System

Abstract

An object of the system according to the embodiment is to collect opinions based on the user's demographic information and update the content based on the feedback.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and an update unit. The reception unit receives demographic information of a user. The generation unit generates an avatar based on the information received by the reception unit. The collection unit collects opinions through the avatar generated by the generation unit. The analysis unit analyzes the opinion collected by the collection unit. The update unit updates the content based on the analysis result obtained by the analysis 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 collect opinions based on users' demographic information and update the content based on that feedback, so there is room for improvement.

[0005] The system according to the embodiment aims to collect opinions based on users' demographic information and update the content based on that feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and an update unit. The reception unit receives demographic information of a user. The generation unit generates an avatar based on the information received by the reception unit. The collection unit collects opinions through the avatar generated by the generation unit. The analysis unit analyzes the opinions collected by the collection unit. The update unit updates content based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect opinions based on the demographic information of users and update the content based on the feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention generates an avatar based on a user's demographic information, and collects, analyzes, and updates opinions. In this system, a user's demographic information is input, and a generation AI creates an avatar. Opinions are collected through the avatar, analyzed, and the content is updated. This allows the system to provide the latest trend information that reflects the user's opinions. For example, a user inputs demographic information such as age, gender, and location, and the generation AI generates an avatar based on that information. Opinions about recent trends and purchases are collected from the user through the generated avatar. The collected opinions are analyzed by the generation AI to understand trend information and purchasing intentions. The content is updated based on the analysis results, and the latest trend information that reflects the user's opinions is provided. This allows the system to quickly and accurately reflect user opinions and provide the latest trend information.

[0029] An information collection system according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and an update unit. The reception unit receives demographic information of a user. The demographic information of a user includes, but is not limited to, for example, age, gender, and location. The reception unit stores the demographic information entered by the user in a database. The reception unit can also receive the demographic information of a user in real time. The generation unit uses a generation AI to generate an avatar based on the information received by the reception unit. The generated avatar may be, for example, a 3D model, a 2D illustration, or a text-based character, but is not limited to these examples. For example, the generation AI analyzes the user's demographic information and generates an optimal avatar. The generation unit can also generate multiple patterns of avatars using the generation AI. The collection unit collects opinions on recent trends and purchases from users through the generated avatar. The collected opinions include, for example, but are not limited to, surveys, feedback, and comments. For example, the collection unit uses an avatar to ask the user a question such as, "What products are you interested in recently?" The collection unit can also collect opinions from users in real time through the generated avatar. The analysis unit uses a generation AI to analyze the opinions collected by the collection unit. Methods for the analysis include, but are not limited to, text mining, statistical analysis, and machine learning. For example, the analysis unit analyzes the opinions collected by the generation AI to understand trend information and purchasing intentions. The analysis unit can also analyze the collected opinions in real time using the generation AI. The update unit updates the content based on the analysis results obtained by the analysis unit. Updates include, but are not limited to, data updates, system improvements, and the addition of new functions. For example, if the analysis results indicate that there is high interest in a particular product, the update unit updates information related to that product. The update unit can also provide the latest trend information reflecting user opinions based on the analysis results.As a result, the information collection system according to the embodiment can quickly and accurately reflect the opinions of users and provide the latest trend information.

[0030] The reception unit can analyze the user's past demographic information and present the optimal input prompt. For example, the reception unit can automatically display the optimal input prompt based on demographic information previously input by the user. The reception unit can also predict and suggest demographic information to be used during a specific time period based on the user's past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) previously used by the user. By analyzing past demographic information, the reception unit can provide the user with the optimal input prompt and improve input efficiency. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's past demographic information into a generation AI and cause the generation AI to present the optimal input prompt.

[0031] The reception unit can filter the demographic information based on the user's current living situation and areas of interest when inputting the demographic information. For example, the reception unit preferentially inputs relevant demographic information based on the user's current living situation (student, working adult, etc.). The reception unit can also filter relevant demographic information based on the user's areas of interest (sports, music, etc.). The reception unit can also provide optimal input prompts taking into account the user's current living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0032] When inputting demographic information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the demographic information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text input field and input the demographic information. Furthermore, if the user selects image input, the reception unit can input the demographic information using image recognition technology. This allows for selecting the optimal input means depending on the user's input method, thereby improving input convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and cause the generation AI to select the optimal input means.

[0033] When inputting demographic information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting relevant demographic information based on the user's current location. The reception unit can also input region-specific demographic information based on the user's location. The reception unit can also provide optimal input prompts by taking into account the user's geographical location information. This allows highly relevant information to be input preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0034] When inputting demographic information, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, analyzes the content of the user's social media posts and inputs related demographic information. The reception unit can also input related demographic information by referring to the activity of the user's friends on social media. The reception unit can also input related demographic information based on the user's social media check-in information. In this way, related demographic information can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity to the generation AI and cause the generation AI to input related information.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting demographic information. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's past feedback to the generation AI and cause the generation AI to customize the input method.

[0036] The generation unit can adjust the level of detail of the avatar based on the importance of demographic information when generating the avatar. For example, the generation unit generates a detailed avatar based on important demographic information. The generation unit can also generate a simplified avatar based on less important demographic information. The generation unit can also dynamically adjust the level of detail of the avatar according to the importance of the demographic information. This allows for the generation of an optimal avatar by adjusting the level of detail of the avatar based on the importance of the demographic information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of demographic information into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0037] When generating an avatar, the generation unit can apply different generation algorithms depending on the category of demographic information. For example, the generation unit can apply different generation algorithms depending on age to generate an avatar. The generation unit can also apply different generation algorithms depending on gender to generate an avatar. The generation unit can also apply different generation algorithms depending on location to generate an avatar. In this way, by applying different generation algorithms depending on the category of demographic information, an optimal avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of demographic information categories into the generation AI and cause the generation AI to apply different generation algorithms.

[0038] When generating an avatar, the generation unit can improve the accuracy of the generation by referring to the user's past avatar generation results. For example, the generation unit generates an optimal avatar based on avatars previously generated by the user. The generation unit can also analyze the user's past avatar generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generation by referring to the user's past avatar generation results. In this way, the accuracy of the generation can be improved by referring to the user's past avatar generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past avatar generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0039] When collecting opinions, the collection unit can analyze the user's past opinion submission history and select the optimal collection method. The collection unit, for example, poses optimal questions based on opinions submitted by the user in the past. The collection unit can also pose optimal questions for a specific time period based on the user's past opinion submission history. The collection unit can also analyze the user's past opinion submission history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past opinion submission history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's past opinion submission history into a generation AI and cause the generation AI to select the optimal collection method.

[0040] When collecting opinions, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit poses relevant questions based on the user's current living situation (student, working adult, etc.). The collection unit can also pose relevant questions based on the user's areas of interest (sports, music, etc.). The collection unit can also provide optimal questions taking into account the user's current living situation and areas of interest. In this way, highly relevant opinions can be collected by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0041] When collecting opinions, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects opinions using voice recognition technology. Furthermore, if the user selects text input, the collection unit can provide a text input field and collect opinions. Furthermore, if the user selects image input, the collection unit can collect opinions using image recognition technology. This allows the optimal collection means to be selected depending on the user's input method, thereby improving the efficiency of opinion collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's input method into a generation AI and cause the generation AI to select the optimal collection means.

[0042] When collecting opinions, the collection unit can prioritize collecting highly relevant opinions by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant opinions based on the user's current location. The collection unit can also prioritize collecting region-specific opinions based on the user's location. The collection unit can also provide an optimal opinion collection prompt by taking into account the user's geographical location information. This makes it possible to prioritize collecting highly relevant opinions by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant opinions.

[0043] When collecting opinions, the collection unit can analyze the user's social media activities and collect related opinions. The collection unit, for example, analyzes the content of the user's social media posts and collects related opinions. The collection unit can also collect related opinions by referring to the activities of the user's friends on social media. The collection unit can also collect related opinions based on the user's social media check-in information. In this way, related opinions can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related opinions.

[0044] When collecting opinions, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can suggest an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially provide a specific collection method based on the user's past feedback. The collection unit can also customize the collection interface by reflecting the user's past feedback. In this way, the optimal collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected opinions. For example, the analysis unit analyzes the interrelationships between the collected opinions and prioritizes the analysis of highly related opinions. The analysis unit can also optimize the analysis algorithm by taking into account the interrelationships between the opinions. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the collected opinions. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the collected opinions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the collected opinions into a generation AI and have the generation AI perform an analysis of the interrelationships.

[0046] The analysis unit can perform the analysis while taking into account the attribute information of the person submitting the opinion. The analysis unit performs the analysis while taking into account attribute information such as the age, gender, and location of the person submitting the opinion. The analysis unit can also optimize the analysis algorithm based on the attribute information of the person submitting the opinion. The analysis unit can also improve the accuracy of the analysis based on the attribute information of the person submitting the opinion. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the person submitting the opinion. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the attribute information of the person submitting the opinion into the generation AI and cause the generation AI to perform an analysis taking into account the attribute information.

[0047] During analysis, the analysis unit can weight the analysis based on the frequency of opinion submission. For example, the analysis unit prioritizes analysis of opinions that are submitted more frequently. The analysis unit can also dynamically adjust the weighting of the analysis based on the submission frequency. The analysis unit can also postpone analysis of opinions that are submitted less frequently. In this way, by weighting the analysis based on the frequency of opinion submission, important opinions can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency of opinion submission to a generation AI and have the generation AI perform the weighting.

[0048] The analysis unit can perform the analysis while taking into account the geographical distribution of opinions. For example, the analysis unit analyzes the geographical distribution of opinions to identify trends specific to a region. The analysis unit can also optimize the analysis algorithm based on the geographical distribution. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution of opinions. In this way, trends specific to a region can be identified by taking into account the geographical distribution of opinions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the geographical distribution of opinions into the generation AI and cause the generation AI to perform an analysis taking into account the geographical distribution.

[0049] The analysis unit can improve the accuracy of the analysis by referring to literature related to the opinion during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to literature related to the opinion. The analysis unit can also optimize the analysis algorithm based on the related literature. The analysis unit can also complement the analysis results based on literature related to the opinion. In this way, the accuracy of the analysis can be improved by referring to literature related to the opinion. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on literature related to the opinion into the generation AI and cause the generation AI to perform analysis by referring to the related literature.

[0050] The analysis unit can perform the analysis taking into account the market value of the opinions. For example, the analysis unit analyzes the market value of the opinions and prioritizes the analysis of opinions with high value. The analysis unit can also dynamically adjust the weighting of the analysis based on the market value. The analysis unit can also improve the accuracy of the analysis based on the market value of the opinions. In this way, by taking into account the market value of the opinions, it is possible to prioritize the analysis of opinions with high value. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the market value of the opinions into the generation AI and cause the generation AI to perform an analysis taking into account the market value.

[0051] The update unit can adjust the level of detail of the update based on the importance of the analysis result during the update. For example, the update unit performs a detailed update based on an important analysis result. The update unit can also perform a simplified update based on an analysis result with a low importance. The update unit can also dynamically adjust the level of detail of the update according to the importance of the analysis result. This makes it possible to provide an optimal update by adjusting the level of detail of the update based on the importance of the analysis result. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the update.

[0052] The update unit can apply different update algorithms depending on the category of the analysis results during the update. The update unit can apply different update algorithms based on, for example, trend information. The update unit can also apply different update algorithms based on purchase intentions. The update unit can also dynamically adjust the update algorithm depending on the category of the analysis results. This makes it possible to provide optimal updates by applying different update algorithms depending on the category of the analysis results. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input data of the category of the analysis results to the generation AI and cause the generation AI to apply different update algorithms.

[0053] During an update, the update unit can improve the accuracy of the update by referring to the user's past update results. The update unit performs an optimal update, for example, based on updates performed by the user in the past. The update unit can also analyze the user's past update results and optimize the update algorithm. The update unit can also improve the accuracy of the update by referring to the user's past update results. In this way, the accuracy of the update can be improved by referring to the user's past update results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the user's past update results into the generation AI and cause the generation AI to improve the accuracy of the update.

[0054] During an update, the update unit can determine the priority of the update based on the submission time of the analysis results. The update unit can determine the priority of the update based on, for example, the latest analysis results. The update unit can also postpone the priority of the update based on analysis results that were submitted earlier. The update unit can also dynamically adjust the priority of the update according to the submission time of the analysis results. In this way, by determining the priority of the update based on the submission time of the analysis results, the latest information can be preferentially reflected. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the update.

[0055] During an update, the update unit can adjust the order of updates based on the relevance of the analysis results. For example, the update unit prioritizes the order of updates based on highly relevant analysis results. The update unit can also postpone the order of updates based on less relevant analysis results. The update unit can also dynamically adjust the order of updates according to the relevance of the analysis results. This allows highly relevant information to be preferentially reflected by adjusting the order of updates based on the relevance of the analysis results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of updates.

[0056] During an update, the update unit can adjust the use of technical terminology in the update according to the user's level of expertise. For example, if the user has technical expertise, the update unit can perform the update using technical terminology. Alternatively, if the user does not have technical expertise, the update unit can perform the update using simpler terminology. The update unit can also dynamically adjust the use of technical terminology in the update according to the user's level of expertise. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing information appropriate for the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When receiving the user's demographic information, the reception unit can refer to the user's past purchase history and automatically input related information. For example, related demographic information is automatically input based on products and services the user has purchased in the past. The reception unit can also predict and suggest information related to a specific time period based on the user's purchase history. Furthermore, the reception unit can provide optimal input prompts based on the user's purchase history. This can improve input efficiency by referring to the user's purchase history.

[0059] The reception unit can analyze the user's past demographic information and present the most appropriate input prompt. For example, the reception unit can automatically display the most appropriate input prompt based on the demographic information the user has previously input. The reception unit can also predict and suggest demographic information to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. In this way, by analyzing past demographic information, the most appropriate input prompt can be provided to the user, improving input efficiency.

[0060] When inputting demographic information, the reception unit can filter the demographic information based on the user's current living situation and areas of interest. For example, the reception unit can preferentially input relevant demographic information based on the user's current living situation (student, working person, etc.). The reception unit can also filter relevant demographic information based on the user's areas of interest (sports, music, etc.). Furthermore, the reception unit can provide optimal input prompts taking into account the user's current living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's living situation and areas of interest.

[0061] When inputting demographic information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the demographic information using voice recognition technology. If the user selects text input, the reception unit can also provide a text input field and input the demographic information. Furthermore, if the user selects image input, the reception unit can also input the demographic information using image recognition technology. This allows the optimal input means to be selected depending on the user's input method, thereby improving input convenience.

[0062] When inputting demographic information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographic location information. For example, the reception unit can prioritize inputting relevant demographic information based on the user's current location. The reception unit can also input region-specific demographic information based on the user's location. Furthermore, the reception unit can provide optimal input prompts by taking into account the user's geographic location information. This allows highly relevant information to be prioritized by taking into account the user's geographic location information.

[0063] When inputting demographic information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can analyze the content of the user's posts on social media and input related demographic information. The reception unit can also input related demographic information by referring to the activities of the user's friends on social media. Furthermore, the reception unit can input related demographic information based on the user's check-in information on social media. In this way, the reception unit can input related demographic information by analyzing the user's social media activity.

[0064] The reception unit can customize the input method by reflecting the user's past feedback when inputting demographic information. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also provide a specific input method with priority based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives the user's demographic information. The user's demographic information includes, for example, age, gender, location, etc. The reception unit can store the demographic information entered by the user in a database and can also receive it in real time. Step 2: The generation unit uses a generation AI to generate an avatar based on the information received by the reception unit. The generated avatar may be, for example, a 3D model, a 2D illustration, or a text-based character. The generation unit analyzes the user's demographic information to generate the optimal avatar and can also generate multiple patterns of avatars. Step 3: The collection unit collects opinions from users about recent trends and purchases through the generated avatar. The collected opinions include surveys, feedback, comments, etc. The collection unit can also collect opinions in real time by having the avatar ask questions to users. Step 4: The analysis unit uses the generation AI to analyze the opinions collected by the collection unit. Methods such as text mining, statistical analysis, and machine learning are used for the analysis. The analysis unit analyzes the collected opinions to grasp trend information and purchasing intentions, and can also analyze them in real time. Step 5: The update unit updates the content based on the analysis results obtained by the analysis unit. Updates include updating data, improving the system, and adding new functions. If the analysis results indicate that there is high interest in a particular product, the update unit updates information related to that product and provides the latest trend information that reflects user opinions.

[0067] (Example 2) A system according to an embodiment of the present invention generates an avatar based on a user's demographic information, and collects, analyzes, and updates opinions. In this system, a user's demographic information is input, and a generation AI creates an avatar. Opinions are collected through the avatar, analyzed, and the content is updated. This allows the system to provide the latest trend information that reflects the user's opinions. For example, a user inputs demographic information such as age, gender, and location, and the generation AI generates an avatar based on that information. Opinions about recent trends and purchases are collected from the user through the generated avatar. The collected opinions are analyzed by the generation AI to understand trend information and purchasing intentions. The content is updated based on the analysis results, and the latest trend information that reflects the user's opinions is provided. This allows the system to quickly and accurately reflect user opinions and provide the latest trend information.

[0068] An information collection system according to an embodiment includes a reception unit, a generation unit, a collection unit, an analysis unit, and an update unit. The reception unit receives demographic information of a user. The demographic information of a user includes, but is not limited to, for example, age, gender, and location. The reception unit stores the demographic information entered by the user in a database. The reception unit can also receive the demographic information of a user in real time. The generation unit uses a generation AI to generate an avatar based on the information received by the reception unit. The generated avatar may be, for example, a 3D model, a 2D illustration, or a text-based character, but is not limited to these examples. For example, the generation AI analyzes the user's demographic information and generates an optimal avatar. The generation unit can also generate multiple patterns of avatars using the generation AI. The collection unit collects opinions on recent trends and purchases from users through the generated avatar. The collected opinions include, for example, but are not limited to, surveys, feedback, and comments. For example, the collection unit uses an avatar to ask the user a question such as, "What products are you interested in recently?" The collection unit can also collect opinions from users in real time through the generated avatar. The analysis unit uses a generation AI to analyze the opinions collected by the collection unit. Methods for the analysis include, but are not limited to, text mining, statistical analysis, and machine learning. For example, the analysis unit analyzes the opinions collected by the generation AI to understand trend information and purchasing intentions. The analysis unit can also analyze the collected opinions in real time using the generation AI. The update unit updates the content based on the analysis results obtained by the analysis unit. Updates include, but are not limited to, data updates, system improvements, and the addition of new functions. For example, if the analysis results indicate that there is high interest in a particular product, the update unit updates information related to that product. The update unit can also provide the latest trend information reflecting user opinions based on the analysis results.As a result, the information collection system according to the embodiment can quickly and accurately reflect the opinions of users and provide the latest trend information.

[0069] The reception unit can estimate the user's emotions and adjust the input method for demographic information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick input of demographic information. This adjusts the input method according to the user's emotions, reducing the burden on the user and improving input accuracy. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] The reception unit can analyze the user's past demographic information and present the optimal input prompt. For example, the reception unit can automatically display the optimal input prompt based on demographic information previously input by the user. The reception unit can also predict and suggest demographic information to be used during a specific time period based on the user's past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) previously used by the user. By analyzing past demographic information, the reception unit can provide the user with the optimal input prompt and improve input efficiency. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's past demographic information into a generation AI and cause the generation AI to present the optimal input prompt.

[0071] The reception unit can filter the demographic information based on the user's current living situation and areas of interest when inputting the demographic information. For example, the reception unit preferentially inputs relevant demographic information based on the user's current living situation (student, working adult, etc.). The reception unit can also filter relevant demographic information based on the user's areas of interest (sports, music, etc.). The reception unit can also provide optimal input prompts taking into account the user's current living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0072] When inputting demographic information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the demographic information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide a text input field and input the demographic information. Furthermore, if the user selects image input, the reception unit can input the demographic information using image recognition technology. This allows for selecting the optimal input means depending on the user's input method, thereby improving input convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and cause the generation AI to select the optimal input means.

[0073] The reception unit can estimate the user's emotions and determine the priority of demographic information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize inputting only the most important demographic information. The reception unit can also provide an option to input detailed demographic information if the user is relaxed. The reception unit can also prioritize inputting minimal demographic information if the user is in a hurry. This allows important information to be input preferentially by prioritizing demographic information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] When inputting demographic information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. The reception unit, for example, prioritizes inputting relevant demographic information based on the user's current location. The reception unit can also input region-specific demographic information based on the user's location. The reception unit can also provide optimal input prompts by taking into account the user's geographical location information. This allows highly relevant information to be input preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0075] When inputting demographic information, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, analyzes the content of the user's social media posts and inputs related demographic information. The reception unit can also input related demographic information by referring to the activity of the user's friends on social media. The reception unit can also input related demographic information based on the user's social media check-in information. In this way, related demographic information can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity to the generation AI and cause the generation AI to input related information.

[0076] The reception unit can customize the input method by reflecting the user's past feedback when inputting demographic information. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's past feedback to the generation AI and cause the generation AI to customize the input method.

[0077] The generation unit can estimate the user's emotions and adjust the avatar's expression style based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an avatar with soft colors. If the user is excited, the generation unit can also generate an avatar with vivid colors. If the user is stressed, the generation unit can also generate an avatar with subdued colors. This allows the avatar's expression style to be adjusted according to the user's emotions, thereby providing an avatar that is suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0078] The generation unit can adjust the level of detail of the avatar based on the importance of demographic information when generating the avatar. For example, the generation unit generates a detailed avatar based on important demographic information. The generation unit can also generate a simplified avatar based on less important demographic information. The generation unit can also dynamically adjust the level of detail of the avatar according to the importance of the demographic information. This allows for the generation of an optimal avatar by adjusting the level of detail of the avatar based on the importance of the demographic information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of demographic information into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0079] When generating an avatar, the generation unit can apply different generation algorithms depending on the category of demographic information. For example, the generation unit can apply different generation algorithms depending on age to generate an avatar. The generation unit can also apply different generation algorithms depending on gender to generate an avatar. The generation unit can also apply different generation algorithms depending on location to generate an avatar. In this way, by applying different generation algorithms depending on the category of demographic information, an optimal avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of demographic information categories into the generation AI and cause the generation AI to apply different generation algorithms.

[0080] When generating an avatar, the generation unit can improve the accuracy of the generation by referring to the user's past avatar generation results. For example, the generation unit generates an optimal avatar based on avatars previously generated by the user. The generation unit can also analyze the user's past avatar generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generation by referring to the user's past avatar generation results. In this way, the accuracy of the generation can be improved by referring to the user's past avatar generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the user's past avatar generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0081] The collection unit can estimate the user's emotions and adjust the opinion collection method based on the estimated user emotions. For example, if the user is relaxed, the collection unit can collect opinions by asking detailed questions. If the user is stressed, the collection unit can also collect opinions by asking simple questions. If the user is in a hurry, the collection unit can also collect opinions by asking questions that can be answered in a short time. This allows opinions to be collected effectively by adjusting the opinion collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0082] When collecting opinions, the collection unit can analyze the user's past opinion submission history and select the optimal collection method. The collection unit, for example, poses optimal questions based on opinions submitted by the user in the past. The collection unit can also pose optimal questions for a specific time period based on the user's past opinion submission history. The collection unit can also analyze the user's past opinion submission history and select the most effective collection method. In this way, the optimal collection method can be selected by analyzing the user's past opinion submission history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's past opinion submission history into a generation AI and cause the generation AI to select the optimal collection method.

[0083] When collecting opinions, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit poses relevant questions based on the user's current living situation (student, working adult, etc.). The collection unit can also pose relevant questions based on the user's areas of interest (sports, music, etc.). The collection unit can also provide optimal questions taking into account the user's current living situation and areas of interest. In this way, highly relevant opinions can be collected by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0084] When collecting opinions, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects opinions using voice recognition technology. Furthermore, if the user selects text input, the collection unit can provide a text input field and collect opinions. Furthermore, if the user selects image input, the collection unit can collect opinions using image recognition technology. This allows the optimal collection means to be selected depending on the user's input method, thereby improving the efficiency of opinion collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's input method into a generation AI and cause the generation AI to select the optimal collection means.

[0085] The collection unit can estimate the user's emotions and determine the priority of opinions to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed opinions. Furthermore, when the user is stressed, the collection unit can also prioritize collecting simple opinions. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting opinions that can be answered in a short time. Thus, by determining the priority of opinions according to the user's emotions, important opinions can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0086] When collecting opinions, the collection unit can prioritize collecting highly relevant opinions by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant opinions based on the user's current location. The collection unit can also prioritize collecting region-specific opinions based on the user's location. The collection unit can also provide an optimal opinion collection prompt by taking into account the user's geographical location information. This makes it possible to prioritize collecting highly relevant opinions by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant opinions.

[0087] When collecting opinions, the collection unit can analyze the user's social media activities and collect related opinions. The collection unit, for example, analyzes the content of the user's social media posts and collects related opinions. The collection unit can also collect related opinions by referring to the activities of the user's friends on social media. The collection unit can also collect related opinions based on the user's social media check-in information. In this way, related opinions can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related opinions.

[0088] When collecting opinions, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can suggest an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially provide a specific collection method based on the user's past feedback. The collection unit can also customize the collection interface by reflecting the user's past feedback. In this way, the optimal collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. The analysis unit can also perform a quick analysis when the user is in a hurry. This enables effective analysis by adjusting the analysis criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the collected opinions. For example, the analysis unit analyzes the interrelationships between the collected opinions and prioritizes the analysis of highly related opinions. The analysis unit can also optimize the analysis algorithm by taking into account the interrelationships between the opinions. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the collected opinions. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the collected opinions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the collected opinions into a generation AI and have the generation AI perform an analysis of the interrelationships.

[0091] The analysis unit can perform the analysis while taking into account the attribute information of the person submitting the opinion. The analysis unit performs the analysis while taking into account attribute information such as the age, gender, and location of the person submitting the opinion. The analysis unit can also optimize the analysis algorithm based on the attribute information of the person submitting the opinion. The analysis unit can also improve the accuracy of the analysis based on the attribute information of the person submitting the opinion. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the person submitting the opinion. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the attribute information of the person submitting the opinion into the generation AI and cause the generation AI to perform an analysis taking into account the attribute information.

[0092] During analysis, the analysis unit can weight the analysis based on the frequency of opinion submission. For example, the analysis unit prioritizes analysis of opinions that are submitted more frequently. The analysis unit can also dynamically adjust the weighting of the analysis based on the submission frequency. The analysis unit can also postpone analysis of opinions that are submitted less frequently. In this way, by weighting the analysis based on the frequency of opinion submission, important opinions can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency of opinion submission to a generation AI and have the generation AI perform the weighting.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, the analysis unit can also display simplified analysis results. If the user is in a hurry, the analysis unit can also display analysis results that can be quickly understood. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0094] The analysis unit can perform the analysis while taking into account the geographical distribution of opinions. For example, the analysis unit analyzes the geographical distribution of opinions to identify trends specific to a region. The analysis unit can also optimize the analysis algorithm based on the geographical distribution. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution of opinions. In this way, trends specific to a region can be identified by taking into account the geographical distribution of opinions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the geographical distribution of opinions into the generation AI and cause the generation AI to perform an analysis taking into account the geographical distribution.

[0095] The analysis unit can improve the accuracy of the analysis by referring to literature related to the opinion during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to literature related to the opinion. The analysis unit can also optimize the analysis algorithm based on the related literature. The analysis unit can also complement the analysis results based on literature related to the opinion. In this way, the accuracy of the analysis can be improved by referring to literature related to the opinion. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on literature related to the opinion into the generation AI and cause the generation AI to perform analysis by referring to the related literature.

[0096] The analysis unit can perform the analysis taking into account the market value of the opinions. For example, the analysis unit analyzes the market value of the opinions and prioritizes the analysis of opinions with high value. The analysis unit can also dynamically adjust the weighting of the analysis based on the market value. The analysis unit can also improve the accuracy of the analysis based on the market value of the opinions. In this way, by taking into account the market value of the opinions, it is possible to prioritize the analysis of opinions with high value. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the market value of the opinions into the generation AI and cause the generation AI to perform an analysis taking into account the market value.

[0097] The update unit can estimate the user's emotion and adjust the update method based on the estimated user's emotion. For example, the update unit can perform a detailed update when the user is relaxed. The update unit can also perform a simplified update when the user is stressed. The update unit can also perform a quick update when the user is in a hurry. This enables effective updates by adjusting the update method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0098] The update unit can adjust the level of detail of the update based on the importance of the analysis result during the update. For example, the update unit performs a detailed update based on an important analysis result. The update unit can also perform a simplified update based on an analysis result with a low importance. The update unit can also dynamically adjust the level of detail of the update according to the importance of the analysis result. This makes it possible to provide an optimal update by adjusting the level of detail of the update based on the importance of the analysis result. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the update.

[0099] The update unit can apply different update algorithms depending on the category of the analysis results during the update. The update unit can apply different update algorithms based on, for example, trend information. The update unit can also apply different update algorithms based on purchase intentions. The update unit can also dynamically adjust the update algorithm depending on the category of the analysis results. This makes it possible to provide optimal updates by applying different update algorithms depending on the category of the analysis results. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input data of the category of the analysis results to the generation AI and cause the generation AI to apply different update algorithms.

[0100] During an update, the update unit can improve the accuracy of the update by referring to the user's past update results. The update unit performs an optimal update, for example, based on updates performed by the user in the past. The update unit can also analyze the user's past update results and optimize the update algorithm. The update unit can also improve the accuracy of the update by referring to the user's past update results. In this way, the accuracy of the update can be improved by referring to the user's past update results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the user's past update results into the generation AI and cause the generation AI to improve the accuracy of the update.

[0101] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated user emotions. For example, if the user is relaxed, the update unit can prioritize detailed updates. Furthermore, if the user is stressed, the update unit can prioritize simplified updates. Furthermore, if the user is in a hurry, the update unit can prioritize quick updates. Thus, by determining the priority of updates according to the user's emotions, important updates can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0102] During an update, the update unit can determine the priority of the update based on the submission time of the analysis results. The update unit can determine the priority of the update based on, for example, the latest analysis results. The update unit can also postpone the priority of the update based on analysis results that were submitted earlier. The update unit can also dynamically adjust the priority of the update according to the submission time of the analysis results. In this way, by determining the priority of the update based on the submission time of the analysis results, the latest information can be preferentially reflected. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the update.

[0103] During an update, the update unit can adjust the order of updates based on the relevance of the analysis results. For example, the update unit prioritizes the order of updates based on highly relevant analysis results. The update unit can also postpone the order of updates based on less relevant analysis results. The update unit can also dynamically adjust the order of updates according to the relevance of the analysis results. This allows highly relevant information to be preferentially reflected by adjusting the order of updates based on the relevance of the analysis results. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of updates.

[0104] During an update, the update unit can adjust the use of technical terminology in the update according to the user's level of expertise. For example, if the user has technical expertise, the update unit can perform the update using technical terminology. Alternatively, if the user does not have technical expertise, the update unit can perform the update using simpler terminology. The update unit can also dynamically adjust the use of technical terminology in the update according to the user's level of expertise. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing information appropriate for the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and update unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives user demographic information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar using a generation AI. The collection unit is realized by the control unit 46A of the smart device 14 and collects opinions from users through the generated avatar. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected opinions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the content based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and update unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives demographic information of the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar using a generation AI. The collection unit is realized by the control unit 46A of the smart glasses 214 and collects opinions from the user through the generated avatar. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected opinions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the content based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and update unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives demographic information of the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar using a generation AI. The collection unit is realized by the control unit 46A of the headset type terminal 314 and collects opinions from users through the generated avatar. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected opinions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the content based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, collection unit, analysis unit, and update unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives demographic information of the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an avatar using a generation AI. The collection unit is realized by the control unit 46A of the robot 414 and collects opinions from users through the generated avatar. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected opinions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the content based on the analysis results.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When receiving the user's demographic information, the reception unit can refer to the user's past purchase history and automatically input related information. For example, related demographic information is automatically input based on products and services the user has purchased in the past. The reception unit can also predict and suggest information related to a specific time period based on the user's purchase history. Furthermore, the reception unit can provide optimal input prompts based on the user's purchase history. This can improve input efficiency by referring to the user's purchase history.

[0107] The reception unit can estimate the user's emotions and adjust the input method for demographic information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of demographic information. In this way, adjusting the input method according to the user's emotions can reduce the burden on the user and improve input accuracy.

[0108] The reception unit can analyze the user's past demographic information and present the most appropriate input prompt. For example, the reception unit can automatically display the most appropriate input prompt based on the demographic information the user has previously input. The reception unit can also predict and suggest demographic information to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. In this way, by analyzing past demographic information, the most appropriate input prompt can be provided to the user, improving input efficiency.

[0109] When inputting demographic information, the reception unit can filter the demographic information based on the user's current living situation and areas of interest. For example, the reception unit can preferentially input relevant demographic information based on the user's current living situation (student, working person, etc.). The reception unit can also filter relevant demographic information based on the user's areas of interest (sports, music, etc.). Furthermore, the reception unit can provide optimal input prompts taking into account the user's current living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's living situation and areas of interest.

[0110] When inputting demographic information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the demographic information using voice recognition technology. If the user selects text input, the reception unit can also provide a text input field and input the demographic information. Furthermore, if the user selects image input, the reception unit can also input the demographic information using image recognition technology. This allows the optimal input means to be selected depending on the user's input method, thereby improving input convenience.

[0111] The reception unit can estimate the user's emotions and determine the priority of demographic information to be input based on the estimated user's emotions. For example, if the user is feeling stressed, only the most important demographic information is input preferentially. The reception unit can also provide the user with an option to input detailed demographic information if the user is relaxed. Furthermore, the reception unit can also input the minimum amount of demographic information preferentially if the user is in a hurry. In this way, by determining the priority of demographic information according to the user's emotions, important information can be input preferentially.

[0112] When inputting demographic information, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographic location information. For example, the reception unit can prioritize inputting relevant demographic information based on the user's current location. The reception unit can also input region-specific demographic information based on the user's location. Furthermore, the reception unit can provide optimal input prompts by taking into account the user's geographic location information. This allows highly relevant information to be prioritized by taking into account the user's geographic location information.

[0113] When inputting demographic information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can analyze the content of the user's posts on social media and input related demographic information. The reception unit can also input related demographic information by referring to the activities of the user's friends on social media. Furthermore, the reception unit can input related demographic information based on the user's check-in information on social media. In this way, the reception unit can input related demographic information by analyzing the user's social media activity.

[0114] The reception unit can customize the input method by reflecting the user's past feedback when inputting demographic information. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also provide a specific input method with priority based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0115] The generation unit can estimate the user's emotions and adjust the avatar's expression style based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an avatar with soft colors. If the user is excited, the generation unit can also generate an avatar with vivid colors. Furthermore, if the user is stressed, the generation unit can also generate an avatar with subdued colors. In this way, by adjusting the avatar's expression style according to the user's emotions, it is possible to provide an avatar that is suitable for the user.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives the user's demographic information. The user's demographic information includes, for example, age, gender, location, etc. The reception unit can store the demographic information entered by the user in a database and can also receive it in real time. Step 2: The generation unit uses a generation AI to generate an avatar based on the information received by the reception unit. The generated avatar may be, for example, a 3D model, a 2D illustration, or a text-based character. The generation unit analyzes the user's demographic information to generate the optimal avatar and can also generate multiple patterns of avatars. Step 3: The collection unit collects opinions from users about recent trends and purchases through the generated avatar. The collected opinions include surveys, feedback, comments, etc. The collection unit can also collect opinions in real time by having the avatar ask questions to users. Step 4: The analysis unit uses the generation AI to analyze the opinions collected by the collection unit. Methods such as text mining, statistical analysis, and machine learning are used for the analysis. The analysis unit analyzes the collected opinions to grasp trend information and purchasing intentions, and can also analyze them in real time. Step 5: The update unit updates the content based on the analysis results obtained by the analysis unit. Updates include updating data, improving the system, and adding new functions. If the analysis results indicate that there is high interest in a particular product, the update unit updates information related to that product and provides the latest trend information that reflects user opinions.

[0118] 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.

[0119] 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.

[0120] 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.

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

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0136] The data processing system 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.

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

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives demographic information of a user; a generation unit that generates an avatar based on the information received by the reception unit; a collection unit that collects opinions through the avatars generated by the generation unit; an analysis unit that analyzes the opinions collected by the collection unit; an update unit that updates the content based on the analysis result obtained by the analysis unit; A system characterized by:

2. The reception unit Inferring user sentiment and adjusting demographic information entry methods based on the estimated user sentiment 2. The system of claim 1.

3. The reception unit Analyzes the user's past demographic information and provides appropriate input prompts 2. The system of claim 1.

4. The reception unit When entering demographic information, filter based on the user's current life situation and interests.

2. The system of claim 1.

5. The reception unit When entering demographic information, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and determine the priority of the demographic information to be input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering demographic information, prioritize relevant information based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When you enter demographic information, analyze your social media activity and enter relevant information 2. The system of claim 1.

Images