System

Abstract

An object of the system according to the embodiment is to propose and implement a funeral and a memorial event that reflect the preference and values of the deceased.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, a generation unit, and a customization unit. The collection unit collects social media data of the deceased. The analysis part analyzes the data collected by the collection part and specifies the preference and sense of values of the deceased. The suggestion unit suggests the theme of the funeral or the memorial event on the basis of the preference or the sense of values specified by the analysis unit. The generation unit generates a memorial item or digital art on the basis of the theme proposed by the proposal unit. The customizing unit customizes the plan by cooperation of family members and friends online.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to plan funerals and memorial events that reflect the preferences and values ​​of the deceased.

[0005] The system according to the embodiment aims to propose and implement funerals and memorial events that reflect the preferences and values ​​of the deceased. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a customization unit. The collection unit collects social media data of the deceased. The analysis unit analyzes the data collected by the collection unit to identify the preferences and values ​​of the deceased. The suggestion unit suggests a theme for the funeral or memorial event based on the preferences and values ​​identified by the analysis unit. The generation unit generates a memorial item or digital art based on the theme suggested by the suggestion unit. The customization unit allows family and friends to collaborate online to customize the plan. [Effects of the Invention]

[0007] The system according to the embodiment can propose and implement funerals and memorial events that reflect the preferences and values ​​of the deceased. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to analyze the deceased's social media data, photos, and writings to understand their preferences and values. The system collects the deceased's social media data, analyzes their photos and writings, and identifies their preferences and values. Based on the analysis results, the system then suggests a theme for a funeral or memorial event and generates special memorial items and digital art. Family and friends collaborate online to customize plans and create a moving, lasting memorial event that reflects the deceased's heart. For example, the system collects data such as photos, writings, and comments posted by the deceased. The system then analyzes the collected data, analyzing the content of the photos, the tone of the writing, and the keywords used to identify the deceased's preferences and values. Based on the analysis results, the system may suggest a nature-themed funeral or memorial event if the deceased loved nature. The system may also generate digital art based on the deceased's photos or paintings of the deceased's favorite landscapes. Finally, family and friends share their opinions and customize the plan through an online platform. This allows the system to create a moving memorial event based on the deceased's preferences and values.

[0029] A memorial event generation system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a customization unit. The collection unit collects social media data of the deceased. For example, the collection unit may collect data such as photos, writings, and comments posted by the deceased. For example, the collection unit may collect photos taken by the deceased on trips and writings about their favorite music. The analysis unit analyzes the data collected by the collection unit to identify the deceased's preferences and values. For example, the analysis unit may analyze the content of photos, the tone of writing, and keywords used. For example, if the deceased was a nature lover, the analysis unit may determine that the post contains many nature-related photos and writings. The suggestion unit suggests a theme for the funeral or memorial event based on the preferences and values ​​identified by the analysis unit. For example, if the deceased loved nature, the suggestion unit may suggest a nature-themed funeral or memorial event. The generation unit generates a memorial item or digital art based on the theme suggested by the suggestion unit. For example, the generation unit can generate digital art based on a photograph of the deceased or a painting of a landscape that the deceased liked. The customization unit allows family and friends to collaborate online to customize the plan. For example, the customization unit allows family and friends to share their opinions and customize the plan through an online platform. In this way, the memorial event generation system according to the embodiment can create a moving memorial event based on the preferences and values ​​of the deceased.

[0030] The collection unit can collect data on photos, writings, or comments posted by the deceased. For example, the collection unit can collect data on photos, writings, or comments posted by the deceased. For example, the collection unit can collect photos taken by the deceased on their travels. The collection unit can also collect writings written by the deceased about their favorite music. Furthermore, the collection unit can collect comments left by the deceased in their interactions with friends. In this way, the collection unit can more accurately identify the preferences and values ​​of the deceased by collecting the posting data of the deceased.

[0031] The analysis unit can identify the preferences and values ​​of the deceased by analyzing the content of the photos, the tone of the text, the keywords used, and the like. The analysis unit can, for example, analyze the content of the photos. For example, if the deceased was a nature lover, the analysis unit can determine that the posts contain many nature-related photos. The analysis unit can also analyze the tone of the text. For example, the analysis unit can determine that the deceased posted many posts written in a positive tone. Furthermore, the analysis unit can analyze the keywords used. For example, the analysis unit can identify keywords frequently used by the deceased and, based on the keywords, identify the preferences and values ​​of the deceased. This allows the analysis unit to identify the preferences and values ​​of the deceased in more detail.

[0032] The suggestion unit can suggest a nature-themed funeral or memorial event if the deceased loved nature. For example, if the deceased loved nature, the suggestion unit can suggest a nature-themed funeral or memorial event. For example, the suggestion unit can suggest decorations incorporating natural scenery. The suggestion unit can also suggest an outdoor ceremony. Furthermore, the suggestion unit can suggest an event using music or video related to nature. In this way, the suggestion unit can suggest a memorial event that reflects the personality of the deceased.

[0033] The generation unit can generate digital art based on a photograph of the deceased or a painting depicting a favorite landscape of the deceased. The generation unit can generate digital art based on, for example, a photograph of the deceased. For example, the generation unit can generate a digital painting from a photograph of the deceased. The generation unit can also generate a painting depicting a favorite landscape of the deceased. For example, the generation unit can generate a painting depicting a landscape of a place the deceased often visited. Furthermore, the generation unit can generate a photomontage from a photograph of the deceased. In this way, the generation unit can provide a special item to commemorate the deceased.

[0034] The customization unit allows family and friends to share their opinions and customize the plan through an online platform. The customization unit allows family and friends to share their opinions and customize the plan through an online platform, for example. For example, the customization unit may provide a dedicated website and a function that allows family and friends to edit together. The customization unit may also provide a chat function that allows opinions to be exchanged in real time. Furthermore, the customization unit may provide a voting function that allows family and friends to collect their opinions and decide on the plan. In this way, the customization unit allows family and friends to work together to create a moving memorial event.

[0035] The collection unit can take security measures for privacy protection when collecting data. The collection unit can take security measures for privacy protection when collecting data, for example. For example, the collection unit can encrypt and store data. The collection unit can also set access restrictions so that only specific users can access the data. Furthermore, the collection unit can introduce an authentication process to verify the identity of users when collecting data. This allows the collection unit to strengthen privacy protection when collecting data.

[0036] The customization unit may provide an interface for family and friends to collaborate online to customize the plan. For example, the customization unit may provide an interface for family and friends to collaborate online to customize the plan. For example, the customization unit may provide a drag-and-drop function to enable a user to easily edit the plan. The customization unit may also provide a real-time editing function to enable multiple users to edit the plan simultaneously. Furthermore, the customization unit may provide a preview function to enable a user to customize the plan while checking the edits. In this way, the customization unit allows family and friends to easily collaborate to customize the plan.

[0037] The collection unit can analyze the frequency of posts made by the deceased in the past and select the optimal collection method. For example, the collection unit can prioritize collecting data from platforms on which the deceased frequently posted. In addition, if the deceased posted intensively during a specific period, the collection unit can also focus on collecting data from that period. Furthermore, if the deceased frequently posted about a specific topic, the collection unit can prioritize collecting data related to that topic. This allows the collection unit to efficiently collect data based on the frequency of posts made by the deceased.

[0038] When collecting data, the collection unit can filter the data based on the areas of interest of the deceased. For example, if the deceased was interested in traveling, the collection unit can prioritize collecting posts related to traveling. Furthermore, if the deceased was interested in music, the collection unit can prioritize collecting posts related to music. Furthermore, if the deceased was interested in cooking, the collection unit can prioritize collecting posts related to cooking. This allows the collection unit to prioritize collecting data related to the areas of interest of the deceased.

[0039] When collecting data, the collection unit can select the optimal collection means depending on the posting method of the deceased. For example, if the deceased posted using voice, the collection unit can prioritize collecting voice data. Furthermore, if the deceased posted using text, the collection unit can also prioritize collecting text data. Furthermore, if the deceased posted using images, the collection unit can also prioritize collecting image data. This allows the collection unit to collect data using the optimal means depending on the posting method of the deceased.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into consideration the geographical location information of the deceased. For example, the collection unit can prioritize collecting data posted by the deceased in a specific location. The collection unit can also prioritize collecting data posted by the deceased while traveling. Furthermore, the collection unit can prioritize collecting data related to the area where the deceased lived. This allows the collection unit to collect highly relevant data based on the geographical location information of the deceased.

[0041] When collecting data, the collection unit can analyze the social media activities of the deceased and collect relevant data. For example, the collection unit can collect data from social media platforms that the deceased frequently used. If the deceased used a specific hashtag, the collection unit can also collect data related to the hashtag. Furthermore, if the deceased participated in a specific group or community, the collection unit can also collect data related to the group or community. This allows the collection unit to collect relevant data based on the social media activities of the deceased.

[0042] When collecting data, the collection unit can customize the collection method by reflecting the deceased's past feedback. For example, the collection unit can prioritize and collect posts for which the deceased gave positive feedback in the past. The collection unit can also collect posts that the deceased gave negative feedback in the past, excluding posts for which the deceased gave negative feedback in the past. Furthermore, the collection unit can adjust the collection method based on posts for which the deceased gave specific feedback in the past. This allows the collection unit to optimize the collection method based on the deceased's past feedback.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also perform a detailed analysis on data containing important keywords. Furthermore, the analysis unit can also perform a detailed analysis on data related to important events. This allows the analysis unit to efficiently perform analysis according to the importance of the data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply the optimal analysis algorithm depending on the data category.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the deceased. For example, the analysis unit can perform analysis on similar data based on the past analysis results of the deceased. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results of the deceased. Furthermore, the analysis unit can also build a feedback loop to improve the accuracy of the analysis based on the past analysis results of the deceased. This allows the analysis unit to improve the accuracy of the analysis by referring to the past analysis results of the deceased.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the time of data posting. For example, the analysis unit can prioritize analysis of data posted by the deceased at a specific time period. The analysis unit can also prioritize analysis of data posted by the deceased at a specific event or anniversary. Furthermore, the analysis unit can also prioritize analysis of data posted by the deceased during a specific season or time period. This allows the analysis unit to efficiently perform analysis based on the time of data posting.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of data related to the preferences and values ​​of the deceased. The analysis unit can also prioritize analysis of data related to the areas of interest of the deceased. Furthermore, the analysis unit can also prioritize analysis of data related to content posted in the past by the deceased. This allows the analysis unit to efficiently perform analysis based on the relevance of the data.

[0048] During the analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of the deceased. For example, the analysis unit can provide analysis results using technical terminology for areas in which the deceased had expertise. The analysis unit can also provide analysis results using concise terminology for areas in which the deceased had general knowledge. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of the deceased. This allows the analysis unit to provide analysis results using optimal terminology according to the level of expertise of the deceased.

[0049] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the theme when making a suggestion. For example, the suggestion unit can make a detailed suggestion for an important theme and a simplified suggestion for a less important theme. The suggestion unit can also make a detailed suggestion for a theme that includes an important keyword. Furthermore, the suggestion unit can also make a detailed suggestion for a theme related to an important event. This allows the suggestion unit to efficiently make suggestions according to the importance of the theme.

[0050] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the theme. For example, the suggestion unit can apply a nature-related suggestion algorithm to a nature-related theme. The suggestion unit can also apply a music-related suggestion algorithm to a music-related theme. Furthermore, the suggestion unit can also apply a movie-related suggestion algorithm to a movie-related theme. This allows the suggestion unit to apply the optimal suggestion algorithm depending on the theme category.

[0051] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the deceased's past suggestion results. For example, the suggestion unit can make suggestions for a similar theme based on the deceased's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the deceased's past suggestion results. Furthermore, the suggestion unit can also build a feedback loop for improving the accuracy of the suggestion based on the deceased's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the deceased's past suggestion results.

[0052] When making suggestions, the suggestion unit can determine the priority of suggestions based on the time of submission of the themes. For example, the suggestion unit can prioritize suggestions of themes that the deceased related to a specific time period. The suggestion unit can also prioritize suggestions of themes that the deceased related to a specific event or anniversary. Furthermore, the suggestion unit can prioritize suggestions of themes that the deceased related to a specific season or time period. This allows the suggestion unit to efficiently make suggestions based on the time of submission of themes.

[0053] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the themes. For example, the suggestion unit can prioritize suggestions of themes related to the preferences and values ​​of the deceased. The suggestion unit can also prioritize suggestions of themes related to the deceased's fields of interest. Furthermore, the suggestion unit can prioritize suggestions of themes related to the deceased's past posts. This allows the suggestion unit to efficiently make suggestions based on the relevance of the themes.

[0054] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion depending on the expertise level of the deceased. For example, the suggestion unit can make a suggestion using technical terms for a field in which the deceased had expertise. The suggestion unit can also make a suggestion using concise terms for a field in which the deceased had general knowledge. Furthermore, the suggestion unit can adjust the way the suggestion is expressed depending on the expertise level of the deceased. This allows the suggestion unit to make a suggestion using optimal terms depending on the expertise level of the deceased.

[0055] The generation unit can adjust the level of detail of the generation based on the importance of the item during generation. For example, the generation unit can perform detailed generation for important items and simple generation for items with low importance. The generation unit can also perform detailed generation for items containing important keywords. Furthermore, the generation unit can also perform detailed generation for items related to important events. This allows the generation unit to efficiently generate items according to the importance of the items.

[0056] The generator can apply different generation algorithms depending on the category of the item during generation. For example, the generator can apply an image generation algorithm to a photo item. The generator can also apply a natural language generation algorithm to a text item. The generator can also apply a voice generation algorithm to a voice item. This allows the generator to apply the optimal generation algorithm depending on the category of the item.

[0057] During generation, the generation unit can improve the accuracy of generation by referring to the deceased's past generation results. For example, the generation unit can generate a similar item based on the deceased's past generation results. The generation unit can also adjust the generation algorithm by referring to the deceased's past generation results. Furthermore, the generation unit can also build a feedback loop to improve the accuracy of generation based on the deceased's past generation results. In this way, the generation unit can improve the accuracy of generation by referring to the deceased's past generation results.

[0058] The generation unit can determine the priority of generation based on the time of submission of the items at the time of generation. For example, the generation unit can prioritize generating items related to a specific time period of the deceased. The generation unit can also prioritize generating items related to a specific event or anniversary of the deceased. Furthermore, the generation unit can prioritize generating items related to a specific season or time period of the deceased. This allows the generation unit to efficiently generate items based on the time of submission of the items.

[0059] The generation unit can adjust the order of generation based on the relevance of the items during generation. For example, the generation unit can prioritize generating items related to the preferences and values ​​of the deceased. The generation unit can also prioritize generating items related to the areas of interest of the deceased. Furthermore, the generation unit can prioritize generating items related to content posted in the past by the deceased. This allows the generation unit to efficiently generate items based on the relevance of the items.

[0060] During generation, the generation unit can adjust the use of technical terminology in the generation according to the expertise level of the deceased. For example, the generation unit can generate items using technical terminology for fields in which the deceased had expertise. The generation unit can also generate items using concise terminology for fields in which the deceased had general knowledge. Furthermore, the generation unit can adjust the expression method in the generation according to the expertise level of the deceased. This allows the generation unit to generate items using optimal terminology according to the expertise level of the deceased.

[0061] During customization, the customization unit can adjust the level of detail of the customization based on the importance of the plan. For example, the customization unit can perform detailed customization for important plans and simple customization for less important plans. The customization unit can also perform detailed customization for plans that include important keywords. Furthermore, the customization unit can also perform detailed customization for plans related to important events. This allows the customization unit to efficiently perform customization according to the importance of the plans.

[0062] During customization, the customization unit can apply different customization algorithms depending on the category of the plan. For example, the customization unit can apply a nature-related customization algorithm to a nature-related plan. The customization unit can also apply a music-related customization algorithm to a music-related plan. Furthermore, the customization unit can apply a movie-related customization algorithm to a movie-related plan. This allows the customization unit to apply the optimal customization algorithm depending on the category of the plan.

[0063] During customization, the customization unit can improve the accuracy of the customization by referring to the deceased's past customization results. For example, the customization unit can customize a similar plan based on the deceased's past customization results. The customization unit can also adjust the customization algorithm by referring to the deceased's past customization results. Furthermore, the customization unit can build a feedback loop for improving the accuracy of the customization based on the deceased's past customization results. In this way, the customization unit can improve the accuracy of the customization by referring to the deceased's past customization results.

[0064] During customization, the customization unit can determine the priority of customization based on the time of submission of the plan. For example, the customization unit can prioritize customization of plans that the deceased related to a specific time period. The customization unit can also prioritize customization of plans that the deceased related to a specific event or anniversary. Furthermore, the customization unit can also prioritize customization of plans that the deceased related to a specific season or time period. This allows the customization unit to efficiently perform customization based on the time of submission of the plan.

[0065] During customization, the customization unit can adjust the order of customization based on the relevance of the plans. For example, the customization unit can prioritize customization of plans related to the preferences and values ​​of the deceased. The customization unit can also prioritize customization of plans related to the areas of interest of the deceased. Furthermore, the customization unit can also prioritize customization of plans related to content posted in the past by the deceased. This allows the customization unit to efficiently perform customization based on the relevance of the plans.

[0066] During customization, the customization unit can adjust the use of technical terms in the customization according to the deceased's level of expertise. For example, the customization unit can perform customization using technical terms for a field in which the deceased had specialized knowledge. The customization unit can also perform customization using concise terms for a field in which the deceased had general knowledge. Furthermore, the customization unit can adjust the way the customization is expressed according to the deceased's level of expertise. This allows the customization unit to perform customization using optimal terms according to the deceased's level of expertise.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] In addition to the deceased's social media data, the collection unit may also collect data from the deceased's digital devices. For example, the collection unit may collect photos, notes, calendar entries, and other information from the deceased's smartphone or computer. The collection unit may also collect the deceased's application usage history, browser bookmarks, and browsing history. Furthermore, the collection unit may collect posts from online forums and communities in which the deceased participated. This allows the collection unit to collect a broader range of data and more precisely identify the deceased's preferences and values.

[0069] When analyzing the data of a deceased person, the analysis unit can classify the data based on the deceased person's life events. For example, the analysis unit can identify and classify data related to important life events such as the deceased person's birthday, wedding anniversary, and birth of a child. The analysis unit can also classify data related to the deceased person's career milestones and life events such as moving and traveling. Furthermore, the analysis unit can analyze and classify data related to the deceased person's health condition and medical history. This allows the analysis unit to organize the data based on the deceased person's life events and provide more detailed analysis results.

[0070] The suggestion unit can make suggestions to recreate places visited or events held by the deceased during their lifetime, based on the preferences and values ​​of the deceased. For example, the suggestion unit can suggest memorial events at restaurants or cafes that the deceased frequently visited. The suggestion unit can also suggest recreating hobby clubs or sporting events that the deceased participated in. Furthermore, the suggestion unit can suggest memorial events themed around places that the deceased traveled to. In this way, the suggestion unit can recreate memories of the deceased and suggest moving memorial events.

[0071] The customization unit can create a digital time capsule for family and friends to share memories of the deceased. For example, the customization unit can provide a platform where family and friends can upload photos, videos, and messages of memories with the deceased. The customization unit can also add content related to the activities and hobbies of the deceased. Furthermore, the customization unit can set the digital time capsule to be opened at a specific date and time. In this way, the customization unit can enable family and friends to share memories of the deceased and create a moving memorial event.

[0072] The customization unit can provide a virtual reality (VR) space in which family and friends can share memories of the deceased. For example, the customization unit can provide a VR space that recreates places the deceased visited or liked during their lifetime. The customization unit can also provide a function that allows family and friends to talk about memories of the deceased within the VR space. Furthermore, the customization unit can provide a function that allows photos and videos of the deceased to be displayed within the VR space. In this way, the customization unit can enable family and friends to share memories of the deceased and create a moving memorial event.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects the deceased's social media data. For example, it can collect data such as photos, text, and comments posted by the deceased. The collection unit can collect photos taken by the deceased on their travels or texts they wrote about their favorite music. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the preferences and values ​​of the deceased. For example, it can analyze the content of photos, the tone of writing, and keywords used. If the deceased was a nature lover, the analysis unit can determine that the collection contains many nature-related photos and writings. Step 3: The suggestion unit suggests a theme for the funeral or memorial event based on the preferences and values ​​identified by the analysis unit. For example, if the deceased loved nature, a nature-themed funeral or memorial event can be suggested. Step 4: The generator generates a memorial item or digital art based on the theme suggested by the suggester. For example, it can generate digital art based on a photograph of the deceased or a painting of a favorite landscape of the deceased. Step 5: The customization department allows family and friends to collaborate online to customize the plan. For example, family and friends can share their opinions and customize the plan through the online platform.

[0075] (Example 2) A system according to an embodiment of the present invention uses AI to analyze the deceased's social media data, photos, and writings to understand their preferences and values. The system collects the deceased's social media data, analyzes their photos and writings, and identifies their preferences and values. Based on the analysis results, the system then suggests a theme for a funeral or memorial event and generates special memorial items and digital art. Family and friends collaborate online to customize plans and create a moving, lasting memorial event that reflects the deceased's heart. For example, the system collects data such as photos, writings, and comments posted by the deceased. The system then analyzes the collected data, analyzing the content of the photos, the tone of the writing, and the keywords used to identify the deceased's preferences and values. Based on the analysis results, the system may suggest a nature-themed funeral or memorial event if the deceased loved nature. The system may also generate digital art based on the deceased's photos or paintings of the deceased's favorite landscapes. Finally, family and friends share their opinions and customize the plan through an online platform. This allows the system to create a moving memorial event based on the deceased's preferences and values.

[0076] A memorial event generation system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a generation unit, and a customization unit. The collection unit collects social media data of the deceased. For example, the collection unit may collect data such as photos, writings, and comments posted by the deceased. For example, the collection unit may collect photos taken by the deceased on trips and writings about their favorite music. The analysis unit analyzes the data collected by the collection unit to identify the deceased's preferences and values. For example, the analysis unit may analyze the content of photos, the tone of writing, and keywords used. For example, if the deceased was a nature lover, the analysis unit may determine that the post contains many nature-related photos and writings. The suggestion unit suggests a theme for the funeral or memorial event based on the preferences and values ​​identified by the analysis unit. For example, if the deceased loved nature, the suggestion unit may suggest a nature-themed funeral or memorial event. The generation unit generates a memorial item or digital art based on the theme suggested by the suggestion unit. For example, the generation unit can generate digital art based on a photograph of the deceased or a painting of a landscape that the deceased liked. The customization unit allows family and friends to collaborate online to customize the plan. For example, the customization unit allows family and friends to share their opinions and customize the plan through an online platform. In this way, the memorial event generation system according to the embodiment can create a moving memorial event based on the preferences and values ​​of the deceased.

[0077] The collection unit can collect data on photos, writings, or comments posted by the deceased. For example, the collection unit can collect data on photos, writings, or comments posted by the deceased. For example, the collection unit can collect photos taken by the deceased on their travels. The collection unit can also collect writings written by the deceased about their favorite music. Furthermore, the collection unit can collect comments left by the deceased in their interactions with friends. In this way, the collection unit can more accurately identify the preferences and values ​​of the deceased by collecting the posting data of the deceased.

[0078] The analysis unit can identify the preferences and values ​​of the deceased by analyzing the content of the photos, the tone of the text, the keywords used, and the like. The analysis unit can, for example, analyze the content of the photos. For example, if the deceased was a nature lover, the analysis unit can determine that the posts contain many nature-related photos. The analysis unit can also analyze the tone of the text. For example, the analysis unit can determine that the deceased posted many posts written in a positive tone. Furthermore, the analysis unit can analyze the keywords used. For example, the analysis unit can identify keywords frequently used by the deceased and, based on the keywords, identify the preferences and values ​​of the deceased. This allows the analysis unit to identify the preferences and values ​​of the deceased in more detail.

[0079] The suggestion unit can suggest a nature-themed funeral or memorial event if the deceased loved nature. For example, if the deceased loved nature, the suggestion unit can suggest a nature-themed funeral or memorial event. For example, the suggestion unit can suggest decorations incorporating natural scenery. The suggestion unit can also suggest an outdoor ceremony. Furthermore, the suggestion unit can suggest an event using music or video related to nature. In this way, the suggestion unit can suggest a memorial event that reflects the personality of the deceased.

[0080] The generation unit can generate digital art based on a photograph of the deceased or a painting depicting a favorite landscape of the deceased. The generation unit can generate digital art based on, for example, a photograph of the deceased. For example, the generation unit can generate a digital painting from a photograph of the deceased. The generation unit can also generate a painting depicting a favorite landscape of the deceased. For example, the generation unit can generate a painting depicting a landscape of a place the deceased often visited. Furthermore, the generation unit can generate a photomontage from a photograph of the deceased. In this way, the generation unit can provide a special item to commemorate the deceased.

[0081] The customization unit allows family and friends to share their opinions and customize the plan through an online platform. The customization unit allows family and friends to share their opinions and customize the plan through an online platform, for example. For example, the customization unit may provide a dedicated website and a function that allows family and friends to edit together. The customization unit may also provide a chat function that allows opinions to be exchanged in real time. Furthermore, the customization unit may provide a voting function that allows family and friends to collect their opinions and decide on the plan. In this way, the customization unit allows family and friends to work together to create a moving memorial event.

[0082] The collection unit can take security measures for privacy protection when collecting data. The collection unit can take security measures for privacy protection when collecting data, for example. For example, the collection unit can encrypt and store data. The collection unit can also set access restrictions so that only specific users can access the data. Furthermore, the collection unit can introduce an authentication process to verify the identity of users when collecting data. This allows the collection unit to strengthen privacy protection when collecting data.

[0083] The customization unit may provide an interface for family and friends to collaborate online to customize the plan. For example, the customization unit may provide an interface for family and friends to collaborate online to customize the plan. For example, the customization unit may provide a drag-and-drop function to enable a user to easily edit the plan. The customization unit may also provide a real-time editing function to enable multiple users to edit the plan simultaneously. Furthermore, the customization unit may provide a preview function to enable a user to customize the plan while checking the edits. In this way, the customization unit allows family and friends to easily collaborate to customize the plan.

[0084] The collection unit can estimate the emotions of the deceased and adjust the timing of data collection based on the estimated emotions of the deceased. For example, if the deceased posted emotionally on a particular event or anniversary, the collection unit can collect data at that timing. In addition, if the deceased posted emotionally during a particular time period, the collection unit can collect data at that time period. Furthermore, if the deceased posted emotionally during a particular season or time of year, the collection unit can collect data at that time period. This allows the collection unit to collect data at the optimal timing based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The collection unit can analyze the frequency of posts made by the deceased in the past and select the optimal collection method. For example, the collection unit can prioritize collecting data from platforms on which the deceased frequently posted. In addition, if the deceased posted intensively during a specific period, the collection unit can also focus on collecting data from that period. Furthermore, if the deceased frequently posted about a specific topic, the collection unit can prioritize collecting data related to that topic. This allows the collection unit to efficiently collect data based on the frequency of posts made by the deceased.

[0086] When collecting data, the collection unit can filter the data based on the areas of interest of the deceased. For example, if the deceased was interested in traveling, the collection unit can prioritize collecting posts related to traveling. Furthermore, if the deceased was interested in music, the collection unit can prioritize collecting posts related to music. Furthermore, if the deceased was interested in cooking, the collection unit can prioritize collecting posts related to cooking. This allows the collection unit to prioritize collecting data related to the areas of interest of the deceased.

[0087] When collecting data, the collection unit can select the optimal collection means depending on the posting method of the deceased. For example, if the deceased posted using voice, the collection unit can prioritize collecting voice data. Furthermore, if the deceased posted using text, the collection unit can also prioritize collecting text data. Furthermore, if the deceased posted using images, the collection unit can also prioritize collecting image data. This allows the collection unit to collect data using the optimal means depending on the posting method of the deceased.

[0088] The collection unit can estimate the emotions of the deceased and determine the priority of data to be collected based on the estimated emotions of the deceased. For example, if the deceased posted emotional content, the collection unit can prioritize collecting those posts. The collection unit can also prioritize collecting posts in which the deceased expressed specific emotions. Furthermore, if the deceased made emotional comments, the collection unit can also prioritize collecting those comments. This allows the collection unit to prioritize collecting data based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into consideration the geographical location information of the deceased. For example, the collection unit can prioritize collecting data posted by the deceased in a specific location. The collection unit can also prioritize collecting data posted by the deceased while traveling. Furthermore, the collection unit can prioritize collecting data related to the area where the deceased lived. This allows the collection unit to collect highly relevant data based on the geographical location information of the deceased.

[0090] When collecting data, the collection unit can analyze the social media activities of the deceased and collect relevant data. For example, the collection unit can collect data from social media platforms that the deceased frequently used. If the deceased used a specific hashtag, the collection unit can also collect data related to the hashtag. Furthermore, if the deceased participated in a specific group or community, the collection unit can also collect data related to the group or community. This allows the collection unit to collect relevant data based on the social media activities of the deceased.

[0091] When collecting data, the collection unit can customize the collection method by reflecting the deceased's past feedback. For example, the collection unit can prioritize and collect posts for which the deceased gave positive feedback in the past. The collection unit can also collect posts that the deceased gave negative feedback in the past, excluding posts for which the deceased gave negative feedback in the past. Furthermore, the collection unit can adjust the collection method based on posts for which the deceased gave specific feedback in the past. This allows the collection unit to optimize the collection method based on the deceased's past feedback.

[0092] The analysis unit can estimate the emotions of the deceased and adjust the expression method of the analysis based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the analysis unit can provide an analysis result that reflects that emotion. The analysis unit can also analyze a post in which the deceased expressed a particular emotion and adjust the analysis result based on that emotion. Furthermore, if the deceased made an emotional comment, the analysis unit can analyze the comment and adjust the analysis result based on the emotion. This allows the analysis unit to provide an analysis result in an optimal expression method based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also perform a detailed analysis on data containing important keywords. Furthermore, the analysis unit can also perform a detailed analysis on data related to important events. This allows the analysis unit to efficiently perform analysis according to the importance of the data.

[0094] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply the optimal analysis algorithm depending on the data category.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the deceased. For example, the analysis unit can perform analysis on similar data based on the past analysis results of the deceased. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results of the deceased. Furthermore, the analysis unit can also build a feedback loop to improve the accuracy of the analysis based on the past analysis results of the deceased. This allows the analysis unit to improve the accuracy of the analysis by referring to the past analysis results of the deceased.

[0096] The analysis unit can estimate the emotions of the deceased and adjust the length of the analysis based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the analysis unit can perform a detailed analysis of that message. The analysis unit can also perform a detailed analysis of a message in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the analysis unit can also perform a detailed analysis of that comment. This allows the analysis unit to perform an optimal length of analysis based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the time of data posting. For example, the analysis unit can prioritize analysis of data posted by the deceased at a specific time period. The analysis unit can also prioritize analysis of data posted by the deceased at a specific event or anniversary. Furthermore, the analysis unit can also prioritize analysis of data posted by the deceased during a specific season or time period. This allows the analysis unit to efficiently perform analysis based on the time of data posting.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of data related to the preferences and values ​​of the deceased. The analysis unit can also prioritize analysis of data related to the areas of interest of the deceased. Furthermore, the analysis unit can also prioritize analysis of data related to content posted in the past by the deceased. This allows the analysis unit to efficiently perform analysis based on the relevance of the data.

[0099] During the analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the level of expertise of the deceased. For example, the analysis unit can provide analysis results using technical terminology for areas in which the deceased had expertise. The analysis unit can also provide analysis results using concise terminology for areas in which the deceased had general knowledge. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the level of expertise of the deceased. This allows the analysis unit to provide analysis results using optimal terminology according to the level of expertise of the deceased.

[0100] The suggestion unit can estimate the emotions of the deceased and adjust the way in which suggestions are expressed based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the suggestion unit can make suggestions that reflect those emotions. The suggestion unit can also make emotion-based suggestions based on posts in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the suggestion unit can also make emotion-based suggestions based on the comment. This allows the suggestion unit to make suggestions using the optimal way of expression based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the theme when making a suggestion. For example, the suggestion unit can make a detailed suggestion for an important theme and a simplified suggestion for a less important theme. The suggestion unit can also make a detailed suggestion for a theme that includes an important keyword. Furthermore, the suggestion unit can also make a detailed suggestion for a theme related to an important event. This allows the suggestion unit to efficiently make suggestions according to the importance of the theme.

[0102] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the theme. For example, the suggestion unit can apply a nature-related suggestion algorithm to a nature-related theme. The suggestion unit can also apply a music-related suggestion algorithm to a music-related theme. Furthermore, the suggestion unit can also apply a movie-related suggestion algorithm to a movie-related theme. This allows the suggestion unit to apply the optimal suggestion algorithm depending on the theme category.

[0103] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the deceased's past suggestion results. For example, the suggestion unit can make suggestions for a similar theme based on the deceased's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the deceased's past suggestion results. Furthermore, the suggestion unit can also build a feedback loop for improving the accuracy of the suggestion based on the deceased's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the deceased's past suggestion results.

[0104] The suggestion unit can estimate the emotions of the deceased and adjust the length of the suggestions based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the suggestion unit can adjust the length of the suggestions based on that message. The suggestion unit can also provide detailed suggestions for posts in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the suggestion unit can adjust the length of the suggestions based on that comment. This allows the suggestion unit to provide suggestions with an optimal length based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When making suggestions, the suggestion unit can determine the priority of suggestions based on the time of submission of the themes. For example, the suggestion unit can prioritize suggestions of themes that the deceased related to a specific time period. The suggestion unit can also prioritize suggestions of themes that the deceased related to a specific event or anniversary. Furthermore, the suggestion unit can prioritize suggestions of themes that the deceased related to a specific season or time period. This allows the suggestion unit to efficiently make suggestions based on the time of submission of themes.

[0106] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of the themes. For example, the suggestion unit can prioritize suggestions of themes related to the preferences and values ​​of the deceased. The suggestion unit can also prioritize suggestions of themes related to the deceased's fields of interest. Furthermore, the suggestion unit can prioritize suggestions of themes related to the deceased's past posts. This allows the suggestion unit to efficiently make suggestions based on the relevance of the themes.

[0107] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion depending on the expertise level of the deceased. For example, the suggestion unit can make a suggestion using technical terms for a field in which the deceased had expertise. The suggestion unit can also make a suggestion using concise terms for a field in which the deceased had general knowledge. Furthermore, the suggestion unit can adjust the way the suggestion is expressed depending on the expertise level of the deceased. This allows the suggestion unit to make a suggestion using optimal terms depending on the expertise level of the deceased.

[0108] The generation unit can estimate the emotions of the deceased and adjust the expression method of the generated item based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the generation unit can generate an item that reflects that emotion. The generation unit can also generate an emotion-based item based on a post in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the generation unit can generate an emotion-based item based on that comment. This allows the generation unit to generate an item using an optimal expression method based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] The generation unit can adjust the level of detail of the generation based on the importance of the item during generation. For example, the generation unit can perform detailed generation for important items and simple generation for items with low importance. The generation unit can also perform detailed generation for items containing important keywords. Furthermore, the generation unit can also perform detailed generation for items related to important events. This allows the generation unit to efficiently generate items according to the importance of the items.

[0110] The generator can apply different generation algorithms depending on the category of the item during generation. For example, the generator can apply an image generation algorithm to a photo item. The generator can also apply a natural language generation algorithm to a text item. The generator can also apply a voice generation algorithm to a voice item. This allows the generator to apply the optimal generation algorithm depending on the category of the item.

[0111] During generation, the generation unit can improve the accuracy of generation by referring to the deceased's past generation results. For example, the generation unit can generate a similar item based on the deceased's past generation results. The generation unit can also adjust the generation algorithm by referring to the deceased's past generation results. Furthermore, the generation unit can also build a feedback loop to improve the accuracy of generation based on the deceased's past generation results. In this way, the generation unit can improve the accuracy of generation by referring to the deceased's past generation results.

[0112] The generation unit can estimate the emotions of the deceased and adjust the length of the generated item based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the generation unit can adjust the length of the item based on that message. The generation unit can also generate detailed items for posts in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the generation unit can adjust the length of the item based on that comment. This allows the generation unit to generate items with an optimal length based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The generation unit can determine the priority of generation based on the time of submission of the items at the time of generation. For example, the generation unit can prioritize generating items related to a specific time period of the deceased. The generation unit can also prioritize generating items related to a specific event or anniversary of the deceased. Furthermore, the generation unit can prioritize generating items related to a specific season or time period of the deceased. This allows the generation unit to efficiently generate items based on the time of submission of the items.

[0114] The generation unit can adjust the order of generation based on the relevance of the items during generation. For example, the generation unit can prioritize generating items related to the preferences and values ​​of the deceased. The generation unit can also prioritize generating items related to the areas of interest of the deceased. Furthermore, the generation unit can prioritize generating items related to content posted in the past by the deceased. This allows the generation unit to efficiently generate items based on the relevance of the items.

[0115] During generation, the generation unit can adjust the use of technical terminology in the generation according to the expertise level of the deceased. For example, the generation unit can generate items using technical terminology for fields in which the deceased had expertise. The generation unit can also generate items using concise terminology for fields in which the deceased had general knowledge. Furthermore, the generation unit can adjust the expression method in the generation according to the expertise level of the deceased. This allows the generation unit to generate items using optimal terminology according to the expertise level of the deceased.

[0116] The customization unit can estimate the emotions of the deceased and adjust the customization expression method based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the customization unit can perform customization that reflects that emotion. The customization unit can also perform emotion-based customization based on a post in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the customization unit can also perform emotion-based customization based on that comment. This allows the customization unit to customize with the optimal expression method based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] During customization, the customization unit can adjust the level of detail of the customization based on the importance of the plan. For example, the customization unit can perform detailed customization for important plans and simple customization for less important plans. The customization unit can also perform detailed customization for plans that include important keywords. Furthermore, the customization unit can also perform detailed customization for plans related to important events. This allows the customization unit to efficiently perform customization according to the importance of the plans.

[0118] During customization, the customization unit can apply different customization algorithms depending on the category of the plan. For example, the customization unit can apply a nature-related customization algorithm to a nature-related plan. The customization unit can also apply a music-related customization algorithm to a music-related plan. Furthermore, the customization unit can apply a movie-related customization algorithm to a movie-related plan. This allows the customization unit to apply the optimal customization algorithm depending on the category of the plan.

[0119] During customization, the customization unit can improve the accuracy of the customization by referring to the deceased's past customization results. For example, the customization unit can customize a similar plan based on the deceased's past customization results. The customization unit can also adjust the customization algorithm by referring to the deceased's past customization results. Furthermore, the customization unit can build a feedback loop for improving the accuracy of the customization based on the deceased's past customization results. In this way, the customization unit can improve the accuracy of the customization by referring to the deceased's past customization results.

[0120] The customization unit can estimate the emotions of the deceased and adjust the length of the customization based on the estimated emotions of the deceased. For example, if the deceased posted an emotional message, the customization unit can adjust the length of the customization based on that message. The customization unit can also perform detailed customization for posts in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the customization unit can adjust the length of the customization based on that comment. This allows the customization unit to customize the optimal length based on the emotions of the deceased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] During customization, the customization unit can determine the priority of customization based on the time of submission of the plan. For example, the customization unit can prioritize customization of plans that the deceased related to a specific time period. The customization unit can also prioritize customization of plans that the deceased related to a specific event or anniversary. Furthermore, the customization unit can also prioritize customization of plans that the deceased related to a specific season or time period. This allows the customization unit to efficiently perform customization based on the time of submission of the plan.

[0122] During customization, the customization unit can adjust the order of customization based on the relevance of the plans. For example, the customization unit can prioritize customization of plans related to the preferences and values ​​of the deceased. The customization unit can also prioritize customization of plans related to the areas of interest of the deceased. Furthermore, the customization unit can also prioritize customization of plans related to content posted in the past by the deceased. This allows the customization unit to efficiently perform customization based on the relevance of the plans.

[0123] During customization, the customization unit can adjust the use of technical terms in the customization according to the deceased's level of expertise. For example, the customization unit can perform customization using technical terms for a field in which the deceased had specialized knowledge. The customization unit can also perform customization using concise terms for a field in which the deceased had general knowledge. Furthermore, the customization unit can adjust the way the customization is expressed according to the deceased's level of expertise. This allows the customization unit to perform customization using optimal terms according to the deceased's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and customization unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect social media data of the deceased using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the preferences and values ​​of the deceased. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests a theme for the funeral or memorial event based on the analysis results. The generation unit is implemented by the control unit 46A of the smart device 14 and generates memorial items or digital art based on the suggested theme. The customization unit is implemented by the control unit 46A of the smart device 14 and allows family and friends to collaborate online to customize the plan. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and customization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect social media data of the deceased using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the preferences and values ​​of the deceased. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests a theme for the funeral or memorial event based on the analysis results. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates memorial items or digital art based on the suggested theme. The customization unit is implemented by the control unit 46A of the smart glasses 214 and allows family and friends to collaborate online to customize the plan. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and customization unit, is implemented, for example, in at least one of the headset-type device 314 and the data processing device 12. For example, the collection unit can collect social media data of the deceased using the camera 42 and microphone 238 of the headset-type device 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the preferences and values ​​of the deceased. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests a theme for the funeral or memorial event based on the analysis results. The generation unit is implemented by the control unit 46A of the headset-type device 314 and generates memorial items or digital art based on the suggested theme. The customization unit is implemented by the control unit 46A of the headset-type device 314 and allows family and friends to collaborate online to customize the plan. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, generation unit, and customization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect social media data of the deceased using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the preferences and values ​​of the deceased. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a theme for the funeral or memorial event based on the analysis results. The generation unit is realized by the control unit 46A of the robot 414 and generates memorial items or digital art based on the suggested theme. The customization unit is realized by the control unit 46A of the robot 414 and allows family and friends to collaborate online to customize the plan.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] In addition to the deceased's social media data, the collection unit may also collect data from the deceased's digital devices. For example, the collection unit may collect photos, notes, calendar entries, and other information from the deceased's smartphone or computer. The collection unit may also collect the deceased's application usage history, browser bookmarks, and browsing history. Furthermore, the collection unit may collect posts from online forums and communities in which the deceased participated. This allows the collection unit to collect a broader range of data and more precisely identify the deceased's preferences and values.

[0126] When analyzing the data of a deceased person, the analysis unit can classify the data based on the deceased person's life events. For example, the analysis unit can identify and classify data related to important life events such as the deceased person's birthday, wedding anniversary, and birth of a child. The analysis unit can also classify data related to the deceased person's career milestones and life events such as moving and traveling. Furthermore, the analysis unit can analyze and classify data related to the deceased person's health condition and medical history. This allows the analysis unit to organize the data based on the deceased person's life events and provide more detailed analysis results.

[0127] The suggestion unit can make suggestions to recreate places visited or events held by the deceased during their lifetime, based on the preferences and values ​​of the deceased. For example, the suggestion unit can suggest memorial events at restaurants or cafes that the deceased frequently visited. The suggestion unit can also suggest recreating hobby clubs or sporting events that the deceased participated in. Furthermore, the suggestion unit can suggest memorial events themed around places that the deceased traveled to. In this way, the suggestion unit can recreate memories of the deceased and suggest moving memorial events.

[0128] The generation unit can generate a voice message that reproduces the voice of the deceased based on data of the deceased. For example, the generation unit can analyze voice data recorded by the deceased before their death and generate a new voice message based on that voice. The generation unit can also generate a voice message that is read in the deceased's voice based on text data of the deceased. Furthermore, the generation unit can generate a voice message that reflects the emotions of the deceased. In this way, the generation unit can reproduce the voice of the deceased and provide a moving memorial item.

[0129] The customization unit can create a digital time capsule for family and friends to share memories of the deceased. For example, the customization unit can provide a platform where family and friends can upload photos, videos, and messages of memories with the deceased. The customization unit can also add content related to the activities and hobbies of the deceased. Furthermore, the customization unit can set the digital time capsule to be opened at a specific date and time. In this way, the customization unit can enable family and friends to share memories of the deceased and create a moving memorial event.

[0130] The collection unit can estimate the emotions of the deceased and adjust the data collection method based on the estimated emotions. For example, if the deceased posted emotional messages, the collection unit can prioritize collecting those messages. Furthermore, when collecting messages in which the deceased expressed specific emotions, the collection unit can determine the priority of collection based on the intensity of the emotions. Furthermore, if the deceased made emotional comments, the collection unit can prioritize collecting those comments. This allows the collection unit to collect data in an optimal manner based on the emotions of the deceased.

[0131] The analysis unit can estimate the emotions of the deceased and adjust the way in which the analysis results are expressed based on the estimated emotions. For example, if the deceased posted an emotional message, the analysis unit can provide an analysis result that reflects that emotion. The analysis unit can also analyze a post in which the deceased expressed a particular emotion and adjust the analysis result based on that emotion. Furthermore, if the deceased made an emotional comment, the analysis unit can analyze the comment and adjust the analysis result based on the emotion. This allows the analysis unit to provide an analysis result in the most appropriate way based on the emotions of the deceased.

[0132] The suggestion unit can estimate the emotions of the deceased and adjust the content of the suggestion based on the estimated emotions. For example, if the deceased posted an emotional message, the suggestion unit can make a suggestion that reflects the emotion. The suggestion unit can also make an emotion-based suggestion based on a post in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the suggestion unit can also make an emotion-based suggestion based on the comment. This allows the suggestion unit to make optimal suggestions based on the emotions of the deceased.

[0133] The generation unit can estimate the emotions of the deceased and adjust the design of the generated item based on the estimated emotions. For example, if the deceased posted an emotional message, the generation unit can generate an item with a design that reflects that emotion. The generation unit can also generate an item with an emotion-based design based on a post in which the deceased expressed a particular emotion. Furthermore, if the deceased made an emotional comment, the generation unit can generate an item with an emotion-based design based on that comment. This allows the generation unit to generate an item with an optimal design based on the emotions of the deceased.

[0134] The customization unit can provide a virtual reality (VR) space in which family and friends can share memories of the deceased. For example, the customization unit can provide a VR space that recreates places the deceased visited or liked during their lifetime. The customization unit can also provide a function that allows family and friends to talk about memories of the deceased within the VR space. Furthermore, the customization unit can provide a function that allows photos and videos of the deceased to be displayed within the VR space. In this way, the customization unit can enable family and friends to share memories of the deceased and create a moving memorial event.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects the deceased's social media data. For example, it can collect data such as photos, text, and comments posted by the deceased. The collection unit can collect photos taken by the deceased on their travels or texts they wrote about their favorite music. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the preferences and values ​​of the deceased. For example, it can analyze the content of photos, the tone of writing, and keywords used. If the deceased was a nature lover, the analysis unit can determine that the collection contains many nature-related photos and writings. Step 3: The suggestion unit suggests a theme for the funeral or memorial event based on the preferences and values ​​identified by the analysis unit. For example, if the deceased loved nature, a nature-themed funeral or memorial event can be suggested. Step 4: The generator generates a memorial item or digital art based on the theme suggested by the suggester. For example, it can generate digital art based on a photograph of the deceased or a painting of a favorite landscape of the deceased. Step 5: The customization department allows family and friends to collaborate online to customize the plan. For example, family and friends can share their opinions and customize the plan through the online platform.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[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 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.

[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 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.

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

[0155] 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.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects social media data of the deceased; an analysis unit that analyzes the data collected by the collection unit and identifies the preferences and values ​​of the deceased; a suggestion unit that suggests a theme for a funeral or memorial event based on the preferences and values ​​identified by the analysis unit; a generation unit that generates a memorial item or digital art based on the theme proposed by the proposal unit; A customization section where family and friends can collaborate online to customize the plan. A system characterized by:

2. The collecting unit Collecting data on photos, texts or comments posted by the deceased The system of claim 1 .

3. The analysis unit Analyze the content of the photos, the tone of the text, and the keywords used to identify the preferences and values ​​of the deceased The system of claim 1 .

4. The proposal unit If the deceased loved nature, suggest a nature-themed funeral or memorial event. The system of claim 1 .

5. The generation unit Generate digital art based on a photograph of the deceased or a painting of their favorite scenery The system of claim 1 .

6. The customization unit Family and friends can share ideas and customize plans through an online platform The system of claim 1 .

7. The collecting unit Implementing security measures to protect privacy when collecting data The system of claim 1 .

8. The customization unit Providing an interface for family and friends to collaborate online and customize plans The system of claim 1 .

Images