System

Abstract

An object of the system according to the embodiment is to analyze position information and a situation of a photograph and propose a new event or a place to visit.SOLUTION: A system according to an embodiment includes a photograph upload unit, a position information acquisition unit, a situation analysis unit, and a proposal unit. The photograph upload unit uploads a photograph. The position information acquisition unit acquires position information of the photograph uploaded by the photograph upload unit. The situation analysis unit analyzes a situation of the photograph on the basis of the position information acquired by the position information acquisition unit. The proposal unit proposes a new event or a place to visit based on the information analyzed by the situation analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem of not being able to fully utilize the location information and context of photos to suggest new events or places to visit.

[0005] The system according to the embodiment aims to analyze the location information and situation of a photo and suggest new events and places to visit. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo upload unit, a location information acquisition unit, a situation analysis unit, and a suggestion unit. The photo upload unit uploads photos. The location information acquisition unit acquires location information of photos uploaded by the photo upload unit. The situation analysis unit analyzes the situation of the photos based on the location information acquired by the location information acquisition unit. The suggestion unit suggests new events and places to visit based on the information analyzed by the situation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the location information and context of the photo and suggest new events and places to visit. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The memory recording system according to an embodiment of the present invention is a system that analyzes the location information and context of photos by simply uploading photos of places a user has visited with their child, automatically creating diary-style memories, and suggesting new events and places to visit. This allows users to easily record memories with their children and plan new experiences even during their busy daily lives.

[0029] A memory recording system according to an embodiment includes a photo upload unit, a location information acquisition unit, a situation analysis unit, and a suggestion unit. The photo upload unit uploads photos of places a user has visited with their child. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo upload unit can also accept photos in file formats such as JPEG, PNG, and RAW. The location information acquisition unit acquires location information for photos uploaded by the photo upload unit. For example, it can analyze GPS data included in the photos to identify visited locations. The location information acquisition unit can also acquire location information using Wi-Fi location information or IP addresses. The situation analysis unit analyzes the situation of the photo based on the location information acquired by the location information acquisition unit. For example, it can use image recognition technology to analyze playground equipment, scenery, and children's expressions in the photos. The situation analysis unit can also use machine learning algorithms to understand the content of the photos and generate diary-style text. The suggestion unit suggests new events and places to visit based on the information analyzed by the situation analysis unit. For example, it can suggest new nearby parks and events based on data on parks and events the user has visited in the past. The suggestion unit can also provide detailed information about suggested events and places. This allows the memory recording system according to the embodiment to record memories and suggest new events and places to visit simply by the user uploading photos. For example, even if the user is unsure where to go on the weekend, the system can automatically make suggestions, saving the user the trouble of making plans. It also makes it easy to look back on past memories, deepening family ties.

[0030] The location information acquisition unit can automatically acquire weather information and seasonal information in addition to the location information of the photo and reflect this in the memory record. For example, the location information acquisition unit automatically acquires weather information for the location based on the location information of the photo and reflects this in the memory record. For example, if the weather was sunny on the day the photo was taken, it will record something like "We played in the park today under clear skies." The location information acquisition unit also automatically acquires seasonal information and reflects this in the memory record. For example, if the photo was taken in spring, it will record something like "We played in the warm spring sunshine." This allows for more detailed memory recording by reflecting weather information and seasonal information.

[0031] The location information acquisition unit can compare the location information of the photo with past visit history and record changes when the user revisits the same place. The location information acquisition unit, for example, compares the location information of the photo with past visit history and records changes when the user revisits the same place. For example, it can record changes such as, "The cherry blossoms were in full bloom the last time I visited, but this time the autumn leaves were at their peak." The location information acquisition unit can also store past visit history in a database and automatically compare it when the user revisits the same place. This allows changes to be recorded when the user revisits the same place by comparing it with past visit history.

[0032] The suggestion unit can automatically acquire and suggest information about nearby tourist spots and restaurants based on the location information of the photo. The suggestion unit, for example, automatically acquires and suggests information about nearby tourist spots based on the location information of the photo. For example, it may suggest, "There is a zoo near this park. Why not visit the zoo next time?" The suggestion unit can also automatically acquire and suggest information about nearby restaurants. For example, it may suggest, "There is a delicious restaurant near this park. Why not go there for lunch?" In this way, by suggesting information about nearby tourist spots and restaurants, it is possible to expand the user's range of activities.

[0033] The suggestion unit can display reviews and ratings from other users who visited the same place based on the location information of the photo. For example, the suggestion unit displays reviews from other users who visited the same place based on the location information of the photo. For example, it displays a review such as "This park is very spacious and is a fun place for children." The suggestion unit can also display ratings from other users. For example, it displays "This park has been rated five stars." Displaying reviews and ratings from other users helps users select a place to visit.

[0034] The situation analysis unit can generate more detailed memories by analyzing audio data and video data in analyzing the situation of photos. For example, the situation analysis unit can analyze audio data in addition to photos to generate more detailed memories. For example, it can analyze the sounds of children's laughter and playing and record, "It was a fun day filled with the sound of children's laughter." The situation analysis unit can also analyze video data to generate more detailed memories. For example, it can analyze a video of children playing and record, "The children were playing happily." In this way, more detailed memories can be generated by analyzing audio data and video data.

[0035] The situation analysis unit can analyze the facial expressions and movements of people in the photograph and record the events of the day more specifically. The situation analysis unit, for example, analyzes the facial expressions of people in the photograph and records the events of the day specifically. For example, if a child is smiling, it records "The child was smiling and having fun." The situation analysis unit can also analyze the movements of people in the photograph and record the events of the day specifically. For example, if a photo shows a child riding on a swing, it records "The child was having fun riding on the swing." In this way, by analyzing the facial expressions and movements of people, events can be recorded more specifically.

[0036] The situation analysis unit can compare the photograph with past photographs in analyzing the photograph situation and record growth and changes. For example, the situation analysis unit can compare the photograph with past photographs in analyzing the photograph situation and record the child's growth. For example, it can record, "The child who was not yet able to walk in the previous photograph was running around energetically this time." The situation analysis unit can also store past photographs in a database and automatically compare them when revisiting the facility. This allows growth and changes to be recorded by comparing with past photographs.

[0037] The situation analysis unit can automatically generate related episodes and stories based on the results of the situation analysis of the photo, thereby enriching memories. The situation analysis unit can automatically generate related episodes and stories based on the results of the situation analysis of the photo, thereby enriching memories. For example, it can record, "After playing in this park, we had ice cream at a nearby cafe." The situation analysis unit can also automatically generate related stories based on the results of the situation analysis of the photo. For example, it can record, "After playing in this park, we had ice cream at a nearby cafe. After that, the children played in the fountain in the park." In this way, memories can be enriched by automatically generating related episodes and stories.

[0038] The suggestion unit can consider current trends and popular spots along with the user's past visit history when selecting suggested events and locations. For example, the suggestion unit suggests new events and locations based on the user's past visit history and current trends. For example, it might suggest, "Why not try a cafe that's popular these days?" The suggestion unit can also use social media data and search engine trend data to understand current trends and reflect them in its suggestions. This allows for more appropriate suggestions by taking into account the user's past visit history and current trends.

[0039] The suggestion unit can analyze the user's interests and concerns when selecting suggested events and places to make personalized suggestions. For example, the suggestion unit can analyze the user's past visit history and interests to suggest personalized events and places. For example, the suggestion unit can suggest, "If you love animals, I recommend this zoo." The suggestion unit can also analyze the user's search history and survey results to understand the user's interests and concerns. This allows for more personalized suggestions by analyzing the user's interests and concerns.

[0040] The suggestion unit can make suggestions according to different seasons and weather conditions when selecting events and locations to suggest. For example, the suggestion unit makes suggestions according to the season when selecting events and locations to suggest. For example, the suggestion unit may suggest, "Why not enjoy cherry blossom viewing in this park in the spring?" The suggestion unit can also make suggestions according to weather conditions. For example, the suggestion unit may suggest, "Why not visit this museum on a rainy day?" This makes it possible to make more appropriate suggestions by making suggestions according to the season and weather conditions.

[0041] The suggestion unit can make suggestions based on reviews and ratings from other users when selecting events and locations to suggest. For example, the suggestion unit makes suggestions based on reviews from other users when selecting events and locations to suggest. For example, the suggestion unit may suggest, "This park has received high ratings from other users." The suggestion unit can also make suggestions based on ratings from other users. For example, the suggestion unit may suggest, "This restaurant has received a five-star rating." By making suggestions based on reviews and ratings from other users, more reliable suggestions can be made.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The memory recording system can further include a voice input unit. The voice input unit allows users to add comments by voice when uploading photos. For example, a user can leave a comment by voice such as "This day was so much fun." The voice input unit can also use voice recognition technology to convert the voice comment into text and reflect it in the memory record. This allows users to easily record memories by voice, enabling more detailed records.

[0044] The suggestion unit can suggest new events and places based on the user's past visit history and current trends. For example, by combining data on places the user has visited in the past with data on current popular spots, the suggestion unit can suggest, "Why don't you try a cafe that's popular these days?" The suggestion unit can also use social media data and search engine trend data to understand current trends and reflect them in its suggestions. This allows users to receive suggestions based on the latest trends.

[0045] The suggestion unit can make suggestions according to different seasons and weather conditions when selecting events and locations to suggest. For example, when selecting events and locations to suggest, suggestions according to the season are made. For example, the suggestion unit may suggest, "Why not enjoy cherry blossom viewing in this park in the spring?" The suggestion unit can also make suggestions according to weather conditions. For example, the suggestion unit may suggest, "Why not visit this museum on a rainy day?" This allows for more appropriate suggestions to be made by making suggestions according to the season and weather conditions.

[0046] The memory recording system may further include a video uploading unit. The video uploading unit allows users to upload videos of places they have visited with their children. For example, videos taken with a smartphone or digital camera can be uploaded to the system. The video uploading unit can also accept videos in file formats such as MP4, AVI, and MOV. This allows users to record memories not only with photos but also with videos, enabling richer records.

[0047] The suggestion unit can make suggestions based on reviews and ratings from other users when selecting events and locations to suggest. For example, when selecting events and locations to suggest, the suggestion unit makes suggestions based on reviews from other users. For example, the suggestion unit may suggest, "This park has received high ratings from other users." The suggestion unit can also make suggestions based on ratings from other users. For example, the suggestion unit may suggest, "This restaurant has received a five-star rating." This makes it possible to make more reliable suggestions by making suggestions based on reviews and ratings from other users.

[0048] The suggestion unit can analyze the user's interests and concerns when selecting suggested events and places to make personalized suggestions. For example, it can analyze the user's past visit history and interests to suggest personalized events and places. For example, it can suggest, "If you love animals, this zoo is recommended." The suggestion unit can also analyze the user's search history and survey results to understand their interests and concerns. This allows for more personalized suggestions by analyzing the user's interests and concerns.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The photo uploader uploads photos of places the user has visited with their child. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo uploader can accept photos in file formats such as JPEG, PNG, and RAW. Step 2: The location information acquisition unit acquires the location information of the photos uploaded by the photo upload unit. For example, it analyzes the GPS data included in the photos to identify the places visited. The location information acquisition unit can also acquire location information using Wi-Fi location information or IP addresses. Step 3: The situation analysis unit analyzes the situation of the photo based on the location information acquired by the location information acquisition unit. For example, it uses image recognition technology to analyze the playground equipment, scenery, and children's expressions in the photo. The situation analysis unit can also use machine learning algorithms to understand the content of the photo and generate diary-style text. Step 4: The suggestion unit suggests new events or places to visit based on the information analyzed by the situation analysis unit. For example, it suggests new parks or events nearby based on data on parks and events the user has visited in the past. The suggestion unit can also provide detailed information about the suggested events or places.

[0051] (Example 2) The memory recording system according to an embodiment of the present invention is a system that analyzes the location information and context of photos by simply uploading photos of places a user has visited with their child, automatically creating diary-style memories, and suggesting new events and places to visit. This allows users to easily record memories with their children and plan new experiences even during their busy daily lives.

[0052] A memory recording system according to an embodiment includes a photo upload unit, a location information acquisition unit, a situation analysis unit, and a suggestion unit. The photo upload unit uploads photos of places a user has visited with their child. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo upload unit can also accept photos in file formats such as JPEG, PNG, and RAW. The location information acquisition unit acquires location information for photos uploaded by the photo upload unit. For example, it can analyze GPS data included in the photos to identify visited locations. The location information acquisition unit can also acquire location information using Wi-Fi location information or IP addresses. The situation analysis unit analyzes the situation of the photo based on the location information acquired by the location information acquisition unit. For example, it can use image recognition technology to analyze playground equipment, scenery, and children's expressions in the photos. The situation analysis unit can also use machine learning algorithms to understand the content of the photos and generate diary-style text. The suggestion unit suggests new events and places to visit based on the information analyzed by the situation analysis unit. For example, it can suggest new nearby parks and events based on data on parks and events the user has visited in the past. The suggestion unit can also provide detailed information about suggested events and places. This allows the memory recording system according to the embodiment to record memories and suggest new events and places to visit simply by the user uploading photos. For example, even if the user is unsure where to go on the weekend, the system can automatically make suggestions, saving the user the trouble of making plans. It also makes it easy to look back on past memories, deepening family ties.

[0053] The location information acquisition unit can automatically acquire weather information and seasonal information in addition to the location information of the photo and reflect this in the memory record. For example, the location information acquisition unit automatically acquires weather information for the location based on the location information of the photo and reflects this in the memory record. For example, if the weather was sunny on the day the photo was taken, it will record something like "We played in the park today under clear skies." The location information acquisition unit also automatically acquires seasonal information and reflects this in the memory record. For example, if the photo was taken in spring, it will record something like "We played in the warm spring sunshine." This allows for more detailed memory recording by reflecting weather information and seasonal information.

[0054] The location information acquisition unit can compare the location information of the photo with past visit history and record changes when the user revisits the same place. The location information acquisition unit, for example, compares the location information of the photo with past visit history and records changes when the user revisits the same place. For example, it can record changes such as, "The cherry blossoms were in full bloom the last time I visited, but this time the autumn leaves were at their peak." The location information acquisition unit can also store past visit history in a database and automatically compare it when the user revisits the same place. This allows changes to be recorded when the user revisits the same place by comparing it with past visit history.

[0055] The situation analysis unit can use an emotion estimation function to analyze the user's emotions when uploading photos and customize the memory record based on those emotions. For example, the situation analysis unit analyzes the user's facial expression when uploading photos and customizes the memory record based on those emotions. For example, if the user is smiling, the situation analysis unit records "It was a fun day." The situation analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user speaks in an excited voice, the situation analysis unit records "It was a very fun day." The situation analysis unit can also use text analysis technology to analyze the user's comments and notes to estimate emotions. For example, if the user comments "It was fun," the situation analysis unit records "It was a fun day." This allows for more personalized memory records by customizing the memory record based on the user's emotions.

[0056] The suggestion unit can automatically acquire and suggest information about nearby tourist spots and restaurants based on the location information of the photo. The suggestion unit, for example, automatically acquires and suggests information about nearby tourist spots based on the location information of the photo. For example, it may suggest, "There is a zoo near this park. Why not visit the zoo next time?" The suggestion unit can also automatically acquire and suggest information about nearby restaurants. For example, it may suggest, "There is a delicious restaurant near this park. Why not go there for lunch?" In this way, by suggesting information about nearby tourist spots and restaurants, it is possible to expand the user's range of activities.

[0057] The suggestion unit can display reviews and ratings from other users who visited the same place based on the location information of the photo. For example, the suggestion unit displays reviews from other users who visited the same place based on the location information of the photo. For example, it displays a review such as "This park is very spacious and is a fun place for children." The suggestion unit can also display ratings from other users. For example, it displays "This park has been rated five stars." Displaying reviews and ratings from other users helps users select a place to visit.

[0058] The suggestion unit can use the emotion estimation function to display the user's emotions in real time when uploading photos and make suggestions to elicit positive emotions. The suggestion unit, for example, analyzes the user's facial expression when uploading photos and displays emotions in real time. For example, if the user is smiling, it displays "What a lovely smile!" The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice and display emotions in real time. For example, if the user speaks in an excited voice, it displays "You look like you're having so much fun!" The suggestion unit can also use text analysis technology to analyze the user's comments and notes and display emotions in real time. For example, if the user comments "I had fun," it displays "It was a fun day!" In this way, the user's emotions are displayed in real time and suggestions to elicit positive emotions are made, improving the user experience.

[0059] The situation analysis unit can generate more detailed memories by analyzing audio data and video data in analyzing the situation of photos. For example, the situation analysis unit can analyze audio data in addition to photos to generate more detailed memories. For example, it can analyze the sounds of children's laughter and playing and record, "It was a fun day filled with the sound of children's laughter." The situation analysis unit can also analyze video data to generate more detailed memories. For example, it can analyze a video of children playing and record, "The children were playing happily." In this way, more detailed memories can be generated by analyzing audio data and video data.

[0060] The situation analysis unit can analyze the facial expressions and movements of people in the photograph and record the events of the day more specifically. The situation analysis unit, for example, analyzes the facial expressions of people in the photograph and records the events of the day specifically. For example, if a child is smiling, it records "The child was smiling and having fun." The situation analysis unit can also analyze the movements of people in the photograph and record the events of the day specifically. For example, if a photo shows a child riding on a swing, it records "The child was having fun riding on the swing." In this way, by analyzing the facial expressions and movements of people, events can be recorded more specifically.

[0061] The situation analysis unit can use the emotion estimation function to analyze the emotions of people in the photo and generate a memorable sentence based on that emotion. For example, the situation analysis unit analyzes the facial expressions of people in the photo and generates a memorable sentence based on that emotion. For example, if a child is smiling, it records, "The child looked like he was having a lot of fun." The situation analysis unit can also use voice analysis technology to analyze the tone and speed of the voice of the person in the photo and estimate their emotion. For example, if the child speaks in an excited voice, it records, "The child looked like he was having a lot of fun." The situation analysis unit can also use text analysis technology to analyze the comments and notes of people in the photo and estimate their emotion. For example, if the child comments, "It was fun," it records, "The child looked like he was having a lot of fun." In this way, by analyzing people's emotions, memorable sentences based on their emotions can be generated.

[0062] The situation analysis unit can compare the photograph with past photographs in analyzing the photograph situation and record growth and changes. For example, the situation analysis unit can compare the photograph with past photographs in analyzing the photograph situation and record the child's growth. For example, it can record, "The child who was not yet able to walk in the previous photograph was running around energetically this time." The situation analysis unit can also store past photographs in a database and automatically compare them when revisiting the facility. This allows growth and changes to be recorded by comparing with past photographs.

[0063] The situation analysis unit can automatically generate related episodes and stories based on the results of the situation analysis of the photo, thereby enriching memories. The situation analysis unit can automatically generate related episodes and stories based on the results of the situation analysis of the photo, thereby enriching memories. For example, it can record, "After playing in this park, we had ice cream at a nearby cafe." The situation analysis unit can also automatically generate related stories based on the results of the situation analysis of the photo. For example, it can record, "After playing in this park, we had ice cream at a nearby cafe. After that, the children played in the fountain in the park." In this way, memories can be enriched by automatically generating related episodes and stories.

[0064] The situation analysis unit can use the emotion estimation function to display the emotions of people in photos in real time and make suggestions to elicit positive emotions. For example, the situation analysis unit analyzes the facial expressions of people in photos and displays their emotions in real time. For example, if a child is smiling, it displays "What a lovely smile!". The situation analysis unit can also use voice analysis technology to analyze the tone and speed of the voices of people in photos and display their emotions in real time. For example, if a child is speaking in an excited voice, it displays "You look like you're having so much fun!". The situation analysis unit can also use text analysis technology to analyze the comments and notes of people in photos and display their emotions in real time. For example, if a child comments "I had fun," it displays "What a fun day!". This improves the user experience by displaying emotions in real time and making suggestions to elicit positive emotions.

[0065] The suggestion unit can consider current trends and popular spots along with the user's past visit history when selecting suggested events and locations. For example, the suggestion unit suggests new events and locations based on the user's past visit history and current trends. For example, it might suggest, "Why not try a cafe that's popular these days?" The suggestion unit can also use social media data and search engine trend data to understand current trends and reflect them in its suggestions. This allows for more appropriate suggestions by taking into account the user's past visit history and current trends.

[0066] The suggestion unit can analyze the user's interests and concerns when selecting suggested events and places to make personalized suggestions. For example, the suggestion unit can analyze the user's past visit history and interests to suggest personalized events and places. For example, the suggestion unit can suggest, "If you love animals, I recommend this zoo." The suggestion unit can also analyze the user's search history and survey results to understand the user's interests and concerns. This allows for more personalized suggestions by analyzing the user's interests and concerns.

[0067] The suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the proposed event or place and make suggestions that elicit positive emotions. For example, the suggestion unit can analyze the user's facial expressions regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user is smiling, the suggestion unit can make a suggestion such as, "This event looks fun!". The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user speaks in an excited voice, the suggestion unit can make a suggestion such as, "This place looks really fun!". The suggestion unit can also use text analysis technology to analyze the user's comments and notes regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user comments that "it looks fun," the suggestion unit can make a suggestion such as, "This event looks fun!". In this way, the user's emotions are analyzed and suggestions that elicit positive emotions are made, improving the user experience.

[0068] The suggestion unit can make suggestions according to different seasons and weather conditions when selecting events and locations to suggest. For example, the suggestion unit makes suggestions according to the season when selecting events and locations to suggest. For example, the suggestion unit may suggest, "Why not enjoy cherry blossom viewing in this park in the spring?" The suggestion unit can also make suggestions according to weather conditions. For example, the suggestion unit may suggest, "Why not visit this museum on a rainy day?" This makes it possible to make more appropriate suggestions by making suggestions according to the season and weather conditions.

[0069] The suggestion unit can make suggestions based on reviews and ratings from other users when selecting events and locations to suggest. For example, the suggestion unit makes suggestions based on reviews from other users when selecting events and locations to suggest. For example, the suggestion unit may suggest, "This park has received high ratings from other users." The suggestion unit can also make suggestions based on ratings from other users. For example, the suggestion unit may suggest, "This restaurant has received a five-star rating." By making suggestions based on reviews and ratings from other users, more reliable suggestions can be made.

[0070] The suggestion unit can use the emotion estimation function to display the user's emotion regarding the proposed event or place in real time and make suggestions that elicit positive emotions. For example, the suggestion unit analyzes the user's facial expression regarding the proposed event or place and displays the emotion in real time. For example, if the user is smiling, it displays "This event looks fun!". The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice regarding the proposed event or place and display the emotion in real time. For example, if the user speaks in an excited voice, it displays "This place looks like a lot of fun!". The suggestion unit can also use text analysis technology to analyze the user's comments and notes regarding the proposed event or place and display the emotion in real time. For example, if the user comments "It looks fun," it displays "This event looks fun!". In this way, the user's emotion is displayed in real time and suggestions that elicit positive emotions are made, improving the user experience.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The memory recording system can further include a voice input unit. The voice input unit allows users to add comments by voice when uploading photos. For example, a user can leave a comment by voice such as "This day was so much fun." The voice input unit can also use voice recognition technology to convert the voice comment into text and reflect it in the memory record. This allows users to easily record memories by voice, enabling more detailed records.

[0073] The suggestion unit can suggest new events and places based on the user's past visit history and current trends. For example, by combining data on places the user has visited in the past with data on current popular spots, the suggestion unit can suggest, "Why don't you try a cafe that's popular these days?" The suggestion unit can also use social media data and search engine trend data to understand current trends and reflect them in its suggestions. This allows users to receive suggestions based on the latest trends.

[0074] The situation analysis unit can use its emotion estimation function to analyze the emotions of people in photos and generate memorable sentences based on those emotions. For example, if a child is smiling, it records, "The child looked like he was having a lot of fun." The situation analysis unit can also use voice analysis technology to analyze the tone and speed of the voices of people in photos to estimate their emotions. For example, if a child speaks in an excited voice, it records, "The child looked like he was having a lot of fun." The situation analysis unit can also use text analysis technology to analyze the comments and notes of people in photos and estimate their emotions. For example, if a child comments, "It was fun," it records, "The child looked like he was having a lot of fun." In this way, by analyzing people's emotions, memorable sentences based on emotions can be generated.

[0075] The suggestion unit can make suggestions according to different seasons and weather conditions when selecting events and locations to suggest. For example, when selecting events and locations to suggest, suggestions according to the season are made. For example, the suggestion unit may suggest, "Why not enjoy cherry blossom viewing in this park in the spring?" The suggestion unit can also make suggestions according to weather conditions. For example, the suggestion unit may suggest, "Why not visit this museum on a rainy day?" This allows for more appropriate suggestions to be made by making suggestions according to the season and weather conditions.

[0076] The suggestion unit can use its emotion estimation function to display the user's emotions regarding proposed events and places in real time and make suggestions that elicit positive emotions. For example, it can analyze the user's facial expressions regarding the proposed events and places and display their emotions in real time. For example, if the user is smiling, it can display "This event looks fun!". The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice regarding the proposed events and places and display their emotions in real time. For example, if the user speaks in an excited voice, it can display "This place looks like a lot of fun!". The suggestion unit can also use text analysis technology to analyze the user's comments and notes regarding the proposed events and places and display their emotions in real time. For example, if the user comments "It looks fun," it can display "This event looks fun!". This improves the user experience by displaying the user's emotions in real time and making suggestions that elicit positive emotions.

[0077] The memory recording system may further include a video uploading unit. The video uploading unit allows users to upload videos of places they have visited with their children. For example, videos taken with a smartphone or digital camera can be uploaded to the system. The video uploading unit can also accept videos in file formats such as MP4, AVI, and MOV. This allows users to record memories not only with photos but also with videos, enabling richer records.

[0078] The suggestion unit can make suggestions based on reviews and ratings from other users when selecting events and locations to suggest. For example, when selecting events and locations to suggest, the suggestion unit makes suggestions based on reviews from other users. For example, the suggestion unit may suggest, "This park has received high ratings from other users." The suggestion unit can also make suggestions based on ratings from other users. For example, the suggestion unit may suggest, "This restaurant has received a five-star rating." This makes it possible to make more reliable suggestions by making suggestions based on reviews and ratings from other users.

[0079] The situation analysis unit can use an emotion estimation function to analyze a user's emotions when uploading photos and customize the memory record based on those emotions. For example, the situation analysis unit can analyze the user's facial expression when uploading photos and customize the memory record based on those emotions. For example, if the user is smiling, the situation analysis unit can record "It was a fun day." The situation analysis unit can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. For example, if the user speaks in an excited voice, the situation analysis unit can record "It was a very fun day." The situation analysis unit can also use text analysis technology to analyze the user's comments and notes and estimate emotions. For example, if the user comments "It was fun," the situation analysis unit can record "It was a fun day." This allows the memory record to be customized based on the user's emotions, enabling more personalized recording.

[0080] The suggestion unit can analyze the user's interests and concerns when selecting suggested events and places to make personalized suggestions. For example, it can analyze the user's past visit history and interests to suggest personalized events and places. For example, it can suggest, "If you love animals, this zoo is recommended." The suggestion unit can also analyze the user's search history and survey results to understand their interests and concerns. This allows for more personalized suggestions by analyzing the user's interests and concerns.

[0081] The suggestion unit can use the emotion estimation function to analyze the user's emotions regarding the proposed event or place and make suggestions that elicit positive emotions. For example, the suggestion unit can analyze the user's facial expressions regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user is smiling, the suggestion unit can make a suggestion such as, "This event looks fun!". The suggestion unit can also use voice analysis technology to analyze the tone and speed of the user's voice regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user speaks in an excited voice, the suggestion unit can make a suggestion such as, "This place looks really fun!". The suggestion unit can also use text analysis technology to analyze the user's comments and notes regarding the proposed event or place and make suggestions that elicit positive emotions. For example, if the user comments, "It looks fun," the suggestion unit can make a suggestion such as, "This event looks fun!". In this way, the user's emotions are analyzed and suggestions that elicit positive emotions are made, improving the user experience.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The photo uploader uploads photos of places the user has visited with their child. For example, photos taken with a smartphone or digital camera can be uploaded to the system. The photo uploader can accept photos in file formats such as JPEG, PNG, and RAW. Step 2: The location information acquisition unit acquires the location information of the photos uploaded by the photo upload unit. For example, it analyzes the GPS data included in the photos to identify the places visited. The location information acquisition unit can also acquire location information using Wi-Fi location information or IP addresses. Step 3: The situation analysis unit analyzes the situation of the photo based on the location information acquired by the location information acquisition unit. For example, it uses image recognition technology to analyze the playground equipment, scenery, and children's expressions in the photo. The situation analysis unit can also use machine learning algorithms to understand the content of the photo and generate diary-style text. Step 4: The suggestion unit suggests new events or places to visit based on the information analyzed by the situation analysis unit. For example, it suggests new parks or events nearby based on data on parks and events the user has visited in the past. The suggestion unit can also provide detailed information about the suggested events or places.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 photo upload section for uploading photos; a location information acquisition unit that acquires location information of the photos uploaded by the photo upload unit; a situation analysis unit that analyzes a situation of a photograph based on the location information acquired by the location information acquisition unit; a suggestion unit that suggests new events and places to visit based on the information analyzed by the situation analysis unit. A system characterized by:

2. The location information acquisition unit In addition to the location information of the photo, weather and seasonal information is automatically acquired and reflected in the record of memories.

2. The system of claim 1.

3. The proposal unit Based on the location information of the photo, information on nearby tourist spots and restaurants is automatically retrieved and suggested.

2. The system of claim 1.

4. The situation analysis unit In analyzing the situation of the photo, audio and video data are also analyzed to generate more detailed memories.

2. The system of claim 1.

5. The proposal unit The selection of the suggested events and locations takes into account current trends and popular spots, along with the user's past visit history.

2. The system of claim 1.

6. The situation analysis unit Analyzing the user's emotions when uploading the photos and customizing the memory record based on those emotions 2. The system of claim 1.

7. The situation analysis unit Analyze the emotions of the people in the photo and generate a memory sentence based on those emotions 2. The system of claim 1.

8. The proposal unit Analyzing the user's feelings about the proposed event or place and making suggestions that elicit positive feelings 2. The system of claim 1.

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