System

Abstract

An object of a system according to an embodiment is to reproduce memories and personalities of individuals by utilizing past information.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a conversation on the basis of the information analyzed by the analysis unit. The providing unit provides the conversation generated by the generating 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 have not adequately utilized past information to recreate personal memories and personalities, and there is room for improvement.

[0005] The system according to the embodiment aims to recreate an individual's memories and personality by utilizing past information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a conversation based on the information analyzed by the analysis unit. The provision unit provides the conversation generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recreate an individual's memories and personality by utilizing past information. [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 retrieves information such as past photos, audio, memos, and emails stored on a mobile phone or in a cloud through an application installed on the mobile phone and provides services in two modes. The system offers a "memory sharing mode," in which the application functions as a partner with which the user can share past memories, and a "personality reproduction mode," in which conversations and sentences are generated that mimic the user's thought patterns based on the user's past information. For example, the system analyzes information such as past photos, audio, memos, and emails to provide conversations that remind the user of past events. The system also generates conversations and sentences that mimic the user's thought patterns, allowing users to create speeches for special occasions or corporate events that reflect the user's personality. This allows the system to provide new entertainment and care services that recreate the user's memories and personality. For example, the system allows users to reminisce about past memories and enjoy them as entertainment. Furthermore, since the data remains even after the user's death, the system can also be used as a service for bereaved families.

[0029] An information processing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past information. Examples of past information include, but are not limited to, text data, audio data, and image data. The collection unit collects information such as past photos, audio, notes, and emails stored on a device or in a cloud. The collection unit can also estimate a user's emotions and select the type of data to collect based on the estimated user emotions. For example, if the user is sad, photos and audio related to happy memories can be preferentially collected. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, natural language processing, image analysis, audio analysis, or other methods, but is not limited to these examples. For example, the analysis unit analyzes the collected information and mimics the user's thought patterns. The analysis unit can also estimate a user's emotions and adjust an analysis algorithm based on the estimated user emotions. For example, if the user is sad, an algorithm is used to provide analysis results that alleviate the user's emotions. The generation unit generates conversations and sentences based on the information analyzed by the analysis unit. The generation of conversations and sentences is performed using, for example, a dialogue system, a chatbot, a text generation algorithm, or the like, but is not limited to these examples. The generation unit generates conversations and sentences based on, for example, analysis results. The generation unit can also estimate a user's emotions and adjust the expression of the generated conversations and sentences based on the estimated user's emotions. For example, if the user is sad, the generation unit generates conversations and sentences using gentle language. The provision unit provides the conversations and sentences generated by the generation unit. The provision can be performed using, for example, a user interface, a notification system, audio output, or the like, but is not limited to these examples. The provision unit provides the generated conversations and sentences to the user, for example. The provision unit can also retain data even after the user's death and provide services to the surviving family. For example, the provision unit can retain data even after the user's death and provide services to the surviving family. As a result, the information processing system according to the embodiment can provide new entertainment and nursing care services that recreate the user's memories and personality.

[0030] The collection unit can collect information on past photos, audio, memos, and emails stored on a device or in a cloud. The collection unit collects, for example, information on past photos, audio, memos, and emails stored on a device or in a cloud. Examples of devices and clouds include, but are not limited to, smartphones, PCs, AWS (registered trademark), Google (registered trademark), and the like. Examples of information on past photos, audio, memos, and emails include, but are not limited to, JPEG, MP3, text files, and email formats. By collecting a variety of information stored on a device or in a cloud, a wide range of the user's past memories and information can be covered. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information stored on a device or in a cloud into AI and have the AI ​​collect the information.

[0031] The analysis unit can analyze the collected information and imitate the user's thought patterns. The analysis unit can, for example, analyze the collected information and imitate the user's thought patterns. The analysis can be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to these examples. The analysis unit can, for example, use a machine learning algorithm to analyze the collected information and imitate the user's thought patterns. The analysis unit can also use a neural network to analyze the collected information and imitate the user's thought patterns. The analysis unit can also use behavioral analysis to analyze the collected information and imitate the user's thought patterns. For example, the analysis unit can analyze collected text data using natural language processing technology to imitate the user's thought patterns. The analysis unit can also analyze collected image data using image analysis technology to imitate the user's thought patterns. The analysis unit can also analyze collected voice data using voice analysis technology to imitate the user's thought patterns. By imitating the user's thought patterns, more natural conversations and sentences can be generated that are more user-like. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected information into AI and have the AI ​​imitate the user's thought patterns.

[0032] The generation unit can generate conversations and sentences based on the analysis results. The generation unit generates conversations and sentences based on, for example, the analysis results. The generation of conversations and sentences is performed using, for example, natural language generation technology, template-based generation, dialogue modeling, or other methods, but is not limited to these examples. The generation unit generates conversations and sentences based on the analysis results using, for example, natural language generation technology. The generation unit can also generate conversations and sentences based on the analysis results using template-based generation. The generation unit can also generate conversations and sentences based on the analysis results using a dialogue model. For example, the generation unit can generate user-like conversations based on the analysis results using natural language generation technology. The generation unit can also generate user-like sentences based on the analysis results using template-based generation. The generation unit can also generate user-like conversations based on the analysis results using a dialogue model. This enables natural communication that utilizes the user's past information by generating conversations and sentences based on the analysis 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 AI. For example, the generation unit can input the analysis results to AI and cause the AI ​​to generate conversations and sentences.

[0033] The providing unit can provide the generated conversations and sentences to the user. For example, the providing unit provides the generated conversations and sentences to the user. The provision can be performed, for example, by a user interface, a notification system, audio output, or other methods, but is not limited to these examples. For example, the providing unit can provide the generated conversations and sentences to the user. The providing unit can also retain data even after the user's death and provide services to the surviving family. For example, the providing unit retains data even after the user's death and provides services to the surviving family. By providing the generated conversations and sentences to the user, the user can look back on past memories and information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated conversations and sentences to AI and have the AI ​​provide them to the user.

[0034] The providing unit can retain data even after the death of a user and provide services to the surviving family members. The providing unit, for example, retains data even after the death of a user and provides services to the surviving family members. Data retention is performed in accordance with standards such as, for example, a data retention period, data protection measures, and a method of access to the surviving family members, but is not limited to such examples. The providing unit, for example, retains data even after the death of a user and provides services to the surviving family members. In this way, by retaining data even after the death of a user and providing services to the surviving family members, the surviving family members can reminisce about memories of the deceased. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can use an AI model to retain data even after the death of a user and provide services to the surviving family members.

[0035] The collection unit can obtain consent to data acquisition. The collection unit, for example, obtains consent to data acquisition. The consent to data acquisition is obtained according to criteria such as, for example, the format of the consent form, the timing of obtaining consent, and the scope of consent, but is not limited to such examples. The collection unit, for example, obtains consent to data acquisition. By obtaining consent to data acquisition, necessary data can be collected while protecting the user's privacy. 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 consent to data acquisition into AI and have the AI ​​obtain consent.

[0036] The providing unit can perform service promotion. The providing unit, for example, performs service promotion. The service promotion is performed according to criteria such as, for example, advertising method, promotion content, and target user, but is not limited to these examples. The providing unit, for example, performs service promotion using advertising method. The providing unit can also set promotion content and perform service promotion to target users. For example, the providing unit performs service promotion using internet advertising. The providing unit can also perform service promotion using social media. The providing unit can also perform email marketing to target users. In this way, service promotion can encourage users to use the service. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the service promotion into AI and leave the execution of the promotion to AI.

[0037] The collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing when collecting data. For example, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing when collecting data. The behavioral pattern analysis can be performed, for example, by analyzing behavioral history, using a machine learning algorithm, or other methods, but is not limited to these examples. For example, the collection unit can analyze the user's past behavioral patterns using the behavioral history analysis. The collection unit can also analyze the user's past behavioral patterns using a machine learning algorithm. For example, if the user has a habit of reviewing photos every night before going to bed, the collection unit can collect data during that time period. If the user has a habit of organizing notes on the weekend, the collection unit can collect data on the weekend. If the user has a habit of checking emails after a specific event, the collection unit can collect data immediately thereafter. This enables efficient data collection by collecting data at the optimal timing based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's behavioral history data into AI and have the AI ​​determine the optimal collection timing.

[0038] The collection unit can prioritize data based on the user's current activity status at the time of collection. For example, the collection unit prioritizes data based on the user's current activity status at the time of collection. The current activity status is determined based on criteria such as, but not limited to, the user's schedule and real-time activity data. For example, the collection unit determines the current activity status by referring to the user's schedule. The collection unit can also analyze real-time activity data to determine the current activity status. For example, when the user is at work, the collection unit prioritizes collecting work-related notes and emails. When the user is on vacation, the collection unit can prioritize collecting travel-related photos and audio. When the user is spending time with family, the collection unit can prioritize collecting data related to family memories. This allows for more appropriate data to be collected by prioritizing data according to the user's activity status. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's activity data into AI and have the AI ​​prioritize the data.

[0039] The collection unit can select the optimal collection means depending on the user's input method during collection. For example, the collection unit selects the optimal collection means depending on the user's input method during collection. Input methods include, but are not limited to, voice input, text input, and image input. For example, for a user using voice input, the collection unit may preferentially collect voice data. For a user using text input, the collection unit may preferentially collect text data. For a user using a large number of images, the collection unit may preferentially collect image data. For example, for a user using voice input, the collection unit may collect voice data using voice recognition technology. For a user using text input, the collection unit may collect text data using text analysis technology. For a user using a large number of images, the collection unit may collect image data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's input data into AI and have the AI ​​select the optimal collection means.

[0040] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information during collection. Geographical location information is acquired by, for example, GPS data, a location information service, or the like, but is not limited to these examples. For example, the collection unit acquires the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using a location information service. For example, when the user is traveling, the collection unit prioritizes collecting photos and notes related to the travel destination. When the user is at home, the collection unit can prioritize collecting data related to memories at home. When the user is at a specific event venue, the collection unit can prioritize collecting data related to the event. This makes it possible to provide more appropriate data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0041] The collection unit may analyze the user's social media activities and collect related data at the time of collection. For example, the collection unit may analyze the user's social media activities and collect related data at the time of collection. Social media activities include, but are not limited to, types of posts, likes, and comments. For example, the collection unit may collect photos shared by the user on social media. The collection unit may also collect notes and comments posted by the user on social media. The collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may collect photos shared by the user on social media. The collection unit may also collect notes and comments posted by the user on social media. The collection unit may also collect related data by referring to the activities of the user's friends on social media. This allows for the provision of more personalized services by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into AI and cause the AI ​​to collect related data.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback during data collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback during data collection. Past feedback can be, for example, survey results, user comments, etc., but is not limited to these examples. For example, the collection unit prioritizes collecting types of data that the user has previously preferred to collect. The collection unit can also adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting data that the user has previously desired to collect. For example, the collection unit prioritizes collecting types of data that the user has previously preferred to collect. The collection unit can also adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting data that the user has previously desired to collect. In this way, by customizing the collection method based on the user's past feedback, more appropriate data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​customize the collection method.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data is determined based on criteria such as, but not limited to, the impact, urgency, and relevance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the urgency of the data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. For example, the analysis unit analyzes data related to important events in detail. The analysis unit can also analyze casual everyday data in a simplified manner. The analysis unit can also analyze data related to a specific theme at a level of detail appropriate to the theme. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into AI and have the AI ​​adjust the level of detail.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, image data, and audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply an audio analysis algorithm to audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply an audio analysis algorithm to audio data. This allows for applying the optimal analysis algorithm depending on the data category, thereby providing more accurate analysis results. Some or all of the above-described 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 the data category into AI and cause the AI ​​to apply the optimal analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past analysis reports and analysis logs. For example, the analysis unit can adjust the analysis algorithm based on the user's previously preferred analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and select an optimal analysis method. For example, the analysis unit can adjust the analysis algorithm based on the user's previously preferred analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can analyze the user's past analysis results and select an optimal analysis method. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0046] The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the analysis unit determines the analysis priority based on the time of data submission during analysis. The time of data submission is determined based on criteria such as, but not limited to, the submission date and time, the submission frequency, etc. The analysis unit determines the analysis priority based on, for example, the submission date and time. The analysis unit can also determine the analysis priority based on the submission frequency. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data related to a specific event. The analysis unit can also prioritize analysis of data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described 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 the time of data submission into AI and have the AI ​​determine the priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the data during analysis. The relevance of the data is determined based on criteria such as, but not limited to, the correlation between the data and the frequency of co-occurrence between the data. For example, the analysis unit adjusts the order of analysis based on the correlation between the data. The analysis unit can also adjust the order of analysis based on the frequency of co-occurrence. For example, the analysis unit analyzes data related to the same event together. The analysis unit can also prioritize analyzing data related to the user's interests. The analysis unit can also prioritize analyzing data related to a specific theme. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-described 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 the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0048] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is determined according to criteria such as, for example, beginner, intermediate, or expert, but is not limited to such examples. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can select appropriate terms to provide analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can select appropriate terms to provide analysis results according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described 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 the user's level of expertise into the AI ​​and have the AI ​​adjust its use of technical terminology.

[0049] The generation unit can adjust the level of detail of the generated content based on the importance of the analysis result at the time of generation. For example, the generation unit adjusts the level of detail of the generated content based on the importance of the analysis result at the time of generation. The importance of the analysis result is determined according to criteria such as, but not limited to, the impact, urgency, and relevance of the data. For example, the generation unit adjusts the level of detail of the generated content based on the impact of the data. The generation unit can also adjust the level of detail of the generated content based on the urgency of the data. The generation unit can also adjust the level of detail of the generated content based on the relevance of the data. For example, the generation unit generates detailed analysis results related to important events. The generation unit can also generate simplified analysis results for everyday, casual analysis results. The generation unit can also generate analysis results related to a specific theme with a level of detail appropriate to the theme. This enables efficient generation by adjusting the level of detail of the generated content based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​adjust the level of detail.

[0050] The generation unit can apply different generation algorithms depending on the category of the analysis result during generation. For example, the generation unit applies different generation algorithms depending on the category of the analysis result during generation. The categories of the analysis result include, but are not limited to, text data, image data, and audio data. For example, the generation unit applies a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a audio generation algorithm to audio data. For example, the generation unit applies a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a audio generation algorithm to audio data. This makes it possible to provide more accurate generation results by applying an optimal generation algorithm depending on the category of the analysis result. 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 the category of the analysis result to AI and cause the AI ​​to apply the optimal generation algorithm.

[0051] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, types of past generation results such as past generation reports and generation logs. For example, the generation unit can adjust the generation algorithm based on generation results that the user previously preferred. The generation unit can also improve the accuracy of generation by referring to the user's past feedback. The generation unit can also analyze the user's past generation results and select an optimal generation method. For example, the generation unit can adjust the generation algorithm based on generation results that the user previously preferred. The generation unit can also improve the accuracy of generation by referring to the user's past feedback. The generation unit can also analyze the user's past generation results and select an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past 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 the user's past generation results into AI and cause the AI ​​to improve the accuracy of generation.

[0052] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the generation unit determines the generation priority based on the submission time of the analysis results at the time of generation. The submission time of the analysis results is determined based on criteria such as, but not limited to, the submission date and time, the submission frequency, etc. The generation unit determines the generation priority based on, for example, the submission date and time. The generation unit can also determine the generation priority based on the submission frequency. For example, the generation unit prioritizes generating recent analysis results. The generation unit can also prioritize generating analysis results related to a specific event. The generation unit can also prioritize generating analysis results within a period specified by the user. This enables efficient generation by determining the generation priority based on the submission time of the analysis 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 the submission time of the analysis results into AI and have the AI ​​determine the priority.

[0053] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit adjusts the order of generation based on the relevance of the analysis results during generation. The relevance of the analysis results is determined based on criteria such as, but not limited to, correlation between data and co-occurrence frequency. For example, the generation unit adjusts the order of generation based on correlation between data. The generation unit can also adjust the order of generation based on co-occurrence frequency. For example, the generation unit generates analysis results related to the same event together. The generation unit can also prioritize generating analysis results related to a user's interests. The generation unit can also prioritize generating analysis results related to a specific theme. In this way, by adjusting the order of generation based on the relevance of the analysis results, more appropriate generation results can be provided. 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 the relevance of the analysis results into AI and have the AI ​​adjust the order of generation.

[0054] The generation unit may adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. For example, the generation unit may adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. The level of expertise may be determined according to, for example, beginner, intermediate, expert, or other criteria, but is not limited to, examples. For example, if the user has technical knowledge, the generation unit may generate conversations and sentences that use a lot of technical terms. Furthermore, if the user does not have technical knowledge, the generation unit may generate conversations and sentences in simple language. Furthermore, the generation unit may select appropriate terms to generate conversations and sentences according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit may generate conversations and sentences that use a lot of technical terms. If the user does not have technical knowledge, the generation unit may generate conversations and sentences in simple language. The generation unit may select appropriate terms to generate conversations and sentences according to the user's level of expertise. In this way, by adjusting the use of technical terms in the content to be generated according to the user's level of expertise, a generated result that is easier to understand may be provided. 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 may input the user's level of expertise into the AI ​​and have the AI ​​adjust the use of technical terms.

[0055] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the data. The past operation history can be, for example, a click history, an operation log, or the like, but is not limited to these examples. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to feedback provided by the user in the past. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to feedback provided by the user in the past. This enables a more appropriate display by selecting the optimal display method based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into AI and cause the AI ​​to select the optimal display method.

[0056] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. The current task is determined based on criteria such as, for example, work content and task priority, but is not limited to these examples. For example, when the user is at work, the providing unit may prioritize displaying work-related information. Furthermore, when the user is on vacation, the providing unit may prioritize displaying relaxing content. Furthermore, when the user is spending time with family, the providing unit may prioritize displaying content related to memories with the family. For example, when the user is at work, the providing unit may prioritize displaying work-related information. When the user is on vacation, the providing unit may prioritize displaying relaxing content. When the user is spending time with family, the providing unit may prioritize displaying content related to memories with the family. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's task data into the AI ​​and have the AI ​​customize the display content.

[0057] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the providing unit improves the delivery method by reflecting user feedback at the time of delivery. Feedback can be, for example, survey results, user comments, etc., but is not limited to these examples. For example, the providing unit adjusts the delivery method based on feedback previously provided by the user. The providing unit can also improve the delivery method by reflecting user feedback in real time. The providing unit can also analyze user feedback and select an optimal delivery method. For example, the providing unit adjusts the delivery method based on feedback previously provided by the user. The providing unit can improve the delivery method by reflecting user feedback in real time. The providing unit can also analyze user feedback and select an optimal delivery method. In this way, by improving the delivery method based on user feedback, more appropriate services can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI and cause the AI ​​to improve the delivery method.

[0058] The providing unit can select the optimal display method taking into account the user's geographical location information when providing the information. For example, the providing unit selects the optimal display method taking into account the user's geographical location information when providing the information. The geographical location information is acquired by, for example, GPS data, a location information service, or the like, but is not limited to these examples. The providing unit acquires the user's geographical location information using, for example, GPS data. The providing unit can also acquire the user's geographical location information using a location information service. For example, when the user is traveling, the providing unit can prioritize displaying information related to the travel destination. When the user is at home, the providing unit can prioritize displaying information related to memories at home. When the user is at a specific event venue, the providing unit can prioritize displaying information related to the event. This makes it possible to provide more appropriate information by selecting the optimal display method based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause the AI ​​to select the optimal display method.

[0059] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the information. The device information is determined according to criteria such as, for example, the device type, screen size, and OS, but is not limited to these examples. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. If the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. This makes it possible to provide more appropriate information by selecting the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0060] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the content. For example, the providing unit makes the display content multilingual in accordance with the user's language setting when providing the content. The language setting is determined based on criteria such as, for example, the user's language selection or the device's language setting, but is not limited to these examples. The providing unit automatically sets the display content based on the user's device's language setting, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes the display content multilingual based on the user's language setting, thereby enabling support for a larger number of users. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language settings into the AI ​​and have the AI ​​execute multilingual display content.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can also acquire the user's biometric information and select the type of data to collect based on the user's health condition. For example, it can monitor the user's heart rate and blood pressure, and prioritize collecting relaxing music and landscape photos if the user is under high stress. It can also analyze the user's sleep patterns and provide relaxing content if the user is sleep deprived. It can also acquire the user's exercise data and collect information useful for recovery after exercise. This makes it possible to provide personalized services based on the user's health condition.

[0063] The analysis unit can also estimate the user's hobbies and interests and adjust the analysis algorithm based on the estimated hobbies and interests. For example, if the user is interested in music, the analysis unit can prioritize analyzing music-related data and recommend music that suits the user's preferences. If the user is interested in sports, the analysis unit can analyze sports-related data and provide the latest sports news and event information. Furthermore, if the user is interested in cooking, the analysis unit can analyze cooking recipes and ingredient information and suggest recipes that suit the user. This makes it possible to provide personalized services based on the user's hobbies and interests.

[0064] The generation unit can also analyze the user's past behavioral patterns and customize the content of the conversations and sentences to be generated. For example, it can generate conversations that include information related to places the user has frequently visited in the past. It can also generate sentences that include topics related to events the user has attended in the past. It can also generate conversations that include information related to products the user has purchased in the past. This makes it possible to provide personalized communication based on the user's past behavioral patterns.

[0065] The providing unit can also obtain real-time location information of the user and customize the content to be provided based on the location information. For example, if the user is traveling, tourist information and restaurant recommendations for the travel destination can be provided. If the user is at a specific event venue, information related to the event can be provided. Furthermore, if the user is at home, entertainment information that can be enjoyed at home can be provided. This makes it possible to provide personalized services based on the user's location information.

[0066] The collection unit can also analyze a user's social media activities and collect related data. For example, it can collect photos and posts shared by the user on social media and provide content based on the user's interests. It can also collect information on accounts the user follows on social media and provide related news and articles. It can also collect information on groups and events the user participates in on social media and provide related content. This makes it possible to provide personalized services based on the user's social media activities.

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

[0068] Step 1: The collection unit collects past information. Past information includes text data, audio data, and image data. The collection unit collects information such as past photos, audio, notes, and emails stored on the device or in the cloud. The collection unit can also estimate the user's emotions and select the type of data to collect based on the estimated emotions. For example, if the user is sad, it will prioritize collecting photos and audio related to happy memories from the past. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. The analysis unit analyzes the collected information and imitates the user's thought patterns. The analysis unit can also estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is sad, an algorithm is used to provide analysis results that alleviate the emotions. Step 3: The generator generates conversations and sentences based on the information analyzed by the analyzer. Conversations and sentences are generated using methods such as dialogue systems, chatbots, and text generation algorithms. The generator generates conversations and sentences based on the analysis results and can also estimate the user's emotions and adjust the way the conversations and sentences are expressed. For example, if the user is sad, the generator generates conversations and sentences using kind language. Step 4: The providing unit provides the conversations and sentences generated by the generating unit. This is done through a user interface, a notification system, audio output, or other methods. The providing unit provides the generated conversations and sentences to the user, and can also retain the data even after the user's death and provide services to the surviving family. For example, the providing unit can retain the data even after the user's death and provide services to the surviving family.

[0069] (Example 2) A system according to an embodiment of the present invention retrieves information such as past photos, audio, memos, and emails stored on a mobile phone or in a cloud through an application installed on the mobile phone and provides services in two modes. The system offers a "memory sharing mode," in which the application functions as a partner with which the user can share past memories, and a "personality reproduction mode," in which conversations and sentences are generated that mimic the user's thought patterns based on the user's past information. For example, the system analyzes information such as past photos, audio, memos, and emails to provide conversations that remind the user of past events. The system also generates conversations and sentences that mimic the user's thought patterns, allowing users to create speeches for special occasions or corporate events that reflect the user's personality. This allows the system to provide new entertainment and care services that recreate the user's memories and personality. For example, the system allows users to reminisce about past memories and enjoy them as entertainment. Furthermore, since the data remains even after the user's death, the system can also be used as a service for bereaved families.

[0070] An information processing system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects past information. Examples of past information include, but are not limited to, text data, audio data, and image data. The collection unit collects information such as past photos, audio, notes, and emails stored on a device or in a cloud. The collection unit can also estimate a user's emotions and select the type of data to collect based on the estimated user emotions. For example, if the user is sad, photos and audio related to happy memories can be preferentially collected. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, natural language processing, image analysis, audio analysis, or other methods, but is not limited to these examples. For example, the analysis unit analyzes the collected information and mimics the user's thought patterns. The analysis unit can also estimate a user's emotions and adjust an analysis algorithm based on the estimated user emotions. For example, if the user is sad, an algorithm is used to provide analysis results that alleviate the user's emotions. The generation unit generates conversations and sentences based on the information analyzed by the analysis unit. The generation of conversations and sentences is performed using, for example, a dialogue system, a chatbot, a text generation algorithm, or the like, but is not limited to these examples. The generation unit generates conversations and sentences based on, for example, analysis results. The generation unit can also estimate a user's emotions and adjust the expression of the generated conversations and sentences based on the estimated user's emotions. For example, if the user is sad, the generation unit generates conversations and sentences using gentle language. The provision unit provides the conversations and sentences generated by the generation unit. The provision can be performed using, for example, a user interface, a notification system, audio output, or the like, but is not limited to these examples. The provision unit provides the generated conversations and sentences to the user, for example. The provision unit can also retain data even after the user's death and provide services to the surviving family. For example, the provision unit can retain data even after the user's death and provide services to the surviving family. As a result, the information processing system according to the embodiment can provide new entertainment and nursing care services that recreate the user's memories and personality.

[0071] The collection unit can collect information on past photos, audio, notes, and emails stored on a device or in a cloud. The collection unit, for example, collects information on past photos, audio, notes, and emails stored on a device or in a cloud. Examples of devices and clouds include, but are not limited to, smartphones, PCs, AWS, Google Cloud, etc. Examples of past photo, audio, note, and email information include, but are not limited to, JPEG, MP3, text files, and email formats. This allows for the collection of a wide range of information stored on a device or in a cloud, thereby covering a wide range of the user's past memories and information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information stored on a device or in a cloud into AI and have the AI ​​collect the information.

[0072] The analysis unit can analyze the collected information and imitate the user's thought patterns. The analysis unit can, for example, analyze the collected information and imitate the user's thought patterns. The analysis can be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to these examples. The analysis unit can, for example, use a machine learning algorithm to analyze the collected information and imitate the user's thought patterns. The analysis unit can also use a neural network to analyze the collected information and imitate the user's thought patterns. The analysis unit can also use behavioral analysis to analyze the collected information and imitate the user's thought patterns. For example, the analysis unit can analyze collected text data using natural language processing technology to imitate the user's thought patterns. The analysis unit can also analyze collected image data using image analysis technology to imitate the user's thought patterns. The analysis unit can also analyze collected voice data using voice analysis technology to imitate the user's thought patterns. By imitating the user's thought patterns, more natural conversations and sentences can be generated that are more user-like. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input collected information into AI and have the AI ​​imitate the user's thought patterns.

[0073] The generation unit can generate conversations and sentences based on the analysis results. The generation unit generates conversations and sentences based on, for example, the analysis results. The generation of conversations and sentences is performed using, for example, natural language generation technology, template-based generation, dialogue modeling, or other methods, but is not limited to these examples. The generation unit generates conversations and sentences based on the analysis results using, for example, natural language generation technology. The generation unit can also generate conversations and sentences based on the analysis results using template-based generation. The generation unit can also generate conversations and sentences based on the analysis results using a dialogue model. For example, the generation unit can generate user-like conversations based on the analysis results using natural language generation technology. The generation unit can also generate user-like sentences based on the analysis results using template-based generation. The generation unit can also generate user-like conversations based on the analysis results using a dialogue model. This enables natural communication that utilizes the user's past information by generating conversations and sentences based on the analysis 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 AI. For example, the generation unit can input the analysis results to AI and cause the AI ​​to generate conversations and sentences.

[0074] The providing unit can provide the generated conversations and sentences to the user. For example, the providing unit provides the generated conversations and sentences to the user. The provision can be performed, for example, by a user interface, a notification system, audio output, or other methods, but is not limited to these examples. For example, the providing unit can provide the generated conversations and sentences to the user. The providing unit can also retain data even after the user's death and provide services to the surviving family. For example, the providing unit retains data even after the user's death and provides services to the surviving family. By providing the generated conversations and sentences to the user, the user can look back on past memories and information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated conversations and sentences to AI and have the AI ​​provide them to the user.

[0075] The providing unit can retain data even after the death of a user and provide services to the surviving family members. The providing unit, for example, retains data even after the death of a user and provides services to the surviving family members. Data retention is performed in accordance with standards such as, for example, a data retention period, data protection measures, and a method of access to the surviving family members, but is not limited to such examples. The providing unit, for example, retains data even after the death of a user and provides services to the surviving family members. In this way, by retaining data even after the death of a user and providing services to the surviving family members, the surviving family members can reminisce about memories of the deceased. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can use an AI model to retain data even after the death of a user and provide services to the surviving family members.

[0076] The collection unit can obtain consent to data acquisition. The collection unit, for example, obtains consent to data acquisition. The consent to data acquisition is obtained according to criteria such as, for example, the format of the consent form, the timing of obtaining consent, and the scope of consent, but is not limited to such examples. The collection unit, for example, obtains consent to data acquisition. By obtaining consent to data acquisition, necessary data can be collected while protecting the user's privacy. 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 consent to data acquisition into AI and have the AI ​​obtain consent.

[0077] The providing unit can perform service promotion. The providing unit, for example, performs service promotion. The service promotion is performed according to criteria such as, for example, advertising method, promotion content, and target user, but is not limited to these examples. The providing unit, for example, performs service promotion using advertising method. The providing unit can also set promotion content and perform service promotion to target users. For example, the providing unit performs service promotion using internet advertising. The providing unit can also perform service promotion using social media. The providing unit can also perform email marketing to target users. In this way, service promotion can encourage users to use the service. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the service promotion into AI and leave the execution of the promotion to AI.

[0078] The collection unit can estimate the user's emotion and select the type of data to collect based on the estimated user's emotion. For example, the collection unit estimates the user's emotion and selects the type of data to collect based on the estimated user's emotion. Emotion estimation can be performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. For example, the collection unit estimates the user's emotion using an emotion recognition algorithm. The collection unit can also estimate the user's emotion using facial expression analysis. The collection unit can also estimate the user's emotion using voice analysis. For example, the collection unit analyzes the user's facial expression data to estimate the emotion. The collection unit can also analyze the user's voice data to estimate the emotion. The collection unit selects the type of data to collect based on the estimated user's emotion. For example, if the user is sad, the collection unit prioritizes collecting photos and audio related to happy memories from the past. If the user is relaxed, the collection unit can collect casual daily notes and emails. If the user is excited, the collection unit can collect data related to special events or trips. This allows for the provision of more personalized services by collecting optimal data according to the user's emotions. 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 may input the user's emotional data into AI and have the AI ​​select data based on the emotions.

[0079] The collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing when collecting data. For example, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing when collecting data. The behavioral pattern analysis can be performed, for example, by analyzing behavioral history, using a machine learning algorithm, or other methods, but is not limited to these examples. For example, the collection unit can analyze the user's past behavioral patterns using the behavioral history analysis. The collection unit can also analyze the user's past behavioral patterns using a machine learning algorithm. For example, if the user has a habit of reviewing photos every night before going to bed, the collection unit can collect data during that time period. If the user has a habit of organizing notes on the weekend, the collection unit can collect data on the weekend. If the user has a habit of checking emails after a specific event, the collection unit can collect data immediately thereafter. This enables efficient data collection by collecting data at the optimal timing based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's behavioral history data into AI and have the AI ​​determine the optimal collection timing.

[0080] The collection unit can prioritize data based on the user's current activity status at the time of collection. For example, the collection unit prioritizes data based on the user's current activity status at the time of collection. The current activity status is determined based on criteria such as, but not limited to, the user's schedule and real-time activity data. For example, the collection unit determines the current activity status by referring to the user's schedule. The collection unit can also analyze real-time activity data to determine the current activity status. For example, when the user is at work, the collection unit prioritizes collecting work-related notes and emails. When the user is on vacation, the collection unit can prioritize collecting travel-related photos and audio. When the user is spending time with family, the collection unit can prioritize collecting data related to family memories. This allows for more appropriate data to be collected by prioritizing data according to the user's activity status. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's activity data into AI and have the AI ​​prioritize the data.

[0081] The collection unit can select the optimal collection means depending on the user's input method during collection. For example, the collection unit selects the optimal collection means depending on the user's input method during collection. Input methods include, but are not limited to, voice input, text input, and image input. For example, for a user using voice input, the collection unit may preferentially collect voice data. For a user using text input, the collection unit may preferentially collect text data. For a user using a large number of images, the collection unit may preferentially collect image data. For example, for a user using voice input, the collection unit may collect voice data using voice recognition technology. For a user using text input, the collection unit may collect text data using text analysis technology. For a user using a large number of images, the collection unit may collect image data using image analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's input data into AI and have the AI ​​select the optimal collection means.

[0082] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, the collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. Emotion estimation can be performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. For example, the collection unit can estimate the user's emotion using an emotion recognition algorithm. The collection unit can also estimate the user's emotion using facial expression analysis. The collection unit can also estimate the user's emotion using voice analysis. For example, the collection unit can analyze the user's facial expression data to estimate the emotion. The collection unit can also analyze the user's voice data to estimate the emotion. The collection unit determines the priority of data to be collected based on the estimated user's emotion. For example, if the user is sad, the collection unit can prioritize collecting data related to past happy memories. If the user is relaxed, the collection unit can prioritize collecting casual everyday data. If the user is excited, the collection unit can prioritize collecting data related to special events. This allows for more personalized services to be provided by determining the priority of data according to the user's emotions. 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 may input the user's emotional data into AI and have the AI ​​determine the priority of data based on the emotions.

[0083] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information during collection. Geographical location information is acquired by, for example, GPS data, a location information service, or the like, but is not limited to these examples. For example, the collection unit acquires the user's geographical location information using GPS data. The collection unit can also acquire the user's geographical location information using a location information service. For example, when the user is traveling, the collection unit prioritizes collecting photos and notes related to the travel destination. When the user is at home, the collection unit can prioritize collecting data related to memories at home. When the user is at a specific event venue, the collection unit can prioritize collecting data related to the event. This makes it possible to provide more appropriate data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0084] The collection unit may analyze the user's social media activities and collect related data at the time of collection. For example, the collection unit may analyze the user's social media activities and collect related data at the time of collection. Social media activities include, but are not limited to, types of posts, likes, and comments. For example, the collection unit may collect photos shared by the user on social media. The collection unit may also collect notes and comments posted by the user on social media. The collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may collect photos shared by the user on social media. The collection unit may also collect notes and comments posted by the user on social media. The collection unit may also collect related data by referring to the activities of the user's friends on social media. This allows for the provision of more personalized services by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into AI and cause the AI ​​to collect related data.

[0085] The collection unit can customize the collection method by reflecting the user's past feedback during data collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback during data collection. Past feedback can be, for example, survey results, user comments, etc., but is not limited to these examples. For example, the collection unit prioritizes collecting types of data that the user has previously preferred to collect. The collection unit can also adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting data that the user has previously desired to collect. For example, the collection unit prioritizes collecting types of data that the user has previously preferred to collect. The collection unit can also adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize collecting data that the user has previously desired to collect. In this way, by customizing the collection method based on the user's past feedback, more appropriate data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​customize the collection method.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. Emotion estimation can be performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. For example, the analysis unit can estimate the user's emotion using an emotion recognition algorithm. The analysis unit can also estimate the user's emotion using facial expression analysis. The analysis unit can also estimate the user's emotion using voice analysis. For example, the analysis unit can analyze the user's facial expression data to estimate the emotion. The analysis unit can also analyze the user's voice data to estimate the emotion. The analysis unit adjusts the analysis algorithm based on the estimated user's emotion. For example, if the user is sad, the analysis unit can use an algorithm that provides an analysis result that soothes the emotion. If the user is relaxed, the analysis unit can use an algorithm that provides a detailed analysis result. If the user is excited, the analysis unit can use an algorithm that provides an interesting analysis result. This allows the analysis algorithm to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotional data into AI and have the AI ​​adjust the algorithm based on the emotion.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data is determined based on criteria such as, but not limited to, the impact, urgency, and relevance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the urgency of the data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. For example, the analysis unit analyzes data related to important events in detail. The analysis unit can also analyze casual everyday data in a simplified manner. The analysis unit can also analyze data related to a specific theme at a level of detail appropriate to the theme. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 the importance of the data into AI and have the AI ​​adjust the level of detail.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, image data, and audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply an audio analysis algorithm to audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply an audio analysis algorithm to audio data. This allows for applying the optimal analysis algorithm depending on the data category, thereby providing more accurate analysis results. Some or all of the above-described 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 the data category into AI and cause the AI ​​to apply the optimal analysis algorithm.

[0089] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past analysis reports and analysis logs. For example, the analysis unit can adjust the analysis algorithm based on the user's previously preferred analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and select an optimal analysis method. For example, the analysis unit can adjust the analysis algorithm based on the user's previously preferred analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can analyze the user's past analysis results and select an optimal analysis method. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the analysis priority based on the estimated user's emotion. Emotion estimation is performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. The analysis unit, for example, estimates the user's emotion using an emotion recognition algorithm. The analysis unit can also estimate the user's emotion using facial expression analysis. The analysis unit can also estimate the user's emotion using voice analysis. For example, the analysis unit analyzes the user's facial expression data to estimate the emotion. The analysis unit can also analyze the user's voice data to estimate the emotion. The analysis unit determines the analysis priority based on the estimated user's emotion. For example, if the user is sad, the analysis unit prioritizes analyzing data that soothes the emotion. If the user is relaxed, the analysis unit can prioritize analyzing data that requires detailed analysis. If the user is excited, the analysis unit can prioritize analyzing data that attracts interest. In this way, by determining the analysis priority according to the user's emotion, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​determine the priority of analysis based on emotion.

[0091] The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the analysis unit determines the analysis priority based on the time of data submission during analysis. The time of data submission is determined based on criteria such as, but not limited to, the submission date and time, the submission frequency, etc. The analysis unit determines the analysis priority based on, for example, the submission date and time. The analysis unit can also determine the analysis priority based on the submission frequency. For example, the analysis unit prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data related to a specific event. The analysis unit can also prioritize analysis of data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described 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 the time of data submission into AI and have the AI ​​determine the priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the data during analysis. The relevance of the data is determined based on criteria such as, but not limited to, the correlation between the data and the frequency of co-occurrence between the data. For example, the analysis unit adjusts the order of analysis based on the correlation between the data. The analysis unit can also adjust the order of analysis based on the frequency of co-occurrence. For example, the analysis unit analyzes data related to the same event together. The analysis unit can also prioritize analyzing data related to the user's interests. The analysis unit can also prioritize analyzing data related to a specific theme. By adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided. Some or all of the above-described 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 the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0093] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is determined according to criteria such as, for example, beginner, intermediate, or expert, but is not limited to such examples. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can select appropriate terms to provide analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can select appropriate terms to provide analysis results according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described 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 the user's level of expertise into the AI ​​and have the AI ​​adjust its use of technical terminology.

[0094] The generation unit can estimate the user's emotion and adjust the expression style of the generated conversation and sentences based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression style of the generated conversation and sentences based on the estimated user's emotion. Emotion estimation is performed using, for example, an emotion recognition algorithm, facial expression analysis, voice analysis, etc., but is not limited to these examples. The generation unit, for example, estimates the user's emotion using an emotion recognition algorithm. The generation unit can also estimate the user's emotion using facial expression analysis. The generation unit can also estimate the user's emotion using voice analysis. For example, the generation unit analyzes the user's facial expression data to estimate the emotion. The generation unit can also analyze the user's voice data to estimate the emotion. The generation unit adjusts the expression style of the generated conversation and sentences based on the estimated user's emotion. For example, if the user is sad, the generation unit generates conversation and sentences using gentle language. If the user is relaxed, the generation unit can also generate conversation and sentences using casual language. The generation unit can also generate conversations and sentences using energetic language when the user is excited. This allows for more personalized communication by adjusting the way the conversation and sentences are expressed according to the user's emotions. 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 the user's emotional data into AI and have the AI ​​adjust the way the expressions are expressed based on the emotions.

[0095] The generation unit can adjust the level of detail of the generated content based on the importance of the analysis result at the time of generation. For example, the generation unit adjusts the level of detail of the generated content based on the importance of the analysis result at the time of generation. The importance of the analysis result is determined according to criteria such as, but not limited to, the impact, urgency, and relevance of the data. For example, the generation unit adjusts the level of detail of the generated content based on the impact of the data. The generation unit can also adjust the level of detail of the generated content based on the urgency of the data. The generation unit can also adjust the level of detail of the generated content based on the relevance of the data. For example, the generation unit generates detailed analysis results related to important events. The generation unit can also generate simplified analysis results for everyday, casual analysis results. The generation unit can also generate analysis results related to a specific theme with a level of detail appropriate to the theme. This enables efficient generation by adjusting the level of detail of the generated content based on the importance of the analysis result. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​adjust the level of detail.

[0096] The generation unit can apply different generation algorithms depending on the category of the analysis result during generation. For example, the generation unit applies different generation algorithms depending on the category of the analysis result during generation. The categories of the analysis result include, but are not limited to, text data, image data, and audio data. For example, the generation unit applies a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a audio generation algorithm to audio data. For example, the generation unit applies a natural language generation algorithm to text data. The generation unit can also apply an image generation algorithm to image data. The generation unit can also apply a audio generation algorithm to audio data. This makes it possible to provide more accurate generation results by applying an optimal generation algorithm depending on the category of the analysis result. 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 the category of the analysis result to AI and cause the AI ​​to apply the optimal generation algorithm.

[0097] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, types of past generation results such as past generation reports and generation logs. For example, the generation unit can adjust the generation algorithm based on generation results that the user previously preferred. The generation unit can also improve the accuracy of generation by referring to the user's past feedback. The generation unit can also analyze the user's past generation results and select an optimal generation method. For example, the generation unit can adjust the generation algorithm based on generation results that the user previously preferred. The generation unit can also improve the accuracy of generation by referring to the user's past feedback. The generation unit can also analyze the user's past generation results and select an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past 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 the user's past generation results into AI and cause the AI ​​to improve the accuracy of generation.

[0098] The generation unit can estimate the user's emotion and adjust the length of the conversations and sentences to be generated based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the conversations and sentences to be generated based on the estimated user's emotion. Emotion estimation can be performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. For example, the generation unit can estimate the user's emotion using an emotion recognition algorithm. The generation unit can also estimate the user's emotion using facial expression analysis. The generation unit can also estimate the user's emotion using voice analysis. For example, the generation unit can analyze the user's facial expression data to estimate the emotion. The generation unit can also analyze the user's voice data to estimate the emotion. The generation unit adjusts the length of the conversations and sentences to be generated based on the estimated user's emotion. For example, if the user is sad, the generation unit can generate short and gentle conversations and sentences. If the user is relaxed, the generation unit can generate longer and more detailed conversations and sentences. If the user is excited, the generation unit can generate energetic and longer conversations and sentences. This allows for more personalized communication by adjusting the length of conversations and sentences according to the user's emotions. 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 may input user emotion data into AI and have the AI ​​adjust the length of conversations and sentences based on the emotion.

[0099] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the generation unit determines the generation priority based on the submission time of the analysis results at the time of generation. The submission time of the analysis results is determined based on criteria such as, but not limited to, the submission date and time, the submission frequency, etc. The generation unit determines the generation priority based on, for example, the submission date and time. The generation unit can also determine the generation priority based on the submission frequency. For example, the generation unit prioritizes generating recent analysis results. The generation unit can also prioritize generating analysis results related to a specific event. The generation unit can also prioritize generating analysis results within a period specified by the user. This enables efficient generation by determining the generation priority based on the submission time of the analysis 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 the submission time of the analysis results into AI and have the AI ​​determine the priority.

[0100] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit adjusts the order of generation based on the relevance of the analysis results during generation. The relevance of the analysis results is determined based on criteria such as, but not limited to, correlation between data and co-occurrence frequency. For example, the generation unit adjusts the order of generation based on correlation between data. The generation unit can also adjust the order of generation based on co-occurrence frequency. For example, the generation unit generates analysis results related to the same event together. The generation unit can also prioritize generating analysis results related to a user's interests. The generation unit can also prioritize generating analysis results related to a specific theme. In this way, by adjusting the order of generation based on the relevance of the analysis results, more appropriate generation results can be provided. 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 the relevance of the analysis results into AI and have the AI ​​adjust the order of generation.

[0101] The generation unit may adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. For example, the generation unit may adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. The level of expertise may be determined according to, for example, beginner, intermediate, expert, or other criteria, but is not limited to, examples. For example, if the user has technical knowledge, the generation unit may generate conversations and sentences that use a lot of technical terms. Furthermore, if the user does not have technical knowledge, the generation unit may generate conversations and sentences in simple language. Furthermore, the generation unit may select appropriate terms to generate conversations and sentences according to the user's level of expertise. For example, if the user has technical knowledge, the generation unit may generate conversations and sentences that use a lot of technical terms. If the user does not have technical knowledge, the generation unit may generate conversations and sentences in simple language. The generation unit may select appropriate terms to generate conversations and sentences according to the user's level of expertise. In this way, by adjusting the use of technical terms in the content to be generated according to the user's level of expertise, a generated result that is easier to understand may be provided. 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 may input the user's level of expertise into the AI ​​and have the AI ​​adjust the use of technical terms.

[0102] The providing unit can estimate the user's emotion and adjust the display method of the conversation and text to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the display method of the conversation and text to be provided based on the estimated user's emotion. Emotion estimation is performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. For example, the providing unit can estimate the user's emotion using an emotion recognition algorithm. The providing unit can also estimate the user's emotion using facial expression analysis. The providing unit can also estimate the user's emotion using voice analysis. For example, the providing unit can analyze the user's facial expression data to estimate the emotion. The providing unit can also analyze the user's voice data to estimate the emotion. The providing unit adjusts the display method of the conversation and text to be provided based on the estimated user's emotion. For example, if the user is sad, the providing unit can display the conversation and text with a background in soft colors. If the user is relaxed, the providing unit can display the conversation and text with a casual design. If the user is excited, the providing unit can display the conversation and text with an energetic design. This allows for a more personalized display by adjusting the display method of conversations and text according to the user's emotions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input user emotion data into AI and have the AI ​​adjust the display method based on the emotion.

[0103] The providing unit can select the optimal display method by referring to the user's past operation history when providing the data. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the data. The past operation history can be, for example, a click history, an operation log, or the like, but is not limited to these examples. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to feedback provided by the user in the past. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to feedback provided by the user in the past. This enables a more appropriate display by selecting the optimal display method based on the user's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into AI and cause the AI ​​to select the optimal display method.

[0104] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. The current task is determined based on criteria such as, for example, work content and task priority, but is not limited to these examples. For example, when the user is at work, the providing unit may prioritize displaying work-related information. Furthermore, when the user is on vacation, the providing unit may prioritize displaying relaxing content. Furthermore, when the user is spending time with family, the providing unit may prioritize displaying content related to memories with the family. For example, when the user is at work, the providing unit may prioritize displaying work-related information. When the user is on vacation, the providing unit may prioritize displaying relaxing content. When the user is spending time with family, the providing unit may prioritize displaying content related to memories with the family. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's task data into the AI ​​and have the AI ​​customize the display content.

[0105] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. For example, the providing unit improves the delivery method by reflecting user feedback at the time of delivery. Feedback can be, for example, survey results, user comments, etc., but is not limited to these examples. For example, the providing unit adjusts the delivery method based on feedback previously provided by the user. The providing unit can also improve the delivery method by reflecting user feedback in real time. The providing unit can also analyze user feedback and select an optimal delivery method. For example, the providing unit adjusts the delivery method based on feedback previously provided by the user. The providing unit can improve the delivery method by reflecting user feedback in real time. The providing unit can also analyze user feedback and select an optimal delivery method. In this way, by improving the delivery method based on user feedback, more appropriate services can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into AI and cause the AI ​​to improve the delivery method.

[0106] The providing unit can estimate the user's emotions and determine the priority of conversations and sentences to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of conversations and sentences to be provided based on the estimated user's emotions. Emotion estimation is performed using, for example, an emotion recognition algorithm, facial expression analysis, or voice analysis, but is not limited to these examples. The providing unit, for example, estimates the user's emotions using an emotion recognition algorithm. The providing unit can also estimate the user's emotions using facial expression analysis. The providing unit can also estimate the user's emotions using voice analysis. For example, the providing unit analyzes the user's facial expression data to estimate the user's emotions. The providing unit can also analyze the user's voice data to estimate the user's emotions. The providing unit determines the priority of conversations and sentences to be provided based on the estimated user's emotions. For example, if the user is sad, conversations and sentences that soothe the user's emotions can be preferentially provided. If the user is relaxed, conversations and sentences containing detailed information can be preferentially provided. If the user is excited, the providing unit can also preferentially provide interesting conversations and sentences. This allows for the provision of a more personalized service by determining the priority of conversations and sentences according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into AI and have the AI ​​determine the priority based on the emotion.

[0107] The providing unit can select the optimal display method taking into account the user's geographical location information when providing the information. For example, the providing unit selects the optimal display method taking into account the user's geographical location information when providing the information. The geographical location information is acquired by, for example, GPS data, a location information service, or the like, but is not limited to these examples. The providing unit acquires the user's geographical location information using, for example, GPS data. The providing unit can also acquire the user's geographical location information using a location information service. For example, when the user is traveling, the providing unit can prioritize displaying information related to the travel destination. When the user is at home, the providing unit can prioritize displaying information related to memories at home. When the user is at a specific event venue, the providing unit can prioritize displaying information related to the event. This makes it possible to provide more appropriate information by selecting the optimal display method based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause the AI ​​to select the optimal display method.

[0108] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the information. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the information. The device information is determined according to criteria such as, for example, the device type, screen size, and OS, but is not limited to these examples. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. If the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. This makes it possible to provide more appropriate information by selecting the optimal display method based on the user's device information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0109] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the content. For example, the providing unit makes the display content multilingual in accordance with the user's language setting when providing the content. The language setting is determined based on criteria such as, for example, the user's language selection or the device's language setting, but is not limited to these examples. The providing unit automatically sets the display content based on the user's device's language setting, for example. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit automatically sets the display content based on the user's device's language setting. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes the display content multilingual based on the user's language setting, thereby enabling support for a larger number of users. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language settings into the AI ​​and have the AI ​​execute multilingual display content. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 38B of the smart device 14 and select the type of data to be collected by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates conversations and sentences based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the conversations and sentences generated by the control unit 46A of the smart device 14 to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and select the type of data to be collected by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates conversations and sentences based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the conversations and sentences generated by the control unit 46A of the smart glasses 214 to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the headset type terminal 314 and select the type of data to be collected by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates conversations and sentences based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the conversations and sentences generated by the control unit 46A of the headset type terminal 314 to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can estimate the user's emotions using the camera 42 and microphone 238 of the robot 414 and select the type of data to be collected by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates conversations and sentences based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit provides the conversations and sentences generated by the control unit 46A of the robot 414 to the user.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The collection unit can also acquire the user's biometric information and select the type of data to collect based on the user's health condition. For example, it can monitor the user's heart rate and blood pressure, and prioritize collecting relaxing music and landscape photos if the user is under high stress. It can also analyze the user's sleep patterns and provide relaxing content if the user is sleep deprived. It can also acquire the user's exercise data and collect information useful for recovery after exercise. This makes it possible to provide personalized services based on the user's health condition.

[0112] The analysis unit can also estimate the user's hobbies and interests and adjust the analysis algorithm based on the estimated hobbies and interests. For example, if the user is interested in music, the analysis unit can prioritize analyzing music-related data and recommend music that suits the user's preferences. If the user is interested in sports, the analysis unit can analyze sports-related data and provide the latest sports news and event information. Furthermore, if the user is interested in cooking, the analysis unit can analyze cooking recipes and ingredient information and suggest recipes that suit the user. This makes it possible to provide personalized services based on the user's hobbies and interests.

[0113] The generation unit can also analyze the user's past behavioral patterns and customize the content of the conversations and sentences to be generated. For example, it can generate conversations that include information related to places the user has frequently visited in the past. It can also generate sentences that include topics related to events the user has attended in the past. It can also generate conversations that include information related to products the user has purchased in the past. This makes it possible to provide personalized communication based on the user's past behavioral patterns.

[0114] The providing unit can also obtain real-time location information of the user and customize the content to be provided based on the location information. For example, if the user is traveling, tourist information and restaurant recommendations for the travel destination can be provided. If the user is at a specific event venue, information related to the event can be provided. Furthermore, if the user is at home, entertainment information that can be enjoyed at home can be provided. This makes it possible to provide personalized services based on the user's location information.

[0115] The collection unit can also analyze a user's social media activities and collect related data. For example, it can collect photos and posts shared by the user on social media and provide content based on the user's interests. It can also collect information on accounts the user follows on social media and provide related news and articles. It can also collect information on groups and events the user participates in on social media and provide related content. This makes it possible to provide personalized services based on the user's social media activities.

[0116] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is sad, data that soothes the emotion is analyzed preferentially. If the user is relaxed, the analysis unit can also analyze data that requires detailed analysis preferentially. If the user is excited, the analysis unit can also analyze data that attracts interest preferentially. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0117] The generation unit can also estimate the user's emotions and adjust the expression style of the generated conversations and sentences based on the estimated user's emotions. For example, if the user is sad, the generation unit can generate conversations and sentences using gentle language. If the user is relaxed, the generation unit can also generate conversations and sentences using casual language. If the user is excited, the generation unit can also generate conversations and sentences using energetic language. This enables more personalized communication by adjusting the expression style of the conversations and sentences according to the user's emotions.

[0118] The providing unit can also estimate the user's emotions and adjust the display method of the conversations and texts to be provided based on the estimated user's emotions. For example, if the user is sad, the conversations and texts are displayed against a background with soft colors. If the user is relaxed, the providing unit can also display the conversations and texts in a casual design. If the user is excited, the providing unit can also display the conversations and texts in an energetic design. This allows for a more personalized display by adjusting the display method of the conversations and texts according to the user's emotions.

[0119] The providing unit can also estimate the user's emotions and determine the priority of conversations and sentences to be provided based on the estimated user's emotions. For example, if the user is sad, conversations and sentences that soothe the user's emotions can be provided preferentially. If the user is relaxed, the providing unit can also provide conversations and sentences that include detailed information preferentially. If the user is excited, the providing unit can also provide interesting conversations and sentences preferentially. In this way, by determining the priority of conversations and sentences according to the user's emotions, a more personalized service can be provided.

[0120] The providing unit can also estimate the user's emotions and select the type of content to provide based on the estimated user's emotions. For example, if the user is sad, the providing unit can provide music or videos that soothe the user's emotions. If the user is relaxed, the providing unit can provide relaxing music or pictures of scenery. If the user is excited, the providing unit can provide energetic music or action movies. This allows for a more personalized service by providing optimal content according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects past information. Past information includes text data, audio data, and image data. The collection unit collects information such as past photos, audio, notes, and emails stored on the device or in the cloud. The collection unit can also estimate the user's emotions and select the type of data to collect based on the estimated emotions. For example, if the user is sad, it will prioritize collecting photos and audio related to happy memories from the past. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. The analysis unit analyzes the collected information and imitates the user's thought patterns. The analysis unit can also estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is sad, an algorithm is used to provide analysis results that alleviate the emotions. Step 3: The generator generates conversations and sentences based on the information analyzed by the analyzer. Conversations and sentences are generated using methods such as dialogue systems, chatbots, and text generation algorithms. The generator generates conversations and sentences based on the analysis results and can also estimate the user's emotions and adjust the way the conversations and sentences are expressed. For example, if the user is sad, the generator generates conversations and sentences using kind language. Step 4: The providing unit provides the conversations and sentences generated by the generating unit. This is done through a user interface, a notification system, audio output, or other methods. The providing unit provides the generated conversations and sentences to the user, and can also retain the data even after the user's death and provide services to the surviving family. For example, the providing unit can retain the data even after the user's death and provide services to the surviving family.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 AI 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 AI 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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, in order to avoid confusion and to 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects past information; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a conversation based on the information analyzed by the analysis unit; a providing unit that provides the conversation generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Collect past photos, audio, notes, and email information stored on your device or in the cloud 2. The system of claim 1.

3. The analysis unit Analyzes collected information and mimics the user's thought patterns 2. The system of claim 1.

4. The generation unit Generate conversations and sentences based on analysis results 2. The system of claim 1.

5. The providing unit Providing generated conversations and sentences to the user 2. The system of claim 1.

6. The providing unit Retaining data even after a user's death and providing services to their surviving family members 2. The system of claim 1.

7. The collecting unit Give consent to data collection 2. The system of claim 1.

8. The providing unit Promote your services 2. The system of claim 1.

Images