user's emotions. For example, if the user is relaxed, the collection unit can display the data in a calm tone. If the user is excited, the collection unit can display the data in a lively tone. Furthermore, if the user is stressed, the collection unit can display the data in a calm tone. This allows the collection unit to adjust the way data is displayed based on the user's emotions and provide more appropriate information.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects personalized data from the user. The personalized data includes the user's personality, hobbies, past behavioral history, etc. The collection unit has an interface through which the user inputs data through the app, and can also estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. Step 2: The generation unit uses a generation AI to generate pseudo-selves based on the data collected by the collection unit. The generation AI may be a text generation AI or a multimodal generation AI, and generates multiple pseudo-selves with different personalities based on data on the user's personality, hobbies, and past behavioral history. Step 3: The opinion collection unit collects the pseudo-opinions generated by the generation unit. When the user inputs a question, the generation AI generates pseudo-opinions for each question and presents them to the user. The opinion collection unit can also estimate the user's emotions and adjust the method of opinion collection based on the estimated emotions. Step 4: The decision unit makes a final decision based on the opinions collected by the opinion collection unit. The decision unit can also estimate the user's emotions and adjust the method of making the final decision based on the estimated emotions.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

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

[0127] The 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.

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

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

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

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0143] The 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.

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

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

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

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 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 personalized data; a generation unit that generates a pseudo self based on the data collected by the collection unit; an opinion collection unit that collects the pseudo opinions of the users generated by the generation unit; a decision unit that makes a final decision based on the opinions collected by the opinion collection unit; Equipped with A system characterized by:

2. The generation unit Generate multiple pseudo-selves with different personalities based on the user's personality, hobbies, and past behavioral history data 2. The system of claim 1.

3. The collecting unit Provides an interface for users to input data through the app 2. The system of claim 1.

4. The collecting unit When a user inputs a question, the generative AI generates a pseudo-opinion for each user and presents it to the user.

2. The system of claim 1.

5. The generation unit Generative AI is equipped with an algorithm to generate different versions of oneself with different personalities 2. The system of claim 1.

6. The collecting unit To estimate a user's emotions and determine the timing of collecting personalized data based on the estimated user's emotions.

2. The system of claim 1.

7. The collecting unit Analyze users' past behavioral history and select the appropriate data collection method 2. The system of claim 1.

8. The collecting unit When collecting personalization data, filter it based on the user's current life situation and interests.

2. The system of claim 1.

Images