System

Abstract

A system is provided.SOLUTION: The system includes a data input means, a data transmission means, a data storage means, a photograph and episode analysis means, a video and narration generation means, a sutra reading and speech generation means, a content integration means, a content transmission means, and a content reproduction means.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] Traditionally, inviting a priest to recite sutras or give a sermon at a memorial service has required expensive costs and schedule adjustments, and often fails to reflect the feelings of the deceased. As a result, the precious time spent remembering the deceased is often left feeling incomplete for the bereaved. There is a need for a way to solve this problem and realize a more personal and heartfelt memorial service. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra chanting and sermon generation means, a content integration means, a content transmission means, and a content playback means. This system allows users to input information about the deceased, and based on that information, AI generates videos and narration commemorating the deceased, as well as appropriate sutra chanting and sermons. As a result, bereaved families can hold more personal and touching memorial services.

[0006] "Data input means" refers to an interface that allows a user to access the memorial service system and input information about the deceased.

[0007] "Data transmission means" refers to a communication means for transmitting information input by the user to the server.

[0008] "Data storage means" refers to storage means for appropriately storing transmitted information on the server side.

[0009] The "photo and episode analysis method" is an analytical algorithm that analyzes the input photos and episodes of the deceased and extracts major themes and keywords from their content.

[0010] The "image and narration generation means" is a generation algorithm for generating appropriate images and narration based on analyzed photos and episodes.

[0011] The "method for generating sutras and sermons" is a generation algorithm for generating appropriate sutras and sermons based on Buddhist teachings and scriptures.

[0012] The "content integration means" is a means for integrating the generated video, narration, sutra recitation, and sermon into one piece of content.

[0013] The "content transmission means" is a communication means for transmitting the integrated content to the user's terminal.

[0014] The "content playback means" is a playback means on a terminal that allows a user to play back transmitted content. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention is composed of a series of processes, starting with a data input means, including a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, and a content playback means.

[0037] First, the user accesses the memorial service system and logs in. They then enter information about the deceased into a dedicated input form, such as a photo of the deceased, anecdotes, posthumous Buddhist name, and the content of the sermon they would like to hear at the memorial service.

[0038] The device then sends the entered data to the server for analysis, and the server stores the received data in a database.

[0039] The server then performs analysis on the stored data: for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords.

[0040] The server then generates video and narration based on the analysis results. It creates a video scenario using the important elements of the photo and the theme of the episode, and generates video based on that scenario. It also generates narration derived from the episode and theme and adds it to the video.

[0041] The server then accesses a Buddhist knowledge database to select a sutra that matches the deceased's story and the family's requests. It then generates an appropriate sermon based on Buddhist teachings and scriptures. The generated sutra and sermon are then created as audio files.

[0042] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0043] Finally, the user receives the integrated content on their device, unzips and saves it, and then plays it back, allowing them to remember the deceased and hold a heartfelt memorial service.

[0044] Specific examples

[0045] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death.

[0046] 1. The user logs in to the system and fills in the input form with the following information: "Photo of the deceased in their garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," and "Sermon on the theme of family love."

[0047] 2. The device sends this information to the server, which receives the data and stores it in a database.

[0048] 3. The server uses image analysis to extract features such as "flowers" and "smiles" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes.

[0049] 4. Based on these analysis results, the server generates a video of the deceased person planting flowers in the garden, and based on the episode, generates a narration about "a precious time when you can feel the love of family" and adds it to the video.

[0050] 5. Next, the server selects a sutra on the theme of "the importance of family" from a Buddhist knowledge database and generates a sermon based on Buddhist teachings.

[0051] 6. The server integrates the generated video, narration, sutra recitation, and sermon, compresses it into a single content, and sends it to the terminal.

[0052] 7. The user unzips, saves, and plays the integrated content on their device, allowing them to hold a heartfelt memorial service for the third anniversary of the death.

[0053] As this example shows, the system allows for a personal and meaningful memorial service for the bereaved.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user accesses the legal system and logs in.

[0057] Step 2:

[0058] The user enters information about the deceased (photos, stories, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen.

[0059] Step 3:

[0060] The terminal transmits the input data to the server.

[0061] Step 4:

[0062] The server stores the received data in a database.

[0063] Step 5:

[0064] The server retrieves the photo data and episode data from the database.

[0065] Step 6:

[0066] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers, etc.).

[0067] Step 7:

[0068] The server analyzes the episodes using natural language processing (NLP) algorithms to extract key themes and keywords.

[0069] Step 8:

[0070] The server creates a video scenario based on the analyzed photo elements and the episode theme.

[0071] Step 9:

[0072] The server uses an AI model to generate footage based on the scenario.

[0073] Step 10:

[0074] The server generates narration based on episodes and themes and adds it to the video.

[0075] Step 11:

[0076] The server accesses a database of Buddhist knowledge and selects sutras that match the stories of the deceased and the requests of the family.

[0077] Step 12:

[0078] The server generates appropriate sermon content based on Buddhist teachings and scriptures.

[0079] Step 13:

[0080] The server creates audio files of the selected sutras and the generated sermons.

[0081] Step 14:

[0082] The server integrates the video, narration, sutra recitation, and sermon into a single piece of content.

[0083] Step 15:

[0084] The server compresses the integrated content and sends it to the terminal.

[0085] Step 16:

[0086] The device unpacks and saves the received content.

[0087] Step 17:

[0088] The user plays back the content on the terminal and performs a memorial service to remember the deceased.

[0089] This series of steps allows the bereaved family to have a personal and heartfelt memorial service.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] Conventional memorial service systems lack an efficient and integrated means for generating personalized content, making it particularly difficult to generate video, narration, sutra readings, and sermons based on information about the deceased. Furthermore, there has been no system that integrates all of this content into one and allows users to easily receive, decompress, and play it, which places a heavy burden on users.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes a data storage means, a photo and episode analysis means, and a video and narration generation means, which allows the server to store and analyze data related to the deceased, and generate video and narration based on the data.

[0095] The server also includes a means for generating sutra chanting and sermons, a content integration means, and a content compression means, which enable it to generate sutra chanting and sermons based on the analysis results, and then integrate and compress them to process them as a single content.

[0096] Furthermore, the terminal includes a content transmitting means, a content receiving means, a content decompressing means, and a content playing means, which enable the terminal to receive, decompress, and play back the integrated content transmitted from the server.

[0097] This allows users to enter information about the deceased and, through a series of processes, easily create and play heartfelt memorial service content.

[0098] "Data input means" refers to a means by which a user inputs information about the deceased.

[0099] The "data transmission means" is a means for transmitting input data from the terminal to the server.

[0100] The "data storage means" is a means for storing received data in a database.

[0101] "Photo and episode analysis means" refers to a means for performing image analysis and natural language processing on photos and episodes of the deceased.

[0102] The "video and narration generating means" is a means for creating video and generating narration based on the analysis results.

[0103] The "means for generating sutra chanting and sermons" is a means for generating sutra chanting and sermons based on a Buddhist knowledge database.

[0104] The "content integration means" is a means for integrating the generated video, narration, sutra recitation, and sermon into one piece of content.

[0105] The "content compression means" is a means for compressing the integrated content.

[0106] The "content transmitting means" is a means for transmitting compressed content from the server to the terminal.

[0107] The "content receiving means" is a means for receiving transmitted content at a terminal.

[0108] The "content decompression means" is a means for decompressing received content at a terminal.

[0109] The "content playback means" is a means for playing back the decompressed content on the terminal.

[0110] The system of the present invention aims to generate personalized content related to the deceased through data processing between the user, the terminal, and the server. This section describes how to specifically implement the system.

[0111] The process begins with the user entering information about the deceased. Using a device such as a PC or smartphone, the user accesses the memorial service system's website through a web browser (e.g., Google Chrome or Firefox) and logs in. Using a dedicated input form, the user enters information such as a photo of the deceased, an anecdote, posthumous Buddhist name, and the desired content of the sermon at the memorial service, and then presses the send button. For example, the user might enter "a photo of the deceased in their garden," "a memorable anecdote about planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," or "a sermon on the theme of family love."

[0112] The device then sends the entered data to the server using HTTPS, with the data being sent in JSON format.

[0113] The server stores the received data in the appropriate format. Specifically, a backend system using the Python Flask framework stores the data in a database such as MySQL or PostgreSQL. After storing, the server returns a response indicating the save was successful.

[0114] During the analysis stage, the server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to analyze the input photo and extract key elements and objects, followed by natural language processing (NLP) algorithms (e.g., spaCy, GPT-3) to extract the main themes and keywords of the episode.

[0115] The server generates video and narration based on the analysis results, using video generation software (e.g., Adobe Premiere, FFmpeg) to generate video from photos of the deceased. Furthermore, it uses voice conversion software such as Google Text-to-Speech or Microsoft Azure TTS to generate narration based on themes derived from the episode and integrate it into the video.

[0116] The server then accesses a Buddhist knowledge database, selects sutras based on the deceased's anecdotes and the family's requests, and generates a sermon. This is done with reference to a database of Buddhist scriptures. The generated sermon and sutras are also created as audio files.

[0117] The generated video, narration, sutra recitation, and sermon are integrated on the server using video editing and compression software such as FFmpeg and compressed into a single piece of content. The integrated content is then sent to the device via a REST API or WebSocket.

[0118] Finally, once the user receives the content, unzips it, and saves it, they can play it using media player software such as VLC Media Player. This system allows users to hold heartfelt memorial services.

[0119] Specific examples

[0120] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death. In this case, the user enters the following information:

[0121] "Photograph in the garden of the deceased"

[0122] "Memorable episode of planting flowers with the deceased"

[0123] "Posthumous Buddhist name: Jiaiin Wako Daishi"

[0124] Sermon on the theme of family love

[0125] Based on this input, the system performs analysis, generates video and narration, generates sutra recitations and sermons, integrates and transmits the content, and ultimately allows users to play heartfelt memorial service content in memory of the deceased.

[0126] This allows the entire process to proceed smoothly and makes it easy for the bereaved to remember the deceased.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] The user uses a terminal to access the memorial service system website and log in. The user enters information about the deceased in the input form (e.g., "Photo of the deceased in the garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," "Sermon on the theme of family love"). The entered information is stored in temporary memory on the terminal.

[0130] Step 2:

[0131] The terminal receives user input, converts the data into JSON format, and sends it to the server via HTTPS. The input is the information entered by the user, and the output is JSON format data.

[0132] Step 3:

[0133] The server parses JSON data received via HTTPS and stores it in a MySQL or PostgreSQL database using the Python Flask framework. The input is the JSON data sent from the terminal, and the output is a new record in the database.

[0134] Step 4:

[0135] The server retrieves the stored data from the database and analyzes the photos and episodes. OpenCV and TensorFlow are used for image analysis of the photos, and spaCy and GPT-3 are used for natural language processing (NLP) of the episodes. The input is the data retrieved from the database, and the output is the analyzed data (for example, the important elements of the photo or the theme of the episode).

[0136] Step 5:

[0137] The server generates video and narration based on the analyzed data. Adobe Premiere and FFmpeg are used to generate video, and Google Text-to-Speech and Microsoft Azure TTS are used to generate narration. The input is the analyzed data, and the output is the generated video and narration files.

[0138] Step 6:

[0139] The server accesses a Buddhist knowledge database and generates sutra chanting and sermons based on the deceased's anecdotes and the family's requests. The database used to generate sutra chanting and sermons is a general Buddhist scripture database. The input is data on anecdotes and requests, and the output is an audio file of the generated sutra chanting and sermon.

[0140] Step 7:

[0141] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content. This integration process uses video editing and compression software such as FFmpeg. The input is the various generated files, and the output is an integrated content file.

[0142] Step 8:

[0143] The server sends the integrated content file to the terminal via REST API or WebSocket. The input is the integrated content file, and the output is the sent content file.

[0144] Step 9:

[0145] The user receives the integrated content on their device and unzips it using ZIP unzipping software (e.g., WinRAR, 7-Zip). The unzipped content is played using media player software such as VLC Media Player. The input is the transmitted content file, and the output is the played video and audio.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Providing a personalized shopping experience for each customer is difficult in today's brick-and-mortar stores. In particular, leveraging past purchase history and individual anecdotes to suggest products and services that customers desire requires advanced technology. Furthermore, with conventional methods, it is difficult to reproduce a customer's past purchase experiences in real time and offer related products and suggestions based on them. A new system is needed to solve these problems and increase customer satisfaction.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a product proposal generation means, a content integration means, a content transmission means, and a content playback means, thereby enabling a customer to input information about products they have purchased in the past and generate personalized proposals based on that information.

[0151] "Data entry means" refers to a device or interface through which a user enters information.

[0152] "Data transmission means" refers to a communication means for transmitting input information to a server or other device.

[0153] "Data storage means" refers to storage or database for storing transmitted information.

[0154] "Photo and episode analysis method" refers to an algorithm that analyzes input photos and text information and extracts important elements and themes.

[0155] "Video and narration generation means" refers to software or algorithms for generating video and narration based on the analysis results.

[0156] "Product proposal generation means" refers to an algorithm that generates personalized product and service proposals for customers based on the analyzed data.

[0157] "Content integration means" refers to a means for combining multiple pieces of content, such as generated video, narration, and product proposals, into one.

[0158] "Content transmission means" refers to a communication means for transmitting the aggregated content to a user device.

[0159] "Content playback means" refers to a function for playing received content on a user device.

[0160] The present invention is a system that allows customers to enjoy a personalized shopping experience in a physical store, and is realized through a series of data processing. Specific embodiments of the system are described below.

[0161] Data Entry Method

[0162] Users log in to a dedicated application through smart glasses and input photos of products they have purchased in the past and related stories. The device is designed to allow users to easily input data using smart glasses.

[0163] Data transmission method

[0164] The smart glasses send the input information to a cloud server using communication methods such as Wi-Fi or Bluetooth, and the communication protocol used is HTTP or WebSocket.

[0165] Data storage means

[0166] The cloud server stores the transmitted data in a NoSQL database (e.g., MongoDB). The stored data includes photo data and text data (episodes).

[0167] Photo and episode analysis tools

[0168] The server analyzes the stored photo and text data, using image analysis algorithms such as TensorFlow to analyze the photos and natural language processing (NLP) algorithms such as spaCy and BERT to analyze the episodes, thereby extracting important elements and themes.

[0169] Video and narration generation method

[0170] The server generates a video and narration based on the analysis results. It uses MoviePy for video generation and gTTS for narration generation. Specifically, it generates a short video that recreates a past shopping experience and a matching narration.

[0171] Product proposal generation means

[0172] The server generates personalized product recommendations based on the analyzed data. The algorithm analyzes past purchase history and current analysis results to suggest the best products and services for the customer.

[0173] Content Integration Methods

[0174] The generated video, narration, and product recommendations are integrated into a single piece of content using a data integration algorithm.

[0175] Content transmission method

[0176] The integrated content is then sent back to the smart glasses using a high-speed communication protocol, enabling real-time content playback.

[0177] Content playback method

[0178] The user's smart glasses then decompress and play the received content, allowing them to navigate the store and receive personalized suggestions and advice based on their past shopping experiences.

[0179] Specific examples

[0180] For example, if a customer enters a photo and story about an item they previously purchased as a "birthday present," the system will process it as follows:

[0181] 1. Data entry: Enter "A necklace purchased as a birthday gift last year."

[0182] 2. Data transmission: Data is sent from the smart glasses to the cloud server.

[0183] 3. Data storage: The cloud server stores the data in MongoDB.

[0184] 4. Data analysis: Analyze photos using TensorFlow and episodes using spaCy.

[0185] 5. Video and narration generation: Create videos with MoviePy and generate narration with gTTS.

[0186] 6. Product suggestion generation: Propose new products relevant to the customer.

[0187] 7. Content integration and playback: The integrated content is played on the smart glasses and provided to the user.

[0188] Prompt Sentence Examples

[0189] Just say, "The necklace I bought as a gift for my birthday last year," and we'll generate special suggestions and narrations based on that.

[0190] The system allows customers to enjoy a personalized shopping experience based on their past purchases.

[0191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0192] Step 1:

[0193] The user logs in to the dedicated application on the smart glasses. The user inputs photos of products they have purchased in the past and related stories. The input data consists of photo files and text data.

[0194] Step 2:

[0195] The smart glasses send the input information to a cloud server via Wi-Fi or Bluetooth. The transmitted data consists of photo files and text data.

[0196] Step 3:

[0197] The server receives the data and stores it in a NoSQL database (e.g., MongoDB). The stored data consists of photo data and text data.

[0198] Step 4:

[0199] The server uses an image analysis algorithm (TensorFlow) to analyze the photo data and extract important elements. For example, it extracts attributes of products such as necklaces. It also uses an NLP algorithm (spaCy or BERT) to extract major themes from the episode text data. For example, it extracts the theme "birthday present."

[0200] Step 5:

[0201] The server generates video and narration based on the analysis results. MoviePy is used to generate the video, combining the analyzed photos and text to create a short video. gTTS is used to generate the narration, generating audio narration of the episode content. For example, a narration could be created such as, "The necklace I gave to a special person for their birthday last year."

[0202] Step 6:

[0203] The server generates personalized product suggestions based on the results of image analysis and NLP. The system uses past purchase history and analysis results to suggest related products currently on sale in the store. For example, it suggests related jewelry and accessory products.

[0204] Step 7:

[0205] The server then combines the generated video, narration, and product recommendations into a single piece of content using a data integration algorithm. The combined content is then compressed.

[0206] Step 8:

[0207] The cloud server sends the integrated content to the smart glasses using high-speed communication protocols (HTTP and WebSocket). The transmitted data is compressed content.

[0208] Step 9:

[0209] The device (smart glasses) decompresses and plays the received content. Through the smart glasses, users can receive personalized suggestions and advice based on their past shopping experiences in real time in the store. For example, they can receive suggestions for related products while watching a video based on their past shopping experiences.

[0210] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0211] The present invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, and an emotion engine that recognizes the user's emotions.

[0212] First, the user accesses the memorial service system and logs in. They enter information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) into a dedicated input screen. At the same time, the emotion engine recognizes emotions from the user's input behavior.

[0213] Next, the device sends the input data, along with the recognized emotion data, to the server, which then stores the received data in a database.

[0214] The server analyzes the stored data. For photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion engine into the analysis.

[0215] The server then generates video and narration based on the analysis results. It creates a video scenario based on the important elements of the photo, the theme of the episode, and the recognized emotions, and generates video based on that scenario. It also generates narration based on the episode, theme, and emotion data, and adds it to the video.

[0216] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions. It then generates appropriate sermon content, referring to Buddhist teachings and scriptures. These are then created as audio files.

[0217] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0218] Finally, the user receives the content on their device, unzips it, saves it, and plays it back. Through the content played back, the user can remember the deceased and hold a heartfelt memorial service. The introduction of an emotion engine enables a more personalized memorial service that responds to the user's emotions.

[0219] Specific examples

[0220] As a concrete example, let us consider a case where a family uses the system for a memorial service for the third anniversary of a death.

[0221] 1. The user logs in to the system and inputs "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "the posthumous Buddhist name of Jiaiin Wako Daishi," and "a sermon on the theme of family love." During this time, the emotion engine recognizes emotions such as "sadness" and "gratitude" from the user's input behavior.

[0222] 2. The device sends this information and emotion data to the server, which receives the data and stores it in a database.

[0223] 3. The server uses image analysis to extract features such as "smiles" and "flowers" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes, and also incorporates emotional data into the analysis.

[0224] 4. Based on the analysis results, the server creates a video scenario with themes such as "smiles," "flowers," "family love," and "sadness," and generates a video based on that scenario. It also generates a narration, such as "A precious time to feel family love and gratitude," and adds it to the video.

[0225] 5. Next, the server selects a sutra from a Buddhist knowledge database on the theme of "the importance of family" to ease the user's "sadness," and generates an appropriate sermon.

[0226] 6. The server integrates these generated materials into a single content, compresses it, and sends it to the terminal.

[0227] 7. The user receives the content on their device, unpacks it, and plays it back. This allows users to hold a more personal memorial service that reflects their emotions and is heartfelt.

[0228] In this way, by incorporating an emotion engine, it becomes possible to hold a memorial service that reflects the user's emotions.

[0229] The processing flow will be explained below.

[0230] Step 1:

[0231] The user accesses the legal system and logs in.

[0232] Step 2:

[0233] The user enters information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen. During this process, the emotion engine recognizes the user's emotions from their input behavior.

[0234] Step 3:

[0235] The terminal transmits the input data and the recognized emotion data to the server.

[0236] Step 4:

[0237] The server stores the received data and emotion data in a database.

[0238] Step 5:

[0239] The server retrieves the photo data, episode data, and emotion data from the database.

[0240] Step 6:

[0241] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers).

[0242] Step 7:

[0243] The server uses natural language processing (NLP) algorithms to analyze the episodes and extract key themes and keywords (e.g., family love, gardening).

[0244] Step 8:

[0245] The server incorporates the emotion data recognized by the emotion engine into the analysis.

[0246] Step 9:

[0247] The server creates a video scenario based on the analyzed photo elements, episode themes, and emotional data.

[0248] Step 10:

[0249] The server uses an AI model to generate footage based on the scenario.

[0250] Step 11:

[0251] The server generates narration based on episode and emotional data and adds it to the video.

[0252] Step 12:

[0253] The server accesses a Buddhist knowledge database and selects sutras appropriate to the deceased's anecdotes, family requests, and perceived emotions.

[0254] Step 13:

[0255] The server generates appropriate sermons from the data based on Buddhist teachings and scriptures.

[0256] Step 14:

[0257] The server creates audio files of the selected sutras and the generated sermons.

[0258] Step 15:

[0259] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content.

[0260] Step 16:

[0261] The server compresses the integrated content and sends it to the terminal.

[0262] Step 17:

[0263] The terminal unpacks and saves the received content.

[0264] Step 18:

[0265] The user plays the integrated content on the terminal and holds a memorial service for the deceased.

[0266] This series of steps allows users to experience an emotionally-charged and personalized memorial service.

[0267] Example 2

[0268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] Conventional memorial service systems provide uniform content without considering the user's feelings, making it difficult to respond to individual requests. Other issues include the complex and time-consuming process of inputting and analyzing information about the deceased, and the mechanical nature of the generated content, which lacks emotion. This creates the problem of insufficient support for users to hold a heartfelt memorial service.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, a video generation means, a voice generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing the user's emotion. This allows for quick and effective generation of personalized memorial service content according to the user's emotion, enabling the user to hold a heartfelt memorial service.

[0271] "Data input means" refers to devices or software that allow the user to input information and emotional data about the deceased.

[0272] "Data transmission means" refers to a communication means for transmitting input data to other devices or systems.

[0273] "Data storage means" refers to devices and software used to store and manage received data.

[0274] "Image analysis means" refers to devices and software for analyzing image data and extracting important elements and features.

[0275] "Natural language processing means" refers to devices or software for analyzing text data and extracting themes and keywords.

[0276] "Image generation means" refers to devices and software for creating images based on the analysis results.

[0277] "Speech generation means" refers to a device or software for creating voice data based on the analysis results.

[0278] "Means for accessing religious knowledge databases" refers to devices or software that access databases containing information about religions and retrieve that information.

[0279] "Content integration means" refers to devices or software that combine generated materials such as video, audio, sutra chanting, and sermons into a single piece of content.

[0280] "Content transmission means" refers to a communication means for transmitting the collected content to the user's terminal.

[0281] "Content playback means" refers to a device or software for playing back received content.

[0282] "Emotion analysis means" refers to devices or software for recognizing and analyzing emotions from user input behavior and other data.

[0283] The present invention is a system including a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, an image generation means, an audio generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing a user's emotions.

[0284] First, the user accesses the memorial service system and logs in. On a dedicated input screen, they input information about the deceased, such as a photo, anecdotes, posthumous Buddhist name, and the desired content of the sermon at the memorial service. At the same time, the emotion analysis means recognizes emotions from the user's input behavior. At this stage, the user inputs specific anecdotes, for example, "memories of planting flowers in the garden with the deceased," and records them in the system. The emotion data recognized at this time is also imported.

[0285] Next, the device sends the input data and the accompanying emotion data to the server. This data is sent using the HTTP protocol and is encrypted with SSL / TLS to ensure data security. The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system.

[0286] The server analyzes the stored data. Specifically, it uses image analysis tools such as Google Vision API and OpenCV to extract important elements and objects from the photos, and natural language processing tools such as TensorFlow and spaCy to extract the main themes and keywords of the episodes. Furthermore, emotional data recognized by the sentiment analysis tool is also incorporated into the analysis.

[0287] The server then generates video and narration based on the analysis results. Adobe Premiere Pro and FFmpeg are used to generate video, creating a video scenario based on the key elements of the photo, the theme of the episode, and the recognized emotions. Amazon Polly is used to generate audio, generating narration and adding it to the video.

[0288] The server then accesses a religious knowledge database to select sutras that fit the deceased's anecdotes, the family's wishes, and the recognized emotions. Using Buddhist teachings and scriptures, the server generates appropriate sermon content. These are then converted into audio files using the AI ​​voice synthesis engine VOCALOID.

[0289] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content. After combining, the content is compressed using FFmpeg and sent to the device. The transmitted content is in MP4 or MP3 format.

[0290] Finally, users can receive the content on their devices, save it, and then play it using VLC Media Player or other multimedia players, allowing users to create a personal memorial service that reflects their emotions and is heartfelt.

[0291] Specific examples

[0292] As a concrete example, let's consider a case in which a family uses this system for a memorial service for the third anniversary of a death. The user inputs a photo of the deceased's garden, a memorable episode of planting flowers with the deceased, the posthumous Buddhist name of Jiaiin Wako Daishi, and a sermon on the theme of family love. During this input, the emotion analysis tool recognizes emotions such as sadness and gratitude. The device then transmits this information and the emotion data to the server. The server uses image analysis to extract features such as smiles and flowers from the photos and natural language processing to extract themes such as family love and gardening from the episode. Based on the analysis results, the server creates a video scenario on the theme of family love, generating a narration such as "A precious time to feel family love and gratitude" and adding it to the video. It also generates sutra readings and an appropriate sermon on the theme of "the importance of family" to ease the user's sadness from a Buddhist knowledge database. These are then integrated, compressed, and sent to the device, where the user can decompress and play them.

[0293] Prompt Sentence Examples

[0294] The following are examples of prompts to use when entering and sending to the system "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," and "a sermon on the theme of family love."

[0295] Prompt statement:

[0296] Please upload a photo of the deceased's garden and enter a memorable story of planting flowers with the deceased in text. Enter "Jiaiin Wako Daishi" as the posthumous Buddhist name, and request a sermon with the theme of family love. Once you have completed the entry, please press the send button.

[0297] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0298] Step 1: User accesses the system and logs in

[0299] A user accesses the system and enters their username and password into the login screen. This allows the system to verify that the user has the appropriate access privileges. If the login is successful, an input screen is displayed. The input requires the user's authentication information (username and password), and the output is the authentication result of the access privileges.

[0300] Step 2: User enters information about the deceased

[0301] The user inputs information such as a photo of the deceased, episodes, posthumous Buddhist name, and desired content of the sermon at the memorial service on a dedicated input screen. During this input, the emotion analysis means analyzes the user's input behavior and recognizes emotions. The input includes photo data and text data (episodes, posthumous Buddhist name, content of the sermon), and the input data and analyzed emotion data are obtained as output.

[0302] Step 3: The device sends the data to the server

[0303] The device sends the input data and the recognized emotion data to the server using the HTTP protocol. The data is encrypted using SSL / TLS. The input includes information about the deceased person and emotion data entered by the user, and the output is the data transmission result.

[0304] Step 4: The server saves the data to the database

[0305] The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system. The input includes the sent information about the deceased and emotion data, and the output is the result stored in the database.

[0306] Step 5: The server parses the data

[0307] The server analyzes the stored data. It uses Google Vision API and OpenCV as image analysis methods to extract important elements from the photos. It uses TensorFlow and spaCy as natural language processing methods to extract major themes and keywords from the episodes. Emotional data recognized by the emotion analysis method is also incorporated into the analysis. The input includes the stored information about the deceased and emotion data, and the analysis results (extracted elements, themes, keywords, and emotion data) are obtained as output.

[0308] Step 6: The server generates the video and narration

[0309] Based on the analysis results, the server creates a video using Adobe Premiere Pro or FFmpeg as a video generation tool. It also uses Amazon Polly to generate an audio file and add narration to the video. The input includes the analysis results, and the generated video and narration are the output.

[0310] Step 7: The server generates the chanting and sermon

[0311] The server accesses a religious knowledge database and selects sutra chanting that matches the deceased's anecdotes, the family's requests, and the recognized emotions. It also generates sermons based on Buddhist teachings. These are created as audio files using an AI speech synthesis engine. The inputs include the analysis results and the system's religious knowledge, and the output is the generated audio files of the sutra chanting and sermon.

[0312] Step 8: The server aggregates the content and sends it to the device

[0313] The server combines the generated video, narration, sutra recitation, and sermons into a single integrated content. It then compresses the content using FFmpeg and sends it to the device. The input includes the various generated media files, and the output is the integrated and compressed content.

[0314] Step 9: User receives and plays content on device

[0315] The received content is decompressed and saved on the user's designated device, and played back using a multimedia player such as VLC Media Player. The input includes consolidated and compressed content, and the output is decompressed and playable content, allowing users to hold a heartfelt memorial service.

[0316] (Application example 2)

[0317] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0318] In virtual stores, personalized product recommendations based on customer emotions and situations are important for improving customer satisfaction. However, current systems are unable to recognize users' facial expressions and voices in real time and make product recommendations based on them. Furthermore, it is difficult to provide an appropriate store experience that takes emotions into account, which ultimately reduces customers' purchasing motivation. To solve this problem, a system is needed that recognizes emotions, makes product recommendations based on emotions, and provides a personalized customer experience.

[0319] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations based on emotion recognition, and a means for recognizing customer facial expressions and voices in a virtual store. This enables real-time product recommendations based on customer emotions and a personalized customer experience.

[0320] A "data entry means" is an interface or device used by a user to enter information.

[0321] The "data transmission means" is a communication means for transmitting input data to a data processing device such as a server.

[0322] The "data storage means" is a storage device for storing the transmitted data.

[0323] "Photo and episode analysis means" refers to software or algorithms used to analyze the input photos and episodes.

[0324] The "video and narration generating means" is a program for generating video and narration based on the analyzed data.

[0325] The "Means for Generating Sutras and Sermons" is a system for generating sutras and sermons based on Buddhist teachings and scriptures.

[0326] The "content integration means" is a mechanism for integrating the generated video, narration, sutra recitation, and sermon into a single piece of content.

[0327] The "content transmitting means" is a method for transmitting the integrated content to the user's terminal.

[0328] The "content playback means" is an application for playing the transmitted content on the user's terminal.

[0329] An "emotion recognition engine" is a system that recognizes emotions from a user's input actions, facial expressions, and voice.

[0330] The "means for generating personalized product recommendations based on emotion recognition" is a function for making optimal product recommendations to users based on recognized emotion data.

[0331] "Means for recognizing customers' facial expressions and voices in a virtual store" refers to technology that analyzes customers' facial expressions and voices in real time in a virtual store and recognizes their emotions.

[0332] The system for implementing this invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra chanting and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations after recognizing the user's emotions, and a means for recognizing the facial expressions and voices of customers in a virtual store.

[0333] First, the user enters information about the deceased (such as photos, anecdotes, posthumous Buddhist name, and desired sermon content at the memorial service) through a dedicated screen. The emotion engine recognizes emotions from the user's input actions, facial expressions, and voice. This data is then sent to the server via data transmission means.

[0334] The server stores the received data in a database and uses the photo and episode analysis means to analyze the data based on image analysis algorithms and natural language processing (NLP) algorithms. Based on the analysis results and emotional data, the video and narration generation means creates a video scenario and generates video based on that scenario. Narration is generated based on the episode, theme, and emotional data and added to the video.

[0335] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermon content based on Buddhist teachings and scriptures. These are also created as audio files. The server then integrates the generated video, narration, sutras, and sermon into a single content, compresses it, and sends it to the user's device.

[0336] Users can receive, unpack, and play the content on their devices, allowing them to hold more personal memorial services that reflect their emotions.

[0337] As an example of application in virtual stores, smartphones and smart glasses can recognize customers' facial expressions and voices in real time and analyze the emotional data to recommend the most suitable products to them, allowing customers to enjoy a personalized shopping experience according to their emotions.

[0338] For example, if a user inputs "I'm looking for a gift for a friend," the system will use emotional data to generate a recommendation such as "Other people also enjoyed this product" if the user has positive emotions. Examples of prompts to the generative AI model are as follows:

[0339] Example prompt sentence:

[0340] Generate personalized product recommendations from the following user input and sentiment data:

[0341] User Input: I'm looking for a gift for a friend.

[0342] Emotion data: positive, joy

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] The user logs into the system and uses the data input means to input information about the deceased (such as photos, episodes, posthumous Buddhist name, and desired sermon content at the memorial service). At this time, the emotion recognition engine collects emotional data from the user's input actions, facial expressions, and voice. The input information (e.g., photos, episodes) and emotional data are input. This data is then passed to the data transmission means as output.

[0346] Step 2:

[0347] The terminal transmits the input data (photos, episodes, emotion data) to the server via the data transmission means. It receives the data and emotion data input by the user as input and sends them to the server as output.

[0348] Step 3:

[0349] The server stores the received data in a database using a data storage means. It receives the transmitted data as input and stores it in the database as output.

[0350] Step 4:

[0351] The server analyzes the data using photo and episode analysis methods. Specifically, for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion recognition engine into the analysis. It receives data stored in the database as input and obtains the analysis results as output.

[0352] Step 5:

[0353] The server uses a video and narration generation means to create a video scenario based on the analysis results and generate video based on that scenario. Narration is also generated based on the analysis results and added to the video. The server receives the analysis results as input and generates video and narration as output.

[0354] Step 6:

[0355] The server accesses a Buddhist knowledge database, selects sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermons based on Buddhist teachings and scriptures. These are created as audio files. It receives emotion data and episode information as input, and generates audio files of sutras and sermons as output.

[0356] Step 7:

[0357] The server integrates the generated video, narration, sutra chanting, and sermon into one content using a content integration means, compresses it using a content transmission means, and transmits it to the terminal.The server receives the generated materials (video, narration, sutra chanting, and sermon) as input and transmits the integrated content as output.

[0358] Step 8:

[0359] The user receives the transmitted content on the terminal, decompresses it, and plays it using the content playback means. This allows the user to hold a more personal memorial service that reflects their emotions. The transmitted content is received as input, and the played video and audio are obtained as output.

[0360] Example prompt sentence:

[0361] Generate personalized product recommendations from the following user input and sentiment data:

[0362] User Input: I'm looking for a gift for a friend.

[0363] Emotion data: positive, joy

[0364] 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.

[0365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0366] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0371] 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.

[0372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0373] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0374] 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.

[0375] 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.

[0376] 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 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.

[0377] 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.

[0378] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0379] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0380] The system of the present invention is composed of a series of processes, starting with a data input means, including a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, and a content playback means.

[0381] First, the user accesses the memorial service system and logs in. They then enter information about the deceased into a dedicated input form, such as a photo of the deceased, anecdotes, posthumous Buddhist name, and the content of the sermon they would like to hear at the memorial service.

[0382] The device then sends the entered data to the server for analysis, and the server stores the received data in a database.

[0383] The server then performs analysis on the stored data: for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords.

[0384] The server then generates video and narration based on the analysis results. It creates a video scenario using the important elements of the photo and the theme of the episode, and generates video based on that scenario. It also generates narration derived from the episode and theme and adds it to the video.

[0385] The server then accesses a Buddhist knowledge database to select a sutra that matches the deceased's story and the family's requests. It then generates an appropriate sermon based on Buddhist teachings and scriptures. The generated sutra and sermon are then created as audio files.

[0386] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0387] Finally, the user receives the integrated content on their device, unzips and saves it, and then plays it back, allowing them to remember the deceased and hold a heartfelt memorial service.

[0388] Specific examples

[0389] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death.

[0390] 1. The user logs in to the system and fills in the input form with the following information: "Photo of the deceased in their garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," and "Sermon on the theme of family love."

[0391] 2. The device sends this information to the server, which receives the data and stores it in a database.

[0392] 3. The server uses image analysis to extract features such as "flowers" and "smiles" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes.

[0393] 4. Based on these analysis results, the server generates a video of the deceased person planting flowers in the garden, and based on the episode, generates a narration about "a precious time when you can feel the love of family" and adds it to the video.

[0394] 5. Next, the server selects a sutra on the theme of "the importance of family" from a Buddhist knowledge database and generates a sermon based on Buddhist teachings.

[0395] 6. The server integrates the generated video, narration, sutra recitation, and sermon, compresses it into a single content, and sends it to the terminal.

[0396] 7. The user unzips, saves, and plays the integrated content on their device, allowing them to hold a heartfelt memorial service for the third anniversary of the death.

[0397] As this example shows, the system allows for a personal and meaningful memorial service for the bereaved.

[0398] The processing flow will be explained below.

[0399] Step 1:

[0400] The user accesses the legal system and logs in.

[0401] Step 2:

[0402] The user enters information about the deceased (photos, stories, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen.

[0403] Step 3:

[0404] The terminal transmits the input data to the server.

[0405] Step 4:

[0406] The server stores the received data in a database.

[0407] Step 5:

[0408] The server retrieves the photo data and episode data from the database.

[0409] Step 6:

[0410] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers, etc.).

[0411] Step 7:

[0412] The server analyzes the episodes using natural language processing (NLP) algorithms to extract key themes and keywords.

[0413] Step 8:

[0414] The server creates a video scenario based on the analyzed photo elements and the episode theme.

[0415] Step 9:

[0416] The server uses an AI model to generate footage based on the scenario.

[0417] Step 10:

[0418] The server generates narration based on episodes and themes and adds it to the video.

[0419] Step 11:

[0420] The server accesses a database of Buddhist knowledge and selects sutras that match the stories of the deceased and the requests of the family.

[0421] Step 12:

[0422] The server generates appropriate sermon content based on Buddhist teachings and scriptures.

[0423] Step 13:

[0424] The server creates audio files of the selected sutras and the generated sermons.

[0425] Step 14:

[0426] The server integrates the video, narration, sutra recitation, and sermon into a single piece of content.

[0427] Step 15:

[0428] The server compresses the integrated content and sends it to the terminal.

[0429] Step 16:

[0430] The device unpacks and saves the received content.

[0431] Step 17:

[0432] The user plays back the content on the terminal and performs a memorial service to remember the deceased.

[0433] This series of steps allows the bereaved family to have a personal and heartfelt memorial service.

[0434] Example 1

[0435] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0436] Conventional memorial service systems lack an efficient and integrated means for generating personalized content, making it particularly difficult to generate video, narration, sutra readings, and sermons based on information about the deceased. Furthermore, there has been no system that integrates all of this content into one and allows users to easily receive, decompress, and play it, which places a heavy burden on users.

[0437] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0438] In this invention, the server includes a data storage means, a photo and episode analysis means, and a video and narration generation means, which allows the server to store and analyze data related to the deceased, and generate video and narration based on the data.

[0439] The server also includes a means for generating sutra chanting and sermons, a content integration means, and a content compression means, which enable it to generate sutra chanting and sermons based on the analysis results, and then integrate and compress them to process them as a single content.

[0440] Furthermore, the terminal includes a content transmitting means, a content receiving means, a content decompressing means, and a content playing means, which enable the terminal to receive, decompress, and play back the integrated content transmitted from the server.

[0441] This allows users to enter information about the deceased and, through a series of processes, easily create and play heartfelt memorial service content.

[0442] "Data input means" refers to a means by which a user inputs information about the deceased.

[0443] The "data transmission means" is a means for transmitting input data from the terminal to the server.

[0444] The "data storage means" is a means for storing received data in a database.

[0445] "Photo and episode analysis means" refers to a means for performing image analysis and natural language processing on photos and episodes of the deceased.

[0446] The "video and narration generating means" is a means for creating video and generating narration based on the analysis results.

[0447] The "means for generating sutra chanting and sermons" is a means for generating sutra chanting and sermons based on a Buddhist knowledge database.

[0448] The "content integration means" is a means for integrating the generated video, narration, sutra recitation, and sermon into one piece of content.

[0449] The "content compression means" is a means for compressing the integrated content.

[0450] The "content transmitting means" is a means for transmitting compressed content from the server to the terminal.

[0451] The "content receiving means" is a means for receiving transmitted content at a terminal.

[0452] The "content decompression means" is a means for decompressing received content at a terminal.

[0453] The "content playback means" is a means for playing back the decompressed content on the terminal.

[0454] The system of the present invention aims to generate personalized content related to the deceased through data processing between the user, the terminal, and the server. This section describes how to specifically implement the system.

[0455] The process begins with the user entering information about the deceased. Using a device such as a PC or smartphone, the user accesses the memorial service system's website through a web browser (e.g., Google Chrome or Firefox) and logs in. Using a dedicated input form, the user enters information such as a photo of the deceased, an anecdote, posthumous Buddhist name, and the desired content of the sermon at the memorial service, and then presses the send button. For example, the user might enter "a photo of the deceased in their garden," "a memorable anecdote about planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," or "a sermon on the theme of family love."

[0456] The device then sends the entered data to the server using HTTPS, with the data being sent in JSON format.

[0457] The server stores the received data in the appropriate format. Specifically, a backend system using the Python Flask framework stores the data in a database such as MySQL or PostgreSQL. After storing, the server returns a response indicating the save was successful.

[0458] During the analysis stage, the server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to analyze the input photo and extract key elements and objects, followed by natural language processing (NLP) algorithms (e.g., spaCy, GPT-3) to extract the main themes and keywords of the episode.

[0459] The server generates video and narration based on the analysis results, using video generation software (e.g., Adobe Premiere, FFmpeg) to generate video from photos of the deceased. Furthermore, it uses voice conversion software such as Google Text-to-Speech or Microsoft Azure TTS to generate narration based on themes derived from the episode and integrate it into the video.

[0460] The server then accesses a Buddhist knowledge database, selects sutras based on the deceased's anecdotes and the family's requests, and generates a sermon. This is done with reference to a database of Buddhist scriptures. The generated sermon and sutras are also created as audio files.

[0461] The generated video, narration, sutra recitation, and sermon are integrated on the server using video editing and compression software such as FFmpeg and compressed into a single piece of content. The integrated content is then sent to the device via a REST API or WebSocket.

[0462] Finally, once the user receives the content, unzips it, and saves it, they can play it using media player software such as VLC Media Player. This system allows users to hold heartfelt memorial services.

[0463] Specific examples

[0464] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death. In this case, the user enters the following information:

[0465] "Photograph in the garden of the deceased"

[0466] "Memorable episode of planting flowers with the deceased"

[0467] "Posthumous Buddhist name: Jiaiin Wako Daishi"

[0468] Sermon on the theme of family love

[0469] Based on this input, the system performs analysis, generates video and narration, generates sutra recitations and sermons, integrates and transmits the content, and ultimately allows users to play heartfelt memorial service content in memory of the deceased.

[0470] This allows the entire process to proceed smoothly and makes it easy for the bereaved to remember the deceased.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] The user uses a terminal to access the memorial service system website and log in. The user enters information about the deceased in the input form (e.g., "Photo of the deceased in the garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," "Sermon on the theme of family love"). The entered information is stored in temporary memory on the terminal.

[0474] Step 2:

[0475] The terminal receives user input, converts the data into JSON format, and sends it to the server via HTTPS. The input is the information entered by the user, and the output is JSON format data.

[0476] Step 3:

[0477] The server parses JSON data received via HTTPS and stores it in a MySQL or PostgreSQL database using the Python Flask framework. The input is the JSON data sent from the terminal, and the output is a new record in the database.

[0478] Step 4:

[0479] The server retrieves the stored data from the database and analyzes the photos and episodes. OpenCV and TensorFlow are used for image analysis of the photos, and spaCy and GPT-3 are used for natural language processing (NLP) of the episodes. The input is the data retrieved from the database, and the output is the analyzed data (for example, the important elements of the photo or the theme of the episode).

[0480] Step 5:

[0481] The server generates video and narration based on the analyzed data. Adobe Premiere and FFmpeg are used to generate video, and Google Text-to-Speech and Microsoft Azure TTS are used to generate narration. The input is the analyzed data, and the output is the generated video and narration files.

[0482] Step 6:

[0483] The server accesses a Buddhist knowledge database and generates sutra chanting and sermons based on the deceased's anecdotes and the family's requests. The database used to generate sutra chanting and sermons is a general Buddhist scripture database. The input is data on anecdotes and requests, and the output is an audio file of the generated sutra chanting and sermon.

[0484] Step 7:

[0485] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content. This integration process uses video editing and compression software such as FFmpeg. The input is the various generated files, and the output is an integrated content file.

[0486] Step 8:

[0487] The server sends the integrated content file to the terminal via REST API or WebSocket. The input is the integrated content file, and the output is the sent content file.

[0488] Step 9:

[0489] The user receives the integrated content on their device and unzips it using ZIP unzipping software (e.g., WinRAR, 7-Zip). The unzipped content is played using media player software such as VLC Media Player. The input is the transmitted content file, and the output is the played video and audio.

[0490] (Application example 1)

[0491] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] Providing a personalized shopping experience for each customer is difficult in today's brick-and-mortar stores. In particular, leveraging past purchase history and individual anecdotes to suggest products and services that customers desire requires advanced technology. Furthermore, with conventional methods, it is difficult to reproduce a customer's past purchase experiences in real time and offer related products and suggestions based on them. A new system is needed to solve these problems and increase customer satisfaction.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0494] In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a product proposal generation means, a content integration means, a content transmission means, and a content playback means, thereby enabling a customer to input information about products they have purchased in the past and generate personalized proposals based on that information.

[0495] "Data entry means" refers to a device or interface through which a user enters information.

[0496] "Data transmission means" refers to a communication means for transmitting input information to a server or other device.

[0497] "Data storage means" refers to storage or database for storing transmitted information.

[0498] "Photo and episode analysis method" refers to an algorithm that analyzes input photos and text information and extracts important elements and themes.

[0499] "Video and narration generation means" refers to software or algorithms for generating video and narration based on the analysis results.

[0500] "Product proposal generation means" refers to an algorithm that generates personalized product and service proposals for customers based on the analyzed data.

[0501] "Content integration means" refers to a means for combining multiple pieces of content, such as generated video, narration, and product proposals, into one.

[0502] "Content transmission means" refers to a communication means for transmitting the aggregated content to a user device.

[0503] "Content playback means" refers to a function for playing received content on a user device.

[0504] The present invention is a system that allows customers to enjoy a personalized shopping experience in a physical store, and is realized through a series of data processing. Specific embodiments of the system are described below.

[0505] Data Entry Method

[0506] Users log in to a dedicated application through smart glasses and input photos of products they have purchased in the past and related stories. The device is designed to allow users to easily input data using smart glasses.

[0507] Data transmission method

[0508] The smart glasses send the input information to a cloud server using communication methods such as Wi-Fi or Bluetooth, and the communication protocol used is HTTP or WebSocket.

[0509] Data storage means

[0510] The cloud server stores the transmitted data in a NoSQL database (e.g., MongoDB). The stored data includes photo data and text data (episodes).

[0511] Photo and episode analysis tools

[0512] The server analyzes the stored photo and text data, using image analysis algorithms such as TensorFlow to analyze the photos and natural language processing (NLP) algorithms such as spaCy and BERT to analyze the episodes, thereby extracting important elements and themes.

[0513] Video and narration generation method

[0514] The server generates a video and narration based on the analysis results. It uses MoviePy for video generation and gTTS for narration generation. Specifically, it generates a short video that recreates a past shopping experience and a matching narration.

[0515] Product proposal generation means

[0516] The server generates personalized product recommendations based on the analyzed data. The algorithm analyzes past purchase history and current analysis results to suggest the best products and services for the customer.

[0517] Content Integration Methods

[0518] The generated video, narration, and product recommendations are integrated into a single piece of content using a data integration algorithm.

[0519] Content transmission method

[0520] The integrated content is then sent back to the smart glasses using a high-speed communication protocol, enabling real-time content playback.

[0521] Content playback method

[0522] The user's smart glasses then decompress and play the received content, allowing them to navigate the store and receive personalized suggestions and advice based on their past shopping experiences.

[0523] Specific examples

[0524] For example, if a customer enters a photo and story about an item they previously purchased as a "birthday present," the system will process it as follows:

[0525] 1. Data entry: Enter "A necklace purchased as a birthday gift last year."

[0526] 2. Data transmission: Data is sent from the smart glasses to the cloud server.

[0527] 3. Data storage: The cloud server stores the data in MongoDB.

[0528] 4. Data analysis: Analyze photos using TensorFlow and episodes using spaCy.

[0529] 5. Video and narration generation: Create videos with MoviePy and generate narration with gTTS.

[0530] 6. Product suggestion generation: Propose new products relevant to the customer.

[0531] 7. Content integration and playback: The integrated content is played on the smart glasses and provided to the user.

[0532] Prompt Sentence Examples

[0533] Just say, "The necklace I bought as a gift for my birthday last year," and we'll generate special suggestions and narrations based on that.

[0534] The system allows customers to enjoy a personalized shopping experience based on their past purchases.

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] The user logs in to the dedicated application on the smart glasses. The user inputs photos of products they have purchased in the past and related stories. The input data consists of photo files and text data.

[0538] Step 2:

[0539] The smart glasses send the input information to a cloud server via Wi-Fi or Bluetooth. The transmitted data consists of photo files and text data.

[0540] Step 3:

[0541] The server receives the data and stores it in a NoSQL database (e.g., MongoDB). The stored data consists of photo data and text data.

[0542] Step 4:

[0543] The server uses an image analysis algorithm (TensorFlow) to analyze the photo data and extract important elements. For example, it extracts attributes of products such as necklaces. It also uses an NLP algorithm (spaCy or BERT) to extract major themes from the episode text data. For example, it extracts the theme "birthday present."

[0544] Step 5:

[0545] The server generates video and narration based on the analysis results. MoviePy is used to generate the video, combining the analyzed photos and text to create a short video. gTTS is used to generate the narration, generating audio narration of the episode content. For example, a narration could be created such as, "The necklace I gave to a special person for their birthday last year."

[0546] Step 6:

[0547] The server generates personalized product suggestions based on the results of image analysis and NLP. The system uses past purchase history and analysis results to suggest related products currently on sale in the store. For example, it suggests related jewelry and accessory products.

[0548] Step 7:

[0549] The server then combines the generated video, narration, and product recommendations into a single piece of content using a data integration algorithm. The combined content is then compressed.

[0550] Step 8:

[0551] The cloud server sends the integrated content to the smart glasses using high-speed communication protocols (HTTP and WebSocket). The transmitted data is compressed content.

[0552] Step 9:

[0553] The device (smart glasses) decompresses and plays the received content. Through the smart glasses, users can receive personalized suggestions and advice based on their past shopping experiences in real time in the store. For example, they can receive suggestions for related products while watching a video based on their past shopping experiences.

[0554] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0555] The present invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, and an emotion engine that recognizes the user's emotions.

[0556] First, the user accesses the memorial service system and logs in. They enter information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) into a dedicated input screen. At the same time, the emotion engine recognizes emotions from the user's input behavior.

[0557] Next, the device sends the input data, along with the recognized emotion data, to the server, which then stores the received data in a database.

[0558] The server analyzes the stored data. For photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion engine into the analysis.

[0559] The server then generates video and narration based on the analysis results. It creates a video scenario based on the important elements of the photo, the theme of the episode, and the recognized emotions, and generates video based on that scenario. It also generates narration based on the episode, theme, and emotion data, and adds it to the video.

[0560] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions. It then generates appropriate sermon content, referring to Buddhist teachings and scriptures. These are then created as audio files.

[0561] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0562] Finally, the user receives the content on their device, unzips it, saves it, and plays it back. Through the content played back, the user can remember the deceased and hold a heartfelt memorial service. The introduction of an emotion engine enables a more personalized memorial service that responds to the user's emotions.

[0563] Specific examples

[0564] As a concrete example, let us consider a case where a family uses the system for a memorial service for the third anniversary of a death.

[0565] 1. The user logs in to the system and inputs "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "the posthumous Buddhist name of Jiaiin Wako Daishi," and "a sermon on the theme of family love." During this time, the emotion engine recognizes emotions such as "sadness" and "gratitude" from the user's input behavior.

[0566] 2. The device sends this information and emotion data to the server, which receives the data and stores it in a database.

[0567] 3. The server uses image analysis to extract features such as "smiles" and "flowers" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes, and also incorporates emotional data into the analysis.

[0568] 4. Based on the analysis results, the server creates a video scenario with themes such as "smiles," "flowers," "family love," and "sadness," and generates a video based on that scenario. It also generates a narration, such as "A precious time to feel family love and gratitude," and adds it to the video.

[0569] 5. Next, the server selects a sutra from a Buddhist knowledge database on the theme of "the importance of family" to ease the user's "sadness," and generates an appropriate sermon.

[0570] 6. The server integrates these generated materials into a single content, compresses it, and sends it to the terminal.

[0571] 7. The user receives the content on their device, unpacks it, and plays it back. This allows users to hold a more personal memorial service that reflects their emotions and is heartfelt.

[0572] In this way, by incorporating an emotion engine, it becomes possible to hold a memorial service that reflects the user's emotions.

[0573] The processing flow will be explained below.

[0574] Step 1:

[0575] The user accesses the legal system and logs in.

[0576] Step 2:

[0577] The user enters information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen. During this process, the emotion engine recognizes the user's emotions from their input behavior.

[0578] Step 3:

[0579] The terminal transmits the input data and the recognized emotion data to the server.

[0580] Step 4:

[0581] The server stores the received data and emotion data in a database.

[0582] Step 5:

[0583] The server retrieves the photo data, episode data, and emotion data from the database.

[0584] Step 6:

[0585] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers).

[0586] Step 7:

[0587] The server uses natural language processing (NLP) algorithms to analyze the episodes and extract key themes and keywords (e.g., family love, gardening).

[0588] Step 8:

[0589] The server incorporates the emotion data recognized by the emotion engine into the analysis.

[0590] Step 9:

[0591] The server creates a video scenario based on the analyzed photo elements, episode themes, and emotional data.

[0592] Step 10:

[0593] The server uses an AI model to generate footage based on the scenario.

[0594] Step 11:

[0595] The server generates narration based on episode and emotional data and adds it to the video.

[0596] Step 12:

[0597] The server accesses a Buddhist knowledge database and selects sutras appropriate to the deceased's anecdotes, family requests, and perceived emotions.

[0598] Step 13:

[0599] The server generates appropriate sermons from the data based on Buddhist teachings and scriptures.

[0600] Step 14:

[0601] The server creates audio files of the selected sutras and the generated sermons.

[0602] Step 15:

[0603] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content.

[0604] Step 16:

[0605] The server compresses the integrated content and sends it to the terminal.

[0606] Step 17:

[0607] The terminal unpacks and saves the received content.

[0608] Step 18:

[0609] The user plays the integrated content on the terminal and holds a memorial service for the deceased.

[0610] This series of steps allows users to experience an emotionally-charged and personalized memorial service.

[0611] Example 2

[0612] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0613] Conventional memorial service systems provide uniform content without considering the user's feelings, making it difficult to respond to individual requests. Other issues include the complex and time-consuming process of inputting and analyzing information about the deceased, and the mechanical nature of the generated content, which lacks emotion. This creates the problem of insufficient support for users to hold a heartfelt memorial service.

[0614] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, a video generation means, a voice generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing the user's emotion. This allows for quick and effective generation of personalized memorial service content according to the user's emotion, enabling the user to hold a heartfelt memorial service.

[0615] "Data input means" refers to devices or software that allow the user to input information and emotional data about the deceased.

[0616] "Data transmission means" refers to a communication means for transmitting input data to other devices or systems.

[0617] "Data storage means" refers to devices and software used to store and manage received data.

[0618] "Image analysis means" refers to devices and software for analyzing image data and extracting important elements and features.

[0619] "Natural language processing means" refers to devices or software for analyzing text data and extracting themes and keywords.

[0620] "Image generation means" refers to devices and software for creating images based on the analysis results.

[0621] "Speech generation means" refers to a device or software for creating voice data based on the analysis results.

[0622] "Means for accessing religious knowledge databases" refers to devices or software that access databases containing information about religions and retrieve that information.

[0623] "Content integration means" refers to devices or software that combine generated materials such as video, audio, sutra chanting, and sermons into a single piece of content.

[0624] "Content transmission means" refers to a communication means for transmitting the collected content to the user's terminal.

[0625] "Content playback means" refers to a device or software for playing back received content.

[0626] "Emotion analysis means" refers to devices or software for recognizing and analyzing emotions from user input behavior and other data.

[0627] The present invention is a system including a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, an image generation means, an audio generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing a user's emotions.

[0628] First, the user accesses the memorial service system and logs in. On a dedicated input screen, they input information about the deceased, such as a photo, anecdotes, posthumous Buddhist name, and the desired content of the sermon at the memorial service. At the same time, the emotion analysis means recognizes emotions from the user's input behavior. At this stage, the user inputs specific anecdotes, for example, "memories of planting flowers in the garden with the deceased," and records them in the system. The emotion data recognized at this time is also imported.

[0629] Next, the device sends the input data and the accompanying emotion data to the server. This data is sent using the HTTP protocol and is encrypted with SSL / TLS to ensure data security. The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system.

[0630] The server analyzes the stored data. Specifically, it uses image analysis tools such as Google Vision API and OpenCV to extract important elements and objects from the photos, and natural language processing tools such as TensorFlow and spaCy to extract the main themes and keywords of the episodes. Furthermore, emotional data recognized by the sentiment analysis tool is also incorporated into the analysis.

[0631] The server then generates video and narration based on the analysis results. Adobe Premiere Pro and FFmpeg are used to generate video, creating a video scenario based on the key elements of the photo, the theme of the episode, and the recognized emotions. Amazon Polly is used to generate audio, generating narration and adding it to the video.

[0632] The server then accesses a religious knowledge database to select sutras that fit the deceased's anecdotes, the family's wishes, and the recognized emotions. Using Buddhist teachings and scriptures, the server generates appropriate sermon content. These are then converted into audio files using the AI ​​voice synthesis engine VOCALOID.

[0633] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content. After combining, the content is compressed using FFmpeg and sent to the device. The transmitted content is in MP4 or MP3 format.

[0634] Finally, users can receive the content on their devices, save it, and then play it using VLC Media Player or other multimedia players, allowing users to create a personal memorial service that reflects their emotions and is heartfelt.

[0635] Specific examples

[0636] As a concrete example, let's consider a case in which a family uses this system for a memorial service for the third anniversary of a death. The user inputs a photo of the deceased's garden, a memorable episode of planting flowers with the deceased, the posthumous Buddhist name of Jiaiin Wako Daishi, and a sermon on the theme of family love. During this input, the emotion analysis tool recognizes emotions such as sadness and gratitude. The device then transmits this information and the emotion data to the server. The server uses image analysis to extract features such as smiles and flowers from the photos and natural language processing to extract themes such as family love and gardening from the episode. Based on the analysis results, the server creates a video scenario on the theme of family love, generating a narration such as "A precious time to feel family love and gratitude" and adding it to the video. It also generates sutra readings and an appropriate sermon on the theme of "the importance of family" to ease the user's sadness from a Buddhist knowledge database. These are then integrated, compressed, and sent to the device, where the user can decompress and play them.

[0637] Prompt Sentence Examples

[0638] The following are examples of prompts to use when entering and sending to the system "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," and "a sermon on the theme of family love."

[0639] Prompt statement:

[0640] Please upload a photo of the deceased's garden and enter a memorable story of planting flowers with the deceased in text. Enter "Jiaiin Wako Daishi" as the posthumous Buddhist name, and request a sermon with the theme of family love. Once you have completed the entry, please press the send button.

[0641] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0642] Step 1: User accesses the system and logs in

[0643] A user accesses the system and enters their username and password into the login screen. This allows the system to verify that the user has the appropriate access privileges. If the login is successful, an input screen is displayed. The input requires the user's authentication information (username and password), and the output is the authentication result of the access privileges.

[0644] Step 2: User enters information about the deceased

[0645] The user inputs information such as a photo of the deceased, episodes, posthumous Buddhist name, and desired content of the sermon at the memorial service on a dedicated input screen. During this input, the emotion analysis means analyzes the user's input behavior and recognizes emotions. The input includes photo data and text data (episodes, posthumous Buddhist name, content of the sermon), and the input data and analyzed emotion data are obtained as output.

[0646] Step 3: The device sends the data to the server

[0647] The device sends the input data and the recognized emotion data to the server using the HTTP protocol. The data is encrypted using SSL / TLS. The input includes information about the deceased person and emotion data entered by the user, and the output is the data transmission result.

[0648] Step 4: The server saves the data to the database

[0649] The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system. The input includes the sent information about the deceased and emotion data, and the output is the result stored in the database.

[0650] Step 5: The server parses the data

[0651] The server analyzes the stored data. It uses Google Vision API and OpenCV as image analysis methods to extract important elements from the photos. It uses TensorFlow and spaCy as natural language processing methods to extract major themes and keywords from the episodes. Emotional data recognized by the emotion analysis method is also incorporated into the analysis. The input includes the stored information about the deceased and emotion data, and the analysis results (extracted elements, themes, keywords, and emotion data) are obtained as output.

[0652] Step 6: The server generates the video and narration

[0653] Based on the analysis results, the server creates a video using Adobe Premiere Pro or FFmpeg as a video generation tool. It also uses Amazon Polly to generate an audio file and add narration to the video. The input includes the analysis results, and the generated video and narration are the output.

[0654] Step 7: The server generates the chanting and sermon

[0655] The server accesses a religious knowledge database and selects sutra chanting that matches the deceased's anecdotes, the family's requests, and the recognized emotions. It also generates sermons based on Buddhist teachings. These are created as audio files using an AI speech synthesis engine. The inputs include the analysis results and the system's religious knowledge, and the output is the generated audio files of the sutra chanting and sermon.

[0656] Step 8: The server aggregates the content and sends it to the device

[0657] The server combines the generated video, narration, sutra recitation, and sermons into a single integrated content. It then compresses the content using FFmpeg and sends it to the device. The input includes the various generated media files, and the output is the integrated and compressed content.

[0658] Step 9: User receives and plays content on device

[0659] The received content is decompressed and saved on the user's designated device, and played back using a multimedia player such as VLC Media Player. The input includes consolidated and compressed content, and the output is decompressed and playable content, allowing users to hold a heartfelt memorial service.

[0660] (Application example 2)

[0661] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0662] In virtual stores, personalized product recommendations based on customer emotions and situations are important for improving customer satisfaction. However, current systems are unable to recognize users' facial expressions and voices in real time and make product recommendations based on them. Furthermore, it is difficult to provide an appropriate store experience that takes emotions into account, which ultimately reduces customers' purchasing motivation. To solve this problem, a system is needed that recognizes emotions, makes product recommendations based on emotions, and provides a personalized customer experience.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations based on emotion recognition, and a means for recognizing customer facial expressions and voices in a virtual store. This enables real-time product recommendations based on customer emotions and a personalized customer experience.

[0664] A "data entry means" is an interface or device used by a user to enter information.

[0665] The "data transmission means" is a communication means for transmitting input data to a data processing device such as a server.

[0666] The "data storage means" is a storage device for storing the transmitted data.

[0667] "Photo and episode analysis means" refers to software or algorithms used to analyze the input photos and episodes.

[0668] The "video and narration generating means" is a program for generating video and narration based on the analyzed data.

[0669] The "Means for Generating Sutras and Sermons" is a system for generating sutras and sermons based on Buddhist teachings and scriptures.

[0670] The "content integration means" is a mechanism for integrating the generated video, narration, sutra recitation, and sermon into a single piece of content.

[0671] The "content transmitting means" is a method for transmitting the integrated content to the user's terminal.

[0672] The "content playback means" is an application for playing the transmitted content on the user's terminal.

[0673] An "emotion recognition engine" is a system that recognizes emotions from a user's input actions, facial expressions, and voice.

[0674] The "means for generating personalized product recommendations based on emotion recognition" is a function for making optimal product recommendations to users based on recognized emotion data.

[0675] "Means for recognizing customers' facial expressions and voices in a virtual store" refers to technology that analyzes customers' facial expressions and voices in real time in a virtual store and recognizes their emotions.

[0676] The system for implementing this invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra chanting and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations after recognizing the user's emotions, and a means for recognizing the facial expressions and voices of customers in a virtual store.

[0677] First, the user enters information about the deceased (such as photos, anecdotes, posthumous Buddhist name, and desired sermon content at the memorial service) through a dedicated screen. The emotion engine recognizes emotions from the user's input actions, facial expressions, and voice. This data is then sent to the server via data transmission means.

[0678] The server stores the received data in a database and uses the photo and episode analysis means to analyze the data based on image analysis algorithms and natural language processing (NLP) algorithms. Based on the analysis results and emotional data, the video and narration generation means creates a video scenario and generates video based on that scenario. Narration is generated based on the episode, theme, and emotional data and added to the video.

[0679] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermon content based on Buddhist teachings and scriptures. These are also created as audio files. The server then integrates the generated video, narration, sutras, and sermon into a single content, compresses it, and sends it to the user's device.

[0680] Users can receive, unpack, and play the content on their devices, allowing them to hold more personal memorial services that reflect their emotions.

[0681] As an example of application in virtual stores, smartphones and smart glasses can recognize customers' facial expressions and voices in real time and analyze the emotional data to recommend the most suitable products to them, allowing customers to enjoy a personalized shopping experience according to their emotions.

[0682] For example, if a user inputs "I'm looking for a gift for a friend," the system will use emotional data to generate a recommendation such as "Other people also enjoyed this product" if the user has positive emotions. Examples of prompts to the generative AI model are as follows:

[0683] Example prompt sentence:

[0684] Generate personalized product recommendations from the following user input and sentiment data:

[0685] User Input: I'm looking for a gift for a friend.

[0686] Emotion data: positive, joy

[0687] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0688] Step 1:

[0689] The user logs into the system and uses the data input means to input information about the deceased (such as photos, episodes, posthumous Buddhist name, and desired sermon content at the memorial service). At this time, the emotion recognition engine collects emotional data from the user's input actions, facial expressions, and voice. The input information (e.g., photos, episodes) and emotional data are input. This data is then passed to the data transmission means as output.

[0690] Step 2:

[0691] The terminal transmits the input data (photos, episodes, emotion data) to the server via the data transmission means. It receives the data and emotion data input by the user as input and sends them to the server as output.

[0692] Step 3:

[0693] The server stores the received data in a database using a data storage means. It receives the transmitted data as input and stores it in the database as output.

[0694] Step 4:

[0695] The server analyzes the data using photo and episode analysis methods. Specifically, for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion recognition engine into the analysis. It receives data stored in the database as input and obtains the analysis results as output.

[0696] Step 5:

[0697] The server uses a video and narration generation means to create a video scenario based on the analysis results and generate video based on that scenario. Narration is also generated based on the analysis results and added to the video. The server receives the analysis results as input and generates video and narration as output.

[0698] Step 6:

[0699] The server accesses a Buddhist knowledge database, selects sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermons based on Buddhist teachings and scriptures. These are created as audio files. It receives emotion data and episode information as input, and generates audio files of sutras and sermons as output.

[0700] Step 7:

[0701] The server integrates the generated video, narration, sutra chanting, and sermon into one content using a content integration means, compresses it using a content transmission means, and transmits it to the terminal.The server receives the generated materials (video, narration, sutra chanting, and sermon) as input and transmits the integrated content as output.

[0702] Step 8:

[0703] The user receives the transmitted content on the terminal, decompresses it, and plays it using the content playback means. This allows the user to hold a more personal memorial service that reflects their emotions. The transmitted content is received as input, and the played video and audio are obtained as output.

[0704] Example prompt sentence:

[0705] Generate personalized product recommendations from the following user input and sentiment data:

[0706] User Input: I'm looking for a gift for a friend.

[0707] Emotion data: positive, joy

[0708] 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.

[0709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 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.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0715] 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.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] 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.

[0719] 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.

[0720] 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 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.

[0721] 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.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] The system of the present invention is composed of a series of processes, starting with a data input means, including a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, and a content playback means.

[0725] First, the user accesses the memorial service system and logs in. They then enter information about the deceased into a dedicated input form, such as a photo of the deceased, anecdotes, posthumous Buddhist name, and the content of the sermon they would like to hear at the memorial service.

[0726] The device then sends the entered data to the server for analysis, and the server stores the received data in a database.

[0727] The server then performs analysis on the stored data: for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords.

[0728] The server then generates video and narration based on the analysis results. It creates a video scenario using the important elements of the photo and the theme of the episode, and generates video based on that scenario. It also generates narration derived from the episode and theme and adds it to the video.

[0729] The server then accesses a Buddhist knowledge database to select a sutra that matches the deceased's story and the family's requests. It then generates an appropriate sermon based on Buddhist teachings and scriptures. The generated sutra and sermon are then created as audio files.

[0730] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0731] Finally, the user receives the integrated content on their device, unzips and saves it, and then plays it back, allowing them to remember the deceased and hold a heartfelt memorial service.

[0732] Specific examples

[0733] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death.

[0734] 1. The user logs in to the system and fills in the input form with the following information: "Photo of the deceased in their garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," and "Sermon on the theme of family love."

[0735] 2. The device sends this information to the server, which receives the data and stores it in a database.

[0736] 3. The server uses image analysis to extract features such as "flowers" and "smiles" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes.

[0737] 4. Based on these analysis results, the server generates a video of the deceased person planting flowers in the garden, and based on the episode, generates a narration about "a precious time when you can feel the love of family" and adds it to the video.

[0738] 5. Next, the server selects a sutra on the theme of "the importance of family" from a Buddhist knowledge database and generates a sermon based on Buddhist teachings.

[0739] 6. The server integrates the generated video, narration, sutra recitation, and sermon, compresses it into a single content, and sends it to the terminal.

[0740] 7. The user unzips, saves, and plays the integrated content on their device, allowing them to hold a heartfelt memorial service for the third anniversary of the death.

[0741] As this example shows, the system allows for a personal and meaningful memorial service for the bereaved.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] The user accesses the legal system and logs in.

[0745] Step 2:

[0746] The user enters information about the deceased (photos, stories, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen.

[0747] Step 3:

[0748] The terminal transmits the input data to the server.

[0749] Step 4:

[0750] The server stores the received data in a database.

[0751] Step 5:

[0752] The server retrieves the photo data and episode data from the database.

[0753] Step 6:

[0754] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers, etc.).

[0755] Step 7:

[0756] The server analyzes the episodes using natural language processing (NLP) algorithms to extract key themes and keywords.

[0757] Step 8:

[0758] The server creates a video scenario based on the analyzed photo elements and the episode theme.

[0759] Step 9:

[0760] The server uses an AI model to generate footage based on the scenario.

[0761] Step 10:

[0762] The server generates narration based on episodes and themes and adds it to the video.

[0763] Step 11:

[0764] The server accesses a database of Buddhist knowledge and selects sutras that match the stories of the deceased and the requests of the family.

[0765] Step 12:

[0766] The server generates appropriate sermon content based on Buddhist teachings and scriptures.

[0767] Step 13:

[0768] The server creates audio files of the selected sutras and the generated sermons.

[0769] Step 14:

[0770] The server integrates the video, narration, sutra recitation, and sermon into a single piece of content.

[0771] Step 15:

[0772] The server compresses the integrated content and sends it to the terminal.

[0773] Step 16:

[0774] The device unpacks and saves the received content.

[0775] Step 17:

[0776] The user plays back the content on the terminal and performs a memorial service to remember the deceased.

[0777] This series of steps allows the bereaved family to have a personal and heartfelt memorial service.

[0778] Example 1

[0779] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0780] Conventional memorial service systems lack an efficient and integrated means for generating personalized content, making it particularly difficult to generate video, narration, sutra readings, and sermons based on information about the deceased. Furthermore, there has been no system that integrates all of this content into one and allows users to easily receive, decompress, and play it, which places a heavy burden on users.

[0781] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0782] In this invention, the server includes a data storage means, a photo and episode analysis means, and a video and narration generation means, which allows the server to store and analyze data related to the deceased, and generate video and narration based on the data.

[0783] The server also includes a means for generating sutra chanting and sermons, a content integration means, and a content compression means, which enable it to generate sutra chanting and sermons based on the analysis results, and then integrate and compress them to process them as a single content.

[0784] Furthermore, the terminal includes a content transmitting means, a content receiving means, a content decompressing means, and a content playing means, which enable the terminal to receive, decompress, and play back the integrated content transmitted from the server.

[0785] This allows users to enter information about the deceased and, through a series of processes, easily create and play heartfelt memorial service content.

[0786] "Data input means" refers to a means by which a user inputs information about the deceased.

[0787] The "data transmission means" is a means for transmitting input data from the terminal to the server.

[0788] The "data storage means" is a means for storing received data in a database.

[0789] "Photo and episode analysis means" refers to a means for performing image analysis and natural language processing on photos and episodes of the deceased.

[0790] The "video and narration generating means" is a means for creating video and generating narration based on the analysis results.

[0791] The "means for generating sutra chanting and sermons" is a means for generating sutra chanting and sermons based on a Buddhist knowledge database.

[0792] The "content integration means" is a means for integrating the generated video, narration, sutra recitation, and sermon into one piece of content.

[0793] The "content compression means" is a means for compressing the integrated content.

[0794] The "content transmitting means" is a means for transmitting compressed content from the server to the terminal.

[0795] The "content receiving means" is a means for receiving transmitted content at a terminal.

[0796] The "content decompression means" is a means for decompressing received content at a terminal.

[0797] The "content playback means" is a means for playing back the decompressed content on the terminal.

[0798] The system of the present invention aims to generate personalized content related to the deceased through data processing between the user, the terminal, and the server. This section describes how to specifically implement the system.

[0799] The process begins with the user entering information about the deceased. Using a device such as a PC or smartphone, the user accesses the memorial service system's website through a web browser (e.g., Google Chrome or Firefox) and logs in. Using a dedicated input form, the user enters information such as a photo of the deceased, an anecdote, posthumous Buddhist name, and the desired content of the sermon at the memorial service, and then presses the send button. For example, the user might enter "a photo of the deceased in their garden," "a memorable anecdote about planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," or "a sermon on the theme of family love."

[0800] The device then sends the entered data to the server using HTTPS, with the data being sent in JSON format.

[0801] The server stores the received data in the appropriate format. Specifically, a backend system using the Python Flask framework stores the data in a database such as MySQL or PostgreSQL. After storing, the server returns a response indicating the save was successful.

[0802] During the analysis stage, the server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to analyze the input photo and extract key elements and objects, followed by natural language processing (NLP) algorithms (e.g., spaCy, GPT-3) to extract the main themes and keywords of the episode.

[0803] The server generates video and narration based on the analysis results, using video generation software (e.g., Adobe Premiere, FFmpeg) to generate video from photos of the deceased. Furthermore, it uses voice conversion software such as Google Text-to-Speech or Microsoft Azure TTS to generate narration based on themes derived from the episode and integrate it into the video.

[0804] The server then accesses a Buddhist knowledge database, selects sutras based on the deceased's anecdotes and the family's requests, and generates a sermon. This is done with reference to a database of Buddhist scriptures. The generated sermon and sutras are also created as audio files.

[0805] The generated video, narration, sutra recitation, and sermon are integrated on the server using video editing and compression software such as FFmpeg and compressed into a single piece of content. The integrated content is then sent to the device via a REST API or WebSocket.

[0806] Finally, once the user receives the content, unzips it, and saves it, they can play it using media player software such as VLC Media Player. This system allows users to hold heartfelt memorial services.

[0807] Specific examples

[0808] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death. In this case, the user enters the following information:

[0809] "Photograph in the garden of the deceased"

[0810] "Memorable episode of planting flowers with the deceased"

[0811] "Posthumous Buddhist name: Jiaiin Wako Daishi"

[0812] Sermon on the theme of family love

[0813] Based on this input, the system performs analysis, generates video and narration, generates sutra recitations and sermons, integrates and transmits the content, and ultimately allows users to play heartfelt memorial service content in memory of the deceased.

[0814] This allows the entire process to proceed smoothly and makes it easy for the bereaved to remember the deceased.

[0815] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0816] Step 1:

[0817] The user uses a terminal to access the memorial service system website and log in. The user enters information about the deceased in the input form (e.g., "Photo of the deceased in the garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," "Sermon on the theme of family love"). The entered information is stored in temporary memory on the terminal.

[0818] Step 2:

[0819] The terminal receives user input, converts the data into JSON format, and sends it to the server via HTTPS. The input is the information entered by the user, and the output is JSON format data.

[0820] Step 3:

[0821] The server parses JSON data received via HTTPS and stores it in a MySQL or PostgreSQL database using the Python Flask framework. The input is the JSON data sent from the terminal, and the output is a new record in the database.

[0822] Step 4:

[0823] The server retrieves the stored data from the database and analyzes the photos and episodes. OpenCV and TensorFlow are used for image analysis of the photos, and spaCy and GPT-3 are used for natural language processing (NLP) of the episodes. The input is the data retrieved from the database, and the output is the analyzed data (for example, the important elements of the photo or the theme of the episode).

[0824] Step 5:

[0825] The server generates video and narration based on the analyzed data. Adobe Premiere and FFmpeg are used to generate video, and Google Text-to-Speech and Microsoft Azure TTS are used to generate narration. The input is the analyzed data, and the output is the generated video and narration files.

[0826] Step 6:

[0827] The server accesses a Buddhist knowledge database and generates sutra chanting and sermons based on the deceased's anecdotes and the family's requests. The database used to generate sutra chanting and sermons is a general Buddhist scripture database. The input is data on anecdotes and requests, and the output is an audio file of the generated sutra chanting and sermon.

[0828] Step 7:

[0829] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content. This integration process uses video editing and compression software such as FFmpeg. The input is the various generated files, and the output is an integrated content file.

[0830] Step 8:

[0831] The server sends the integrated content file to the terminal via REST API or WebSocket. The input is the integrated content file, and the output is the sent content file.

[0832] Step 9:

[0833] The user receives the integrated content on their device and unzips it using ZIP unzipping software (e.g., WinRAR, 7-Zip). The unzipped content is played using media player software such as VLC Media Player. The input is the transmitted content file, and the output is the played video and audio.

[0834] (Application example 1)

[0835] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0836] Providing a personalized shopping experience for each customer is difficult in today's brick-and-mortar stores. In particular, leveraging past purchase history and individual anecdotes to suggest products and services that customers desire requires advanced technology. Furthermore, with conventional methods, it is difficult to reproduce a customer's past purchase experiences in real time and offer related products and suggestions based on them. A new system is needed to solve these problems and increase customer satisfaction.

[0837] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0838] In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a product proposal generation means, a content integration means, a content transmission means, and a content playback means, thereby enabling a customer to input information about products they have purchased in the past and generate personalized proposals based on that information.

[0839] "Data entry means" refers to a device or interface through which a user enters information.

[0840] "Data transmission means" refers to a communication means for transmitting input information to a server or other device.

[0841] "Data storage means" refers to storage or database for storing transmitted information.

[0842] "Photo and episode analysis method" refers to an algorithm that analyzes input photos and text information and extracts important elements and themes.

[0843] "Video and narration generation means" refers to software or algorithms for generating video and narration based on the analysis results.

[0844] "Product proposal generation means" refers to an algorithm that generates personalized product and service proposals for customers based on the analyzed data.

[0845] "Content integration means" refers to a means for combining multiple pieces of content, such as generated video, narration, and product proposals, into one.

[0846] "Content transmission means" refers to a communication means for transmitting the aggregated content to a user device.

[0847] "Content playback means" refers to a function for playing received content on a user device.

[0848] The present invention is a system that allows customers to enjoy a personalized shopping experience in a physical store, and is realized through a series of data processing. Specific embodiments of the system are described below.

[0849] Data Entry Method

[0850] Users log in to a dedicated application through smart glasses and input photos of products they have purchased in the past and related stories. The device is designed to allow users to easily input data using smart glasses.

[0851] Data transmission method

[0852] The smart glasses send the input information to a cloud server using communication methods such as Wi-Fi or Bluetooth, and the communication protocol used is HTTP or WebSocket.

[0853] Data storage means

[0854] The cloud server stores the transmitted data in a NoSQL database (e.g., MongoDB). The stored data includes photo data and text data (episodes).

[0855] Photo and episode analysis tools

[0856] The server analyzes the stored photo and text data, using image analysis algorithms such as TensorFlow to analyze the photos and natural language processing (NLP) algorithms such as spaCy and BERT to analyze the episodes, thereby extracting important elements and themes.

[0857] Video and narration generation method

[0858] The server generates a video and narration based on the analysis results. It uses MoviePy for video generation and gTTS for narration generation. Specifically, it generates a short video that recreates a past shopping experience and a matching narration.

[0859] Product proposal generation means

[0860] The server generates personalized product recommendations based on the analyzed data. The algorithm analyzes past purchase history and current analysis results to suggest the best products and services for the customer.

[0861] Content Integration Methods

[0862] The generated video, narration, and product recommendations are integrated into a single piece of content using a data integration algorithm.

[0863] Content transmission method

[0864] The integrated content is then sent back to the smart glasses using a high-speed communication protocol, enabling real-time content playback.

[0865] Content playback method

[0866] The user's smart glasses then decompress and play the received content, allowing them to navigate the store and receive personalized suggestions and advice based on their past shopping experiences.

[0867] Specific examples

[0868] For example, if a customer enters a photo and story about an item they previously purchased as a "birthday present," the system will process it as follows:

[0869] 1. Data entry: Enter "A necklace purchased as a birthday gift last year."

[0870] 2. Data transmission: Data is sent from the smart glasses to the cloud server.

[0871] 3. Data storage: The cloud server stores the data in MongoDB.

[0872] 4. Data analysis: Analyze photos using TensorFlow and episodes using spaCy.

[0873] 5. Video and narration generation: Create videos with MoviePy and generate narration with gTTS.

[0874] 6. Product suggestion generation: Propose new products relevant to the customer.

[0875] 7. Content integration and playback: The integrated content is played on the smart glasses and provided to the user.

[0876] Prompt Sentence Examples

[0877] Just say, "The necklace I bought as a gift for my birthday last year," and we'll generate special suggestions and narrations based on that.

[0878] The system allows customers to enjoy a personalized shopping experience based on their past purchases.

[0879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0880] Step 1:

[0881] The user logs in to the dedicated application on the smart glasses. The user inputs photos of products they have purchased in the past and related stories. The input data consists of photo files and text data.

[0882] Step 2:

[0883] The smart glasses send the input information to a cloud server via Wi-Fi or Bluetooth. The transmitted data consists of photo files and text data.

[0884] Step 3:

[0885] The server receives the data and stores it in a NoSQL database (e.g., MongoDB). The stored data consists of photo data and text data.

[0886] Step 4:

[0887] The server uses an image analysis algorithm (TensorFlow) to analyze the photo data and extract important elements. For example, it extracts attributes of products such as necklaces. It also uses an NLP algorithm (spaCy or BERT) to extract major themes from the episode text data. For example, it extracts the theme "birthday present."

[0888] Step 5:

[0889] The server generates video and narration based on the analysis results. MoviePy is used to generate the video, combining the analyzed photos and text to create a short video. gTTS is used to generate the narration, generating audio narration of the episode content. For example, a narration could be created such as, "The necklace I gave to a special person for their birthday last year."

[0890] Step 6:

[0891] The server generates personalized product suggestions based on the results of image analysis and NLP. The system uses past purchase history and analysis results to suggest related products currently on sale in the store. For example, it suggests related jewelry and accessory products.

[0892] Step 7:

[0893] The server then combines the generated video, narration, and product recommendations into a single piece of content using a data integration algorithm. The combined content is then compressed.

[0894] Step 8:

[0895] The cloud server sends the integrated content to the smart glasses using high-speed communication protocols (HTTP and WebSocket). The transmitted data is compressed content.

[0896] Step 9:

[0897] The device (smart glasses) decompresses and plays the received content. Through the smart glasses, users can receive personalized suggestions and advice based on their past shopping experiences in real time in the store. For example, they can receive suggestions for related products while watching a video based on their past shopping experiences.

[0898] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0899] The present invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, and an emotion engine that recognizes the user's emotions.

[0900] First, the user accesses the memorial service system and logs in. They enter information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) into a dedicated input screen. At the same time, the emotion engine recognizes emotions from the user's input behavior.

[0901] Next, the device sends the input data, along with the recognized emotion data, to the server, which then stores the received data in a database.

[0902] The server analyzes the stored data. For photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion engine into the analysis.

[0903] The server then generates video and narration based on the analysis results. It creates a video scenario based on the important elements of the photo, the theme of the episode, and the recognized emotions, and generates video based on that scenario. It also generates narration based on the episode, theme, and emotion data, and adds it to the video.

[0904] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions. It then generates appropriate sermon content, referring to Buddhist teachings and scriptures. These are then created as audio files.

[0905] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[0906] Finally, the user receives the content on their device, unzips it, saves it, and plays it back. Through the content played back, the user can remember the deceased and hold a heartfelt memorial service. The introduction of an emotion engine enables a more personalized memorial service that responds to the user's emotions.

[0907] Specific examples

[0908] As a concrete example, let us consider a case where a family uses the system for a memorial service for the third anniversary of a death.

[0909] 1. The user logs in to the system and inputs "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "the posthumous Buddhist name of Jiaiin Wako Daishi," and "a sermon on the theme of family love." During this time, the emotion engine recognizes emotions such as "sadness" and "gratitude" from the user's input behavior.

[0910] 2. The device sends this information and emotion data to the server, which receives the data and stores it in a database.

[0911] 3. The server uses image analysis to extract features such as "smiles" and "flowers" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes, and also incorporates emotional data into the analysis.

[0912] 4. Based on the analysis results, the server creates a video scenario with themes such as "smiles," "flowers," "family love," and "sadness," and generates a video based on that scenario. It also generates a narration, such as "A precious time to feel family love and gratitude," and adds it to the video.

[0913] 5. Next, the server selects a sutra from a Buddhist knowledge database on the theme of "the importance of family" to ease the user's "sadness," and generates an appropriate sermon.

[0914] 6. The server integrates these generated materials into a single content, compresses it, and sends it to the terminal.

[0915] 7. The user receives the content on their device, unpacks it, and plays it back. This allows users to hold a more personal memorial service that reflects their emotions and is heartfelt.

[0916] In this way, by incorporating an emotion engine, it becomes possible to hold a memorial service that reflects the user's emotions.

[0917] The processing flow will be explained below.

[0918] Step 1:

[0919] The user accesses the legal system and logs in.

[0920] Step 2:

[0921] The user enters information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen. During this process, the emotion engine recognizes the user's emotions from their input behavior.

[0922] Step 3:

[0923] The terminal transmits the input data and the recognized emotion data to the server.

[0924] Step 4:

[0925] The server stores the received data and emotion data in a database.

[0926] Step 5:

[0927] The server retrieves the photo data, episode data, and emotion data from the database.

[0928] Step 6:

[0929] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers).

[0930] Step 7:

[0931] The server uses natural language processing (NLP) algorithms to analyze the episodes and extract key themes and keywords (e.g., family love, gardening).

[0932] Step 8:

[0933] The server incorporates the emotion data recognized by the emotion engine into the analysis.

[0934] Step 9:

[0935] The server creates a video scenario based on the analyzed photo elements, episode themes, and emotional data.

[0936] Step 10:

[0937] The server uses an AI model to generate footage based on the scenario.

[0938] Step 11:

[0939] The server generates narration based on episode and emotional data and adds it to the video.

[0940] Step 12:

[0941] The server accesses a Buddhist knowledge database and selects sutras appropriate to the deceased's anecdotes, family requests, and perceived emotions.

[0942] Step 13:

[0943] The server generates appropriate sermons from the data based on Buddhist teachings and scriptures.

[0944] Step 14:

[0945] The server creates audio files of the selected sutras and the generated sermons.

[0946] Step 15:

[0947] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content.

[0948] Step 16:

[0949] The server compresses the integrated content and sends it to the terminal.

[0950] Step 17:

[0951] The terminal unpacks and saves the received content.

[0952] Step 18:

[0953] The user plays the integrated content on the terminal and holds a memorial service for the deceased.

[0954] This series of steps allows users to experience an emotionally-charged and personalized memorial service.

[0955] Example 2

[0956] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0957] Conventional memorial service systems provide uniform content without considering the user's feelings, making it difficult to respond to individual requests. Other issues include the complex and time-consuming process of inputting and analyzing information about the deceased, and the mechanical nature of the generated content, which lacks emotion. This creates the problem of insufficient support for users to hold a heartfelt memorial service.

[0958] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, a video generation means, a voice generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing the user's emotion. This allows for quick and effective generation of personalized memorial service content according to the user's emotion, enabling the user to hold a heartfelt memorial service.

[0959] "Data input means" refers to devices or software that allow the user to input information and emotional data about the deceased.

[0960] "Data transmission means" refers to a communication means for transmitting input data to other devices or systems.

[0961] "Data storage means" refers to devices and software used to store and manage received data.

[0962] "Image analysis means" refers to devices and software for analyzing image data and extracting important elements and features.

[0963] "Natural language processing means" refers to devices or software for analyzing text data and extracting themes and keywords.

[0964] "Image generation means" refers to devices and software for creating images based on the analysis results.

[0965] "Speech generation means" refers to a device or software for creating voice data based on the analysis results.

[0966] "Means for accessing religious knowledge databases" refers to devices or software that access databases containing information about religions and retrieve that information.

[0967] "Content integration means" refers to devices or software that combine generated materials such as video, audio, sutra chanting, and sermons into a single piece of content.

[0968] "Content transmission means" refers to a communication means for transmitting the collected content to the user's terminal.

[0969] "Content playback means" refers to a device or software for playing back received content.

[0970] "Emotion analysis means" refers to devices or software for recognizing and analyzing emotions from user input behavior and other data.

[0971] The present invention is a system including a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, an image generation means, an audio generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing a user's emotions.

[0972] First, the user accesses the memorial service system and logs in. On a dedicated input screen, they input information about the deceased, such as a photo, anecdotes, posthumous Buddhist name, and the desired content of the sermon at the memorial service. At the same time, the emotion analysis means recognizes emotions from the user's input behavior. At this stage, the user inputs specific anecdotes, for example, "memories of planting flowers in the garden with the deceased," and records them in the system. The emotion data recognized at this time is also imported.

[0973] Next, the device sends the input data and the accompanying emotion data to the server. This data is sent using the HTTP protocol and is encrypted with SSL / TLS to ensure data security. The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system.

[0974] The server analyzes the stored data. Specifically, it uses image analysis tools such as Google Vision API and OpenCV to extract important elements and objects from the photos, and natural language processing tools such as TensorFlow and spaCy to extract the main themes and keywords of the episodes. Furthermore, emotional data recognized by the sentiment analysis tool is also incorporated into the analysis.

[0975] The server then generates video and narration based on the analysis results. Adobe Premiere Pro and FFmpeg are used to generate video, creating a video scenario based on the key elements of the photo, the theme of the episode, and the recognized emotions. Amazon Polly is used to generate audio, generating narration and adding it to the video.

[0976] The server then accesses a religious knowledge database to select sutras that fit the deceased's anecdotes, the family's wishes, and the recognized emotions. Using Buddhist teachings and scriptures, the server generates appropriate sermon content. These are then converted into audio files using the AI ​​voice synthesis engine VOCALOID.

[0977] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content. After combining, the content is compressed using FFmpeg and sent to the device. The transmitted content is in MP4 or MP3 format.

[0978] Finally, users can receive the content on their devices, save it, and then play it using VLC Media Player or other multimedia players, allowing users to create a personal memorial service that reflects their emotions and is heartfelt.

[0979] Specific examples

[0980] As a concrete example, let's consider a case in which a family uses this system for a memorial service for the third anniversary of a death. The user inputs a photo of the deceased's garden, a memorable episode of planting flowers with the deceased, the posthumous Buddhist name of Jiaiin Wako Daishi, and a sermon on the theme of family love. During this input, the emotion analysis tool recognizes emotions such as sadness and gratitude. The device then transmits this information and the emotion data to the server. The server uses image analysis to extract features such as smiles and flowers from the photos and natural language processing to extract themes such as family love and gardening from the episode. Based on the analysis results, the server creates a video scenario on the theme of family love, generating a narration such as "A precious time to feel family love and gratitude" and adding it to the video. It also generates sutra readings and an appropriate sermon on the theme of "the importance of family" to ease the user's sadness from a Buddhist knowledge database. These are then integrated, compressed, and sent to the device, where the user can decompress and play them.

[0981] Prompt Sentence Examples

[0982] The following are examples of prompts to use when entering and sending to the system "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," and "a sermon on the theme of family love."

[0983] Prompt statement:

[0984] Please upload a photo of the deceased's garden and enter a memorable story of planting flowers with the deceased in text. Enter "Jiaiin Wako Daishi" as the posthumous Buddhist name, and request a sermon with the theme of family love. Once you have completed the entry, please press the send button.

[0985] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0986] Step 1: User accesses the system and logs in

[0987] A user accesses the system and enters their username and password into the login screen. This allows the system to verify that the user has the appropriate access privileges. If the login is successful, an input screen is displayed. The input requires the user's authentication information (username and password), and the output is the authentication result of the access privileges.

[0988] Step 2: User enters information about the deceased

[0989] The user inputs information such as a photo of the deceased, episodes, posthumous Buddhist name, and desired content of the sermon at the memorial service on a dedicated input screen. During this input, the emotion analysis means analyzes the user's input behavior and recognizes emotions. The input includes photo data and text data (episodes, posthumous Buddhist name, content of the sermon), and the input data and analyzed emotion data are obtained as output.

[0990] Step 3: The device sends the data to the server

[0991] The device sends the input data and the recognized emotion data to the server using the HTTP protocol. The data is encrypted using SSL / TLS. The input includes information about the deceased person and emotion data entered by the user, and the output is the data transmission result.

[0992] Step 4: The server saves the data to the database

[0993] The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system. The input includes the sent information about the deceased and emotion data, and the output is the result stored in the database.

[0994] Step 5: The server parses the data

[0995] The server analyzes the stored data. It uses Google Vision API and OpenCV as image analysis methods to extract important elements from the photos. It uses TensorFlow and spaCy as natural language processing methods to extract major themes and keywords from the episodes. Emotional data recognized by the emotion analysis method is also incorporated into the analysis. The input includes the stored information about the deceased and emotion data, and the analysis results (extracted elements, themes, keywords, and emotion data) are obtained as output.

[0996] Step 6: The server generates the video and narration

[0997] Based on the analysis results, the server creates a video using Adobe Premiere Pro or FFmpeg as a video generation tool. It also uses Amazon Polly to generate an audio file and add narration to the video. The input includes the analysis results, and the generated video and narration are the output.

[0998] Step 7: The server generates the chanting and sermon

[0999] The server accesses a religious knowledge database and selects sutra chanting that matches the deceased's anecdotes, the family's requests, and the recognized emotions. It also generates sermons based on Buddhist teachings. These are created as audio files using an AI speech synthesis engine. The inputs include the analysis results and the system's religious knowledge, and the output is the generated audio files of the sutra chanting and sermon.

[1000] Step 8: The server aggregates the content and sends it to the device

[1001] The server combines the generated video, narration, sutra recitation, and sermons into a single integrated content. It then compresses the content using FFmpeg and sends it to the device. The input includes the various generated media files, and the output is the integrated and compressed content.

[1002] Step 9: User receives and plays content on device

[1003] The received content is decompressed and saved on the user's designated device, and played back using a multimedia player such as VLC Media Player. The input includes consolidated and compressed content, and the output is decompressed and playable content, allowing users to hold a heartfelt memorial service.

[1004] (Application example 2)

[1005] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1006] In virtual stores, personalized product recommendations based on customer emotions and situations are important for improving customer satisfaction. However, current systems are unable to recognize users' facial expressions and voices in real time and make product recommendations based on them. Furthermore, it is difficult to provide an appropriate store experience that takes emotions into account, which ultimately reduces customers' purchasing motivation. To solve this problem, a system is needed that recognizes emotions, makes product recommendations based on emotions, and provides a personalized customer experience.

[1007] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations based on emotion recognition, and a means for recognizing customer facial expressions and voices in a virtual store. This enables real-time product recommendations based on customer emotions and a personalized customer experience.

[1008] A "data entry means" is an interface or device used by a user to enter information.

[1009] The "data transmission means" is a communication means for transmitting input data to a data processing device such as a server.

[1010] The "data storage means" is a storage device for storing the transmitted data.

[1011] "Photo and episode analysis means" refers to software or algorithms used to analyze the input photos and episodes.

[1012] The "video and narration generating means" is a program for generating video and narration based on the analyzed data.

[1013] The "Means for Generating Sutras and Sermons" is a system for generating sutras and sermons based on Buddhist teachings and scriptures.

[1014] The "content integration means" is a mechanism for integrating the generated video, narration, sutra recitation, and sermon into a single piece of content.

[1015] The "content transmitting means" is a method for transmitting the integrated content to the user's terminal.

[1016] The "content playback means" is an application for playing the transmitted content on the user's terminal.

[1017] An "emotion recognition engine" is a system that recognizes emotions from a user's input actions, facial expressions, and voice.

[1018] The "means for generating personalized product recommendations based on emotion recognition" is a function for making optimal product recommendations to users based on recognized emotion data.

[1019] "Means for recognizing customers' facial expressions and voices in a virtual store" refers to technology that analyzes customers' facial expressions and voices in real time in a virtual store and recognizes their emotions.

[1020] The system for implementing this invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra chanting and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations after recognizing the user's emotions, and a means for recognizing the facial expressions and voices of customers in a virtual store.

[1021] First, the user enters information about the deceased (such as photos, anecdotes, posthumous Buddhist name, and desired sermon content at the memorial service) through a dedicated screen. The emotion engine recognizes emotions from the user's input actions, facial expressions, and voice. This data is then sent to the server via data transmission means.

[1022] The server stores the received data in a database and uses the photo and episode analysis means to analyze the data based on image analysis algorithms and natural language processing (NLP) algorithms. Based on the analysis results and emotional data, the video and narration generation means creates a video scenario and generates video based on that scenario. Narration is generated based on the episode, theme, and emotional data and added to the video.

[1023] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermon content based on Buddhist teachings and scriptures. These are also created as audio files. The server then integrates the generated video, narration, sutras, and sermon into a single content, compresses it, and sends it to the user's device.

[1024] Users can receive, unpack, and play the content on their devices, allowing them to hold more personal memorial services that reflect their emotions.

[1025] As an example of application in virtual stores, smartphones and smart glasses can recognize customers' facial expressions and voices in real time and analyze the emotional data to recommend the most suitable products to them, allowing customers to enjoy a personalized shopping experience according to their emotions.

[1026] For example, if a user inputs "I'm looking for a gift for a friend," the system will use emotional data to generate a recommendation such as "Other people also enjoyed this product" if the user has positive emotions. Examples of prompts to the generative AI model are as follows:

[1027] Example prompt sentence:

[1028] Generate personalized product recommendations from the following user input and sentiment data:

[1029] User Input: I'm looking for a gift for a friend.

[1030] Emotion data: positive, joy

[1031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1032] Step 1:

[1033] The user logs into the system and uses the data input means to input information about the deceased (such as photos, episodes, posthumous Buddhist name, and desired sermon content at the memorial service). At this time, the emotion recognition engine collects emotional data from the user's input actions, facial expressions, and voice. The input information (e.g., photos, episodes) and emotional data are input. This data is then passed to the data transmission means as output.

[1034] Step 2:

[1035] The terminal transmits the input data (photos, episodes, emotion data) to the server via the data transmission means. It receives the data and emotion data input by the user as input and sends them to the server as output.

[1036] Step 3:

[1037] The server stores the received data in a database using a data storage means. It receives the transmitted data as input and stores it in the database as output.

[1038] Step 4:

[1039] The server analyzes the data using photo and episode analysis methods. Specifically, for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion recognition engine into the analysis. It receives data stored in the database as input and obtains the analysis results as output.

[1040] Step 5:

[1041] The server uses a video and narration generation means to create a video scenario based on the analysis results and generate video based on that scenario. Narration is also generated based on the analysis results and added to the video. The server receives the analysis results as input and generates video and narration as output.

[1042] Step 6:

[1043] The server accesses a Buddhist knowledge database, selects sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermons based on Buddhist teachings and scriptures. These are created as audio files. It receives emotion data and episode information as input, and generates audio files of sutras and sermons as output.

[1044] Step 7:

[1045] The server integrates the generated video, narration, sutra chanting, and sermon into one content using a content integration means, compresses it using a content transmission means, and transmits it to the terminal.The server receives the generated materials (video, narration, sutra chanting, and sermon) as input and transmits the integrated content as output.

[1046] Step 8:

[1047] The user receives the transmitted content on the terminal, decompresses it, and plays it using the content playback means. This allows the user to hold a more personal memorial service that reflects their emotions. The transmitted content is received as input, and the played video and audio are obtained as output.

[1048] Example prompt sentence:

[1049] Generate personalized product recommendations from the following user input and sentiment data:

[1050] User Input: I'm looking for a gift for a friend.

[1051] Emotion data: positive, joy

[1052] 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.

[1053] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1054] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1055] [Fourth embodiment]

[1056] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1057] 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.

[1058] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1059] 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.

[1060] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1061] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1062] 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.

[1063] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1064] 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.

[1065] 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 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.

[1066] 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.

[1067] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1068] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1069] The system of the present invention is composed of a series of processes, starting with a data input means, including a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, and a content playback means.

[1070] First, the user accesses the memorial service system and logs in. They then enter information about the deceased into a dedicated input form, such as a photo of the deceased, anecdotes, posthumous Buddhist name, and the content of the sermon they would like to hear at the memorial service.

[1071] The device then sends the entered data to the server for analysis, and the server stores the received data in a database.

[1072] The server then performs analysis on the stored data: for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords.

[1073] The server then generates video and narration based on the analysis results. It creates a video scenario using the important elements of the photo and the theme of the episode, and generates video based on that scenario. It also generates narration derived from the episode and theme and adds it to the video.

[1074] The server then accesses a Buddhist knowledge database to select a sutra that matches the deceased's story and the family's requests. It then generates an appropriate sermon based on Buddhist teachings and scriptures. The generated sutra and sermon are then created as audio files.

[1075] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[1076] Finally, the user receives the integrated content on their device, unzips and saves it, and then plays it back, allowing them to remember the deceased and hold a heartfelt memorial service.

[1077] Specific examples

[1078] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death.

[1079] 1. The user logs in to the system and fills in the input form with the following information: "Photo of the deceased in their garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," and "Sermon on the theme of family love."

[1080] 2. The device sends this information to the server, which receives the data and stores it in a database.

[1081] 3. The server uses image analysis to extract features such as "flowers" and "smiles" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes.

[1082] 4. Based on these analysis results, the server generates a video of the deceased person planting flowers in the garden, and based on the episode, generates a narration about "a precious time when you can feel the love of family" and adds it to the video.

[1083] 5. Next, the server selects a sutra on the theme of "the importance of family" from a Buddhist knowledge database and generates a sermon based on Buddhist teachings.

[1084] 6. The server integrates the generated video, narration, sutra recitation, and sermon, compresses it into a single content, and sends it to the terminal.

[1085] 7. The user unzips, saves, and plays the integrated content on their device, allowing them to hold a heartfelt memorial service for the third anniversary of the death.

[1086] As this example shows, the system allows for a personal and meaningful memorial service for the bereaved.

[1087] The processing flow will be explained below.

[1088] Step 1:

[1089] The user accesses the legal system and logs in.

[1090] Step 2:

[1091] The user enters information about the deceased (photos, stories, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen.

[1092] Step 3:

[1093] The terminal transmits the input data to the server.

[1094] Step 4:

[1095] The server stores the received data in a database.

[1096] Step 5:

[1097] The server retrieves the photo data and episode data from the database.

[1098] Step 6:

[1099] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers, etc.).

[1100] Step 7:

[1101] The server analyzes the episodes using natural language processing (NLP) algorithms to extract key themes and keywords.

[1102] Step 8:

[1103] The server creates a video scenario based on the analyzed photo elements and the episode theme.

[1104] Step 9:

[1105] The server uses an AI model to generate footage based on the scenario.

[1106] Step 10:

[1107] The server generates narration based on episodes and themes and adds it to the video.

[1108] Step 11:

[1109] The server accesses a database of Buddhist knowledge and selects sutras that match the stories of the deceased and the requests of the family.

[1110] Step 12:

[1111] The server generates appropriate sermon content based on Buddhist teachings and scriptures.

[1112] Step 13:

[1113] The server creates audio files of the selected sutras and the generated sermons.

[1114] Step 14:

[1115] The server integrates the video, narration, sutra recitation, and sermon into a single piece of content.

[1116] Step 15:

[1117] The server compresses the integrated content and sends it to the terminal.

[1118] Step 16:

[1119] The device unpacks and saves the received content.

[1120] Step 17:

[1121] The user plays back the content on the terminal and performs a memorial service to remember the deceased.

[1122] This series of steps allows the bereaved family to have a personal and heartfelt memorial service.

[1123] Example 1

[1124] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1125] Conventional memorial service systems lack an efficient and integrated means for generating personalized content, making it particularly difficult to generate video, narration, sutra readings, and sermons based on information about the deceased. Furthermore, there has been no system that integrates all of this content into one and allows users to easily receive, decompress, and play it, which places a heavy burden on users.

[1126] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1127] In this invention, the server includes a data storage means, a photo and episode analysis means, and a video and narration generation means, which allows the server to store and analyze data related to the deceased, and generate video and narration based on the data.

[1128] The server also includes a means for generating sutra chanting and sermons, a content integration means, and a content compression means, which enable it to generate sutra chanting and sermons based on the analysis results, and then integrate and compress them to process them as a single content.

[1129] Furthermore, the terminal includes a content transmitting means, a content receiving means, a content decompressing means, and a content playing means, which enable the terminal to receive, decompress, and play back the integrated content transmitted from the server.

[1130] This allows users to enter information about the deceased and, through a series of processes, easily create and play heartfelt memorial service content.

[1131] "Data input means" refers to a means by which a user inputs information about the deceased.

[1132] The "data transmission means" is a means for transmitting input data from the terminal to the server.

[1133] The "data storage means" is a means for storing received data in a database.

[1134] "Photo and episode analysis means" refers to a means for performing image analysis and natural language processing on photos and episodes of the deceased.

[1135] The "video and narration generating means" is a means for creating video and generating narration based on the analysis results.

[1136] The "means for generating sutra chanting and sermons" is a means for generating sutra chanting and sermons based on a Buddhist knowledge database.

[1137] The "content integration means" is a means for integrating the generated video, narration, sutra recitation, and sermon into one piece of content.

[1138] The "content compression means" is a means for compressing the integrated content.

[1139] The "content transmitting means" is a means for transmitting compressed content from the server to the terminal.

[1140] The "content receiving means" is a means for receiving transmitted content at a terminal.

[1141] The "content decompression means" is a means for decompressing received content at a terminal.

[1142] The "content playback means" is a means for playing back the decompressed content on the terminal.

[1143] The system of the present invention aims to generate personalized content related to the deceased through data processing between the user, the terminal, and the server. This section describes how to specifically implement the system.

[1144] The process begins with the user entering information about the deceased. Using a device such as a PC or smartphone, the user accesses the memorial service system's website through a web browser (e.g., Google Chrome or Firefox) and logs in. Using a dedicated input form, the user enters information such as a photo of the deceased, an anecdote, posthumous Buddhist name, and the desired content of the sermon at the memorial service, and then presses the send button. For example, the user might enter "a photo of the deceased in their garden," "a memorable anecdote about planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," or "a sermon on the theme of family love."

[1145] The device then sends the entered data to the server using HTTPS, with the data being sent in JSON format.

[1146] The server stores the received data in the appropriate format. Specifically, a backend system using the Python Flask framework stores the data in a database such as MySQL or PostgreSQL. After storing, the server returns a response indicating the save was successful.

[1147] During the analysis stage, the server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to analyze the input photo and extract key elements and objects, followed by natural language processing (NLP) algorithms (e.g., spaCy, GPT-3) to extract the main themes and keywords of the episode.

[1148] The server generates video and narration based on the analysis results, using video generation software (e.g., Adobe Premiere, FFmpeg) to generate video from photos of the deceased. Furthermore, it uses voice conversion software such as Google Text-to-Speech or Microsoft Azure TTS to generate narration based on themes derived from the episode and integrate it into the video.

[1149] The server then accesses a Buddhist knowledge database, selects sutras based on the deceased's anecdotes and the family's requests, and generates a sermon. This is done with reference to a database of Buddhist scriptures. The generated sermon and sutras are also created as audio files.

[1150] The generated video, narration, sutra recitation, and sermon are integrated on the server using video editing and compression software such as FFmpeg and compressed into a single piece of content. The integrated content is then sent to the device via a REST API or WebSocket.

[1151] Finally, once the user receives the content, unzips it, and saves it, they can play it using media player software such as VLC Media Player. This system allows users to hold heartfelt memorial services.

[1152] Specific examples

[1153] As a concrete example, let's say a family uses this system for a memorial service for the third anniversary of a death. In this case, the user enters the following information:

[1154] "Photograph in the garden of the deceased"

[1155] "Memorable episode of planting flowers with the deceased"

[1156] "Posthumous Buddhist name: Jiaiin Wako Daishi"

[1157] Sermon on the theme of family love

[1158] Based on this input, the system performs analysis, generates video and narration, generates sutra recitations and sermons, integrates and transmits the content, and ultimately allows users to play heartfelt memorial service content in memory of the deceased.

[1159] This allows the entire process to proceed smoothly and makes it easy for the bereaved to remember the deceased.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user uses a terminal to access the memorial service system website and log in. The user enters information about the deceased in the input form (e.g., "Photo of the deceased in the garden," "Memorable episode of planting flowers with the deceased," "Posthumous Buddhist name: Jiaiin Wako Daishi," "Sermon on the theme of family love"). The entered information is stored in temporary memory on the terminal.

[1163] Step 2:

[1164] The terminal receives user input, converts the data into JSON format, and sends it to the server via HTTPS. The input is the information entered by the user, and the output is JSON format data.

[1165] Step 3:

[1166] The server parses JSON data received via HTTPS and stores it in a MySQL or PostgreSQL database using the Python Flask framework. The input is the JSON data sent from the terminal, and the output is a new record in the database.

[1167] Step 4:

[1168] The server retrieves the stored data from the database and analyzes the photos and episodes. OpenCV and TensorFlow are used for image analysis of the photos, and spaCy and GPT-3 are used for natural language processing (NLP) of the episodes. The input is the data retrieved from the database, and the output is the analyzed data (for example, the important elements of the photo or the theme of the episode).

[1169] Step 5:

[1170] The server generates video and narration based on the analyzed data. Adobe Premiere and FFmpeg are used to generate video, and Google Text-to-Speech and Microsoft Azure TTS are used to generate narration. The input is the analyzed data, and the output is the generated video and narration files.

[1171] Step 6:

[1172] The server accesses a Buddhist knowledge database and generates sutra chanting and sermons based on the deceased's anecdotes and the family's requests. The database used to generate sutra chanting and sermons is a general Buddhist scripture database. The input is data on anecdotes and requests, and the output is an audio file of the generated sutra chanting and sermon.

[1173] Step 7:

[1174] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content. This integration process uses video editing and compression software such as FFmpeg. The input is the various generated files, and the output is an integrated content file.

[1175] Step 8:

[1176] The server sends the integrated content file to the terminal via REST API or WebSocket. The input is the integrated content file, and the output is the sent content file.

[1177] Step 9:

[1178] The user receives the integrated content on their device and unzips it using ZIP unzipping software (e.g., WinRAR, 7-Zip). The unzipped content is played using media player software such as VLC Media Player. The input is the transmitted content file, and the output is the played video and audio.

[1179] (Application example 1)

[1180] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] Providing a personalized shopping experience for each customer is difficult in today's brick-and-mortar stores. In particular, leveraging past purchase history and individual anecdotes to suggest products and services that customers desire requires advanced technology. Furthermore, with conventional methods, it is difficult to reproduce a customer's past purchase experiences in real time and offer related products and suggestions based on them. A new system is needed to solve these problems and increase customer satisfaction.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1183] In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a product proposal generation means, a content integration means, a content transmission means, and a content playback means, thereby enabling a customer to input information about products they have purchased in the past and generate personalized proposals based on that information.

[1184] "Data entry means" refers to a device or interface through which a user enters information.

[1185] "Data transmission means" refers to a communication means for transmitting input information to a server or other device.

[1186] "Data storage means" refers to storage or database for storing transmitted information.

[1187] "Photo and episode analysis method" refers to an algorithm that analyzes input photos and text information and extracts important elements and themes.

[1188] "Video and narration generation means" refers to software or algorithms for generating video and narration based on the analysis results.

[1189] "Product proposal generation means" refers to an algorithm that generates personalized product and service proposals for customers based on the analyzed data.

[1190] "Content integration means" refers to a means for combining multiple pieces of content, such as generated video, narration, and product proposals, into one.

[1191] "Content transmission means" refers to a communication means for transmitting the aggregated content to a user device.

[1192] "Content playback means" refers to a function for playing received content on a user device.

[1193] The present invention is a system that allows customers to enjoy a personalized shopping experience in a physical store, and is realized through a series of data processing. Specific embodiments of the system are described below.

[1194] Data Entry Method

[1195] Users log in to a dedicated application through smart glasses and input photos of products they have purchased in the past and related stories. The device is designed to allow users to easily input data using smart glasses.

[1196] Data transmission method

[1197] The smart glasses send the input information to a cloud server using communication methods such as Wi-Fi or Bluetooth, and the communication protocol used is HTTP or WebSocket.

[1198] Data storage means

[1199] The cloud server stores the transmitted data in a NoSQL database (e.g., MongoDB). The stored data includes photo data and text data (episodes).

[1200] Photo and episode analysis tools

[1201] The server analyzes the stored photo and text data, using image analysis algorithms such as TensorFlow to analyze the photos and natural language processing (NLP) algorithms such as spaCy and BERT to analyze the episodes, thereby extracting important elements and themes.

[1202] Video and narration generation method

[1203] The server generates a video and narration based on the analysis results. It uses MoviePy for video generation and gTTS for narration generation. Specifically, it generates a short video that recreates a past shopping experience and a matching narration.

[1204] Product proposal generation means

[1205] The server generates personalized product recommendations based on the analyzed data. The algorithm analyzes past purchase history and current analysis results to suggest the best products and services for the customer.

[1206] Content Integration Methods

[1207] The generated video, narration, and product recommendations are integrated into a single piece of content using a data integration algorithm.

[1208] Content transmission method

[1209] The integrated content is then sent back to the smart glasses using a high-speed communication protocol, enabling real-time content playback.

[1210] Content playback method

[1211] The user's smart glasses then decompress and play the received content, allowing them to navigate the store and receive personalized suggestions and advice based on their past shopping experiences.

[1212] Specific examples

[1213] For example, if a customer enters a photo and story about an item they previously purchased as a "birthday present," the system will process it as follows:

[1214] 1. Data entry: Enter "A necklace purchased as a birthday gift last year."

[1215] 2. Data transmission: Data is sent from the smart glasses to the cloud server.

[1216] 3. Data storage: The cloud server stores the data in MongoDB.

[1217] 4. Data analysis: Analyze photos using TensorFlow and episodes using spaCy.

[1218] 5. Video and narration generation: Create videos with MoviePy and generate narration with gTTS.

[1219] 6. Product suggestion generation: Propose new products relevant to the customer.

[1220] 7. Content integration and playback: The integrated content is played on the smart glasses and provided to the user.

[1221] Prompt Sentence Examples

[1222] Just say, "The necklace I bought as a gift for my birthday last year," and we'll generate special suggestions and narrations based on that.

[1223] The system allows customers to enjoy a personalized shopping experience based on their past purchases.

[1224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1225] Step 1:

[1226] The user logs in to the dedicated application on the smart glasses. The user inputs photos of products they have purchased in the past and related stories. The input data consists of photo files and text data.

[1227] Step 2:

[1228] The smart glasses send the input information to a cloud server via Wi-Fi or Bluetooth. The transmitted data consists of photo files and text data.

[1229] Step 3:

[1230] The server receives the data and stores it in a NoSQL database (e.g., MongoDB). The stored data consists of photo data and text data.

[1231] Step 4:

[1232] The server uses an image analysis algorithm (TensorFlow) to analyze the photo data and extract important elements. For example, it extracts attributes of products such as necklaces. It also uses an NLP algorithm (spaCy or BERT) to extract major themes from the episode text data. For example, it extracts the theme "birthday present."

[1233] Step 5:

[1234] The server generates video and narration based on the analysis results. MoviePy is used to generate the video, combining the analyzed photos and text to create a short video. gTTS is used to generate the narration, generating audio narration of the episode content. For example, a narration could be created such as, "The necklace I gave to a special person for their birthday last year."

[1235] Step 6:

[1236] The server generates personalized product suggestions based on the results of image analysis and NLP. The system uses past purchase history and analysis results to suggest related products currently on sale in the store. For example, it suggests related jewelry and accessory products.

[1237] Step 7:

[1238] The server then combines the generated video, narration, and product recommendations into a single piece of content using a data integration algorithm. The combined content is then compressed.

[1239] Step 8:

[1240] The cloud server sends the integrated content to the smart glasses using high-speed communication protocols (HTTP and WebSocket). The transmitted data is compressed content.

[1241] Step 9:

[1242] The device (smart glasses) decompresses and plays the received content. Through the smart glasses, users can receive personalized suggestions and advice based on their past shopping experiences in real time in the store. For example, they can receive suggestions for related products while watching a video based on their past shopping experiences.

[1243] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1244] The present invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, and an emotion engine that recognizes the user's emotions.

[1245] First, the user accesses the memorial service system and logs in. They enter information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) into a dedicated input screen. At the same time, the emotion engine recognizes emotions from the user's input behavior.

[1246] Next, the device sends the input data, along with the recognized emotion data, to the server, which then stores the received data in a database.

[1247] The server analyzes the stored data. For photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion engine into the analysis.

[1248] The server then generates video and narration based on the analysis results. It creates a video scenario based on the important elements of the photo, the theme of the episode, and the recognized emotions, and generates video based on that scenario. It also generates narration based on the episode, theme, and emotion data, and adds it to the video.

[1249] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions. It then generates appropriate sermon content, referring to Buddhist teachings and scriptures. These are then created as audio files.

[1250] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content, which is then compressed and sent to the terminal.

[1251] Finally, the user receives the content on their device, unzips it, saves it, and plays it back. Through the content played back, the user can remember the deceased and hold a heartfelt memorial service. The introduction of an emotion engine enables a more personalized memorial service that responds to the user's emotions.

[1252] Specific examples

[1253] As a concrete example, let us consider a case where a family uses the system for a memorial service for the third anniversary of a death.

[1254] 1. The user logs in to the system and inputs "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "the posthumous Buddhist name of Jiaiin Wako Daishi," and "a sermon on the theme of family love." During this time, the emotion engine recognizes emotions such as "sadness" and "gratitude" from the user's input behavior.

[1255] 2. The device sends this information and emotion data to the server, which receives the data and stores it in a database.

[1256] 3. The server uses image analysis to extract features such as "smiles" and "flowers" from the photos, and uses NLP to extract themes such as "family love" and "gardening" from the episodes, and also incorporates emotional data into the analysis.

[1257] 4. Based on the analysis results, the server creates a video scenario with themes such as "smiles," "flowers," "family love," and "sadness," and generates a video based on that scenario. It also generates a narration, such as "A precious time to feel family love and gratitude," and adds it to the video.

[1258] 5. Next, the server selects a sutra from a Buddhist knowledge database on the theme of "the importance of family" to ease the user's "sadness," and generates an appropriate sermon.

[1259] 6. The server integrates these generated materials into a single content, compresses it, and sends it to the terminal.

[1260] 7. The user receives the content on their device, unpacks it, and plays it back. This allows users to hold a more personal memorial service that reflects their emotions and is heartfelt.

[1261] In this way, by incorporating an emotion engine, it becomes possible to hold a memorial service that reflects the user's emotions.

[1262] The processing flow will be explained below.

[1263] Step 1:

[1264] The user accesses the legal system and logs in.

[1265] Step 2:

[1266] The user enters information about the deceased (photos, anecdotes, posthumous Buddhist name, desired sermon content at the memorial service, etc.) on a dedicated input screen. During this process, the emotion engine recognizes the user's emotions from their input behavior.

[1267] Step 3:

[1268] The terminal transmits the input data and the recognized emotion data to the server.

[1269] Step 4:

[1270] The server stores the received data and emotion data in a database.

[1271] Step 5:

[1272] The server retrieves the photo data, episode data, and emotion data from the database.

[1273] Step 6:

[1274] The server uses image analysis algorithms to analyze the photo and extract important elements (e.g., smiles, flowers).

[1275] Step 7:

[1276] The server uses natural language processing (NLP) algorithms to analyze the episodes and extract key themes and keywords (e.g., family love, gardening).

[1277] Step 8:

[1278] The server incorporates the emotion data recognized by the emotion engine into the analysis.

[1279] Step 9:

[1280] The server creates a video scenario based on the analyzed photo elements, episode themes, and emotional data.

[1281] Step 10:

[1282] The server uses an AI model to generate footage based on the scenario.

[1283] Step 11:

[1284] The server generates narration based on episode and emotional data and adds it to the video.

[1285] Step 12:

[1286] The server accesses a Buddhist knowledge database and selects sutras appropriate to the deceased's anecdotes, family requests, and perceived emotions.

[1287] Step 13:

[1288] The server generates appropriate sermons from the data based on Buddhist teachings and scriptures.

[1289] Step 14:

[1290] The server creates audio files of the selected sutras and the generated sermons.

[1291] Step 15:

[1292] The server integrates the generated video, narration, sutra recitation, and sermon into a single piece of content.

[1293] Step 16:

[1294] The server compresses the integrated content and sends it to the terminal.

[1295] Step 17:

[1296] The terminal unpacks and saves the received content.

[1297] Step 18:

[1298] The user plays the integrated content on the terminal and holds a memorial service for the deceased.

[1299] This series of steps allows users to experience an emotionally-charged and personalized memorial service.

[1300] Example 2

[1301] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1302] Conventional memorial service systems provide uniform content without considering the user's feelings, making it difficult to respond to individual requests. Other issues include the complex and time-consuming process of inputting and analyzing information about the deceased, and the mechanical nature of the generated content, which lacks emotion. This creates the problem of insufficient support for users to hold a heartfelt memorial service.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, a video generation means, a voice generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing the user's emotion. This allows for quick and effective generation of personalized memorial service content according to the user's emotion, enabling the user to hold a heartfelt memorial service.

[1304] "Data input means" refers to devices or software that allow the user to input information and emotional data about the deceased.

[1305] "Data transmission means" refers to a communication means for transmitting input data to other devices or systems.

[1306] "Data storage means" refers to devices and software used to store and manage received data.

[1307] "Image analysis means" refers to devices and software for analyzing image data and extracting important elements and features.

[1308] "Natural language processing means" refers to devices or software for analyzing text data and extracting themes and keywords.

[1309] "Image generation means" refers to devices and software for creating images based on the analysis results.

[1310] "Speech generation means" refers to a device or software for creating voice data based on the analysis results.

[1311] "Means for accessing religious knowledge databases" refers to devices or software that access databases containing information about religions and retrieve that information.

[1312] "Content integration means" refers to devices or software that combine generated materials such as video, audio, sutra chanting, and sermons into a single piece of content.

[1313] "Content transmission means" refers to a communication means for transmitting the collected content to the user's terminal.

[1314] "Content playback means" refers to a device or software for playing back received content.

[1315] "Emotion analysis means" refers to devices or software for recognizing and analyzing emotions from user input behavior and other data.

[1316] The present invention is a system including a data input means, a data transmission means, a data storage means, an image analysis means, a natural language processing means, an image generation means, an audio generation means, a religious knowledge database access means, a content integration means, a content transmission means, a content playback means, and an emotion analysis means for recognizing a user's emotions.

[1317] First, the user accesses the memorial service system and logs in. On a dedicated input screen, they input information about the deceased, such as a photo, anecdotes, posthumous Buddhist name, and the desired content of the sermon at the memorial service. At the same time, the emotion analysis means recognizes emotions from the user's input behavior. At this stage, the user inputs specific anecdotes, for example, "memories of planting flowers in the garden with the deceased," and records them in the system. The emotion data recognized at this time is also imported.

[1318] Next, the device sends the input data and the accompanying emotion data to the server. This data is sent using the HTTP protocol and is encrypted with SSL / TLS to ensure data security. The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system.

[1319] The server analyzes the stored data. Specifically, it uses image analysis tools such as Google Vision API and OpenCV to extract important elements and objects from the photos, and natural language processing tools such as TensorFlow and spaCy to extract the main themes and keywords of the episodes. Furthermore, emotional data recognized by the sentiment analysis tool is also incorporated into the analysis.

[1320] The server then generates video and narration based on the analysis results. Adobe Premiere Pro and FFmpeg are used to generate video, creating a video scenario based on the key elements of the photo, the theme of the episode, and the recognized emotions. Amazon Polly is used to generate audio, generating narration and adding it to the video.

[1321] The server then accesses a religious knowledge database to select sutras that fit the deceased's anecdotes, the family's wishes, and the recognized emotions. Using Buddhist teachings and scriptures, the server generates appropriate sermon content. These are then converted into audio files using the AI ​​voice synthesis engine VOCALOID.

[1322] The server combines the generated video, narration, sutra reading, and sermon into a single piece of content. After combining, the content is compressed using FFmpeg and sent to the device. The transmitted content is in MP4 or MP3 format.

[1323] Finally, users can receive the content on their devices, save it, and then play it using VLC Media Player or other multimedia players, allowing users to create a personal memorial service that reflects their emotions and is heartfelt.

[1324] Specific examples

[1325] As a concrete example, let's consider a case in which a family uses this system for a memorial service for the third anniversary of a death. The user inputs a photo of the deceased's garden, a memorable episode of planting flowers with the deceased, the posthumous Buddhist name of Jiaiin Wako Daishi, and a sermon on the theme of family love. During this input, the emotion analysis tool recognizes emotions such as sadness and gratitude. The device then transmits this information and the emotion data to the server. The server uses image analysis to extract features such as smiles and flowers from the photos and natural language processing to extract themes such as family love and gardening from the episode. Based on the analysis results, the server creates a video scenario on the theme of family love, generating a narration such as "A precious time to feel family love and gratitude" and adding it to the video. It also generates sutra readings and an appropriate sermon on the theme of "the importance of family" to ease the user's sadness from a Buddhist knowledge database. These are then integrated, compressed, and sent to the device, where the user can decompress and play them.

[1326] Prompt Sentence Examples

[1327] The following are examples of prompts to use when entering and sending to the system "a photo of the deceased person in their garden," "a memorable episode of planting flowers with the deceased," "posthumous Buddhist name: Jiaiin Wako Daishi," and "a sermon on the theme of family love."

[1328] Prompt statement:

[1329] Please upload a photo of the deceased's garden and enter a memorable story of planting flowers with the deceased in text. Enter "Jiaiin Wako Daishi" as the posthumous Buddhist name, and request a sermon with the theme of family love. Once you have completed the entry, please press the send button.

[1330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1331] Step 1: User accesses the system and logs in

[1332] A user accesses the system and enters their username and password into the login screen. This allows the system to verify that the user has the appropriate access privileges. If the login is successful, an input screen is displayed. The input requires the user's authentication information (username and password), and the output is the authentication result of the access privileges.

[1333] Step 2: User enters information about the deceased

[1334] The user inputs information such as a photo of the deceased, episodes, posthumous Buddhist name, and desired content of the sermon at the memorial service on a dedicated input screen. During this input, the emotion analysis means analyzes the user's input behavior and recognizes emotions. The input includes photo data and text data (episodes, posthumous Buddhist name, content of the sermon), and the input data and analyzed emotion data are obtained as output.

[1335] Step 3: The device sends the data to the server

[1336] The device sends the input data and the recognized emotion data to the server using the HTTP protocol. The data is encrypted using SSL / TLS. The input includes information about the deceased person and emotion data entered by the user, and the output is the data transmission result.

[1337] Step 4: The server saves the data to the database

[1338] The server stores the received data in a database. MySQL or PostgreSQL is used as the database management system. The input includes the sent information about the deceased and emotion data, and the output is the result stored in the database.

[1339] Step 5: The server parses the data

[1340] The server analyzes the stored data. It uses Google Vision API and OpenCV as image analysis methods to extract important elements from the photos. It uses TensorFlow and spaCy as natural language processing methods to extract major themes and keywords from the episodes. Emotional data recognized by the emotion analysis method is also incorporated into the analysis. The input includes the stored information about the deceased and emotion data, and the analysis results (extracted elements, themes, keywords, and emotion data) are obtained as output.

[1341] Step 6: The server generates the video and narration

[1342] Based on the analysis results, the server creates a video using Adobe Premiere Pro or FFmpeg as a video generation tool. It also uses Amazon Polly to generate an audio file and add narration to the video. The input includes the analysis results, and the generated video and narration are the output.

[1343] Step 7: The server generates the chanting and sermon

[1344] The server accesses a religious knowledge database and selects sutra chanting that matches the deceased's anecdotes, the family's requests, and the recognized emotions. It also generates sermons based on Buddhist teachings. These are created as audio files using an AI speech synthesis engine. The inputs include the analysis results and the system's religious knowledge, and the output is the generated audio files of the sutra chanting and sermon.

[1345] Step 8: The server aggregates the content and sends it to the device

[1346] The server combines the generated video, narration, sutra recitation, and sermons into a single integrated content. It then compresses the content using FFmpeg and sends it to the device. The input includes the various generated media files, and the output is the integrated and compressed content.

[1347] Step 9: User receives and plays content on device

[1348] The received content is decompressed and saved on the user's designated device, and played back using a multimedia player such as VLC Media Player. The input includes consolidated and compressed content, and the output is decompressed and playable content, allowing users to hold a heartfelt memorial service.

[1349] (Application example 2)

[1350] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1351] In virtual stores, personalized product recommendations based on customer emotions and situations are important for improving customer satisfaction. However, current systems are unable to recognize users' facial expressions and voices in real time and make product recommendations based on them. Furthermore, it is difficult to provide an appropriate store experience that takes emotions into account, which ultimately reduces customers' purchasing motivation. To solve this problem, a system is needed that recognizes emotions, makes product recommendations based on emotions, and provides a personalized customer experience.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra recitation and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations based on emotion recognition, and a means for recognizing customer facial expressions and voices in a virtual store. This enables real-time product recommendations based on customer emotions and a personalized customer experience.

[1353] A "data entry means" is an interface or device used by a user to enter information.

[1354] The "data transmission means" is a communication means for transmitting input data to a data processing device such as a server.

[1355] The "data storage means" is a storage device for storing the transmitted data.

[1356] "Photo and episode analysis means" refers to software or algorithms used to analyze the input photos and episodes.

[1357] The "video and narration generating means" is a program for generating video and narration based on the analyzed data.

[1358] The "Means for Generating Sutras and Sermons" is a system for generating sutras and sermons based on Buddhist teachings and scriptures.

[1359] The "content integration means" is a mechanism for integrating the generated video, narration, sutra recitation, and sermon into a single piece of content.

[1360] The "content transmitting means" is a method for transmitting the integrated content to the user's terminal.

[1361] The "content playback means" is an application for playing the transmitted content on the user's terminal.

[1362] An "emotion recognition engine" is a system that recognizes emotions from a user's input actions, facial expressions, and voice.

[1363] The "means for generating personalized product recommendations based on emotion recognition" is a function for making optimal product recommendations to users based on recognized emotion data.

[1364] "Means for recognizing customers' facial expressions and voices in a virtual store" refers to technology that analyzes customers' facial expressions and voices in real time in a virtual store and recognizes their emotions.

[1365] The system for implementing this invention is composed of a system including a data input means, a data transmission means, a data storage means, a photo and episode analysis means, a video and narration generation means, a sutra chanting and sermon generation means, a content integration means, a content transmission means, a content playback means, an emotion recognition engine, a means for generating personalized product recommendations after recognizing the user's emotions, and a means for recognizing the facial expressions and voices of customers in a virtual store.

[1366] First, the user enters information about the deceased (such as photos, anecdotes, posthumous Buddhist name, and desired sermon content at the memorial service) through a dedicated screen. The emotion engine recognizes emotions from the user's input actions, facial expressions, and voice. This data is then sent to the server via data transmission means.

[1367] The server stores the received data in a database and uses the photo and episode analysis means to analyze the data based on image analysis algorithms and natural language processing (NLP) algorithms. Based on the analysis results and emotional data, the video and narration generation means creates a video scenario and generates video based on that scenario. Narration is generated based on the episode, theme, and emotional data and added to the video.

[1368] The server then accesses a Buddhist knowledge database to select sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermon content based on Buddhist teachings and scriptures. These are also created as audio files. The server then integrates the generated video, narration, sutras, and sermon into a single content, compresses it, and sends it to the user's device.

[1369] Users can receive, unpack, and play the content on their devices, allowing them to hold more personal memorial services that reflect their emotions.

[1370] As an example of application in virtual stores, smartphones and smart glasses can recognize customers' facial expressions and voices in real time and analyze the emotional data to recommend the most suitable products to them, allowing customers to enjoy a personalized shopping experience according to their emotions.

[1371] For example, if a user inputs "I'm looking for a gift for a friend," the system will use emotional data to generate a recommendation such as "Other people also enjoyed this product" if the user has positive emotions. Examples of prompts to the generative AI model are as follows:

[1372] Example prompt sentence:

[1373] Generate personalized product recommendations from the following user input and sentiment data:

[1374] User Input: I'm looking for a gift for a friend.

[1375] Emotion data: positive, joy

[1376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1377] Step 1:

[1378] The user logs into the system and uses the data input means to input information about the deceased (such as photos, episodes, posthumous Buddhist name, and desired sermon content at the memorial service). At this time, the emotion recognition engine collects emotional data from the user's input actions, facial expressions, and voice. The input information (e.g., photos, episodes) and emotional data are input. This data is then passed to the data transmission means as output.

[1379] Step 2:

[1380] The terminal transmits the input data (photos, episodes, emotion data) to the server via the data transmission means. It receives the data and emotion data input by the user as input and sends them to the server as output.

[1381] Step 3:

[1382] The server stores the received data in a database using a data storage means. It receives the transmitted data as input and stores it in the database as output.

[1383] Step 4:

[1384] The server analyzes the data using photo and episode analysis methods. Specifically, for photos, it uses image analysis algorithms to extract important elements and objects, and for episodes, it uses natural language processing (NLP) algorithms to extract key themes and keywords. It also incorporates emotional data recognized by an emotion recognition engine into the analysis. It receives data stored in the database as input and obtains the analysis results as output.

[1385] Step 5:

[1386] The server uses a video and narration generation means to create a video scenario based on the analysis results and generate video based on that scenario. Narration is also generated based on the analysis results and added to the video. The server receives the analysis results as input and generates video and narration as output.

[1387] Step 6:

[1388] The server accesses a Buddhist knowledge database, selects sutras that fit the deceased's anecdotes, the family's requests, and the recognized emotions, and generates appropriate sermons based on Buddhist teachings and scriptures. These are created as audio files. It receives emotion data and episode information as input, and generates audio files of sutras and sermons as output.

[1389] Step 7:

[1390] The server integrates the generated video, narration, sutra chanting, and sermon into one content using a content integration means, compresses it using a content transmission means, and transmits it to the terminal.The server receives the generated materials (video, narration, sutra chanting, and sermon) as input and transmits the integrated content as output.

[1391] Step 8:

[1392] The user receives the transmitted content on the terminal, decompresses it, and plays it using the content playback means. This allows the user to hold a more personal memorial service that reflects their emotions. The transmitted content is received as input, and the played video and audio are obtained as output.

[1393] Example prompt sentence:

[1394] Generate personalized product recommendations from the following user input and sentiment data:

[1395] User Input: I'm looking for a gift for a friend.

[1396] Emotion data: positive, joy

[1397] 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.

[1398] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1399] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1400] 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.

[1401] 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 includes both affect 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.

[1402] 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.

[1403] 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).

[1404] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1405] 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."

[1406] 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.

[1407] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1408] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1413] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] The following is further disclosed regarding the above embodiment.

[1419] (Claim 1)

[1420] a data input means;

[1421] data transmission means;

[1422] A data storage means;

[1423] and the means of analyzing photographs and episodes.

[1424] a video and narration generating means;

[1425] A means for generating sutra chanting and sermons;

[1426] a content integration means;

[1427] content transmission means;

[1428] content playback means;

[1429] A system including:

[1430] (Claim 2)

[1431] 10. The system of claim 1, wherein a user inputs information about the deceased person.

[1432] (Claim 3)

[1433] The system according to claim 1, wherein the data analysis means is used to analyze the photographs and episodes.

[1434] "Example 1"

[1435] (Claim 1)

[1436] a data input means;

[1437] data transmission means;

[1438] A data storage means;

[1439] and the means of analyzing photographs and episodes.

[1440] a video and narration generating means;

[1441] A means for generating sutra chanting and sermons;

[1442] a content integration means;

[1443] content compression means;

[1444] content transmission means;

[1445] content receiving means;

[1446] content decompression means;

[1447] content playback means;

[1448] A system including:

[1449] (Claim 2)

[1450] 2. The system of claim 1, wherein the user enters information about the deceased into a dedicated form and submits it.

[1451] (Claim 3)

[1452] 10. The system of claim 1, wherein the terminal transmits the transmitted data to a server for analysis.

[1453] (Claim 4)

[1454] The system of claim 1, wherein the server stores the received data in a database and begins analysis.

[1455] (Claim 5)

[1456] The system of claim 1, wherein the server performs image analysis of the photos and natural language processing of the episodes.

[1457] (Claim 6)

[1458] The system according to claim 1, wherein the server generates video and narration based on the analysis results.

[1459] (Claim 7)

[1460] The system of claim 1, wherein the server accesses a Buddhist knowledge database and generates sutra recitations and sermons.

[1461] (Claim 8)

[1462] The system according to claim 1, wherein the server integrates and compresses the generated video, narration, sutra recitation, and sermon into a single content.

[1463] (Claim 9)

[1464] 10. The system of claim 1, wherein the server transmits the integrated content to the terminal.

[1465] (Claim 10)

[1466] The system of claim 1, wherein the user receives the integrated content on a terminal, decompresses and saves it, and then plays it.

[1467] "Application Example 1"

[1468] (Claim 1)

[1469] a data input means;

[1470] data transmission means;

[1471] A data storage means;

[1472] and the means of analyzing photographs and episodes.

[1473] a video and narration generating means;

[1474] a product proposal generating means;

[1475] a content integration means;

[1476] content transmission means;

[1477] content playback means;

[1478] A system including:

[1479] (Claim 2)

[1480] 10. The system of claim 1, wherein a user inputs information about products purchased in the past.

[1481] (Claim 3)

[1482] The system of claim 1, wherein the data analysis means is used to analyze the photos and episodes and generate personalized suggestions.

[1483] "Example 2: Combining Emotion Engines"

[1484] (Claim 1)

[1485] a data input means;

[1486] data transmission means;

[1487] A data storage means;

[1488] Image analysis means;

[1489] natural language processing means;

[1490] An image generating means;

[1491] A voice generating means;

[1492] A means of accessing a religious knowledge database;

[1493] a content integration means;

[1494] content transmission means;

[1495] content playback means;

[1496] and emotion analysis means for recognizing the emotions of the user.

[1497] (Claim 2)

[1498] 10. The system of claim 1, wherein the user inputs information about the deceased and emotional data.

[1499] (Claim 3)

[1500] 10. The system of claim 1, wherein the data analysis means is used to analyze the photographs, episodes, and emotion data.

[1501] "Application example 2 when combining emotion engines"

[1502] (Claim 1)

[1503] a data input means;

[1504] data transmission means;

[1505] A data storage means;

[1506] and the means of analyzing photographs and episodes.

[1507] a video and narration generating means;

[1508] A means for generating sutra chanting and sermons;

[1509] a content integration means;

[1510] content transmission means;

[1511] content playback means;

[1512] Emotion recognition engine,

[1513] A means for generating personalized product recommendations based on emotion recognition;

[1514] A means for recognizing facial expressions and voices of customers in a virtual store;

[1515] A system including:

[1516] (Claim 2)

[1517] 10. The system of claim 1, wherein a user inputs information about the deceased person.

[1518] (Claim 3)

[1519] The system according to claim 1, wherein the data analysis means is used to analyze the photographs and episodes. [Explanation of symbols]

[1520] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a data input means; data transmission means; A data storage means; and the means of analyzing photographs and episodes. a video and narration generating means; A means for generating sutra chanting and sermons; a content integration means; content transmission means; content playback means; A system including:

2. The system of claim 1, wherein a user inputs information about the deceased.

3. 2. The system according to claim 1, wherein the data analysis means is used to analyze the photographs and episodes.

Images