system

Abstract

An object of a system according to an embodiment is to efficiently search for past memories of a user and describe the memories in detail.SOLUTION: A system includes a data storage part, a retrieval part, an explanation part, and an alternative answer part. The data storage unit stores data such as past photographs, mail history, and SNS posts of the user. The retrieval part retrieves the memory of the user based on the data stored by the data storage part. The explanation part explains the memories retrieved by the retrieval part in detail together with related moving images and images. The alternative answer unit provides a plausible answer when there is no corresponding data.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 techniques have had the problem of making it difficult to efficiently search for and describe a user's past memories in detail.

[0005] The system according to the embodiment aims to efficiently search for and provide detailed explanations of a user's past memories. [Means for solving the problem]

[0006] The system according to the embodiment includes a data storage unit, a search unit, an explanation unit, and an alternative answer unit. The data storage unit stores data such as the user's past photos, email history, and social media posts. The search unit searches for the user's memories based on the data stored by the data storage unit. The explanation unit provides a detailed explanation of the memories found by the search unit, including related videos and images. The alternative answer unit provides a plausible answer when there is no relevant data. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for and describe in detail the user's past memories. [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 private service according to an embodiment of the present invention is a system that uses a generation AI to search past data for personal memories and provide detailed explanations. This system accumulates data such as past photos, email history, and social media posts. When a user asks the generation AI about a memory, the system provides a detailed explanation, including videos and images related to the memory. Even if no relevant data is available, the generation AI provides a plausible answer. This allows the private service to easily reminisce about the user's memories. For example, if a user wants to know more about a particular event or trip, the generation AI searches for related data and provides an explanation with videos and images, vividly reviving the memory. Furthermore, even if no relevant data is available, the generation AI provides a plausible answer, ensuring that the user always has a satisfying experience.

[0029] A private service according to an embodiment includes a data storage unit, a search unit, an explanation unit, and an alternative answer unit. The data storage unit stores data such as a user's past photos, email history, and social media posts. For example, the data includes travel photos taken by the user in the past, email exchanges with friends, and social media posts. The search unit searches for the user's memories based on the data stored by the data storage unit. For example, when a user asks the generation AI, "Tell me about your memories of last summer's trip," the search unit searches for those memories based on the stored data. The explanation unit provides a detailed explanation of the memories retrieved by the search unit, including related videos and images. For example, the explanation may be in the form of, "During your last summer trip, you enjoyed yourself at the beach with your friends. Look at this photo." The alternative answer unit provides a plausible answer when no relevant data is available. For example, the explanation may be in the form of, "I couldn't find any specific data, but summer trips typically involve enjoying beach activities. I'm sure you had a wonderful time." This allows the private service according to an embodiment to provide a detailed explanation of the user's memories and provide a satisfactory answer even when no relevant data is available.

[0030] The data storage unit can also store users' voice memos and video logs and convert them into text using voice recognition technology. For example, the data storage unit collects voice memos recorded by users on a daily basis and converts them into text using voice recognition technology. For example, the data storage unit automatically collects voice data recorded through a voice memo app on a smartphone and saves it as text data. The data storage unit also collects video logs taken by users and converts the audio in the videos into text using voice recognition technology. For example, the data storage unit analyzes the audio portion of videos taken while traveling and saves the conversation content and location descriptions as text data. The data storage unit also integrates the converted voice memo and video log data with other data (photos, email history, social media posts) to improve the accuracy of memory searches. For example, the data storage unit links the content of voice memos to related photos and emails. In this way, converting voice memos and video logs into text improves the accuracy of memory searches.

[0031] The data storage unit collects location information data of a user and can search for memories based on the places visited. The data storage unit, for example, collects location information data from the user's smartphone and records the places visited. For example, GPS data is used to store the location information of tourist attractions and restaurants visited by the user. The data storage unit also automatically links photos and social media posts related to the places visited by the user based on the location information data. For example, it associates photos taken at a specific location with social media posts related to that location. The data storage unit also searches for related memories based on the location information data when the user wants to remember a specific place. For example, in response to a request such as "Tell me about memories of the beach you visited last summer," the data storage unit displays related photos and posts using the location information data. This allows for more accurate memory searches by searching for memories based on the location information data.

[0032] The data storage unit can also share data with family and friends, allowing them to accumulate shared memories. For example, the data storage unit creates albums and folders for family and friends to share and accumulate shared memories. For example, photos and videos from a family trip can be compiled into a single album. The data storage unit also adds a function to search for shared memories based on shared data. For example, in response to a request such as "Tell me about your memories of last year's family trip," it can display photos and videos taken by all family members. The data storage unit also adds a function to add comments and tags to data shared by family and friends. For example, each member can add comments to photos in a shared album and share memories. This makes it easier to accumulate shared memories by sharing data with family and friends.

[0033] The data storage unit may have a function that allows users to scan and upload handwritten notes or diary entries. The data storage unit, for example, provides a function that allows users to scan and upload handwritten notes or diary entries. For example, the data storage unit may scan handwritten notes using a smartphone camera and save them as digital data. The data storage unit may also convert the scanned handwritten notes or diary entries into text data, making them available for memory search. For example, the data storage unit may convert handwritten characters into text using OCR technology and save the text in a searchable database. The data storage unit may also provide a function to add tags and comments to handwritten notes or diary entries. For example, tags related to specific events or emotions may be added to handwritten data, making it easier to search for later. This makes it easier to store memories by scanning and uploading handwritten notes or diary entries.

[0034] The search unit can analyze a user's voice input and search for memories using natural language processing technology. For example, when a user asks about memories by voice, the search unit converts the voice into text using voice recognition technology. For example, a voice input such as "Tell me about your memories of last summer's trip" is converted into text data. The search unit also analyzes the voice-input text data using natural language processing technology to search for memories. For example, keywords are extracted from the voice-input text and related data is searched for. The search unit also uses a generation AI to search for memories based on the voice-input text data and display related photos and videos. For example, related data is displayed in response to a voice input such as "Tell me about your memories of last summer's trip." In this way, the accuracy of memory search is improved by analyzing voice input and using natural language processing technology.

[0035] The search unit can refer to the user's past search history and prioritize searching for related memories. The search unit, for example, records the user's past search history and references it when searching for memories. For example, it saves keywords and phrases searched in the past and uses them the next time it searches. The search unit also develops an algorithm that prioritizes searching for related memories based on the past search history. For example, it prioritizes displaying data related to keywords searched in the past. The search unit also adds a function that analyzes the user's search history and automatically suggests related memories. For example, it suggests memories that the user may be interested in based on the past search history. This allows the user to prioritize searching for related memories by referring to the past search history.

[0036] The search unit may have a function that allows images and videos to be used as input when a user asks about memories. The search unit provides a function that allows images and videos to be used as input when a user asks about memories. For example, a user can upload a specific photo or video and search for memories related to it. The search unit also uses image recognition technology to analyze the content of the uploaded image or video and search for related memories. For example, it identifies places and people appearing in the photo and displays data related to them. The search unit also uses a generative AI to search for memories based on images and videos uploaded by the user and displays related data. For example, in response to a request such as "Tell me about travel memories that are shown in this photo," related data is displayed. This improves the accuracy of memory searches by using images and videos as input.

[0037] The search unit may have a function to associate memories with those of other users when a user asks about memories. For example, the search unit provides a function to associate memories with those of other users when a user asks about memories. For example, memories of family and friends are shared and related data is displayed. The search unit also develops an algorithm to search for related memories based on memory data of other users. For example, related memories are displayed based on data shared by family and friends. The search unit also adds a function to link memories with those of other users when a user asks about memories. For example, in response to a request such as "Tell me about memories of trips you took with friends," data of friends is displayed. This allows for richer memory searches by associating with memories of other users.

[0038] The description unit can provide a more realistic experience by synthesizing and using the user's voice when describing the details of the memories. For example, the description unit uses the user's past voice data to synthesize the user's voice when describing the details of the memories. For example, the description unit analyzes voice memos recorded by the user in the past and uses them to describe the memories. The description unit also uses voice synthesis technology to generate a voice that imitates the user's voice and uses it to describe the details of the memories. For example, the description unit learns the characteristics of the user's voice and generates a natural voice. The description unit also synthesizes and uses the user's voice to make the detailed description of the memories feel more realistic. For example, the description unit provides an experience where the user is recounting their memories in their own voice. In this way, synthesizing and using the user's voice makes the detailed description of the memories feel more realistic.

[0039] The description unit can add related news and event information to the detailed description of the memory to explain the historical background. For example, the description unit adds news and event information from that time to the detailed description of the memory. For example, it searches for news and events from the time the user traveled and incorporates them into the description of the memory. The description unit also explains the historical background of the memory based on related news and event information. For example, it explains events that were held in places the user visited and social events that occurred during that time. The description unit also builds a system that automatically collects news and event information and adds it to the detailed description of the memory. For example, it automatically searches for news articles related to a specific time period or place and incorporates them into the description of the memory. In this way, by adding related news and event information, the historical background of the memory becomes clearer.

[0040] The description unit may have a function of playing music selected by the user in the background while the detailed description of the memory is being provided. The description unit, for example, provides a function that allows the user to select music to be used in the detailed description of the memory. For example, the user selects music that the user likes or music related to the memory and plays it in the background. The description unit also works with a music streaming service to build a system that automatically plays music selected by the user. For example, the description unit works with Spotify or Apple Music to play a playlist selected by the user. The description unit also plays music selected by the user in the background to enrich the detailed description of the memory emotionally. For example, playing music related to the memory allows the user to re-experience the emotions of that time. In this way, playing music selected by the user in the background enriches the detailed description of the memory emotionally.

[0041] The description unit may have a function that reflects a theme or style selected by the user in the detailed description of memories. The description unit, for example, provides a function that allows the user to select a theme or style to be used in the detailed description of memories. For example, a nostalgic theme or a modern style may be selected and reflected in the description. The description unit also builds a system that changes the format and design of the detailed description of memories according to the theme or style. For example, the description unit changes the background or font of the description to match the selected theme. The description unit also reflects the theme or style selected by the user to make the detailed description of memories more personalized and provide a special experience for the user. For example, the description unit provides a description tailored to the user's preferences. This allows the detailed description of memories to be more personalized by reflecting the theme or style selected by the user.

[0042] The alternative answer unit can generate a plausible scenario based on the user's past behavioral patterns even when there is no relevant data. The alternative answer unit, for example, analyzes the user's past behavioral patterns and generates a plausible scenario even when there is no relevant data. For example, it generates a hypothetical travel scenario based on past travel history and behavioral patterns. The alternative answer unit also develops a behavioral pattern analysis algorithm and automatically generates a plausible scenario based on the user's past data. For example, it suggests a scenario that the user is likely to enjoy based on past behavioral data. The alternative answer unit also generates an emotionally satisfying scenario based on the user's past behavioral patterns even when there is no relevant data. For example, it suggests a scenario that the user is likely to be moved by based on past behavioral data. In this way, even when there is no relevant data, plausible scenarios are generated based on past behavioral patterns, thereby improving user satisfaction.

[0043] The alternative answer unit can refer to data of the user's friends and family to generate related memories even when there is no relevant data. The alternative answer unit, for example, refers to data of the user's friends and family to generate related memories even when there is no relevant data. For example, it generates virtual memories based on photos and videos shared by family and friends. The alternative answer unit also develops an algorithm to generate scenarios that the user can easily empathize with emotionally based on the data of friends and family. For example, it suggests scenarios that the user is likely to enjoy based on the data of family and friends. The alternative answer unit also generates emotionally satisfying scenarios based on the data of the user's friends and family even when there is no relevant data. For example, it suggests scenarios that the user is likely to be moved by based on the data of family and friends. In this way, even when there is no relevant data, it is possible to generate related memories by referring to the data of friends and family.

[0044] The alternative answer unit can generate a plausible scenario based on a theme or style selected by the user even when relevant data is unavailable. The alternative answer unit generates a plausible scenario even when relevant data is unavailable, for example, based on a theme or style selected by the user. For example, it generates a hypothetical travel scenario that matches the theme selected by the user. The alternative answer unit also develops an algorithm that generates an emotionally satisfying scenario even when relevant data is unavailable, depending on the theme or style. For example, it proposes an emotional scenario that matches the theme selected by the user. The alternative answer unit also builds a system that generates an emotionally satisfying scenario even when relevant data is unavailable, based on the theme or style selected by the user. For example, it automatically generates a scenario that matches the user's preferences. This improves user satisfaction by generating a scenario based on the theme or style even when relevant data is unavailable.

[0045] The alternative answer unit can generate plausible scenarios using music and images selected by the user even when relevant data is unavailable. The alternative answer unit generates plausible scenarios even when relevant data is unavailable, for example, by using music and images selected by the user. For example, a virtual travel scenario is generated with music selected by the user playing in the background. The alternative answer unit also develops an algorithm that generates emotionally satisfying scenarios even when relevant data is unavailable, based on music and images. For example, it proposes moving scenarios that match the music and images selected by the user. The alternative answer unit also builds a system that generates emotionally satisfying scenarios even when relevant data is unavailable, based on music and images selected by the user. For example, it automatically generates scenarios that match the user's preferences. In this way, user satisfaction is improved by generating scenarios using music and images even when relevant data is unavailable.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The search unit can analyze the user's voice input and search for memories using natural language processing technology. For example, when a user asks about memories by voice, the voice is converted into text using speech recognition technology. For example, a voice input such as "Tell me about your memories of last summer's trip" is converted into text data. The search unit also uses natural language processing technology to analyze the voice-input text data and search for memories. For example, keywords are extracted from the voice-input text and related data is searched for. The search unit also uses a generation AI to search for memories based on the voice-input text data and display related photos and videos. For example, related data is displayed in response to a voice input such as "Tell me about your memories of last summer's trip." In this way, analyzing voice input and using natural language processing technology improves the accuracy of memory searches.

[0048] The data storage unit can collect location data of a user and search for memories based on the places visited. For example, it can collect location data from the user's smartphone and record the places visited. For example, it can use GPS data to save the locations of tourist attractions and restaurants visited by the user. The data storage unit also automatically links photos and social media posts related to the places visited by the user based on the location data. For example, it can associate photos taken at a specific location with social media posts related to that location. When the user wants to remember a specific place, the data storage unit can search for related memories based on the location data. For example, in response to a request such as "Tell me about memories of the beach you visited last summer," it can display related photos and posts using the location data. This allows for more accurate memory searches by searching for memories based on the location data.

[0049] The data storage unit can also share data with family and friends, allowing them to accumulate shared memories. For example, family and friends can create albums and folders to share and accumulate shared memories. For example, photos and videos from a family trip can be compiled into a single album. The data storage unit also adds a function to search for shared memories based on shared data. For example, in response to a request such as "Tell me about your memories of last year's family trip," it can display photos and videos taken by all family members. The data storage unit also adds a function to add comments and tags to data shared by family and friends. For example, each member can add comments to photos in a shared album and share their memories. This makes it easier to accumulate shared memories by sharing data with family and friends.

[0050] The data storage unit may have a function that allows users to scan and upload handwritten notes or diary entries. For example, a function that allows users to scan and upload handwritten notes or diary entries may be provided. For example, handwritten notes may be scanned using a smartphone camera and saved as digital data. The data storage unit may also convert the scanned handwritten notes or diary entries into text data, making them available for memory search. For example, handwritten characters may be converted to text using OCR technology and saved in a searchable database. The data storage unit may also provide a function to add tags and comments to handwritten notes or diary entries. For example, tags related to specific events or emotions may be added to handwritten data, making it easier to search for later. This makes it easier to store memories by scanning and uploading handwritten notes or diary entries.

[0051] The search unit can refer to the user's past search history and prioritize searching for related memories. For example, the search unit can record the user's past search history and refer to it when searching for memories. For example, it can save keywords and phrases searched in the past and use them the next time it searches. The search unit can also develop an algorithm that prioritizes searching for related memories based on the past search history. For example, it can prioritize displaying data related to keywords searched in the past. The search unit can also analyze the user's search history and add a function that automatically suggests related memories. For example, it can suggest memories that the user may be interested in based on the past search history. This allows the user to prioritize searching for related memories by referring to the past search history.

[0052] The description unit can provide a more realistic experience by synthesizing and using the user's voice when describing the details of memories. For example, the description unit uses the user's past voice data to synthesize the user's voice when describing the details of memories. For example, it analyzes voice memos recorded by the user in the past and uses them to describe the memories. The description unit also uses voice synthesis technology to generate a voice that imitates the user's voice and uses it when describing the details of memories. For example, it learns the characteristics of the user's voice and generates a natural voice. The description unit also synthesizes and uses the user's voice to make the detailed description of memories feel more realistic. For example, it provides an experience where the user is recounting their memories in their own voice. In this way, by synthesizing and using the user's voice, the detailed description of memories feels more realistic.

[0053] The description unit can add related news and event information to the detailed description of the memory to explain the historical background. For example, news and event information from that time is added to the detailed description of the memory. For example, news and events from the time the user traveled are searched for and incorporated into the description of the memory. The description unit also explains the historical background of the memory based on related news and event information. For example, it explains events that were held in places the user visited and social events that occurred during that time. The description unit also builds a system that automatically collects news and event information and adds it to the detailed description of the memory. For example, it automatically searches for news articles related to a specific time period or place and incorporates them into the description of the memory. In this way, adding related news and event information makes the historical background of the memory clearer.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data storage unit stores data such as the user's past photos, email history, and social media posts. For example, this includes photos the user took on a trip, email exchanges with friends, and content posted to social media. Step 2: The search unit searches for the user's memories based on the data accumulated by the data accumulation unit. For example, if a user asks the generating AI, "Tell me about your memories of last summer's trip," the search unit will search for those memories based on the accumulated data. Step 3: The description section provides a detailed explanation of the memories found by the search section, including related videos and images. For example, it might say, "During your trip last summer, you had fun at the beach with your friends. Look at this photo." Step 4: The alternative answer section provides a plausible answer in the absence of relevant data, such as, "I couldn't find any specific data, but summer trips typically involve beach activities. I'm sure you had a great time."

[0056] (Example 2) A private service according to an embodiment of the present invention is a system that uses a generation AI to search past data for personal memories and provide detailed explanations. This system accumulates data such as past photos, email history, and social media posts. When a user asks the generation AI about a memory, the system provides a detailed explanation, including videos and images related to the memory. Even if no relevant data is available, the generation AI provides a plausible answer. This allows the private service to easily reminisce about the user's memories. For example, if a user wants to know more about a particular event or trip, the generation AI searches for related data and provides an explanation with videos and images, vividly reviving the memory. Furthermore, even if no relevant data is available, the generation AI provides a plausible answer, ensuring that the user always has a satisfying experience.

[0057] A private service according to an embodiment includes a data storage unit, a search unit, an explanation unit, and an alternative answer unit. The data storage unit stores data such as a user's past photos, email history, and social media posts. For example, the data includes travel photos taken by the user in the past, email exchanges with friends, and social media posts. The search unit searches for the user's memories based on the data stored by the data storage unit. For example, when a user asks the generation AI, "Tell me about your memories of last summer's trip," the search unit searches for those memories based on the stored data. The explanation unit provides a detailed explanation of the memories retrieved by the search unit, including related videos and images. For example, the explanation may be in the form of, "During your last summer trip, you enjoyed yourself at the beach with your friends. Look at this photo." The alternative answer unit provides a plausible answer when no relevant data is available. For example, the explanation may be in the form of, "I couldn't find any specific data, but summer trips typically involve enjoying beach activities. I'm sure you had a wonderful time." This allows the private service according to an embodiment to provide a detailed explanation of the user's memories and provide a satisfactory answer even when no relevant data is available.

[0058] The data storage unit can also store users' voice memos and video logs and convert them into text using voice recognition technology. For example, the data storage unit collects voice memos recorded by users on a daily basis and converts them into text using voice recognition technology. For example, the data storage unit automatically collects voice data recorded through a voice memo app on a smartphone and saves it as text data. The data storage unit also collects video logs taken by users and converts the audio in the videos into text using voice recognition technology. For example, the data storage unit analyzes the audio portion of videos taken while traveling and saves the conversation content and location descriptions as text data. The data storage unit also integrates the converted voice memo and video log data with other data (photos, email history, social media posts) to improve the accuracy of memory searches. For example, the data storage unit links the content of voice memos to related photos and emails. In this way, converting voice memos and video logs into text improves the accuracy of memory searches.

[0059] The data storage unit collects location information data of a user and can search for memories based on the places visited. The data storage unit, for example, collects location information data from the user's smartphone and records the places visited. For example, GPS data is used to store the location information of tourist attractions and restaurants visited by the user. The data storage unit also automatically links photos and social media posts related to the places visited by the user based on the location information data. For example, it associates photos taken at a specific location with social media posts related to that location. The data storage unit also searches for related memories based on the location information data when the user wants to remember a specific place. For example, in response to a request such as "Tell me about memories of the beach you visited last summer," the data storage unit displays related photos and posts using the location information data. This allows for more accurate memory searches by searching for memories based on the location information data.

[0060] The data storage unit uses the emotion estimation function to record the emotions expressed by a user when uploading specific data, and can prioritize the storage of emotionally significant data. For example, when a user uploads a photo or video, the data storage unit uses the emotion estimation function to record the emotion expressed at the time. For example, it uses facial recognition technology to analyze the user's facial expression and calculate an emotion score. The data storage unit also prioritizes the storage of data with a high emotion score, and prioritizes the display of emotionally significant data when searching for memories. For example, it identifies photos and videos that show strong emotions such as joy or surprise and reflects them in search results. The data storage unit also tags data that the user expresses specific emotions based on the emotion estimation data, allowing the data to be easily searched later. For example, it automatically assigns tags such as "moving moment" or "fun memories." This prioritizes the storage of emotionally significant data, thereby improving the accuracy of memory searches.

[0061] The data storage unit can also share data with family and friends, allowing them to accumulate shared memories. For example, the data storage unit creates albums and folders for family and friends to share and accumulate shared memories. For example, photos and videos from a family trip can be compiled into a single album. The data storage unit also adds a function to search for shared memories based on shared data. For example, in response to a request such as "Tell me about your memories of last year's family trip," it can display photos and videos taken by all family members. The data storage unit also adds a function to add comments and tags to data shared by family and friends. For example, each member can add comments to photos in a shared album and share memories. This makes it easier to accumulate shared memories by sharing data with family and friends.

[0062] The data storage unit may have a function that allows users to scan and upload handwritten notes or diary entries. The data storage unit, for example, provides a function that allows users to scan and upload handwritten notes or diary entries. For example, the data storage unit may scan handwritten notes using a smartphone camera and save them as digital data. The data storage unit may also convert the scanned handwritten notes or diary entries into text data, making them available for memory search. For example, the data storage unit may convert handwritten characters into text using OCR technology and save the text in a searchable database. The data storage unit may also provide a function to add tags and comments to handwritten notes or diary entries. For example, tags related to specific events or emotions may be added to handwritten data, making it easier to search for later. This makes it easier to store memories by scanning and uploading handwritten notes or diary entries.

[0063] The data accumulation unit can use the emotion estimation function to analyze the emotions of a user when uploading data in real time and make suggestions to elicit positive emotions. The data accumulation unit, for example, uses the emotion estimation function to analyze emotions in real time when a user uploads data. For example, it uses a camera or microphone to analyze the user's facial expressions and voice and calculates an emotion score. The data accumulation unit also makes suggestions to elicit positive emotions based on the results of the emotion analysis. For example, it displays encouraging messages or positive comments for photos uploaded by the user. The data accumulation unit also provides feedback in real time based on the emotion estimation data when the user uploads data, and offers advice to strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. In this way, the user's emotions are analyzed in real time and positive emotions are elicited, thereby improving user satisfaction.

[0064] The search unit can analyze a user's voice input and search for memories using natural language processing technology. For example, when a user asks about memories by voice, the search unit converts the voice into text using voice recognition technology. For example, a voice input such as "Tell me about your memories of last summer's trip" is converted into text data. The search unit also analyzes the voice-input text data using natural language processing technology to search for memories. For example, keywords are extracted from the voice-input text and related data is searched for. The search unit also uses a generation AI to search for memories based on the voice-input text data and display related photos and videos. For example, related data is displayed in response to a voice input such as "Tell me about your memories of last summer's trip." In this way, the accuracy of memory search is improved by analyzing voice input and using natural language processing technology.

[0065] The search unit can refer to the user's past search history and prioritize searching for related memories. The search unit, for example, records the user's past search history and references it when searching for memories. For example, it saves keywords and phrases searched in the past and uses them the next time it searches. The search unit also develops an algorithm that prioritizes searching for related memories based on the past search history. For example, it prioritizes displaying data related to keywords searched in the past. The search unit also adds a function that analyzes the user's search history and automatically suggests related memories. For example, it suggests memories that the user may be interested in based on the past search history. This allows the user to prioritize searching for related memories by referring to the past search history.

[0066] The search unit uses the emotion estimation function to analyze the emotion expressed when a user asks about memories, and can prioritize searching for emotionally significant memories. For example, when a user asks about memories, the search unit uses the emotion estimation function to analyze the emotion expressed at the time. For example, the search unit analyzes the tone and facial expression during voice input to calculate an emotion score. The search unit also prioritizes searching for memories with high emotion scores and provides them to the user. For example, an algorithm is developed that prioritizes displaying emotionally significant memories. The search unit also enables the user to easily search for emotionally significant memories based on the emotion estimation data. For example, data related to emotions such as "moving moments" and "fun memories" is prioritized and displayed. This prioritizes searching for emotionally significant memories, thereby improving user satisfaction.

[0067] The search unit may have a function that allows images and videos to be used as input when a user asks about memories. The search unit provides a function that allows images and videos to be used as input when a user asks about memories. For example, a user can upload a specific photo or video and search for memories related to it. The search unit also uses image recognition technology to analyze the content of the uploaded image or video and search for related memories. For example, it identifies places and people appearing in the photo and displays data related to them. The search unit also uses a generative AI to search for memories based on images and videos uploaded by the user and displays related data. For example, in response to a request such as "Tell me about travel memories that are shown in this photo," related data is displayed. This improves the accuracy of memory searches by using images and videos as input.

[0068] The search unit may have a function to associate memories with those of other users when a user asks about memories. For example, the search unit provides a function to associate memories with those of other users when a user asks about memories. For example, memories of family and friends are shared and related data is displayed. The search unit also develops an algorithm to search for related memories based on memory data of other users. For example, related memories are displayed based on data shared by family and friends. The search unit also adds a function to link memories with those of other users when a user asks about memories. For example, in response to a request such as "Tell me about memories of trips you took with friends," data of friends is displayed. This allows for richer memory searches by associating with memories of other users.

[0069] The search unit uses the emotion estimation function to analyze the emotions of the user when asking about memories in real time and make suggestions that will elicit positive emotions. For example, the search unit uses the emotion estimation function to analyze emotions in real time when the user asks about memories. For example, it analyzes the tone and facial expression during voice input and calculates an emotion score. The search unit also makes suggestions that will elicit positive emotions based on the results of the emotion analysis. For example, it displays encouraging messages and positive comments in response to the memories asked by the user. The search unit also provides feedback in real time based on the emotion estimation data when the user asks about memories, and offers advice that will strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. In this way, the user's emotions are analyzed in real time and positive emotions are elicited, thereby improving user satisfaction.

[0070] The description unit can provide a more realistic experience by synthesizing and using the user's voice when describing the details of the memories. For example, the description unit uses the user's past voice data to synthesize the user's voice when describing the details of the memories. For example, the description unit analyzes voice memos recorded by the user in the past and uses them to describe the memories. The description unit also uses voice synthesis technology to generate a voice that imitates the user's voice and uses it to describe the details of the memories. For example, the description unit learns the characteristics of the user's voice and generates a natural voice. The description unit also synthesizes and uses the user's voice to make the detailed description of the memories feel more realistic. For example, the description unit provides an experience where the user is recounting their memories in their own voice. In this way, synthesizing and using the user's voice makes the detailed description of the memories feel more realistic.

[0071] The description unit can add related news and event information to the detailed description of the memory to explain the historical background. For example, the description unit adds news and event information from that time to the detailed description of the memory. For example, it searches for news and events from the time the user traveled and incorporates them into the description of the memory. The description unit also explains the historical background of the memory based on related news and event information. For example, it explains events that were held in places the user visited and social events that occurred during that time. The description unit also builds a system that automatically collects news and event information and adds it to the detailed description of the memory. For example, it automatically searches for news articles related to a specific time period or place and incorporates them into the description of the memory. In this way, by adding related news and event information, the historical background of the memory becomes clearer.

[0072] The explanation unit can use the emotion estimation function to analyze the emotions of the user when listening to memories and provide an explanation that is easy to empathize with emotionally. For example, when a user listens to memories, the explanation unit uses the emotion estimation function to analyze the emotions at that time. For example, it analyzes voice and facial expressions and calculates an emotion score. The explanation unit also prioritizes explaining memories with high emotion scores, providing an experience that is easy for the user to empathize with emotionally. For example, it prioritizes explaining moving memories and happy memories. The explanation unit also builds a system that provides explanations that are easy for the user to empathize with emotionally based on the emotion estimation data. For example, it automatically generates explanations that match the user's emotions. As a result, user satisfaction is improved by providing explanations that are easy for the user to empathize with emotionally.

[0073] The description unit may have a function of playing music selected by the user in the background while the detailed description of the memory is being provided. The description unit, for example, provides a function that allows the user to select music to be used in the detailed description of the memory. For example, the user selects music that the user likes or music related to the memory and plays it in the background. The description unit also works with a music streaming service to build a system that automatically plays music selected by the user. For example, the description unit works with Spotify or Apple Music to play a playlist selected by the user. The description unit also plays music selected by the user in the background to enrich the detailed description of the memory emotionally. For example, playing music related to the memory allows the user to re-experience the emotions of that time. In this way, playing music selected by the user in the background enriches the detailed description of the memory emotionally.

[0074] The description unit may have a function that reflects a theme or style selected by the user in the detailed description of memories. The description unit, for example, provides a function that allows the user to select a theme or style to be used in the detailed description of memories. For example, a nostalgic theme or a modern style may be selected and reflected in the description. The description unit also builds a system that changes the format and design of the detailed description of memories according to the theme or style. For example, the description unit changes the background or font of the description to match the selected theme. The description unit also reflects the theme or style selected by the user to make the detailed description of memories more personalized and provide a special experience for the user. For example, the description unit provides a description tailored to the user's preferences. This allows the detailed description of memories to be more personalized by reflecting the theme or style selected by the user.

[0075] The explanation unit can use the emotion estimation function to analyze the emotions of the user when listening to the user's memories in real time and make suggestions to elicit positive emotions. For example, the explanation unit uses the emotion estimation function to analyze the emotions of the user when listening to the user's memories. For example, it analyzes voice and facial expressions and calculates an emotion score. The explanation unit also makes suggestions to elicit positive emotions based on the results of the emotion analysis. For example, it displays encouraging messages and positive comments when the user listens to the user's memories. The explanation unit also provides feedback in real time based on the emotion estimation data when the user listens to the user's memories, and offers advice to strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. In this way, the user's emotions are analyzed in real time and positive emotions are elicited, thereby improving user satisfaction.

[0076] The alternative answer unit can generate a plausible scenario based on the user's past behavioral patterns even when there is no relevant data. The alternative answer unit, for example, analyzes the user's past behavioral patterns and generates a plausible scenario even when there is no relevant data. For example, it generates a hypothetical travel scenario based on past travel history and behavioral patterns. The alternative answer unit also develops a behavioral pattern analysis algorithm and automatically generates a plausible scenario based on the user's past data. For example, it suggests a scenario that the user is likely to enjoy based on past behavioral data. The alternative answer unit also generates an emotionally satisfying scenario based on the user's past behavioral patterns even when there is no relevant data. For example, it suggests a scenario that the user is likely to be moved by based on past behavioral data. In this way, even when there is no relevant data, plausible scenarios are generated based on past behavioral patterns, thereby improving user satisfaction.

[0077] The alternative answer unit can refer to data of the user's friends and family to generate related memories even when there is no relevant data. The alternative answer unit, for example, refers to data of the user's friends and family to generate related memories even when there is no relevant data. For example, it generates virtual memories based on photos and videos shared by family and friends. The alternative answer unit also develops an algorithm to generate scenarios that the user can easily empathize with emotionally based on the data of friends and family. For example, it suggests scenarios that the user is likely to enjoy based on the data of family and friends. The alternative answer unit also generates emotionally satisfying scenarios based on the data of the user's friends and family even when there is no relevant data. For example, it suggests scenarios that the user is likely to be moved by based on the data of family and friends. In this way, even when there is no relevant data, it is possible to generate related memories by referring to the data of friends and family.

[0078] The alternative answer unit can use the emotion estimation function to analyze the user's emotions when there is no relevant data and provide an emotionally satisfying answer. For example, when there is no relevant data, the alternative answer unit uses the emotion estimation function to analyze the user's emotions at that time. For example, it analyzes voice and facial expressions and calculates an emotion score. The alternative answer unit also develops an algorithm that provides an answer that is emotionally satisfying to the user even when the emotion score is low. For example, it suggests a scenario that is likely to move the user based on the emotion score. The alternative answer unit also builds a system that provides an emotionally satisfying answer based on the emotion estimation data even when there is no relevant data. For example, it automatically generates an answer that matches the user's emotions. In this way, even when there is no relevant data, the user's satisfaction is improved by analyzing emotions and providing a satisfying answer.

[0079] The alternative answer unit can generate a plausible scenario based on a theme or style selected by the user even when relevant data is unavailable. The alternative answer unit generates a plausible scenario even when relevant data is unavailable, for example, based on a theme or style selected by the user. For example, it generates a hypothetical travel scenario that matches the theme selected by the user. The alternative answer unit also develops an algorithm that generates an emotionally satisfying scenario even when relevant data is unavailable, depending on the theme or style. For example, it proposes an emotional scenario that matches the theme selected by the user. The alternative answer unit also builds a system that generates an emotionally satisfying scenario even when relevant data is unavailable, based on the theme or style selected by the user. For example, it automatically generates a scenario that matches the user's preferences. This improves user satisfaction by generating a scenario based on the theme or style even when relevant data is unavailable.

[0080] The alternative answer unit can generate plausible scenarios using music and images selected by the user even when relevant data is unavailable. The alternative answer unit generates plausible scenarios even when relevant data is unavailable, for example, by using music and images selected by the user. For example, a virtual travel scenario is generated with music selected by the user playing in the background. The alternative answer unit also develops an algorithm that generates emotionally satisfying scenarios even when relevant data is unavailable, based on music and images. For example, it proposes moving scenarios that match the music and images selected by the user. The alternative answer unit also builds a system that generates emotionally satisfying scenarios even when relevant data is unavailable, based on music and images selected by the user. For example, it automatically generates scenarios that match the user's preferences. In this way, user satisfaction is improved by generating scenarios using music and images even when relevant data is unavailable.

[0081] The alternative answer unit can use the emotion estimation function to analyze the user's emotions in real time when there is no relevant data and make suggestions to elicit positive emotions. The alternative answer unit can, for example, use the emotion estimation function to analyze emotions in real time when there is no relevant data for the user. For example, it can analyze voice and facial expressions and calculate an emotion score. The alternative answer unit can also make suggestions to elicit positive emotions based on the results of the emotion analysis. For example, it can display encouraging messages and positive comments even when there is no relevant data for the user. The alternative answer unit can also provide feedback in real time based on the emotion estimation data when there is no relevant data for the user, and offer advice to strengthen positive emotions. For example, it can display appropriate encouragement or praise based on the input content. In this way, even when there is no relevant data, emotions can be analyzed in real time and positive emotions can be elicited, thereby improving user satisfaction.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The search unit can analyze the user's voice input and search for memories using natural language processing technology. For example, when a user asks about memories by voice, the voice is converted into text using speech recognition technology. For example, a voice input such as "Tell me about your memories of last summer's trip" is converted into text data. The search unit also uses natural language processing technology to analyze the voice-input text data and search for memories. For example, keywords are extracted from the voice-input text and related data is searched for. The search unit also uses a generation AI to search for memories based on the voice-input text data and display related photos and videos. For example, related data is displayed in response to a voice input such as "Tell me about your memories of last summer's trip." In this way, analyzing voice input and using natural language processing technology improves the accuracy of memory searches.

[0084] The data storage unit can collect location data of a user and search for memories based on the places visited. For example, it can collect location data from the user's smartphone and record the places visited. For example, it can use GPS data to save the locations of tourist attractions and restaurants visited by the user. The data storage unit also automatically links photos and social media posts related to the places visited by the user based on the location data. For example, it can associate photos taken at a specific location with social media posts related to that location. When the user wants to remember a specific place, the data storage unit can search for related memories based on the location data. For example, in response to a request such as "Tell me about memories of the beach you visited last summer," it can display related photos and posts using the location data. This allows for more accurate memory searches by searching for memories based on the location data.

[0085] The data storage unit uses the emotion estimation function to record the emotions expressed by users when they upload specific data, allowing it to prioritize the storage of emotionally significant data. For example, when a user uploads a photo or video, the emotion estimation function records the emotions expressed at the time. For example, facial recognition technology is used to analyze the user's facial expressions and calculate an emotion score. The data storage unit also prioritizes the storage of data with high emotion scores, allowing it to prioritize the display of emotionally significant data when searching for memories. For example, it identifies photos and videos that show strong emotions such as joy or surprise and reflects them in search results. The data storage unit also tags data that the user expresses specific emotions based on the emotion estimation data, allowing it to be easily searched for later. For example, it automatically assigns tags such as "moving moments" or "fun memories." This prioritizes the storage of emotionally significant data, improving the accuracy of memory searches.

[0086] The data storage unit can also share data with family and friends, allowing them to accumulate shared memories. For example, family and friends can create albums and folders to share and accumulate shared memories. For example, photos and videos from a family trip can be compiled into a single album. The data storage unit also adds a function to search for shared memories based on shared data. For example, in response to a request such as "Tell me about your memories of last year's family trip," it can display photos and videos taken by all family members. The data storage unit also adds a function to add comments and tags to data shared by family and friends. For example, each member can add comments to photos in a shared album and share their memories. This makes it easier to accumulate shared memories by sharing data with family and friends.

[0087] The data storage unit may have a function that allows users to scan and upload handwritten notes or diary entries. For example, a function that allows users to scan and upload handwritten notes or diary entries may be provided. For example, handwritten notes may be scanned using a smartphone camera and saved as digital data. The data storage unit may also convert the scanned handwritten notes or diary entries into text data, making them available for memory search. For example, handwritten characters may be converted to text using OCR technology and saved in a searchable database. The data storage unit may also provide a function to add tags and comments to handwritten notes or diary entries. For example, tags related to specific events or emotions may be added to handwritten data, making it easier to search for later. This makes it easier to store memories by scanning and uploading handwritten notes or diary entries.

[0088] The search unit can refer to the user's past search history and prioritize searching for related memories. For example, the search unit can record the user's past search history and refer to it when searching for memories. For example, it can save keywords and phrases searched in the past and use them the next time it searches. The search unit can also develop an algorithm that prioritizes searching for related memories based on the past search history. For example, it can prioritize displaying data related to keywords searched in the past. The search unit can also analyze the user's search history and add a function that automatically suggests related memories. For example, it can suggest memories that the user may be interested in based on the past search history. This allows the user to prioritize searching for related memories by referring to the past search history.

[0089] The search unit uses the emotion estimation function to analyze the emotion a user expresses when asking about memories, and can prioritize searching for emotionally significant memories. For example, when a user asks about memories, the emotion estimation function is used to analyze the emotion at that time. For example, the tone and facial expression during voice input are analyzed to calculate an emotion score. The search unit also prioritizes searching for memories with a high emotion score and provides them to the user. For example, an algorithm is developed to prioritize displaying emotionally significant memories. The search unit also enables the user to easily search for emotionally significant memories based on the emotion estimation data. For example, data related to emotions such as "moving moments" and "fun memories" is prioritized and displayed. This prioritizes searching for emotionally significant memories, thereby improving user satisfaction.

[0090] The description unit can provide a more realistic experience by synthesizing and using the user's voice when describing the details of memories. For example, the description unit uses the user's past voice data to synthesize the user's voice when describing the details of memories. For example, it analyzes voice memos recorded by the user in the past and uses them to describe the memories. The description unit also uses voice synthesis technology to generate a voice that imitates the user's voice and uses it when describing the details of memories. For example, it learns the characteristics of the user's voice and generates a natural voice. The description unit also synthesizes and uses the user's voice to make the detailed description of memories feel more realistic. For example, it provides an experience where the user is recounting their memories in their own voice. In this way, by synthesizing and using the user's voice, the detailed description of memories feels more realistic.

[0091] The description unit can add related news and event information to the detailed description of the memory to explain the historical background. For example, news and event information from that time is added to the detailed description of the memory. For example, news and events from the time the user traveled are searched for and incorporated into the description of the memory. The description unit also explains the historical background of the memory based on related news and event information. For example, it explains events that were held in places the user visited and social events that occurred during that time. The description unit also builds a system that automatically collects news and event information and adds it to the detailed description of the memory. For example, it automatically searches for news articles related to a specific time period or place and incorporates them into the description of the memory. In this way, adding related news and event information makes the historical background of the memory clearer.

[0092] The explanation unit can use the emotion estimation function to analyze the emotions a user feels when listening to a memory and provide an explanation that is easy to empathize with emotionally. For example, when a user listens to a memory, the emotion estimation function is used to analyze the emotion at that time. For example, the voice and facial expression are analyzed and an emotion score is calculated. The explanation unit also prioritizes explaining memories with a high emotion score, providing an experience that is easy for the user to empathize with emotionally. For example, moving memories and happy memories are prioritized. The explanation unit also builds a system that provides explanations that are easy for the user to empathize with emotionally based on the emotion estimation data. For example, an explanation that matches the user's emotions is automatically generated. This improves user satisfaction by providing explanations that are easy for the user to empathize with emotionally.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data storage unit stores data such as the user's past photos, email history, and social media posts. For example, this includes photos the user took on a trip, email exchanges with friends, and content posted to social media. Step 2: The search unit searches for the user's memories based on the data accumulated by the data accumulation unit. For example, if a user asks the generating AI, "Tell me about your memories of last summer's trip," the search unit will search for those memories based on the accumulated data. Step 3: The description section provides a detailed explanation of the memories found by the search section, including related videos and images. For example, it might say, "During your trip last summer, you had fun at the beach with your friends. Look at this photo." Step 4: The alternative answer section provides a plausible answer in the absence of relevant data, such as, "I couldn't find any specific data, but summer trips typically involve beach activities. I'm sure you had a great time."

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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. [Explanation of symbols]

[0162] 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 data storage unit that stores data such as users' past photos, email history, and SNS posts; a search unit that searches for memories of the user based on the data stored by the data storage unit; an explanation section that explains in detail the memories searched by the search section, using related videos and images; An alternative answer section that provides a plausible answer when there is no relevant data. A system characterized by:

2. The data storage unit The system also stores the user's voice memos and video logs, and converts the voice memos and video logs into text using voice recognition technology.

2. The system of claim 1.

3. The data storage unit Collecting location data of the user and retrieving memories based on the places visited 2. The system of claim 1.

4. The data storage unit The emotions of the user when uploading specific data are recorded, and emotionally significant data is preferentially stored.

2. The system of claim 1.

5. The data storage unit Share data with family and friends to build shared memories 2. The system of claim 1.

6. The data storage unit The user has the ability to scan and upload handwritten notes and diaries.

2. The system of claim 1.

Images