system

Abstract

The system according to the embodiment aims to automatically organize travel memories and suggest plans for the next trip. [Solution] A system according to an embodiment includes a recognition unit, a generation unit, a planning unit, and a sharing unit. The recognition unit analyzes metadata of photos to identify the location, time, and accompanying people. The generation unit generates text describing travel scenes and memories based on the information identified by the recognition unit. The planning unit proposes plans for the next trip based on the text and photos generated by the generation unit. The sharing unit generates and shares articles based on the plans proposed by the planning unit.

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] With conventional technology, the process of organizing travel memories and planning the next trip was tedious and time-consuming.

[0005] The system according to the embodiment aims to automatically organize travel memories and suggest plans for the next trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a generation unit, a planning unit, and a sharing unit. The recognition unit analyzes the metadata of the photo to identify the location, time, and accompanying people. The generation unit generates text describing the travel scenery and memories based on the information identified by the recognition unit. The planning unit proposes plans for the next trip based on the text and photos generated by the generation unit. The sharing unit generates and shares articles based on the plans proposed by the planning unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically organize travel memories and suggest plans for the next trip. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel memory organization system according to an embodiment of the present invention combines AI technology with photo and location data, such as a Google® account, to automatically organize and record travel memories. This system automatically recognizes the location, time, and people who accompanied the user from photos and automatically creates a travel log. Next, AI-generated text vividly captures travel scenes and memories. Furthermore, a recommended travel plan creation function suggests plans for the next trip. Finally, an article sharing function allows users to share travel memories with family and friends. For example, the automatic recognition of the location, time, and people who accompanied the user from photos uses facial recognition technology and location analysis technology to identify the people in the photo and determine the location where the photo was taken. Next, the AI-generated text generation uses natural language processing technology to analyze the content of the photo and generate text describing the scene and events at the time. Furthermore, the recommended travel plan creation function uses a clustering algorithm to analyze past travel data and user preferences to suggest new travel destinations and plans. Finally, the article sharing function compiles the AI-generated text and photos in HTML format, allowing them to be shared via social media or email. In this way, the travel memory organization system uses photo and location data from Google accounts and other sources, along with AI technology, to automatically organize and record travel memories and also support planning for the next trip. This allows the travel memory organization system to automatically organize and record the user's travel memories. It can also suggest plans for the next trip and generate and share articles.

[0029] A travel memory organization system according to an embodiment includes a recognition unit, a generation unit, a planning unit, and a sharing unit. The recognition unit analyzes metadata of a photo to identify the location, time, and accompanying people. Photo metadata includes, but is not limited to, location information, timestamps, and person tags. The recognition unit uses, for example, facial recognition technology to identify people appearing in the photo. The recognition unit can also identify the location where the photo was taken using location information analysis technology. For example, the facial recognition technology can use deep learning-based facial recognition or Haar feature-based face detection. The location information analysis technology can use GPS data analysis or Wi-Fi location information analysis. The generation unit generates text describing travel scenes and memories based on the information identified by the recognition unit. The generation unit analyzes the content of the photo using, for example, natural language processing technology to generate text describing the scene and events at the time. The natural language processing technology can include, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The planning unit proposes a plan for the next trip based on the text and photos generated by the generation unit. The planning unit analyzes past travel data and the user's preferences using, for example, a clustering algorithm to propose new travel destinations and plans. Examples of clustering algorithms include, but are not limited to, K-means and hierarchical clustering. The sharing unit generates and shares articles based on the plans proposed by the planning unit. For example, the sharing unit compiles the text and photos generated by the AI ​​in HTML format so that they can be shared via social media or email. Generating the HTML format includes, but is not limited to, the use of HTML tags and the application of CSS styles. As a result, the travel memory organizing system according to the embodiment can automatically organize and record the user's travel memories. It can also propose plans for the next trip and generate and share articles.

[0030] The recognition unit can identify people appearing in a photograph using facial recognition technology. Examples of facial recognition technology include, but are not limited to, deep learning-based facial recognition and Haar feature-based face detection. The recognition unit can identify people appearing in a photograph using, for example, deep learning-based facial recognition technology. Deep learning-based facial recognition technology learns large amounts of facial image data to achieve highly accurate facial recognition. The recognition unit can also identify people appearing in a photograph using Haar feature-based face detection technology. Haar feature-based face detection technology extracts facial features in an image and identifies the location of the face. Furthermore, the recognition unit can also identify attribute information (e.g., age, gender, etc.) of people appearing in a photograph using facial recognition technology. Thus, people appearing in a photograph can be identified using facial recognition technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input photo data to a generation AI and cause the generation AI to perform facial recognition.

[0031] The recognition unit can identify the location where the photo was taken using location information analysis technology. Location information analysis technology includes, but is not limited to, GPS data analysis and Wi-Fi location information analysis, for example. The recognition unit can identify the location where the photo was taken using, for example, GPS data analysis technology. The GPS data analysis technology analyzes GPS data embedded in the photo to identify the location where the photo was taken. The recognition unit can also identify the location where the photo was taken using Wi-Fi location information analysis technology. The Wi-Fi location information analysis technology identifies the location where the photo was taken based on information about the Wi-Fi access point at the location where the photo was taken. Furthermore, the recognition unit can also identify detailed information about the location where the photo was taken (e.g., address, name of tourist spot, etc.) using location information analysis technology. In this way, the location where the photo was taken can be identified using location information analysis technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input photo data to a generation AI and have the generation AI perform location information analysis.

[0032] The generation unit can analyze the content of the photo using natural language processing technology and generate sentences about the scene or event of the scene. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The generation unit can analyze the content of the photo using, for example, morphological analysis technology and generate sentences about the scene or event of the scene. Morphological analysis technology divides sentences into words and analyzes the meaning of each word. The generation unit can also analyze the content of the photo using grammatical analysis technology and generate sentences about the scene or event of the scene. Grammatical analysis technology analyzes the structure of the sentence and generates grammatically correct sentences. Furthermore, the generation unit can analyze the content of the photo using semantic analysis technology and generate sentences about the scene or event of the scene. Semantic analysis technology analyzes the meaning of the sentence and generates semantically consistent sentences. In this way, the use of natural language processing technology makes it possible to analyze the content of the photo and generate sentences about the scene or event of the scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input photo data into the generation AI and have the generation AI generate text.

[0033] The planning unit can use a clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. Examples of clustering algorithms include, but are not limited to, K-means and hierarchical clustering. The planning unit can use, for example, a K-means clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. The K-means clustering algorithm divides data into K clusters and classifies the data based on the center of each cluster. The planning unit can also use a hierarchical clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. The hierarchical clustering algorithm classifies data hierarchically and classifies data based on the characteristics of each hierarchical level. Furthermore, the planning unit can use a clustering algorithm to propose optimal travel plans based on user preferences and past travel data. Thus, by using a clustering algorithm, past travel data and user preferences can be analyzed and new travel destinations and plans can be proposed. Some or all of the above-described processing in the planning unit may be performed using, for example, AI or without AI. For example, the planning unit can input travel data into the generation AI and have the generation AI execute a travel plan proposal.

[0034] The sharing unit can compile AI-generated text and photos in HTML format so that they can be shared on social media or email. Generating the HTML format includes, but is not limited to, using HTML tags and applying CSS styles, for example. The sharing unit, for example, compiles AI-generated text and photos in HTML format using HTML tags. The HTML tags structure the text and photos and convert them into a format that can be displayed in a web browser. The sharing unit can also apply CSS styles to compile AI-generated text and photos in HTML format. The CSS styles adjust the appearance of the text and photos structured with HTML tags and convert them into a visually appealing format. The sharing unit can also compile AI-generated text and photos in HTML format so that they can be shared on social media or email. This makes it possible to compile AI-generated text and photos in HTML format so that they can be shared on social media or email. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the generated HTML data to the generation AI and cause the generation AI to share the data on social media or email.

[0035] The recognition unit can refer to the user's calendar information in addition to the photo metadata and analyze the photos based on events and schedules. For example, the recognition unit can refer to the user's calendar information in addition to the photo metadata and analyze the photos based on events and schedules. Calendar information includes, but is not limited to, Google Calendar and Outlook (registered trademark) Calendar. For example, the recognition unit analyzes photos based on the dates of events registered in the user's calendar. Based on the calendar information, the recognition unit can prioritize analysis of photos related to specific events. The recognition unit can also prioritize analysis of photos taken at locations related to the user's schedule. Furthermore, the recognition unit can extract and analyze photos related to specific events from the calendar information. This enables photo analysis based on events and schedules by referring to the user's calendar information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input calendar information to the generation AI and cause the generation AI to analyze photos based on events and schedules.

[0036] When analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. For example, when analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. Weather information includes, but is not limited to, weather data APIs and weather forecast services. For example, the recognition unit obtains weather information for that day based on the date and time the photo was taken and reflects it in the analysis. Based on the weather information, the recognition unit can distinguish between photos taken on sunny days and photos taken on rainy days and analyze them accordingly. The recognition unit can also refer to weather information to identify the lighting conditions at the time of shooting and improve analysis accuracy. In this way, by referring to the weather information, the environment at the time of shooting can be identified and analysis accuracy can be improved. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input weather information to a generation AI and cause the generation AI to identify the environment at the time of shooting.

[0037] The recognition unit can improve the analysis accuracy by referring to the content of the user's social media posts when analyzing a photo. The recognition unit can improve the analysis accuracy by referring to the content of the user's social media posts when analyzing a photo, for example. The content of the social media posts includes, but is not limited to, text, images, and location information. The recognition unit can, for example, identify people appearing in a photo from the content of the user's social media posts. The location where the photo was taken can be identified based on the content of the social media posts. The recognition unit can also analyze the content of the photo in detail by referring to the content of the social media posts. In this way, the analysis accuracy can be improved by referring to the content of the user's social media posts. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input the content of the social media posts into a generation AI and cause the generation AI to analyze the photo.

[0038] The recognition unit can analyze the user's voice memo when analyzing a photo and associate it with the content of the photo. For example, when analyzing a photo, the recognition unit can analyze the user's voice memo and associate it with the content of the photo. Voice memos include, for example, events and impressions from a trip, but are not limited to such examples. For example, the recognition unit can identify people and places appearing in the photo from the user's voice memo. The scene in which the photo was taken can be analyzed in detail based on the content of the voice memo. The recognition unit can also improve the accuracy of analysis by analyzing the voice memo and associating it with the content of the photo. In this way, by analyzing the user's voice memo, it can be associated with the content of the photo and improve the accuracy of analysis. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input the voice memo into a generation AI and have the generation AI associate it with the content of the photo.

[0039] The generation unit can provide consistency in the generated text by referring to the user's past travel records. For example, the generation unit can provide consistency in the generated text by referring to the user's past travel records. Past travel records include, but are not limited to, travel destinations, travel itineraries, and travel events. For example, the generation unit can quote episodes from the user's past travel records when visiting the same place. Based on the past travel records, the style and tone of the text can be made consistent. The generation unit can also incorporate related events into the text by referring to the user's past travel records. In this way, consistency can be provided in the generated text by referring to the user's past travel records. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input past travel records into a generation AI and have the generation AI perform text consistency.

[0040] The generation unit can add relevant historical background and cultural information to the generated text based on the results of photo analysis. For example, the generation unit can add relevant historical background and cultural information to the generated text based on the results of photo analysis. Examples of historical background and cultural information include, but are not limited to, the history of the location where the photo was taken, its cultural significance, and related events. For example, the generation unit can add historical background related to the location where the photo was taken to the text. Cultural information related to buildings and scenery appearing in the photo can be incorporated into the text. The generation unit can also provide information about the history and culture of the location based on the results of photo analysis. By adding relevant historical background and cultural information based on the results of photo analysis, richer text can be generated. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the results of photo analysis into a generation AI and cause the generation AI to add historical background and cultural information.

[0041] The generation unit can add individual episodes to the generated text by referring to the user's social media posts. For example, the generation unit can add individual episodes to the generated text by referring to the user's social media posts. The social media posts include, but are not limited to, for example, text, images, and location information. For example, the generation unit can quote specific episodes from the user's social media posts. The generation unit can add individual episodes to the text based on the social media posts. The generation unit can also incorporate events related to the text by referring to the user's social media posts. In this way, individual episodes can be added to the generated text by referring to the user's social media posts. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the social media posts into a generation AI and cause the generation AI to add individual episodes.

[0042] The generation unit can convert the user's voice memos into text and incorporate them into the generated text. For example, the generation unit converts the user's voice memos into text and incorporates them into the generated text. Voice memos include, for example, events and impressions from a trip, but are not limited to these examples. For example, the generation unit converts the user's voice memos into text and quotes them in the text. Episodes related to the text can be added based on the content of the voice memos. The generation unit can also convert the user's voice memos into text and incorporate them into the text to ensure consistency. In this way, by converting the user's voice memos into text and incorporating them, consistency can be ensured in the generated text. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the voice memos into a generation AI and have the generation AI perform the text conversion.

[0043] The planning unit can provide a travel plan that matches the user's preferences by referring to the user's past travel history when proposing a travel plan. For example, the planning unit can provide a travel plan that matches the user's preferences by referring to the user's past travel history when proposing a travel plan. Past travel history includes, but is not limited to, travel destinations, travel itineraries, and travel events. For example, the planning unit can suggest travel destinations that match the user's preferences based on the user's past travel history. Based on the past travel history, the planning unit can also suggest activities that the user might be interested in. The planning unit can also suggest accommodations that match the user's preferences by referring to the user's past travel history. In this way, by referring to the user's past travel history, a travel plan that matches the user's preferences can be provided. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past travel history into a generation AI and have the generation AI execute a travel plan suggestion.

[0044] The planning unit can propose an appropriate time for the proposed travel plan by taking into account seasonal and event information. For example, the planning unit proposes an appropriate time for the proposed travel plan by taking into account seasonal and event information. Seasonal and event information includes, but is not limited to, events held in specific seasons and the attractions of tourist spots in each season. For example, the planning unit proposes travel destinations according to the season. It can propose a travel plan tailored to the time when a specific event is held. The planning unit can also propose an optimal travel time based on seasonal and event information. In this way, it is possible to propose an optimal travel time by taking into account seasonal and event information. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input seasonal and event information into the generation AI and cause the generation AI to propose an appropriate time.

[0045] The planning unit can add related activities to the proposed travel plan by referring to the user's social media posts. For example, the planning unit can add related activities to the proposed travel plan by referring to the user's social media posts. The social media posts include, but are not limited to, for example, text, images, and location information. For example, the planning unit can suggest activities that the user might be interested in based on the user's social media posts. Activities related to the travel plan can be added based on the social media posts. The planning unit can also suggest events related to the travel plan by referring to the user's social media posts. In this way, related activities can be added by referring to the user's social media posts. Some or all of the above-described processing in the planning unit can be performed using, for example, AI, or can be performed without using AI. For example, the planning unit can input the social media posts into a generation AI and cause the generation AI to add activities.

[0046] The planning unit can analyze the user's voice memos to incorporate places and activities of interest into the proposed travel plan. For example, the planning unit analyzes the user's voice memos to incorporate places and activities of interest into the proposed travel plan. Voice memos include, but are not limited to, events and impressions that occurred during the trip. For example, the planning unit can suggest places of interest from the user's voice memos. Activities related to the travel plan can be added based on the content of the voice memos. The planning unit can also analyze the user's voice memos to suggest events related to the travel plan. In this way, places and activities of interest can be incorporated into the travel plan by analyzing the user's voice memos. Some or all of the above-described processing in the planning unit can be performed using, for example, AI, or without AI. For example, the planning unit can input voice memos into a generation AI and have the generation AI incorporate the voice memos into the travel plan.

[0047] The sharing unit can ensure consistency in the shared article by referring to the user's past posting style. For example, the sharing unit can ensure consistency in the shared article by referring to the user's past posting style. Past posting styles include, but are not limited to, the tone of writing, layout, and types of images used. For example, the sharing unit can ensure consistency in the tone and style of the article based on the user's past posting style. The sharing unit can adjust the layout of the article by referring to the past posting style. The sharing unit can also ensure consistency in the content of the article based on the user's past posting style. In this way, consistency can be ensured in the shared article by referring to the user's past posting style. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the past posting style into a generation AI and cause the generation AI to ensure consistency in the article.

[0048] The sharing unit can add related map and route information to the shared article based on the photo analysis results. For example, the sharing unit adds related map and route information to the shared article based on the photo analysis results. Map and route information includes, but is not limited to, a map of the photo's location and routes from the photo's location to other tourist attractions. For example, the sharing unit adds a related map to the article based on the photo's location. Route information from the photo's location to other tourist attractions can be added to the article based on the photo analysis results. The sharing unit can also incorporate map information around the photo's location into the article by referring to the photo analysis results. This allows for providing a richer article by adding related map and route information based on the photo analysis results. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the photo analysis results to a generation AI and cause the generation AI to add map and route information.

[0049] The sharing unit can add relevant hashtags to an article to be shared by referring to the user's social media posts. For example, the sharing unit can add relevant hashtags to an article to be shared by referring to the user's social media posts. Hashtags include, but are not limited to, the names of travel destinations, event names, and keywords related to a specific theme. For example, the sharing unit can extract relevant hashtags from the user's social media posts and add them to the article. It can suggest hashtags suitable for the article based on the social media posts. The sharing unit can also add trending hashtags related to the article by referring to the user's social media posts. In this way, it is possible to add relevant hashtags by referring to the user's social media posts. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the social media posts to a generation AI and cause the generation AI to add hashtags.

[0050] The sharing unit can convert the user's voice memos into text and incorporate them as quotes into the article to be shared. For example, the sharing unit converts the user's voice memos into text and incorporates them as quotes into the article to be shared. Voice memos include, for example, events and impressions from a trip, but are not limited to such examples. For example, the sharing unit converts the user's voice memos into text and incorporates them as quotes into the article. Episodes related to the article can be added based on the content of the voice memos. The sharing unit can also convert the user's voice memos into text to ensure consistency in the content of the article. In this way, by converting the user's voice memos into text and incorporating them as quotes, it is possible to ensure consistency in the content of the article. Some or all of the above-mentioned processing in the sharing unit can be performed, for example, using AI, or can be performed without using AI. For example, the sharing unit can input the voice memos into a generation AI and have the generation AI perform the text conversion.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The recognition unit can refer to the user's calendar information and analyze photos based on events and schedules. For example, it can analyze photos based on the dates of events registered in the user's calendar and prioritize analysis of photos related to specific events. It can also prioritize analysis of photos taken at locations related to the user's schedule. It can also extract and analyze photos related to specific events from the calendar information. This makes it possible to analyze photos based on events and schedules by referring to the user's calendar information.

[0053] When analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. For example, based on the date and time the photo was taken, weather information for that day can be acquired and reflected in the analysis. Based on the weather information, photos taken on sunny days can be analyzed separately from photos taken on rainy days. Furthermore, by referring to the weather information, the lighting conditions at the time of shooting can be identified, improving the accuracy of the analysis. In this way, by referring to the weather information, the environment at the time of shooting can be identified, improving the accuracy of the analysis.

[0054] The planning unit can propose an appropriate time for a proposed travel plan by taking into account seasonal and event information. For example, it can propose travel destinations by taking into account events held in a particular season and the attractiveness of tourist spots for each season. It can also propose travel plans that match the time when a particular event is held. Furthermore, it can propose the best time to travel based on seasonal and event information. This makes it possible to propose the best time to travel by taking into account seasonal and event information.

[0055] When analyzing a photo, the recognition unit can improve the accuracy of the analysis by referring to the content posted by the user on social media. For example, it is possible to identify the person appearing in the photo from the content posted by the user on social media. It is also possible to identify the location where the photo was taken based on the content posted by the user on social media. Furthermore, it is possible to analyze the content of the photo in detail by referring to the content posted by the user on social media. In this way, it is possible to improve the accuracy of the analysis by referring to the content posted by the user on social media.

[0056] The generation unit can reference the user's past travel records to ensure consistency in the generated text. For example, it can quote episodes from the user's past travel records when visiting the same place. It can also make the style and tone of the text consistent based on the past travel records. It can also incorporate related events into the text by referencing the user's past travel records. In this way, it is possible to ensure consistency in the generated text by referencing the user's past travel records.

[0057] The sharing unit can add related map and route information to the shared article based on the results of photo analysis. For example, a related map can be added to the article based on the location where the photo was taken. Route information from the photo location to other tourist spots can also be added to the article based on the results of photo analysis. Furthermore, map information around the photo location can be incorporated into the article by referring to the results of photo analysis. This makes it possible to provide richer articles by adding related map and route information based on the results of photo analysis.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The recognition unit analyzes the photo's metadata to identify the location, time, and accompanying people. Photo metadata includes location information, timestamps, and person tags. The recognition unit uses facial recognition technology to identify people in the photo and location analysis technology to determine the location where the photo was taken. Facial recognition technology includes deep learning-based facial recognition and Haar feature-based face detection, while location analysis technology includes GPS data analysis and Wi-Fi location analysis. Step 2: The generation unit generates text describing the travel scenes and memories based on the information identified by the recognition unit. The generation unit analyzes the content of the photo using natural language processing technology and generates text describing the scenes and events of the location. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The planning unit proposes the next travel plan based on the text and photos generated by the generation unit. The planning unit uses a clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. Clustering algorithms include K-means and hierarchical clustering. Step 4: The Sharing Department generates and shares articles based on the plans proposed by the Planning Department. The Sharing Department compiles the AI-generated text and photos in HTML format, making them shareable via social media and email. Generating the HTML format includes using HTML tags and applying CSS styles.

[0060] (Example 2) A travel memory organization system according to an embodiment of the present invention combines AI technology with photo and location data, such as a Google account, to automatically organize and record travel memories. This system automatically recognizes the location, time, and people who accompanied the user from photos and creates a travel log. Next, AI-generated text vividly captures travel scenes and memories. Furthermore, a recommended travel plan creation function suggests plans for the next trip. Finally, an article sharing function allows users to share travel memories with family and friends. For example, the automatic recognition of the location, time, and people who accompanied the user from photos uses facial recognition technology and location analysis technology to identify the people in the photo and the location where the photo was taken. Next, the AI-generated text generation uses natural language processing technology to analyze the content of the photo and generate text describing the scene and events at the time. Furthermore, the recommended travel plan creation function uses a clustering algorithm to analyze past travel data and user preferences to suggest new travel destinations and plans. Finally, the article sharing function compiles the AI-generated text and photos in HTML format, allowing them to be shared via social media or email. In this way, the travel memory organization system uses photo and location data from Google accounts and other sources, along with AI technology, to automatically organize and record travel memories and also support planning for the next trip. This allows the travel memory organization system to automatically organize and record the user's travel memories. It can also suggest plans for the next trip and generate and share articles.

[0061] A travel memory organization system according to an embodiment includes a recognition unit, a generation unit, a planning unit, and a sharing unit. The recognition unit analyzes metadata of a photo to identify the location, time, and accompanying people. Photo metadata includes, but is not limited to, location information, timestamps, and person tags. The recognition unit uses, for example, facial recognition technology to identify people appearing in the photo. The recognition unit can also identify the location where the photo was taken using location information analysis technology. For example, the facial recognition technology can use deep learning-based facial recognition or Haar feature-based face detection. The location information analysis technology can use GPS data analysis or Wi-Fi location information analysis. The generation unit generates text describing travel scenes and memories based on the information identified by the recognition unit. The generation unit analyzes the content of the photo using, for example, natural language processing technology to generate text describing the scene and events at the time. The natural language processing technology can include, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The planning unit proposes a plan for the next trip based on the text and photos generated by the generation unit. The planning unit analyzes past travel data and the user's preferences using, for example, a clustering algorithm to propose new travel destinations and plans. Examples of clustering algorithms include, but are not limited to, K-means and hierarchical clustering. The sharing unit generates and shares articles based on the plans proposed by the planning unit. For example, the sharing unit compiles the text and photos generated by the AI ​​in HTML format so that they can be shared via social media or email. Generating the HTML format includes, but is not limited to, the use of HTML tags and the application of CSS styles. As a result, the travel memory organizing system according to the embodiment can automatically organize and record the user's travel memories. It can also propose plans for the next trip and generate and share articles.

[0062] The recognition unit can identify people appearing in a photograph using facial recognition technology. Examples of facial recognition technology include, but are not limited to, deep learning-based facial recognition and Haar feature-based face detection. The recognition unit can identify people appearing in a photograph using, for example, deep learning-based facial recognition technology. Deep learning-based facial recognition technology learns large amounts of facial image data to achieve highly accurate facial recognition. The recognition unit can also identify people appearing in a photograph using Haar feature-based face detection technology. Haar feature-based face detection technology extracts facial features in an image and identifies the location of the face. Furthermore, the recognition unit can also identify attribute information (e.g., age, gender, etc.) of people appearing in a photograph using facial recognition technology. Thus, people appearing in a photograph can be identified using facial recognition technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input photo data to a generation AI and cause the generation AI to perform facial recognition.

[0063] The recognition unit can identify the location where the photo was taken using location information analysis technology. Location information analysis technology includes, but is not limited to, GPS data analysis and Wi-Fi location information analysis, for example. The recognition unit can identify the location where the photo was taken using, for example, GPS data analysis technology. The GPS data analysis technology analyzes GPS data embedded in the photo to identify the location where the photo was taken. The recognition unit can also identify the location where the photo was taken using Wi-Fi location information analysis technology. The Wi-Fi location information analysis technology identifies the location where the photo was taken based on information about the Wi-Fi access point at the location where the photo was taken. Furthermore, the recognition unit can also identify detailed information about the location where the photo was taken (e.g., address, name of tourist spot, etc.) using location information analysis technology. In this way, the location where the photo was taken can be identified using location information analysis technology. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input photo data to a generation AI and have the generation AI perform location information analysis.

[0064] The generation unit can analyze the content of the photo using natural language processing technology and generate sentences about the scene or event of the scene. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The generation unit can analyze the content of the photo using, for example, morphological analysis technology and generate sentences about the scene or event of the scene. Morphological analysis technology divides sentences into words and analyzes the meaning of each word. The generation unit can also analyze the content of the photo using grammatical analysis technology and generate sentences about the scene or event of the scene. Grammatical analysis technology analyzes the structure of the sentence and generates grammatically correct sentences. Furthermore, the generation unit can analyze the content of the photo using semantic analysis technology and generate sentences about the scene or event of the scene. Semantic analysis technology analyzes the meaning of the sentence and generates semantically consistent sentences. In this way, the use of natural language processing technology makes it possible to analyze the content of the photo and generate sentences about the scene or event of the scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input photo data into the generation AI and have the generation AI generate text.

[0065] The planning unit can use a clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. Examples of clustering algorithms include, but are not limited to, K-means and hierarchical clustering. The planning unit can use, for example, a K-means clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. The K-means clustering algorithm divides data into K clusters and classifies the data based on the center of each cluster. The planning unit can also use a hierarchical clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. The hierarchical clustering algorithm classifies data hierarchically and classifies data based on the characteristics of each hierarchical level. Furthermore, the planning unit can use a clustering algorithm to propose optimal travel plans based on user preferences and past travel data. Thus, by using a clustering algorithm, past travel data and user preferences can be analyzed and new travel destinations and plans can be proposed. Some or all of the above-described processing in the planning unit may be performed using, for example, AI or without AI. For example, the planning unit can input travel data into the generation AI and have the generation AI execute a travel plan proposal.

[0066] The sharing unit can compile AI-generated text and photos in HTML format so that they can be shared on social media or email. Generating the HTML format includes, but is not limited to, using HTML tags and applying CSS styles, for example. The sharing unit, for example, compiles AI-generated text and photos in HTML format using HTML tags. The HTML tags structure the text and photos and convert them into a format that can be displayed in a web browser. The sharing unit can also apply CSS styles to compile AI-generated text and photos in HTML format. The CSS styles adjust the appearance of the text and photos structured with HTML tags and convert them into a visually appealing format. The sharing unit can also compile AI-generated text and photos in HTML format so that they can be shared on social media or email. This makes it possible to compile AI-generated text and photos in HTML format so that they can be shared on social media or email. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the generated HTML data to the generation AI and cause the generation AI to share the data on social media or email.

[0067] The recognition unit can estimate a user's emotion and adjust the analysis order of photos based on the estimated user's emotion. The recognition unit, for example, estimates a user's emotion and adjusts the analysis order of photos based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. The recognition unit, for example, estimates a user's emotion using facial expression recognition technology and adjusts the analysis order of photos based on the estimated emotion. The facial expression recognition technology analyzes a user's facial expression to estimate the emotion. The recognition unit can also estimate a user's emotion using voice analysis technology and adjust the analysis order of photos based on the estimated emotion. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the recognition unit can estimate a user's emotion using text analysis technology and adjust the analysis order of photos based on the estimated emotion. The text analysis technology analyzes a user's text messages to estimate the emotion. This enables more appropriate analysis by adjusting the analysis order of photos based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, or without, an AI. For example, the recognition unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0068] The recognition unit can refer to the user's calendar information in addition to the photo metadata and analyze the photo based on events and schedules. For example, the recognition unit can refer to the user's calendar information in addition to the photo metadata and analyze the photo based on events and schedules. Calendar information includes, but is not limited to, Google Calendar and Outlook Calendar. For example, the recognition unit analyzes photos based on the dates of events registered in the user's calendar. Based on the calendar information, the recognition unit can prioritize analysis of photos related to specific events. The recognition unit can also prioritize analysis of photos taken at locations related to the user's schedule. Furthermore, the recognition unit can extract and analyze photos related to specific events from the calendar information. This enables photo analysis based on events and schedules by referring to the user's calendar information. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input calendar information to the generation AI and cause the generation AI to analyze photos based on events and schedules.

[0069] When analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. For example, when analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. Weather information includes, but is not limited to, weather data APIs and weather forecast services. For example, the recognition unit obtains weather information for that day based on the date and time the photo was taken and reflects it in the analysis. Based on the weather information, the recognition unit can distinguish between photos taken on sunny days and photos taken on rainy days and analyze them accordingly. The recognition unit can also refer to weather information to identify the lighting conditions at the time of shooting and improve analysis accuracy. In this way, by referring to the weather information, the environment at the time of shooting can be identified and analysis accuracy can be improved. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input weather information to a generation AI and cause the generation AI to identify the environment at the time of shooting.

[0070] The recognition unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The recognition unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. The recognition unit, for example, estimates the user's emotion using facial expression recognition technology and adjusts the display method of the analysis results based on the estimated emotion. The facial expression recognition technology analyzes the user's facial expression to estimate the emotion. The recognition unit can also estimate the user's emotion using voice analysis technology and adjust the display method of the analysis results based on the estimated emotion. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the recognition unit can estimate the user's emotion using text analysis technology and adjust the display method of the analysis results based on the estimated emotion. The text analysis technology analyzes the user's text message to estimate the emotion. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, or without, an AI. For example, the recognition unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0071] The recognition unit can improve the analysis accuracy by referring to the content of the user's social media posts when analyzing a photo. The recognition unit can improve the analysis accuracy by referring to the content of the user's social media posts when analyzing a photo, for example. The content of the social media posts includes, but is not limited to, text, images, and location information. The recognition unit can, for example, identify people appearing in a photo from the content of the user's social media posts. The location where the photo was taken can be identified based on the content of the social media posts. The recognition unit can also analyze the content of the photo in detail by referring to the content of the social media posts. In this way, the analysis accuracy can be improved by referring to the content of the user's social media posts. Some or all of the above-described processing in the recognition unit can be performed using, for example, AI, or can be performed without using AI. For example, the recognition unit can input the content of the social media posts into a generation AI and cause the generation AI to analyze the photo.

[0072] The recognition unit can analyze the user's voice memo when analyzing a photo and associate it with the content of the photo. For example, when analyzing a photo, the recognition unit can analyze the user's voice memo and associate it with the content of the photo. Voice memos include, for example, events and impressions from a trip, but are not limited to such examples. For example, the recognition unit can identify people and places appearing in the photo from the user's voice memo. The scene in which the photo was taken can be analyzed in detail based on the content of the voice memo. The recognition unit can also improve the accuracy of analysis by analyzing the voice memo and associating it with the content of the photo. In this way, by analyzing the user's voice memo, it can be associated with the content of the photo and improve the accuracy of analysis. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input the voice memo into a generation AI and have the generation AI associate it with the content of the photo.

[0073] The generation unit can estimate the user's emotion and adjust the tone and style of the text based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the tone and style of the text based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can estimate the user's emotion using facial expression recognition technology and adjust the tone and style of the text based on the estimated emotion. The facial expression recognition technology analyzes the user's facial expression to estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology and adjust the tone and style of the text based on the estimated emotion. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit can estimate the user's emotion using text analysis technology and adjust the tone and style of the text based on the estimated emotion. The text analysis technology analyzes the user's text message to estimate the emotion. This makes it possible to generate more appropriate text by adjusting the tone and style of the text based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the tone and style of the text.

[0074] The generation unit can provide consistency in the generated text by referring to the user's past travel records. For example, the generation unit can provide consistency in the generated text by referring to the user's past travel records. Past travel records include, but are not limited to, travel destinations, travel itineraries, and travel events. For example, the generation unit can quote episodes from the user's past travel records when visiting the same place. Based on the past travel records, the style and tone of the text can be made consistent. The generation unit can also incorporate related events into the text by referring to the user's past travel records. In this way, consistency can be provided in the generated text by referring to the user's past travel records. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input past travel records into a generation AI and have the generation AI perform text consistency.

[0075] The generation unit can add relevant historical background and cultural information to the generated text based on the results of photo analysis. For example, the generation unit can add relevant historical background and cultural information to the generated text based on the results of photo analysis. Examples of historical background and cultural information include, but are not limited to, the history of the location where the photo was taken, its cultural significance, and related events. For example, the generation unit can add historical background related to the location where the photo was taken to the text. Cultural information related to buildings and scenery appearing in the photo can be incorporated into the text. The generation unit can also provide information about the history and culture of the location based on the results of photo analysis. By adding relevant historical background and cultural information based on the results of photo analysis, richer text can be generated. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the results of photo analysis into a generation AI and cause the generation AI to add historical background and cultural information.

[0076] The generation unit can estimate the user's emotion and adjust the length of the sentence based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the sentence based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. The generation unit, for example, estimates the user's emotion using facial expression recognition technology and adjusts the length of the sentence based on the estimated emotion. The facial expression recognition technology analyzes the user's facial expression to estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology and adjust the length of the sentence based on the estimated emotion. The voice analysis technology analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit can estimate the user's emotion using text analysis technology and adjust the length of the sentence based on the estimated emotion. The text analysis technology analyzes the user's text message and estimates the emotion. This allows for more appropriate sentences to be generated by adjusting the length of the sentence based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the sentence.

[0077] The generation unit can add individual episodes to the generated text by referring to the user's social media posts. For example, the generation unit can add individual episodes to the generated text by referring to the user's social media posts. The social media posts include, but are not limited to, for example, text, images, and location information. For example, the generation unit can quote specific episodes from the user's social media posts. The generation unit can add individual episodes to the text based on the social media posts. The generation unit can also incorporate events related to the text by referring to the user's social media posts. In this way, individual episodes can be added to the generated text by referring to the user's social media posts. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the social media posts into a generation AI and cause the generation AI to add individual episodes.

[0078] The generation unit can convert the user's voice memos into text and incorporate them into the generated text. For example, the generation unit converts the user's voice memos into text and incorporates them into the generated text. Voice memos include, for example, events and impressions from a trip, but are not limited to these examples. For example, the generation unit converts the user's voice memos into text and quotes them in the text. Episodes related to the text can be added based on the content of the voice memos. The generation unit can also convert the user's voice memos into text and incorporate them into the text to ensure consistency. In this way, by converting the user's voice memos into text and incorporating them, consistency can be ensured in the generated text. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the voice memos into a generation AI and have the generation AI perform the text conversion.

[0079] The planning unit can estimate the user's emotions and adjust the content of the proposed travel plan based on the estimated user emotions. The planning unit, for example, estimates the user's emotions and adjusts the content of the proposed travel plan based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. The planning unit, for example, estimates the user's emotions using facial expression recognition technology and adjusts the content of the travel plan based on the estimated emotions. The facial expression recognition technology analyzes the user's facial expressions and estimates the emotions. The planning unit can also estimate the user's emotions using voice analysis technology and adjust the content of the travel plan based on the estimated emotions. The voice analysis technology analyzes the tone and speed of the user's voice and estimates the emotions. Furthermore, the planning unit can estimate the user's emotions using text analysis technology and adjust the content of the travel plan based on the estimated emotions. The text analysis technology analyzes the user's text messages and estimates the emotions. This allows the content of the travel plan to be adjusted based on the user's emotions, thereby proposing a more appropriate travel plan. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit may input the user's emotion data into the generation AI and have the generation AI adjust the contents of the travel plan.

[0080] The planning unit can provide a travel plan that matches the user's preferences by referring to the user's past travel history when proposing a travel plan. For example, the planning unit can provide a travel plan that matches the user's preferences by referring to the user's past travel history when proposing a travel plan. Past travel history includes, but is not limited to, travel destinations, travel itineraries, and travel events. For example, the planning unit can suggest travel destinations that match the user's preferences based on the user's past travel history. Based on the past travel history, the planning unit can also suggest activities that the user might be interested in. The planning unit can also suggest accommodations that match the user's preferences by referring to the user's past travel history. In this way, by referring to the user's past travel history, a travel plan that matches the user's preferences can be provided. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past travel history into a generation AI and have the generation AI execute a travel plan suggestion.

[0081] The planning unit can propose an appropriate time for the proposed travel plan by taking into account seasonal and event information. For example, the planning unit proposes an appropriate time for the proposed travel plan by taking into account seasonal and event information. Seasonal and event information includes, but is not limited to, events held in specific seasons and the attractions of tourist spots in each season. For example, the planning unit proposes travel destinations according to the season. It can propose a travel plan tailored to the time when a specific event is held. The planning unit can also propose an optimal travel time based on seasonal and event information. In this way, it is possible to propose an optimal travel time by taking into account seasonal and event information. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input seasonal and event information into the generation AI and cause the generation AI to propose an appropriate time.

[0082] The planning unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. For example, the planning unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the planning unit can estimate the user's emotions using facial expression recognition technology and prioritize travel plans based on the estimated emotions. The facial expression recognition technology analyzes the user's facial expressions and estimates the emotions. The planning unit can also estimate the user's emotions using voice analysis technology and prioritize travel plans based on the estimated emotions. The voice analysis technology analyzes the tone and speed of the user's voice and estimates the emotions. Furthermore, the planning unit can estimate the user's emotions using text analysis technology and prioritize travel plans based on the estimated emotions. The text analysis technology analyzes the user's text messages and estimates the emotions. This allows the system to provide more appropriate travel plans by prioritizing travel plans based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit may input the user's emotion data into the generation AI and have the generation AI determine the priorities of the travel plan.

[0083] The planning unit can add related activities to the proposed travel plan by referring to the user's social media posts. For example, the planning unit can add related activities to the proposed travel plan by referring to the user's social media posts. The social media posts include, but are not limited to, for example, text, images, and location information. For example, the planning unit can suggest activities that the user might be interested in based on the user's social media posts. Activities related to the travel plan can be added based on the social media posts. The planning unit can also suggest events related to the travel plan by referring to the user's social media posts. In this way, related activities can be added by referring to the user's social media posts. Some or all of the above-described processing in the planning unit can be performed using, for example, AI, or can be performed without using AI. For example, the planning unit can input the social media posts into a generation AI and cause the generation AI to add activities.

[0084] The planning unit can analyze the user's voice memos to incorporate places and activities of interest into the proposed travel plan. For example, the planning unit analyzes the user's voice memos to incorporate places and activities of interest into the proposed travel plan. Voice memos include, but are not limited to, events and impressions that occurred during the trip. For example, the planning unit can suggest places of interest from the user's voice memos. Activities related to the travel plan can be added based on the content of the voice memos. The planning unit can also analyze the user's voice memos to suggest events related to the travel plan. In this way, places and activities of interest can be incorporated into the travel plan by analyzing the user's voice memos. Some or all of the above-described processing in the planning unit can be performed using, for example, AI, or without AI. For example, the planning unit can input voice memos into a generation AI and have the generation AI incorporate the voice memos into the travel plan.

[0085] The sharing unit can estimate a user's emotion and adjust the layout and design of an article based on the estimated user's emotion. For example, the sharing unit can estimate a user's emotion and adjust the layout and design of an article based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the sharing unit can estimate a user's emotion using facial expression recognition technology and adjust the layout and design of an article based on the estimated emotion. The facial expression recognition technology analyzes a user's facial expression and estimates the emotion. The sharing unit can also estimate a user's emotion using voice analysis technology and adjust the layout and design of an article based on the estimated emotion. The voice analysis technology analyzes the tone and speed of a user's voice and estimates the emotion. Furthermore, the sharing unit can estimate a user's emotion using text analysis technology and adjust the layout and design of an article based on the estimated emotion. The text analysis technology analyzes a user's text message and estimates the emotion. This makes it possible to provide more appropriate articles by adjusting the layout and design of an article based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit may input user emotion data into the generation AI and cause the generation AI to adjust the layout and design of the article.

[0086] The sharing unit can ensure consistency in the shared article by referring to the user's past posting style. For example, the sharing unit can ensure consistency in the shared article by referring to the user's past posting style. Past posting styles include, but are not limited to, the tone of writing, layout, and types of images used. For example, the sharing unit can ensure consistency in the tone and style of the article based on the user's past posting style. The sharing unit can adjust the layout of the article by referring to the past posting style. The sharing unit can also ensure consistency in the content of the article based on the user's past posting style. In this way, consistency can be ensured in the shared article by referring to the user's past posting style. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input the past posting style into a generation AI and cause the generation AI to ensure consistency in the article.

[0087] The sharing unit can add related map and route information to the shared article based on the photo analysis results. For example, the sharing unit adds related map and route information to the shared article based on the photo analysis results. Map and route information includes, but is not limited to, a map of the photo's location and routes from the photo's location to other tourist attractions. For example, the sharing unit adds a related map to the article based on the photo's location. Route information from the photo's location to other tourist attractions can be added to the article based on the photo analysis results. The sharing unit can also incorporate map information around the photo's location into the article by referring to the photo analysis results. This allows for providing a richer article by adding related map and route information based on the photo analysis results. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the photo analysis results to a generation AI and cause the generation AI to add map and route information.

[0088] The sharing unit can estimate a user's emotions and adjust the timing of publishing an article based on the estimated user's emotions. The sharing unit, for example, estimates a user's emotions and adjusts the timing of publishing an article based on the estimated user's emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. The sharing unit, for example, estimates a user's emotions using facial expression recognition technology and adjusts the timing of publishing an article based on the estimated emotions. The facial expression recognition technology analyzes a user's facial expressions and estimates the emotions. The sharing unit can also estimate a user's emotions using voice analysis technology and adjust the timing of publishing an article based on the estimated emotions. The voice analysis technology analyzes the tone and speed of the user's voice and estimates the emotions. Furthermore, the sharing unit can estimate a user's emotions using text analysis technology and adjust the timing of publishing an article based on the estimated emotions. The text analysis technology analyzes a user's text messages and estimates the emotions. In this way, by adjusting the timing of publishing an article based on the user's emotions, the article can be published at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit may input user emotion data into the generation AI and cause the generation AI to adjust the publication timing of an article.

[0089] The sharing unit can add relevant hashtags to an article to be shared by referring to the user's social media posts. For example, the sharing unit can add relevant hashtags to an article to be shared by referring to the user's social media posts. Hashtags include, but are not limited to, the names of travel destinations, event names, and keywords related to a specific theme. For example, the sharing unit can extract relevant hashtags from the user's social media posts and add them to the article. It can suggest hashtags suitable for the article based on the social media posts. The sharing unit can also add trending hashtags related to the article by referring to the user's social media posts. In this way, it is possible to add relevant hashtags by referring to the user's social media posts. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the social media posts to a generation AI and cause the generation AI to add hashtags.

[0090] The sharing unit can convert the user's voice memos into text and incorporate them as quotes into the article to be shared. For example, the sharing unit converts the user's voice memos into text and incorporates them as quotes into the article to be shared. Voice memos include, for example, events and impressions from a trip, but are not limited to such examples. For example, the sharing unit converts the user's voice memos into text and incorporates them as quotes into the article. Episodes related to the article can be added based on the content of the voice memos. The sharing unit can also convert the user's voice memos into text to ensure consistency in the content of the article. In this way, by converting the user's voice memos into text and incorporating them as quotes, it is possible to ensure consistency in the content of the article. Some or all of the above-mentioned processing in the sharing unit can be performed, for example, using AI, or can be performed without using AI. For example, the sharing unit can input the voice memos into a generation AI and have the generation AI perform the text conversion. === Hard Collateral 1-1 === Each of the multiple elements, including the recognition unit, generation unit, planning unit, and sharing unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit analyzes metadata of photos using the camera 42 of the smart device 14 and location information analysis technology to identify the location, time, and accompanying people. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the content of photos using natural language processing technology and generates text describing the scene and events of the location. The planning unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes past travel data and user preferences using a clustering algorithm to suggest new travel destinations and plans. The sharing unit, implemented, for example, by the control unit 46A of the smart device 14, compiles AI-generated text and photos in HTML format and makes them shareable via social media or email. === Hard Collateral 1-2 === Each of the multiple elements, including the recognition unit, generation unit, planning unit, and sharing unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit analyzes metadata of photos using the camera 42 of the smart glasses 214 and location information analysis technology to identify the location, time, and accompanying people. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the content of photos using natural language processing technology and generates text describing the scene and events of the location. The planning unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes past travel data and user preferences using a clustering algorithm to suggest new travel destinations and plans. The sharing unit, implemented, for example, by the control unit 46A of the smart glasses 214, compiles AI-generated text and photos in HTML format and makes them shareable via social media or email. === Hard Collateral 1-3 === Each of the multiple elements, including the recognition unit, generation unit, planning unit, and sharing unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recognition unit analyzes metadata of photos using the camera 42 of the headset-type terminal 314 and location information analysis technology to identify the location, time, and accompanying people. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the content of photos using natural language processing technology and generates text describing the scene and events at the time. The planning unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes past travel data and user preferences using a clustering algorithm to suggest new travel destinations and plans. The sharing unit, implemented, for example, by the control unit 46A of the headset-type terminal 314, compiles AI-generated text and photos in HTML format and makes them shareable via social media or email. === Hard Collateral 1-4 === Each of the multiple elements, including the recognition unit, generation unit, planning unit, and sharing unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit analyzes metadata of photos using the robot 414's camera 42 and location information analysis technology to identify the location, time, and accompanying people. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the content of photos using natural language processing technology and generates text describing the scene and events of the location. The planning unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes past travel data and user preferences using a clustering algorithm to suggest new travel destinations and plans. The sharing unit, implemented, for example, by the control unit 46A of the robot 414, compiles AI-generated text and photos in HTML format and makes them shareable via social media or email.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The recognition unit can refer to the user's calendar information and analyze photos based on events and schedules. For example, it can analyze photos based on the dates of events registered in the user's calendar and prioritize analysis of photos related to specific events. It can also prioritize analysis of photos taken at locations related to the user's schedule. It can also extract and analyze photos related to specific events from the calendar information. This makes it possible to analyze photos based on events and schedules by referring to the user's calendar information.

[0093] When analyzing a photo, the recognition unit can refer to weather information to identify the environment at the time of shooting. For example, based on the date and time the photo was taken, weather information for that day can be acquired and reflected in the analysis. Based on the weather information, photos taken on sunny days can be analyzed separately from photos taken on rainy days. Furthermore, by referring to the weather information, the lighting conditions at the time of shooting can be identified, improving the accuracy of the analysis. In this way, by referring to the weather information, the environment at the time of shooting can be identified, improving the accuracy of the analysis.

[0094] The generation unit can estimate the user's emotion and adjust the tone and style of the text based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition technology and adjust the tone and style of the text based on the estimated emotion. Alternatively, the generation unit can estimate the user's emotion using voice analysis technology and adjust the tone and style of the text based on the estimated emotion. Furthermore, the generation unit can estimate the user's emotion using text analysis technology and adjust the tone and style of the text based on the estimated emotion. In this way, more appropriate text can be generated by adjusting the tone and style of the text based on the user's emotion.

[0095] The planning unit can propose an appropriate time for a proposed travel plan by taking into account seasonal and event information. For example, it can propose travel destinations by taking into account events held in a particular season and the attractiveness of tourist spots for each season. It can also propose travel plans that match the time when a particular event is held. Furthermore, it can propose the best time to travel based on seasonal and event information. This makes it possible to propose the best time to travel by taking into account seasonal and event information.

[0096] The sharing unit can estimate the user's emotions and adjust the layout and design of the article based on the estimated user's emotions. For example, it can estimate the user's emotions using facial expression recognition technology and adjust the layout and design of the article based on the estimated emotions. It can also estimate the user's emotions using voice analysis technology and adjust the layout and design of the article based on the estimated emotions. It can also estimate the user's emotions using text analysis technology and adjust the layout and design of the article based on the estimated emotions. In this way, by adjusting the layout and design of the article based on the user's emotions, it is possible to provide more appropriate articles.

[0097] When analyzing a photo, the recognition unit can improve the accuracy of the analysis by referring to the content posted by the user on social media. For example, it is possible to identify the person appearing in the photo from the content posted by the user on social media. It is also possible to identify the location where the photo was taken based on the content posted by the user on social media. Furthermore, it is possible to analyze the content of the photo in detail by referring to the content posted by the user on social media. In this way, it is possible to improve the accuracy of the analysis by referring to the content posted by the user on social media.

[0098] The generation unit can reference the user's past travel records to ensure consistency in the generated text. For example, it can quote episodes from the user's past travel records when visiting the same place. It can also make the style and tone of the text consistent based on the past travel records. It can also incorporate related events into the text by referencing the user's past travel records. In this way, it is possible to ensure consistency in the generated text by referencing the user's past travel records.

[0099] The planning unit can estimate the user's emotions and adjust the contents of the proposed travel plan based on the estimated user emotions. For example, the planning unit can estimate the user's emotions using facial expression recognition technology and adjust the contents of the travel plan based on the estimated emotions. Alternatively, the planning unit can estimate the user's emotions using voice analysis technology and adjust the contents of the travel plan based on the estimated emotions. Furthermore, the planning unit can estimate the user's emotions using text analysis technology and adjust the contents of the travel plan based on the estimated emotions. By adjusting the contents of the travel plan based on the user's emotions, a more appropriate travel plan can be proposed.

[0100] The sharing unit can add related map and route information to the shared article based on the results of photo analysis. For example, a related map can be added to the article based on the location where the photo was taken. Route information from the photo location to other tourist spots can also be added to the article based on the results of photo analysis. Furthermore, map information around the photo location can be incorporated into the article by referring to the results of photo analysis. This makes it possible to provide richer articles by adding related map and route information based on the results of photo analysis.

[0101] The sharing unit can estimate a user's emotions and adjust the timing of publishing an article based on the estimated user's emotions. For example, the sharing unit can estimate a user's emotions using facial expression recognition technology and adjust the timing of publishing an article based on the estimated emotions. The sharing unit can also estimate a user's emotions using voice analysis technology and adjust the timing of publishing an article based on the estimated emotions. Furthermore, the sharing unit can estimate a user's emotions using text analysis technology and adjust the timing of publishing an article based on the estimated emotions. In this way, by adjusting the timing of publishing an article based on the user's emotions, the article can be published at a more appropriate time.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The recognition unit analyzes the photo's metadata to identify the location, time, and accompanying people. Photo metadata includes location information, timestamps, and person tags. The recognition unit uses facial recognition technology to identify people in the photo and location analysis technology to determine the location where the photo was taken. Facial recognition technology includes deep learning-based facial recognition and Haar feature-based face detection, while location analysis technology includes GPS data analysis and Wi-Fi location analysis. Step 2: The generation unit generates text describing the travel scenes and memories based on the information identified by the recognition unit. The generation unit analyzes the content of the photo using natural language processing technology and generates text describing the scenes and events of the location. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The planning unit proposes the next travel plan based on the text and photos generated by the generation unit. The planning unit uses a clustering algorithm to analyze past travel data and user preferences and propose new travel destinations and plans. Clustering algorithms include K-means and hierarchical clustering. Step 4: The Sharing Department generates and shares articles based on the plans proposed by the Planning Department. The Sharing Department compiles the AI-generated text and photos in HTML format, making them shareable via social media and email. Generating the HTML format includes using HTML tags and applying CSS styles.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

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

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

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

[0127] The 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.

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

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

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

[0131] Fig. 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.

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

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

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

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

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

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

[0143] The 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.

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 recognition unit that analyzes the metadata of photos to identify the location, time, and accompanying people; a generation unit that generates travel scenes and memories as text based on the information identified by the recognition unit; a planning unit that proposes a plan for the next trip based on the text and photos generated by the generation unit; a sharing unit that generates and shares articles based on the plan proposed by the planning unit. A system characterized by:

2. The recognition unit Use facial recognition technology to identify people in photos 2. The system of claim 1.

3. The recognition unit Identifying the shooting location using location information analysis technology 2. The system of claim 1.

4. The generation unit Analyze the content of a photo using natural language processing technology and generate text describing the scene or event.

2. The system of claim 1.

5. The planning unit Using clustering algorithms to analyze past travel data and user preferences, the system suggests new travel destinations and plans.

2. The system of claim 1.

6. The common part is AI-generated text and photos will be compiled in HTML format and made available for sharing via social media and email.

2. The system of claim 1.

7. The recognition unit Estimate the user's emotions and adjust the analysis order of photos based on the estimated user emotions.

2. The system of claim 1.

8. The recognition unit In addition to photo metadata, look at the user's calendar information to identify how to analyze photos based on events and appointments.

2. The system of claim 1.

Images