System

Abstract

To provide a system which utilizes photographs and voice data of a decedent and responds to a user's questions, in order to provide grief care.SOLUTION: A system includes an intake part, an analysis part, and a response generation part. The intake part takes in photographs or voice data of a decedent. The analysis part analyzes the data taken in by the intake part, and learns the features of the decedent. The response generation part generates responses to a user's questions on the basis of the features learned by the analysis part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had limited means for people to look back on memories of the deceased, making it ineffective as a form of grief care.

[0005] The system according to the embodiment aims to provide grief care by utilizing photographs and audio data of the deceased and responding to questions from the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an import unit, an analysis unit, and a response generation unit. The import unit imports photographic or audio data of the deceased. The analysis unit analyzes the data imported by the import unit and learns characteristics of the deceased. The response generation unit generates a response to a user's question based on the characteristics learned by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide grief care by utilizing photographs and audio data of the deceased and responding to questions from the user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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).

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The grief care system according to the embodiment of the present invention is a system that imports photos and audio data of the deceased and links with a generation AI, so that the deceased in the photo answers questions. This grief care system imports photos and audio data of the deceased, and the generation AI analyzes these data and learns the characteristics of the deceased. When a user asks a question, the generation AI generates a response based on the characteristics of the deceased and displays the deceased in the photo as if he or she is answering. This mechanism is effective as grief care to overcome the sadness of losing a family member. For example, photos and audio data of the deceased are imported into the system. At this time, a photo of the deceased's face and recorded data of the voice are uploaded. For example, a photo of the deceased smiling and audio data of the deceased speaking are input into the system. Next, the generation AI analyzes the imported data and learns the characteristics of the deceased. The generation AI analyzes the facial features, tone of voice, speaking style, etc. of the deceased, and models the characteristics of the deceased. For example, facial features are extracted from a photo of the deceased smiling, and the tone of voice and speaking style are learned from the audio data. When a user asks a question, the generation AI generates a response based on the characteristics of the deceased. For example, if you ask "How are you?", the generation AI will generate a response based on the characteristics of the deceased, saying "I'm fine," and the deceased will be displayed in the photo as if they are answering. The deceased's face and mouth will move as they do so. This mechanism is effective as grief care to help people overcome the sadness of losing a family member. In particular, it can provide peace of mind to elderly people who are mentally and physically limited through conversation with the deceased. For example, hearing the voice of the deceased can provide a sense of security, and seeing the face of the deceased can provide emotional support. In this way, the grief care system allows users to experience conversation with the deceased.

[0029] The grief care system according to the embodiment includes an import unit, an analysis unit, and a response generation unit. The import unit imports a photo and audio data of the deceased. The photo and audio data of the deceased include, but are not limited to, JPEG, MP3, and WAV formats. The import unit, for example, reads a facial photo of the deceased with a scanner and saves it as digital data. The import unit can also record audio data of the deceased with a microphone and save it as digital data. For example, the import unit scans a photo of the deceased's smile and saves it in JPEG format. The import unit can also record the voice of the deceased with a microphone and save it in MP3 format. The analysis unit analyzes the data imported by the import unit and learns the characteristics of the deceased. The analysis unit, for example, uses an image recognition algorithm to analyze the facial features of the deceased. The analysis unit can also use a voice analysis algorithm to analyze the tone of voice and speaking style of the deceased. For example, the analysis unit extracts feature points of the deceased's face and analyzes the facial contour and eye position. The analysis unit can also learn the tone of voice and speaking patterns of the deceased. The response generation unit generates a response to a question from the user based on the characteristics learned by the analysis unit. The response generation unit generates a response using, for example, natural language generation technology. The response generation unit can also generate a response using template-based response generation technology. For example, the response generation unit generates a response based on the characteristics of the deceased in response to a question from the user. For example, when the user asks, "How are you?", the response generation unit generates a response, "I'm fine." In this way, the grief care system according to the embodiment allows the user to experience a conversation with the deceased.

[0030] The capture unit captures the photograph and voice data of the deceased. The photograph and voice data of the deceased include, but are not limited to, JPEG, MP3, and WAV formats. For example, the capture unit reads the face photo of the deceased with a scanner and saves it as digital data. The capture unit can also record the voice data of the deceased with a microphone and save it as digital data. For example, the capture unit scans a photo of the smiling face of the deceased and saves it in JPEG format. The capture unit can also record the voice of the deceased with a microphone and save it in MP3 format. In order to capture these data efficiently, it is recommended that the capture unit use a high-resolution scanner and a high-sensitivity microphone. By using a scanner with a resolution of 600 dpi or more, it is possible to capture details clearly. By using a microphone with a noise canceling function, it is possible to obtain clear voice data. The capture unit also has a function of automatically classifying the captured data and saving it in a database. For example, photo data is tagged with dates, places, and people, and voice data is classified based on speakers and content. This allows the subsequent analysis and response generation parts to use the data efficiently. Furthermore, the import part provides an intuitive interface so that users can import data easily. For example, photos can be imported by drag-and-drop operation, and audio can be recorded with one click. This allows users to easily import data of the deceased into the system.

[0031] The analysis unit analyzes the data captured by the capture unit and learns the characteristics of the deceased. The analysis unit, for example, uses an image recognition algorithm to analyze the facial features of the deceased. The analysis unit can also analyze the tone of voice and speech of the deceased using a voice analysis algorithm. For example, the analysis unit extracts the facial features of the deceased and analyzes the facial outline and eye position. The analysis unit can also learn the tone of voice and speech pattern of the deceased. The analysis unit is equipped with a high-performance processor and GPU to execute these algorithms, and can quickly process large amounts of data. The image recognition algorithm uses deep learning technology and achieves highly accurate facial recognition by learning thousands of photo data. Specifically, a convolutional neural network (CNN) is used to extract facial features and reproduce the face of the deceased with high accuracy. The voice analysis algorithm uses a recurrent neural network (RNN) and a long short-term memory (LSTM) to learn the tone of voice and speech pattern of the deceased. This makes it possible to reproduce the voice of the deceased naturally. The analysis unit stores these learning results in a database and makes them available to the response generation unit. Furthermore, the analysis unit can continuously improve the analysis results by receiving feedback from the user. For example, every time a user adds a photo or voice data of the deceased, the analysis unit learns from the new data and reproduces the characteristics of the deceased more accurately. This allows the analysis unit to always provide highly accurate analysis based on the latest data, improving the performance of the entire system.

[0032] The response generation unit generates a response to the user's question based on the features learned by the analysis unit. The response generation unit generates a response using, for example, natural language generation technology. The response generation unit can also generate a response using template-based response generation technology. For example, the response generation unit generates a response based on the characteristics of the deceased in response to a user's question. For example, when the user asks, "How are you?", the response generation unit generates a response, "I'm fine." The response generation unit can generate more natural and diverse responses by combining these technologies. The natural language generation technology uses a generation AI to generate an appropriate response to the user's question. Specifically, a transformer model is used to analyze the user's question and generate a response based on the characteristics of the deceased. The template-based response generation technology selects an appropriate response to the user's question based on a response template prepared in advance. This allows the response generation unit to provide a quick and highly accurate response. Furthermore, the response generation unit can continuously improve the accuracy and naturalness of the response by receiving user feedback. For example, a function is provided for the user to evaluate the satisfaction with the response, and the response generation algorithm is adjusted based on the evaluation result. The response generator can also generate responses according to the user's emotions and circumstances. For example, if the user is sad, the response generator generates a response that offers comforting words. This allows the response generator to provide a more personalized response to the user, maximizing the effectiveness of the grief care system.

[0033] The analysis unit may include a learning unit that analyzes the facial features or tone of voice and speaking style of the deceased and learns the features of the deceased. The analysis unit may use, for example, a face recognition algorithm to analyze the facial features of the deceased. For example, the analysis unit may extract the facial feature points of the deceased and analyze the position of the eyes, the shape of the nose, the contour of the face, and the like. The analysis unit may also use a voice analysis algorithm to analyze the tone of the deceased's voice. For example, the analysis unit may analyze the pitch, intensity, and quality of the voice of the deceased. The analysis unit may also learn the speech pattern to analyze the speech style of the deceased. For example, the analysis unit may analyze the speaking speed, intonation, choice of words, and the like of the deceased. This allows the analysis unit to learn the features of the deceased in detail. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may use an AI model to extract the facial feature points of the deceased.

[0034] The response generation unit may include a display unit that displays the generated response. The response generation unit may include, for example, a text display unit to display the generated response in text. For example, the response generation unit displays the response to the user's question in text format. The response generation unit may also include a graphical display unit to graphically display the generated response. For example, the response generation unit displays the deceased's face moving and his mouth moving. The response generation unit may also include an audio output unit to output the generated response as audio. For example, the response generation unit plays back the response in the voice of the deceased. This allows the response generation unit to visually display the generated response. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit may use an AI model to display the generated response.

[0035] The capture unit can automatically evaluate the quality of the data when capturing the photo and voice data of the deceased and select appropriate data. The capture unit, for example, uses an image analysis algorithm to evaluate the resolution of the image. For example, the capture unit evaluates the resolution of the photo of the deceased and preferentially captures a photo with a high resolution. The capture unit can also use a voice analysis algorithm to evaluate the noise level of the voice data. For example, the capture unit evaluates the noise level of the voice data of the deceased and selects a voice with less noise. The capture unit can also use an image analysis algorithm to evaluate the brightness and contrast of the photo. For example, the capture unit evaluates the brightness and contrast of the photo of the deceased and selects the optimal photo. This allows the capture unit to evaluate the quality of the data and select the optimal data. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the photo data of the deceased to the generation AI and cause the generation AI to execute the quality evaluation of the data.

[0036] When the capture unit captures the photo or voice data of the deceased, the capture unit can select an appropriate capture method by referring to the user's past capture history. The capture unit, for example, refers to the format of data previously captured by the user. For example, the capture unit refers to the format of data previously captured by the user and captures the data in the same format. The capture unit can also refer to the amount of data previously captured by the user. For example, the capture unit refers to the amount of data previously captured by the user and captures the same amount. The capture unit can also refer to the timing of data previously captured by the user. For example, the capture unit refers to the timing of data previously captured by the user and captures the data at the same timing. This allows the capture unit to provide an optimal capture method by referring to the user's past capture history. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the user's past capture history data to the generation AI and cause the generation AI to select the optimal capture method.

[0037] When the capture unit captures the photographs and voice data of the deceased, the capture unit can preferentially capture data with high relevance based on the geographical location information of the user. The capture unit, for example, uses GPS data to acquire the geographical location information of the user. For example, the capture unit acquires the user's current location from the GPS data, and preferentially captures the photographs and voice data of the deceased related to that location. The capture unit can also use location estimation from an IP address to acquire the geographical location information of the user. For example, the capture unit estimates a location from the user's IP address, and preferentially captures data related to that location. This allows the capture unit to capture data with high relevance based on the geographical location information of the user. For example, when the user is in the hometown of the deceased, the capture unit preferentially captures photos related to that location. In addition, when the user is traveling, the capture unit can also preferentially capture data related to the travel destination. Furthermore, when the user is at home, the capture unit can also preferentially capture photos taken at home. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the import unit can input a user's geographic location data into the generation AI and cause the generation AI to select highly relevant data.

[0038] The capture unit can analyze the social media activity of the user and capture related data when capturing the photos and voice data of the deceased. The capture unit uses, for example, a social media API to analyze the social media activity of the user. For example, the capture unit acquires and captures photos posted by the user on social media. The capture unit can also acquire and capture photos tagged by the user on social media. Furthermore, the capture unit can acquire and capture voice data shared by the user on social media. This allows the capture unit to analyze the social media activity of the user and efficiently capture related data. For example, the capture unit captures photos posted by the user on social media. The capture unit can also capture photos tagged by the user on social media. Furthermore, the capture unit can also capture voice data shared by the user on social media. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related data.

[0039] During the analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationship between the photo and audio data of the deceased. The analysis unit, for example, compares the timestamps of the photo and audio data of the deceased. For example, the analysis unit compares the timestamps of the photo and audio data of the deceased and preferentially analyzes highly relevant data. The analysis unit can also compare the contents of the photo and audio data of the deceased and preferentially analyze the matching parts. Furthermore, the analysis unit can also refer to the metadata of the photo and audio data of the deceased and analyze the highly relevant data. This allows the analysis unit to improve the accuracy of the analysis by taking into account the interrelationship between the photo and audio data of the deceased. For example, the timestamps of the photo and audio data of the deceased are compared and highly relevant data is analyzed. Furthermore, the contents of the photo and audio data of the deceased are compared and preferentially analyze the matching parts. Furthermore, the metadata of the photo and audio data of the deceased can be referred to and the highly relevant data is analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input correlation data between photographs and audio data of the deceased into the generation AI, and have the generation AI improve the accuracy of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the photo or voice data of the deceased during the analysis. The analysis unit applies a face recognition algorithm to, for example, a family photo of the deceased. For example, the analysis unit applies a face recognition algorithm to the family photo of the deceased to analyze the facial features. The analysis unit can also apply a voice recognition algorithm to the voice data of the deceased. For example, the analysis unit applies a voice recognition algorithm to the voice data of the deceased to analyze the voice features. Furthermore, the analysis unit can also apply an image analysis algorithm to a landscape photo of the deceased. For example, the analysis unit applies an image analysis algorithm to a landscape photo of the deceased to analyze the image features. This allows the analysis unit to apply different analysis algorithms depending on the category of the photo or voice data of the deceased. For example, the analysis unit applies a face recognition algorithm to the family photo of the deceased. Also, a voice recognition algorithm can be applied to the voice data of the deceased. Furthermore, an image analysis algorithm can be applied to the landscape photo of the deceased. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of photographs and audio data of the deceased into the generation AI and have the generation AI apply an analysis algorithm.

[0041] During the analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the deceased's photo and voice data. The analysis unit uses a timestamp, for example, to obtain the time of submission of the deceased's photo and voice data. For example, the analysis unit refers to the timestamp of the deceased's photo and voice data and preferentially analyzes the most recently submitted data. The analysis unit can also preferentially analyze data related to a specific event. For example, the analysis unit preferentially analyzes data related to the deceased's birthday or anniversary. Furthermore, the analysis unit can also preferentially analyze data from a time specified by the user. This allows the analysis unit to determine the priority of the analysis based on the time of submission of the deceased's photo and voice data. For example, the most recently submitted data is analyzed preferentially. Furthermore, the analysis unit can also preferentially analyze data related to a specific event. Furthermore, the analysis unit can also preferentially analyze data from a time specified by the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input timestamp data of the deceased's photo and voice data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0042] The analysis unit can improve the accuracy of the analysis based on related literature of the deceased's photograph and voice data at the time of analysis. The analysis unit uses, for example, an academic paper database to refer to related literature of the deceased's photograph and voice data. For example, the analysis unit refers to related academic papers and improves the accuracy of the analysis. The analysis unit can also refer to related patent documents using a patent document database. For example, the analysis unit refers to related patent documents and improves the accuracy of the analysis. Furthermore, the analysis unit can also refer to related technical documents using a technical document database. For example, the analysis unit refers to related technical documents and improves the accuracy of the analysis. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature of the deceased's photograph and voice data. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related patent documents. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related technical documents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data for photographs and audio data of the deceased into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0043] The response generation unit can adjust the level of detail of the response based on the characteristics of the deceased when generating the response. The response generation unit uses a characteristic database of the deceased, for example, to adjust the level of detail of the response based on the characteristics of the deceased. For example, if the deceased prefers detailed explanations, the response generation unit generates a detailed response. Also, if the deceased prefers concise explanations, the response generation unit can generate a concise response. Furthermore, if the deceased prefers humor, the response generation unit can generate a response including humor. This allows the response generation unit to adjust the level of detail of the response based on the characteristics of the deceased. For example, if the deceased prefers detailed explanations, the response generation unit generates a detailed response. Also, if the deceased prefers concise explanations, the response generation unit can generate a concise response. Furthermore, if the deceased prefers humor, the response generation unit can generate a response including humor. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input characteristic data of the deceased to the generation AI and cause the generation AI to adjust the level of detail of the response.

[0044] The response generation unit can apply different response algorithms depending on the category of the user's question when generating a response. The response generation unit uses a response algorithm database, for example, to apply different response algorithms depending on the category of the user's question. For example, the response generation unit applies an emotion recognition algorithm to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. This allows the response generation unit to apply different response algorithms depending on the category of the user's question. For example, an emotion recognition algorithm can be applied to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input the user's question data to the generation AI and cause the generation AI to apply the response algorithm.

[0045] The response generation unit can determine the priority of responses based on the time of submission of the user's question when generating a response. The response generation unit uses a timestamp, for example, to obtain the time of submission of the user's question. For example, the response generation unit refers to the timestamp of the user's question and responds preferentially to a recent question. The response generation unit can also respond preferentially to a question related to a specific event. Furthermore, the response generation unit can also respond preferentially to a question at a time specified by the user. This allows the response generation unit to determine the priority of responses based on the time of submission of the user's question. For example, the response generation unit can respond preferentially to a recent question. Furthermore, the response generation unit can also respond preferentially to a question at a time specified by the user. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input timestamp data of the user's question to the generation AI and cause the generation AI to execute the determination of the priority of responses.

[0046] The response generation unit can adjust the order of responses based on the relevance of the user's questions when generating responses. The response generation unit uses a relevance evaluation algorithm, for example, to evaluate the relevance of the user's questions. For example, the response generation unit analyzes the content of the user's questions and responds preferentially to questions with high relevance. The response generation unit can also respond next to questions with medium relevance. Furthermore, the response generation unit can also respond last to questions with low relevance. This allows the response generation unit to adjust the order of responses based on the relevance of the user's questions. For example, a question with high relevance is responded to preferentially. The response generation unit can also respond next to questions with medium relevance. Furthermore, the response generation unit can also respond last to questions with low relevance. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input the user's question data to the generation AI and cause the generation AI to adjust the order of responses.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit uses a learning database, for example, to refer to past learning data. For example, the learning unit analyzes past learning data and selects an optimal learning algorithm. The learning unit can also adjust the learning algorithm by referring to patterns of past learning data. Furthermore, the learning unit can evaluate the accuracy of past learning data and optimize the learning algorithm. Thereby, the learning unit can optimize the learning algorithm by referring to past learning data and improve the accuracy of learning. For example, the past learning data is analyzed and an optimal learning algorithm is selected. The learning algorithm can also be adjusted by referring to patterns of past learning data. Furthermore, the accuracy of past learning data can be evaluated and the learning algorithm can be optimized. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input past learning data to the generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0048] During learning, the learning unit can weight the learning data based on the time of submission of the deceased's photo and voice data. The learning unit uses a timestamp, for example, to acquire the time of submission of the deceased's photo and voice data. For example, the learning unit refers to the timestamp of the deceased's photo and voice data, and learns by weighting the most recently submitted data highly. The learning unit can also learn by weighting data related to a specific event highly. Furthermore, the learning unit can learn by weighting data from a time specified by the user highly. This allows the learning unit to weight the learning data based on the time of submission of the deceased's photo and voice data. For example, the learning unit learns by weighting the most recently submitted data highly. Furthermore, the learning unit can learn by weighting data related to a specific event highly. Furthermore, the learning unit can learn by weighting data from a time specified by the user highly. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input timestamp data of the deceased's photo and voice data to the generation AI, and cause the generation AI to execute weighting of the learning data.

[0049] The display unit can select an appropriate display method by referring to the user's past operation history at the time of display. The display unit uses an operation log database, for example, to refer to the user's past operation history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also select an optimal display layout from the user's past operation history. Furthermore, the display unit can adjust the display method by referring to the font and color used by the user in the past. Thereby, the display unit can provide an optimal display method by referring to the user's past operation history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also select an optimal display layout from the user's past operation history. Furthermore, the display method can be adjusted by referring to the font and color used by the user in the past. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's operation history data to the generation AI and cause the generation AI to select the display method.

[0050] The display unit can select an appropriate display method based on the device information of the user at the time of display. The display unit uses, for example, a device information database to acquire the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method optimized for a wide screen. This allows the display unit to provide an optimal display method based on the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method optimized for a wide screen. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information data of the user to the generation AI and cause the generation AI to select the display method.

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

[0052] The response generation unit may include a display unit that displays the generated response. For example, the response generation unit may include a text display unit for displaying the generated response in text. The response generation unit may also include a graphical display unit for displaying the generated response graphically. The response generation unit may also include an audio output unit for outputting the generated response as audio. This allows the user to visually and audibly confirm the response.

[0053] The capture unit can automatically evaluate the quality of the data when capturing the photographs and audio data of the deceased, and select appropriate data. For example, an image analysis algorithm can be used to evaluate the resolution of the image. Also, an audio analysis algorithm can be used to evaluate the noise level of the audio data. Furthermore, an image analysis algorithm can be used to evaluate the brightness and contrast of the photograph. This allows the quality of the data to be evaluated and the optimal data to be selected.

[0054] When the capture unit captures photos and audio data of the deceased, the capture unit can prioritize capture of highly relevant data based on the geographical location information of the user. For example, if the user is in the hometown of the deceased, photos related to that place can be prioritized. Also, if the user is traveling, data related to the travel destination can be prioritized. Furthermore, if the user is at home, photos taken at home can be prioritized. This makes it possible to capture highly relevant data based on the geographical location information of the user.

[0055] During the analysis, the analysis unit can improve the accuracy of the analysis based on the correlation between the deceased's photograph and audio data. For example, the timestamps of the deceased's photograph and audio data can be collated, and data with high correlation can be analyzed preferentially. The analysis unit can also compare the contents of the deceased's photograph and audio data, and analyze matching parts preferentially. Furthermore, the analysis unit can also refer to the metadata of the deceased's photograph and audio data to analyze data with high correlation. This allows the analysis accuracy to be improved by taking into account the correlation between the deceased's photograph and audio data.

[0056] When generating a response, the response generator can apply different response algorithms depending on the category of the user's question. For example, an emotion recognition algorithm can be applied to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. In this way, different response algorithms can be applied depending on the category of the user's question.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The capture unit captures the photograph and voice data of the deceased. For example, a photograph of the face of the deceased is read by a scanner and saved as digital data. The voice data of the deceased can also be recorded by a microphone and saved as digital data. The capture unit saves the data in a format such as JPEG, MP3, or WAV. Step 2: The analysis unit analyzes the data captured by the capture unit and learns the characteristics of the deceased. For example, it uses an image recognition algorithm to analyze the facial features of the deceased, and uses a voice analysis algorithm to analyze the tone of voice and speaking style of the deceased. This allows the analysis unit to extract the facial features of the deceased, analyze the facial contour and eye position, and learn the tone of voice and speaking pattern. Step 3: The response generation unit generates a response to the user's question based on the features learned by the analysis unit. For example, the response generation unit generates a response based on the features of the deceased to the user's question using natural language generation technology or template-based response generation technology. This allows the user to experience a conversation with the deceased.

[0059] (Example 2) The grief care system according to the embodiment of the present invention is a system that imports photos and audio data of the deceased and links with a generation AI, so that the deceased in the photo answers questions. This grief care system imports photos and audio data of the deceased, and the generation AI analyzes these data and learns the characteristics of the deceased. When a user asks a question, the generation AI generates a response based on the characteristics of the deceased and displays the deceased in the photo as if he or she is answering. This mechanism is effective as grief care to overcome the sadness of losing a family member. For example, photos and audio data of the deceased are imported into the system. At this time, a photo of the deceased's face and recorded data of the voice are uploaded. For example, a photo of the deceased smiling and audio data of the deceased speaking are input into the system. Next, the generation AI analyzes the imported data and learns the characteristics of the deceased. The generation AI analyzes the facial features, tone of voice, speaking style, etc. of the deceased, and models the characteristics of the deceased. For example, facial features are extracted from a photo of the deceased smiling, and the tone of voice and speaking style are learned from the audio data. When a user asks a question, the generation AI generates a response based on the characteristics of the deceased. For example, if you ask "How are you?", the generation AI will generate a response based on the characteristics of the deceased, saying "I'm fine," and the deceased will be displayed in the photo as if they are answering. The deceased's face and mouth will move as they do so. This mechanism is effective as grief care to help people overcome the sadness of losing a family member. In particular, it can provide peace of mind to elderly people who are mentally and physically limited through conversation with the deceased. For example, hearing the voice of the deceased can provide a sense of security, and seeing the face of the deceased can provide emotional support. In this way, the grief care system allows users to experience conversation with the deceased.

[0060] The grief care system according to the embodiment includes an import unit, an analysis unit, and a response generation unit. The import unit imports a photo and audio data of the deceased. The photo and audio data of the deceased include, but are not limited to, JPEG, MP3, and WAV formats. The import unit, for example, reads a facial photo of the deceased with a scanner and saves it as digital data. The import unit can also record audio data of the deceased with a microphone and save it as digital data. For example, the import unit scans a photo of the deceased's smile and saves it in JPEG format. The import unit can also record the voice of the deceased with a microphone and save it in MP3 format. The analysis unit analyzes the data imported by the import unit and learns the characteristics of the deceased. The analysis unit, for example, uses an image recognition algorithm to analyze the facial features of the deceased. The analysis unit can also use a voice analysis algorithm to analyze the tone of voice and speaking style of the deceased. For example, the analysis unit extracts feature points of the deceased's face and analyzes the facial contour and eye position. The analysis unit can also learn the tone of voice and speaking patterns of the deceased. The response generation unit generates a response to a question from the user based on the characteristics learned by the analysis unit. The response generation unit generates a response using, for example, natural language generation technology. The response generation unit can also generate a response using template-based response generation technology. For example, the response generation unit generates a response based on the characteristics of the deceased in response to a question from the user. For example, when the user asks, "How are you?", the response generation unit generates a response, "I'm fine." In this way, the grief care system according to the embodiment allows the user to experience a conversation with the deceased.

[0061] The capture unit captures the photograph and voice data of the deceased. The photograph and voice data of the deceased include, but are not limited to, JPEG, MP3, and WAV formats. For example, the capture unit reads the face photo of the deceased with a scanner and saves it as digital data. The capture unit can also record the voice data of the deceased with a microphone and save it as digital data. For example, the capture unit scans a photo of the smiling face of the deceased and saves it in JPEG format. The capture unit can also record the voice of the deceased with a microphone and save it in MP3 format. In order to capture these data efficiently, it is recommended that the capture unit use a high-resolution scanner and a high-sensitivity microphone. By using a scanner with a resolution of 600 dpi or more, it is possible to capture details clearly. By using a microphone with a noise canceling function, it is possible to obtain clear voice data. The capture unit also has a function of automatically classifying the captured data and saving it in a database. For example, photo data is tagged with dates, places, and people, and voice data is classified based on speakers and content. This allows the subsequent analysis and response generation parts to use the data efficiently. Furthermore, the import part provides an intuitive interface so that users can import data easily. For example, photos can be imported by drag-and-drop operation, and audio can be recorded with one click. This allows users to easily import data of the deceased into the system.

[0062] The analysis unit analyzes the data captured by the capture unit and learns the characteristics of the deceased. The analysis unit, for example, uses an image recognition algorithm to analyze the facial features of the deceased. The analysis unit can also analyze the tone of voice and speech of the deceased using a voice analysis algorithm. For example, the analysis unit extracts the facial features of the deceased and analyzes the facial outline and eye position. The analysis unit can also learn the tone of voice and speech pattern of the deceased. The analysis unit is equipped with a high-performance processor and GPU to execute these algorithms, and can quickly process large amounts of data. The image recognition algorithm uses deep learning technology and achieves highly accurate facial recognition by learning thousands of photo data. Specifically, a convolutional neural network (CNN) is used to extract facial features and reproduce the face of the deceased with high accuracy. The voice analysis algorithm uses a recurrent neural network (RNN) and a long short-term memory (LSTM) to learn the tone of voice and speech pattern of the deceased. This makes it possible to reproduce the voice of the deceased naturally. The analysis unit stores these learning results in a database and makes them available to the response generation unit. Furthermore, the analysis unit can continuously improve the analysis results by receiving feedback from the user. For example, every time a user adds a photo or voice data of the deceased, the analysis unit learns from the new data and reproduces the characteristics of the deceased more accurately. This allows the analysis unit to always provide highly accurate analysis based on the latest data, improving the performance of the entire system.

[0063] The response generation unit generates a response to the user's question based on the features learned by the analysis unit. The response generation unit generates a response using, for example, natural language generation technology. The response generation unit can also generate a response using template-based response generation technology. For example, the response generation unit generates a response based on the characteristics of the deceased in response to a user's question. For example, when the user asks, "How are you?", the response generation unit generates a response, "I'm fine." The response generation unit can generate more natural and diverse responses by combining these technologies. The natural language generation technology uses a generation AI to generate an appropriate response to the user's question. Specifically, a transformer model is used to analyze the user's question and generate a response based on the characteristics of the deceased. The template-based response generation technology selects an appropriate response to the user's question based on a response template prepared in advance. This allows the response generation unit to provide a quick and highly accurate response. Furthermore, the response generation unit can continuously improve the accuracy and naturalness of the response by receiving user feedback. For example, a function is provided for the user to evaluate the satisfaction with the response, and the response generation algorithm is adjusted based on the evaluation result. The response generator can also generate responses according to the user's emotions and circumstances. For example, if the user is sad, the response generator generates a response that offers comforting words. This allows the response generator to provide a more personalized response to the user, maximizing the effectiveness of the grief care system.

[0064] The analysis unit may include a learning unit that analyzes the facial features or tone of voice and speaking style of the deceased and learns the features of the deceased. The analysis unit may use, for example, a face recognition algorithm to analyze the facial features of the deceased. For example, the analysis unit may extract the facial feature points of the deceased and analyze the position of the eyes, the shape of the nose, the contour of the face, and the like. The analysis unit may also use a voice analysis algorithm to analyze the tone of the deceased's voice. For example, the analysis unit may analyze the pitch, intensity, and quality of the voice of the deceased. The analysis unit may also learn the speech pattern to analyze the speech style of the deceased. For example, the analysis unit may analyze the speaking speed, intonation, choice of words, and the like of the deceased. This allows the analysis unit to learn the features of the deceased in detail. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may use an AI model to extract the facial feature points of the deceased.

[0065] The response generation unit may include a display unit that displays the generated response. The response generation unit may include, for example, a text display unit to display the generated response in text. For example, the response generation unit displays the response to the user's question in text format. The response generation unit may also include a graphical display unit to graphically display the generated response. For example, the response generation unit displays the deceased's face moving and his mouth moving. The response generation unit may also include an audio output unit to output the generated response as audio. For example, the response generation unit plays back the response in the voice of the deceased. This allows the response generation unit to visually display the generated response. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit may use an AI model to display the generated response.

[0066] The capture unit can estimate the user's emotion and adjust the capture timing of the photo and voice data of the deceased person based on the estimated user's emotion. The capture unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the capture unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The capture unit can also use voice analysis technology to estimate the user's emotion. For example, the capture unit analyzes the tone and speed of the user's voice to estimate the emotion. The capture unit can also use biometric data to estimate the user's emotion. For example, the capture unit collects the user's heart rate and electrodermal activity with a sensor and estimates the emotion. This allows the capture unit to adjust the capture timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0067] The capture unit can automatically evaluate the quality of the data when capturing the photo and voice data of the deceased and select appropriate data. The capture unit, for example, uses an image analysis algorithm to evaluate the resolution of the image. For example, the capture unit evaluates the resolution of the photo of the deceased and preferentially captures a photo with a high resolution. The capture unit can also use a voice analysis algorithm to evaluate the noise level of the voice data. For example, the capture unit evaluates the noise level of the voice data of the deceased and selects a voice with less noise. The capture unit can also use an image analysis algorithm to evaluate the brightness and contrast of the photo. For example, the capture unit evaluates the brightness and contrast of the photo of the deceased and selects the optimal photo. This allows the capture unit to evaluate the quality of the data and select the optimal data. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the photo data of the deceased to the generation AI and cause the generation AI to execute the quality evaluation of the data.

[0068] When the capture unit captures the photo or voice data of the deceased, the capture unit can select an appropriate capture method by referring to the user's past capture history. The capture unit, for example, refers to the format of data previously captured by the user. For example, the capture unit refers to the format of data previously captured by the user and captures the data in the same format. The capture unit can also refer to the amount of data previously captured by the user. For example, the capture unit refers to the amount of data previously captured by the user and captures the same amount. The capture unit can also refer to the timing of data previously captured by the user. For example, the capture unit refers to the timing of data previously captured by the user and captures the data at the same timing. This allows the capture unit to provide an optimal capture method by referring to the user's past capture history. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the user's past capture history data to the generation AI and cause the generation AI to select the optimal capture method.

[0069] The capture unit can estimate the user's emotion and determine the priority of the data to be captured based on the estimated user's emotion. The capture unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the capture unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The capture unit can also use voice analysis technology to estimate the user's emotion. For example, the capture unit analyzes the tone and speed of the user's voice to estimate the emotion. The capture unit can also use biometric data to estimate the user's emotion. For example, the capture unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the capture unit to determine the priority of the data to be captured according to the user's emotion. For example, if the user is feeling sad, memorable photos are preferentially captured. Also, if the user is relaxed, data can be randomly captured. Furthermore, if the user is in a hurry, important data can be preferentially captured. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the capture unit may be performed, for example, using AI or may be performed without using AI. For example, the capture unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0070] When the capture unit captures the photographs and voice data of the deceased, the capture unit can preferentially capture data with high relevance based on the geographical location information of the user. The capture unit, for example, uses GPS data to acquire the geographical location information of the user. For example, the capture unit acquires the user's current location from the GPS data, and preferentially captures the photographs and voice data of the deceased related to that location. The capture unit can also use location estimation from an IP address to acquire the geographical location information of the user. For example, the capture unit estimates a location from the user's IP address, and preferentially captures data related to that location. This allows the capture unit to capture data with high relevance based on the geographical location information of the user. For example, when the user is in the hometown of the deceased, the capture unit preferentially captures photos related to that location. In addition, when the user is traveling, the capture unit can also preferentially capture data related to the travel destination. Furthermore, when the user is at home, the capture unit can also preferentially capture photos taken at home. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the import unit can input a user's geographic location data into the generation AI and cause the generation AI to select highly relevant data.

[0071] The capture unit can analyze the social media activity of the user and capture related data when capturing the photos and voice data of the deceased. The capture unit uses, for example, a social media API to analyze the social media activity of the user. For example, the capture unit acquires and captures photos posted by the user on social media. The capture unit can also acquire and capture photos tagged by the user on social media. Furthermore, the capture unit can acquire and capture voice data shared by the user on social media. This allows the capture unit to analyze the social media activity of the user and efficiently capture related data. For example, the capture unit captures photos posted by the user on social media. The capture unit can also capture photos tagged by the user on social media. Furthermore, the capture unit can also capture voice data shared by the user on social media. A part or all of the above-mentioned processing in the capture unit may be performed, for example, using AI, or may be performed without using AI. For example, the capture unit can input the social media activity data of the user to the generation AI and cause the generation AI to select related data.

[0072] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also use voice analysis technology to estimate the user's emotion. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also use biometric data to estimate the user's emotion. For example, the analysis unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the analysis unit to adjust the accuracy of the analysis according to the user's emotion. For example, if the user feels sad, the accuracy of the analysis can be increased to generate a more accurate response. Also, if the user is relaxed, the accuracy of the analysis can be kept normal. Furthermore, if the user is in a hurry, the accuracy of the analysis can be reduced to generate a quick response. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0073] During the analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationship between the photo and audio data of the deceased. The analysis unit, for example, compares the timestamps of the photo and audio data of the deceased. For example, the analysis unit compares the timestamps of the photo and audio data of the deceased and preferentially analyzes highly relevant data. The analysis unit can also compare the contents of the photo and audio data of the deceased and preferentially analyze the matching parts. Furthermore, the analysis unit can also refer to the metadata of the photo and audio data of the deceased and analyze the highly relevant data. This allows the analysis unit to improve the accuracy of the analysis by taking into account the interrelationship between the photo and audio data of the deceased. For example, the timestamps of the photo and audio data of the deceased are compared and highly relevant data is analyzed. Furthermore, the contents of the photo and audio data of the deceased are compared and preferentially analyze the matching parts. Furthermore, the metadata of the photo and audio data of the deceased can be referred to and highly relevant data is analyzed. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input correlation data between photographs and audio data of the deceased into the generation AI, and have the generation AI improve the accuracy of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the photo or voice data of the deceased during the analysis. The analysis unit applies a face recognition algorithm to, for example, a family photo of the deceased. For example, the analysis unit applies a face recognition algorithm to the family photo of the deceased to analyze the facial features. The analysis unit can also apply a voice recognition algorithm to the voice data of the deceased. For example, the analysis unit applies a voice recognition algorithm to the voice data of the deceased to analyze the voice features. Furthermore, the analysis unit can also apply an image analysis algorithm to a landscape photo of the deceased. For example, the analysis unit applies an image analysis algorithm to a landscape photo of the deceased to analyze the image features. This allows the analysis unit to apply different analysis algorithms depending on the category of the photo or voice data of the deceased. For example, the analysis unit applies a face recognition algorithm to the family photo of the deceased. Also, a voice recognition algorithm can be applied to the voice data of the deceased. Furthermore, an image analysis algorithm can be applied to the landscape photo of the deceased. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input category data of photographs and audio data of the deceased into the generation AI and have the generation AI apply an analysis algorithm.

[0075] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also use voice analysis technology to estimate the user's emotion. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also use biometric data to estimate the user's emotion. For example, the analysis unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the analysis unit to adjust the display method of the analysis result according to the user's emotion. For example, if the user feels sad, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0076] During the analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the deceased's photo and audio data. The analysis unit uses a timestamp, for example, to obtain the time of submission of the deceased's photo and audio data. For example, the analysis unit refers to the timestamp of the deceased's photo and audio data, and prioritizes analysis of the most recently submitted data. The analysis unit can also prioritize analysis of data related to a specific event. For example, the analysis unit can prioritize analysis of data related to the deceased's birthday or anniversary. Furthermore, the analysis unit can also prioritize analysis of data from a time specified by the user. This allows the analysis unit to determine the priority of the analysis based on the time of submission of the deceased's photo and audio data. For example, the most recently submitted data is analyzed. Furthermore, the analysis unit can prioritize analysis of data related to a specific event. Furthermore, the analysis unit can prioritize analysis of data from a time specified by the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input timestamp data of the deceased's photo and audio data to the generation AI, and cause the generation AI to execute the determination of the analysis priority.

[0077] The analysis unit can improve the accuracy of the analysis based on related literature of the deceased's photograph and voice data at the time of analysis. The analysis unit uses, for example, an academic paper database to refer to related literature of the deceased's photograph and voice data. For example, the analysis unit refers to related academic papers and improves the accuracy of the analysis. The analysis unit can also refer to related patent documents using a patent document database. For example, the analysis unit refers to related patent documents and improves the accuracy of the analysis. Furthermore, the analysis unit can also refer to related technical documents using a technical document database. For example, the analysis unit refers to related technical documents and improves the accuracy of the analysis. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature of the deceased's photograph and voice data. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related patent documents. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to related technical documents. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data for photographs and audio data of the deceased into the generation AI, allowing the generation AI to improve the accuracy of the analysis.

[0078] The response generation unit can estimate the user's emotion and adjust the way of expressing the response based on the estimated user's emotion. The response generation unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the response generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The response generation unit can also use voice analysis technology to estimate the user's emotion. For example, the response generation unit analyzes the tone and speed of the user's voice to estimate the emotion. The response generation unit can also use biometric data to estimate the user's emotion. For example, the response generation unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the response generation unit to adjust the way of expressing the response according to the user's emotion. For example, if the user feels sad, a response can be generated in gentle words. Also, if the user is relaxed, a response can be generated in normal words. Furthermore, if the user is in a hurry, a response can be generated in concise words. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0079] The response generation unit can adjust the level of detail of the response based on the characteristics of the deceased when generating the response. The response generation unit uses a characteristic database of the deceased, for example, to adjust the level of detail of the response based on the characteristics of the deceased. For example, if the deceased prefers detailed explanations, the response generation unit generates a detailed response. Also, if the deceased prefers concise explanations, the response generation unit can generate a concise response. Furthermore, if the deceased prefers humor, the response generation unit can generate a response including humor. This allows the response generation unit to adjust the level of detail of the response based on the characteristics of the deceased. For example, if the deceased prefers detailed explanations, the response generation unit generates a detailed response. Also, if the deceased prefers concise explanations, the response generation unit can generate a concise response. Furthermore, if the deceased prefers humor, the response generation unit can generate a response including humor. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input characteristic data of the deceased to the generation AI and cause the generation AI to adjust the level of detail of the response.

[0080] The response generation unit can apply different response algorithms depending on the category of the user's question when generating a response. The response generation unit uses a response algorithm database, for example, to apply different response algorithms depending on the category of the user's question. For example, the response generation unit applies an emotion recognition algorithm to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. This allows the response generation unit to apply different response algorithms depending on the category of the user's question. For example, an emotion recognition algorithm can be applied to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input the user's question data to the generation AI and cause the generation AI to apply the response algorithm.

[0081] The response generation unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The response generation unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the response generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The response generation unit can also use voice analysis technology to estimate the user's emotion. For example, the response generation unit analyzes the tone and speed of the user's voice to estimate the emotion. The response generation unit can also use biometric data to estimate the user's emotion. For example, the response generation unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the response generation unit to adjust the length of the response according to the user's emotion. For example, if the user feels sad, a longer response can be generated. Also, if the user is relaxed, a normal length response can be generated. Furthermore, if the user is in a hurry, a shorter response can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0082] The response generation unit can determine the priority of responses based on the time of submission of the user's question when generating a response. The response generation unit uses a timestamp, for example, to obtain the time of submission of the user's question. For example, the response generation unit refers to the timestamp of the user's question and responds preferentially to a recent question. The response generation unit can also respond preferentially to a question related to a specific event. Furthermore, the response generation unit can also respond preferentially to a question at a time specified by the user. This allows the response generation unit to determine the priority of responses based on the time of submission of the user's question. For example, the response generation unit can respond preferentially to a recent question. Furthermore, the response generation unit can also respond preferentially to a question at a time specified by the user. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input timestamp data of the user's question to the generation AI and cause the generation AI to execute the determination of the priority of responses.

[0083] The response generation unit can adjust the order of responses based on the relevance of the user's questions when generating responses. The response generation unit uses a relevance evaluation algorithm, for example, to evaluate the relevance of the user's questions. For example, the response generation unit analyzes the content of the user's questions and responds preferentially to questions with high relevance. The response generation unit can also respond next to questions with medium relevance. Furthermore, the response generation unit can also respond last to questions with low relevance. This allows the response generation unit to adjust the order of responses based on the relevance of the user's questions. For example, a question with high relevance is responded to preferentially. The response generation unit can also respond next to questions with medium relevance. Furthermore, the response generation unit can also respond last to questions with low relevance. A part or all of the above-mentioned processing in the response generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the response generation unit can input the user's question data to the generation AI and cause the generation AI to adjust the order of responses.

[0084] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. The learning unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The learning unit can also use voice analysis technology to estimate the user's emotion. For example, the learning unit analyzes the tone and speed of the user's voice to estimate the emotion. The learning unit can also use biometric data to estimate the user's emotion. For example, the learning unit collects the user's heart rate and electrodermal activity with a sensor and estimates the emotion. This allows the learning unit to select learning data according to the user's emotion. For example, if the user feels sad, emotional data is preferentially learned. Also, if the user is relaxed, normal data can be learned. Furthermore, if the user is in a hurry, concise data can be learned. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0085] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit uses a learning database, for example, to refer to past learning data. For example, the learning unit analyzes past learning data and selects an optimal learning algorithm. The learning unit can also adjust the learning algorithm by referring to patterns of past learning data. Furthermore, the learning unit can evaluate the accuracy of past learning data and optimize the learning algorithm. Thereby, the learning unit can optimize the learning algorithm by referring to past learning data and improve the accuracy of learning. For example, the past learning data is analyzed and an optimal learning algorithm is selected. The learning algorithm can also be adjusted by referring to patterns of past learning data. Furthermore, the accuracy of past learning data can be evaluated and the learning algorithm can be optimized. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input past learning data to the generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0086] The learning unit can estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. The learning unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The learning unit can also use voice analysis technology to estimate the user's emotion. For example, the learning unit analyzes the tone and speed of the user's voice to estimate the emotion. The learning unit can also use biometric data to estimate the user's emotion. For example, the learning unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the learning unit to adjust the frequency of learning according to the user's emotion. For example, if the user feels sad, the frequency of learning is increased. Also, if the user is relaxed, learning can be done at a normal frequency. Furthermore, if the user is in a hurry, the frequency of learning can be reduced. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0087] During learning, the learning unit can weight the learning data based on the time of submission of the deceased's photo and voice data. The learning unit uses a timestamp, for example, to acquire the time of submission of the deceased's photo and voice data. For example, the learning unit refers to the timestamp of the deceased's photo and voice data, and learns by weighting the most recently submitted data highly. The learning unit can also learn by weighting data related to a specific event highly. Furthermore, the learning unit can learn by weighting data from a time specified by the user highly. This allows the learning unit to weight the learning data based on the time of submission of the deceased's photo and voice data. For example, the learning unit learns by weighting the most recently submitted data highly. Furthermore, the learning unit can learn by weighting data related to a specific event highly. Furthermore, the learning unit can learn by weighting data from a time specified by the user highly. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input timestamp data of the deceased's photo and voice data to the generation AI, and cause the generation AI to execute weighting of the learning data.

[0088] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. The display unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit can also use voice analysis technology to estimate the user's emotion. For example, the display unit analyzes the tone and speed of the user's voice to estimate the emotion. The display unit can also use biometric data to estimate the user's emotion. For example, the display unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the display unit to adjust the display method according to the user's emotion. For example, if the user feels sad, a display method with calm colors can be provided. Also, if the user is relaxed, a display method with bright colors can be provided. Furthermore, if the user is in a hurry, a simple and highly visible display method can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0089] The display unit can select an appropriate display method by referring to the user's past operation history at the time of display. The display unit uses an operation log database, for example, to refer to the user's past operation history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also select an optimal display layout from the user's past operation history. Furthermore, the display unit can adjust the display method by referring to the font and color used by the user in the past. Thereby, the display unit can provide an optimal display method by referring to the user's past operation history. For example, the display unit preferentially provides a display method that the user has previously preferred. The display unit can also select an optimal display layout from the user's past operation history. Furthermore, the display method can be adjusted by referring to the font and color used by the user in the past. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the user's operation history data to the generation AI and cause the generation AI to select the display method.

[0090] The display unit can estimate the user's emotion and adjust the display operation procedure based on the estimated user's emotion. The display unit uses, for example, facial expression recognition technology to estimate the user's emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit can also use voice analysis technology to estimate the user's emotion. For example, the display unit analyzes the tone and speed of the user's voice to estimate the emotion. The display unit can also use biometric data to estimate the user's emotion. For example, the display unit collects the user's heart rate and electrodermal activity with a sensor to estimate the emotion. This allows the display unit to adjust the display operation procedure according to the user's emotion. For example, if the user feels sad, the operation procedure is simplified. Also, if the user is relaxed, detailed operation procedures can be provided. Furthermore, if the user is in a hurry, a procedure that allows quick operation can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0091] The display unit can select an appropriate display method based on the device information of the user at the time of display. The display unit uses, for example, a device information database to acquire the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method optimized for a wide screen. This allows the display unit to provide an optimal display method based on the device information of the user. For example, when the user is using a smartphone, the display unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can also provide a display method optimized for a wide screen. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the device information data of the user to the generation AI and cause the generation AI to select the display method.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The grief care system may further include an emotion estimation unit that estimates the emotion of the user and adjusts the timing of importing photos and audio data of the deceased based on the estimated emotion. For example, if the user is feeling sad, the system will prioritize importing photos of the deceased smiling and audio data of happy memories. Also, if the user is relaxed, the system can import data randomly. Furthermore, if the user is in a hurry, the system can prioritize importing important data. This allows optimal data import according to the user's emotion.

[0094] When analyzing the photos and audio data of the deceased, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user feels sad, the analysis accuracy can be increased to generate a more accurate response. If the user is relaxed, the analysis accuracy can be kept normal. Furthermore, if the user is in a hurry, the analysis accuracy can be lowered to generate a quick response. This enables flexible analysis according to the user's emotions.

[0095] The response generation unit may include a display unit that displays the generated response. For example, the response generation unit may include a text display unit for displaying the generated response in text. The response generation unit may also include a graphical display unit for displaying the generated response graphically. The response generation unit may also include an audio output unit for outputting the generated response as audio. This allows the user to visually and audibly confirm the response.

[0096] The import unit can estimate the user's emotion and determine the priority of data to be imported based on the estimated emotion. For example, if the user feels sad, memorable photos are imported with priority. If the user is relaxed, data can be imported randomly. Furthermore, if the user is in a hurry, important data can be imported with priority. This makes it possible to import optimal data according to the user's emotion.

[0097] The capture unit can automatically evaluate the quality of the data when capturing the photographs and audio data of the deceased, and select appropriate data. For example, an image analysis algorithm can be used to evaluate the resolution of the image. Also, an audio analysis algorithm can be used to evaluate the noise level of the audio data. Furthermore, an image analysis algorithm can be used to evaluate the brightness and contrast of the photograph. This allows the quality of the data to be evaluated and the optimal data to be selected.

[0098] When the capture unit captures photos and audio data of the deceased, the capture unit can prioritize capture of highly relevant data based on the geographical location information of the user. For example, if the user is in the hometown of the deceased, photos related to that place can be prioritized. Also, if the user is traveling, data related to the travel destination can be prioritized. Furthermore, if the user is at home, photos taken at home can be prioritized. This makes it possible to capture highly relevant data based on the geographical location information of the user.

[0099] During the analysis, the analysis unit can improve the accuracy of the analysis based on the correlation between the deceased's photograph and audio data. For example, the timestamps of the deceased's photograph and audio data can be collated, and data with high correlation can be analyzed preferentially. The analysis unit can also compare the contents of the deceased's photograph and audio data, and analyze matching parts preferentially. Furthermore, the analysis unit can also refer to the metadata of the deceased's photograph and audio data to analyze data with high correlation. This allows the analysis accuracy to be improved by taking into account the correlation between the deceased's photograph and audio data.

[0100] The response generation unit can estimate the user's emotion and adjust the way the response is expressed based on the estimated emotion. For example, if the user feels sad, a response can be generated in kind words. If the user is relaxed, a response can be generated in normal words. Furthermore, if the user is in a hurry, a response can be generated in simple words. This makes it possible to provide an optimal response according to the user's emotion.

[0101] When generating a response, the response generator can apply different response algorithms depending on the category of the user's question. For example, an emotion recognition algorithm can be applied to an emotional question. Also, an information retrieval algorithm can be applied to a fact-checking question. Furthermore, a personalized response algorithm can be applied to a personal question. In this way, different response algorithms can be applied depending on the category of the user's question.

[0102] The display unit can estimate the user's emotion and adjust the display method based on the estimated emotion. For example, if the user feels sad, a display method using calm colors can be provided. If the user feels relaxed, a display method using bright colors can be provided. Furthermore, if the user is in a hurry, a simple and highly visible display method can be provided. This makes it possible to provide an optimal display according to the user's emotion.

[0103] The process flow of the second embodiment will be briefly described below.

[0104] Step 1: The capture unit captures the photograph and voice data of the deceased. For example, a photograph of the face of the deceased is read by a scanner and saved as digital data. The voice data of the deceased can also be recorded by a microphone and saved as digital data. The capture unit saves the data in a format such as JPEG, MP3, or WAV. Step 2: The analysis unit analyzes the data captured by the capture unit and learns the characteristics of the deceased. For example, it uses an image recognition algorithm to analyze the facial features of the deceased, and uses a voice analysis algorithm to analyze the tone of voice and speaking style of the deceased. This allows the analysis unit to extract the facial features of the deceased, analyze the facial contour and eye position, and learn the tone of voice and speaking pattern. Step 3: The response generation unit generates a response to the user's question based on the features learned by the analysis unit. For example, the response generation unit generates a response based on the features of the deceased to the user's question using natural language generation technology or template-based response generation technology. This allows the user to experience a conversation with the deceased.

[0105] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0107] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and response generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the capture unit captures a photo or voice data of the deceased using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the characteristics of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the characteristics of the deceased to a user's question. The capture unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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 and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0115] 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 performed in a secure state.

[0116] Fig. 4 shows an example of 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and response generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capture unit captures photos and voice data of the deceased using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the characteristics of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the characteristics of the deceased in response to a user's question. The capture unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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 and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 voice indicating a user input for 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and response generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the capture unit captures a photo or voice data of the deceased using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the characteristics of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the characteristics of the deceased to a user's question. The capture unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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 and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] 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 a voice indicating a user input for 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] Each of the multiple elements including the above-mentioned capture unit, analysis unit, and response generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the capture unit captures a photo or voice data of the deceased using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the characteristics of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response based on the characteristics of the deceased in response to a user's question. The capture unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0159] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0161] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0163] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0166] 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 Universal Serial Bus (USB) 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.

[0167] In addition, 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 upon request from the data processing device 12.

[0168] 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.

[0169] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0170] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0172] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0174] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0176] (Appendix 1) An import unit that imports photographs or audio data of the deceased; An analysis unit that analyzes the data captured by the capture unit and learns characteristics of the deceased; A response generation unit that generates a response to a user's question based on the features learned by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is Equipped with a learning unit that analyzes the facial features, tone of voice, and speaking style of the deceased and learns the characteristics of the deceased 2. The system of claim 1, (Appendix 3) The response generation unit: Equipped with a display unit that displays the generated response 2. The system of claim 1, (Appendix 4) The capture unit is The system estimates the user's emotions and adjusts the timing of capturing photos and audio data of the deceased person based on the estimated user's emotions. 2. The system of claim 1, (Appendix 5) The capture unit is When capturing photos and audio data of the deceased, the quality of the data is automatically evaluated and the appropriate data is selected. 2. The system of claim 1, (Appendix 6) The capture unit is When importing photos and audio data of the deceased, the system selects the appropriate import method by referring to the user's past import history. 2. The system of claim 1, (Appendix 7) The capture unit is Infer user sentiment and prioritize data to be ingested based on the inferred user sentiment 2. The system of claim 1, (Appendix 8) The capture unit is When capturing photos and audio of the deceased, the system prioritizes the most relevant data based on the user's geographic location. 2. The system of claim 1, (Appendix 9) The capture unit is When capturing photos and audio of the deceased, the system analyzes the user's social media activity and captures related data. 2. The system of claim 1, (Appendix 10) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, the accuracy of the analysis is improved based on correlations between photos and audio data of the deceased. 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of the deceased's photo and audio data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the priority of analysis will be determined based on when the deceased's photographs and audio data were submitted. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the accuracy of the analysis is improved based on related literature on photos and audio data of the deceased. 2. The system of claim 1, (Appendix 16) The response generation unit: Inferring a user's emotions and adjusting the way responses are expressed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The response generation unit: When generating responses, adjust the level of detail in the response based on characteristics of the deceased 2. The system of claim 1, (Appendix 18) The response generation unit: When generating responses, different response algorithms are applied depending on the category of the user's question. 2. The system of claim 1, (Appendix 19) The response generation unit: Inferring a user's emotion and adjusting the length of a response based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The response generation unit: When generating responses, prioritize responses based on when the user submitted the question 2. The system of claim 1, (Appendix 21) The response generation unit: When generating responses, adjust the order of responses based on the relevance of the user's question 2. The system of claim 1, (Appendix 22) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The learning unit is During training, the learning algorithm is optimized by referencing past training data. 2. The system of claim 1, (Appendix 24) The learning unit is Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 2. The system of claim 1, (Appendix 25) The learning unit is During training, the training data is weighted based on when photos and audio of the deceased were submitted. 2. The system of claim 1, (Appendix 26) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The display unit is When displaying, the appropriate display method is selected by referring to the user's past operation history. 2. The system of claim 1, (Appendix 28) The display unit is Estimating a user's emotions and adjusting the displayed operation procedure based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The display unit is When displaying, select the appropriate display method based on the user's device information 2. The system of claim 1, [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An import unit that imports photographs or audio data of the deceased; An analysis unit that analyzes the data captured by the capture unit and learns characteristics of the deceased; A response generation unit that generates a response to a user's question based on the features learned by the analysis unit. A system characterized by:

2. The analysis unit is Equipped with a learning unit that analyzes the facial features, tone of voice, and speaking style of the deceased and learns the characteristics of the deceased 2. The system of claim 1 .

3. The response generation unit: Equipped with a display unit that displays the generated response 2. The system of claim 1 .

4. The capture unit is The system estimates the user's emotions and adjusts the timing of capturing photos and audio data of the deceased person based on the estimated user's emotions.

2. The system of claim 1 .

5. The capture unit is When capturing photos and audio data of the deceased, the quality of the data is automatically evaluated and the appropriate data is selected.

2. The system of claim 1 .

6. The capture unit is When importing photos and audio data of the deceased, the system selects the appropriate import method by referring to the user's past import history.

2. The system of claim 1 .

7. The capture unit is Infer user sentiment and prioritize data to be ingested based on the inferred user sentiment 2. The system of claim 1 .

8. The capture unit is When capturing photos and audio of the deceased, the system prioritizes the most relevant data based on the user's geographic location.

2. The system of claim 1 .

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