System

Abstract

An object of the system according to the embodiment is to richly express details of memories and emotions.SOLUTION: A system includes an emotion analysis part, an event analysis part, and an expression generation part. The emotion analysis unit analyzes an emotion of a user. The event analysis unit analyzes preceding and following events on the basis of the emotion analyzed by the emotion analysis unit. An expression generator generates an emotional record based on the prior and subsequent events analyzed by the event analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to express the details and emotions of memories in a rich way.

[0005] The system according to the embodiment aims to express the details and emotions of memories in a rich manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, an event analysis unit, and an expression generation unit. The emotion analysis unit analyzes the emotion of a user. The event analysis unit analyzes preceding and following events based on the emotion analyzed by the emotion analysis unit. The expression generation unit generates an emotional record based on the preceding and following events analyzed by the event analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can express the details and emotions of memories in a rich manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The emotional record generation system according to an embodiment of the present invention is a system in which a generation AI analyzes a user's emotions and events and generates an emotional record, allowing the user to record and look back on the details and emotions of memories in a rich manner.

[0029] An emotional record generation system according to an embodiment includes an emotion analysis unit, an event analysis unit, and an expression generation unit. The emotion analysis unit analyzes a user's emotions. For example, the emotion analysis unit analyzes emotions from text entered by the user. The emotion analysis unit can also analyze the user's voice input and estimate emotions from the tone and rhythm of the voice. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze emotions using facial recognition technology. The event analysis unit analyzes preceding and following events based on the emotions analyzed by the emotion analysis unit. For example, the event analysis unit analyzes preceding and following events from text information provided by the user. The event analysis unit can also analyze photos and videos provided by the user and extract emotions and events from visual information. The event analysis unit can also obtain information from the user's calendar or schedule app and automatically complete the context of events. The expression generation unit generates an emotional record based on the preceding and following events analyzed by the event analysis unit. For example, the expression generation unit generates an emotional record using a generation AI based on the user's emotions and events. The expression generation unit can also use an emotion estimation function to analyze the user's emotions in real time and generate a record based on those emotions. The expression generation unit can also use a generation AI to generate rich expressions that include keywords and phrases provided by the user. This allows the emotional record generation system according to the embodiment to record and look back on the details and emotions of memories in a rich way. For example, by recording travel memories in detail, a user can vividly recall the emotions and events of the time when looking back on those memories later. Furthermore, richer emotional expressions further increase the value of memories.

[0030] The event analysis unit can analyze photos and videos provided by the user, extract emotions and events from the visual information, and incorporate them into the record. For example, if a user provides photos taken during a trip, the event analysis unit analyzes the content of the photos and extracts emotions and events. For example, if there are many smiling faces in the photos, the generation AI can record, "There were many smiling faces during the trip." The event analysis unit can also analyze videos provided by the user and extract emotions and events from the visual information. For example, if there are many smiling faces in the video, the generation AI can record, "There were many smiling faces during the trip." The event analysis unit can also develop algorithms to analyze photos and videos provided by the user and extract emotions and events from the visual information. For example, image recognition technology can be used to analyze the content of photos and videos and extract emotions and events. This allows emotions and events to be extracted from visual information and incorporated into the record, making it possible to record more detailed memories.

[0031] The event analysis unit automatically extracts emotions and events from a user's social media posts and message history, and the generation AI can generate records based on these. For example, the event analysis unit analyzes a user's social media posts and extracts emotions and events. For example, if a user posts "I had a great time with my friends today," the unit records this as "I had a great time with my friends today." The event analysis unit can also analyze a user's message history and extract emotions and events. For example, it can extract information such as "I had fun today" from the message history and record this as "I had fun today." The event analysis unit can also develop algorithms to analyze a user's social media posts and message history and extract emotions and events. For example, it can use text analysis technology to analyze the content of social media posts and message history and extract emotions and events. This allows for the extraction of emotions and events from social media posts and message history and the generation of records, making it possible to record more detailed memories.

[0032] The expression generation unit can support recording of emotions and events in different languages, making it possible to accommodate international users. For example, if a user inputs "I had a great time today" in English, the expression generation unit's generation AI analyzes the emotion and records it as "I had a great time today." The expression generation unit can also develop algorithms to support recording of emotions and events in different languages. For example, multilingual natural language processing technology can be used to generate records of emotions and events in different languages. The expression generation unit can also build a system to support recording of emotions and events in different languages. For example, a generation AI that supports multiple languages ​​can be used to generate records of emotions and events in different languages. This allows for international users to be accommodated by supporting recording of emotions and events in different languages.

[0033] The event analysis unit can acquire information from a user's calendar or schedule app and automatically complete the context of events. For example, the event analysis unit acquires information such as "meeting in the morning, lunch in the afternoon" from the user's calendar, and the generation AI records it as "after the morning meeting, I enjoyed lunch with a colleague in the afternoon." The event analysis unit can also acquire information from a user's schedule app and automatically complete the context of events. For example, the event analysis unit acquires information such as "meeting in the morning, lunch in the afternoon" from the schedule app and records it as "after the morning meeting, I enjoyed lunch with a colleague in the afternoon." The event analysis unit can also acquire information from a user's calendar or schedule app and develop an algorithm to automatically complete the context of events. For example, the schedule analysis algorithm can analyze information from a calendar or schedule app and complete the context of events. This allows for more detailed memory recording by acquiring information from a calendar or schedule app and automatically completing the context of events.

[0034] The event analysis unit can link the user's life log data to generate more detailed records. For example, the event analysis unit analyzes the user's step count data and reflects information such as "I walked a lot during my trip" in the record. For example, it might record "I walked a lot during my trip and enjoyed the beautiful scenery." The event analysis unit can also analyze the user's heart rate data and reflect changes in emotions in the record. For example, if the heart rate increases, it might record this as an "excited moment." The event analysis unit can also develop algorithms to analyze the user's life log data and reflect emotions and events in the record. For example, it might use a life log data analysis algorithm to analyze step count and heart rate data and reflect emotions and events in the record. By linking this with the life log data, it becomes possible to record more detailed memories.

[0035] The event analysis unit may support synchronization of previous and following events between different devices, allowing a user to provide information from multiple devices. For example, the event analysis unit may synchronize information provided by a user from a smartphone and a tablet, and record previous and following events. For example, "integrating photos taken on a smartphone with text entered on a tablet." The event analysis unit may also develop an algorithm for supporting synchronization of previous and following events between different devices. For example, an inter-device data synchronization algorithm may be used to integrate information provided from multiple devices. The event analysis unit may also build a system for supporting synchronization of previous and following events between different devices. For example, a cloud-based data synchronization system may be used to integrate information provided from multiple devices. This allows a user to provide information from multiple devices by supporting synchronization of previous and following events between different devices.

[0036] The expression generation unit can refer to the user's past records and generate consistent expressions. For example, the expression generation unit refers to the user's past records, and the generation AI generates consistent expressions. For example, a new travel record is generated in the same style based on past travel records. The expression generation unit can also refer to the user's past records and develop an algorithm for generating consistent expressions. For example, the past records are retrieved from a database, and their contents are analyzed to generate consistent expressions. The expression generation unit can also build a system for referencing the user's past records and generating consistent expressions. For example, the past records are retrieved from a database, and their contents are analyzed to generate consistent expressions. In this way, by referring to past records and generating consistent expressions, it is possible to create records with a more unified look.

[0037] The expression generation unit enables the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, if the user inputs "fun," the generation AI generates rich expressions based on the keywords. For example, it records "I had a great time and felt fulfilled." The expression generation unit can also develop algorithms that enable the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, it can use text analysis technology to analyze keywords and phrases provided by the user and generate rich expressions based on those keywords and phrases. The expression generation unit can also build a system that enables the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, it can use a text analysis algorithm to analyze keywords and phrases provided by the user and generate rich expressions based on those keywords and phrases. This allows for the generation of rich expressions based on keywords and phrases provided by the user, making it possible to record more detailed memories.

[0038] The expression generation unit can provide a function that can be customized to suit the user's preferences and style. For example, the expression generation unit allows the user to set a preferred expression style, and the generation AI generates a record that matches that style. For example, a concise record is generated for a user who likes simple expressions. The expression generation unit can also develop an algorithm to provide a function that can be customized to suit the user's preferences and style. For example, it generates customized expressions based on the user's settings and past preference data. The expression generation unit can also build a system to provide a function that can be customized to suit the user's preferences and style. For example, it generates customized expressions based on the user's settings and past preference data. This provides a function that can be customized to suit the user's preferences and style, making it possible to record more detailed memories.

[0039] The expression generation unit can support expressions of different genres (for example, poetry and stories) and enable users to generate records in a variety of formats. For example, if a user wants to generate a record in the form of poetry, the expression generation unit uses a generation AI to generate a record that matches the format of poetry. For example, memories of a fun trip can be recorded in the form of poetry. The expression generation unit can also develop algorithms to support expressions of different genres. For example, expressions corresponding to genres such as poetry, stories, and essays can be generated. The expression generation unit can also build a system to support expressions of different genres. For example, expressions corresponding to genres such as poetry, stories, and essays can be generated. In this way, by supporting expressions of different genres, users can generate records in a variety of formats.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] By analyzing the user's physical reactions without using emotion estimation functions, it is possible to reflect this in the records. For example, the user's heart rate and electrodermal activity can be analyzed and their state of stress or relaxation reflected in the records. If the user's heart rate increases, it can be recorded as "At this moment, my heart rate increased and I was excited." Also, if the electrodermal activity decreases, it can be recorded as "I was having a relaxed time." In this way, by analyzing the user's physical reactions and reflecting them in the records, it is possible to record more detailed memories.

[0042] It is possible to analyze a user's behavioral patterns and reflect them in records without using emotion estimation functions. For example, it is possible to analyze a user's movement history and reflect the places visited and the route traveled in the records. It is possible to identify places that a user frequently visits and record "this place is a particularly favorite place." It is also possible to analyze a user's movement speed and length of stay and record "I spent a long time at this place." In this way, by analyzing a user's behavioral patterns and reflecting them in the records, it is possible to record more detailed memories.

[0043] Even without using the emotion estimation function, more detailed records can be generated by linking with the user's life log data. For example, by analyzing the user's step count data, information such as "I walked a lot during my trip" can be reflected in the record. For example, it can record "I walked a lot during my trip and enjoyed the beautiful scenery." It can also analyze the user's heart rate data and reflect changes in emotions in the record. For example, if the heart rate increases, it can record this as a "moment of excitement." By linking with life log data, it becomes possible to record more detailed memories.

[0044] Without using emotion estimation functions, emotions and events can be automatically extracted from a user's SNS posts and message history, and the generation AI can generate records based on these. For example, a user's SNS posts can be analyzed to extract emotions and events. For example, if a user posts "I had a great time with friends today," the AI ​​can record this as "I had a great time with friends today." It can also analyze a user's message history to extract emotions and events. This allows for the extraction of emotions and events from SNS posts and message history and the generation of records, making it possible to record memories in more detail.

[0045] Without using emotion estimation functionality, it is possible to support synchronization of previous and next events between different devices of a user, allowing the user to provide information from multiple devices. For example, information provided by a user from a smartphone and a tablet can be synchronized to record previous and next events. For example, "integrating photos taken on a smartphone with text entered on a tablet." This allows the user to provide information from multiple devices by supporting synchronization of previous and next events between different devices.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the emotion analysis unit analyzes emotions from text entered by the user. The emotion analysis unit can also analyze the user's voice input and infer emotions from the tone and rhythm of the voice. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze emotions using facial recognition technology. Step 2: The event analysis unit analyzes the preceding and following events based on the emotions analyzed by the emotion analysis unit. For example, the event analysis unit analyzes the preceding and following events from text information provided by the user. The event analysis unit can also analyze photos and videos provided by the user to extract emotions and events from visual information. The event analysis unit can also obtain information from the user's calendar or schedule app to automatically complete the context of events. Step 3: The expression generation unit generates an emotional record based on the preceding and following events analyzed by the event analysis unit. For example, the expression generation unit generates an emotional record based on the user's emotions and the events using a generation AI. The expression generation unit can also use an emotion estimation function to analyze the user's emotions in real time and generate a record based on those emotions. The expression generation unit can also generate rich expressions based on keywords and phrases provided by the user using a generation AI.

[0048] (Example 2) The emotional record generation system according to an embodiment of the present invention is a system in which a generation AI analyzes a user's emotions and events and generates an emotional record, allowing the user to record and look back on the details and emotions of memories in a rich manner.

[0049] An emotional record generation system according to an embodiment includes an emotion analysis unit, an event analysis unit, and an expression generation unit. The emotion analysis unit analyzes a user's emotions. For example, the emotion analysis unit analyzes emotions from text entered by the user. The emotion analysis unit can also analyze the user's voice input and estimate emotions from the tone and rhythm of the voice. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze emotions using facial recognition technology. The event analysis unit analyzes preceding and following events based on the emotions analyzed by the emotion analysis unit. For example, the event analysis unit analyzes preceding and following events from text information provided by the user. The event analysis unit can also analyze photos and videos provided by the user and extract emotions and events from visual information. The event analysis unit can also obtain information from the user's calendar or schedule app and automatically complete the context of events. The expression generation unit generates an emotional record based on the preceding and following events analyzed by the event analysis unit. For example, the expression generation unit generates an emotional record using a generation AI based on the user's emotions and events. The expression generation unit can also use an emotion estimation function to analyze the user's emotions in real time and generate a record based on those emotions. The expression generation unit can also use a generation AI to generate rich expressions that include keywords and phrases provided by the user. This allows the emotional record generation system according to the embodiment to record and look back on the details and emotions of memories in a rich way. For example, by recording travel memories in detail, a user can vividly recall the emotions and events of the time when looking back on those memories later. Furthermore, richer emotional expressions further increase the value of memories.

[0050] The emotion analysis unit can analyze a user's voice input, infer emotions from the tone and rhythm of the voice, and reflect these in the recording. For example, if a user inputs, "I had a lot of fun today," the generative AI analyzes the tone and rhythm of the voice to infer emotions. For example, if the user speaks in a bright tone, the generative AI might record, "I had a lot of fun today. I really enjoyed spending time with my friends." The emotion analysis unit can also record the user's voice and infer emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The emotion analysis unit can also analyze the user's voice data in real time to infer emotions. For example, it can analyze the tone and rhythm of the voice in real time to infer emotions. This allows for more accurate emotional expression by inferring emotions from the tone and rhythm of the voice and reflecting them in the recording.

[0051] The event analysis unit can analyze photos and videos provided by the user, extract emotions and events from the visual information, and incorporate them into the record. For example, if a user provides photos taken during a trip, the event analysis unit analyzes the content of the photos and extracts emotions and events. For example, if there are many smiling faces in the photos, the generation AI can record, "There were many smiling faces during the trip." The event analysis unit can also analyze videos provided by the user and extract emotions and events from the visual information. For example, if there are many smiling faces in the video, the generation AI can record, "There were many smiling faces during the trip." The event analysis unit can also develop algorithms to analyze photos and videos provided by the user and extract emotions and events from the visual information. For example, image recognition technology can be used to analyze the content of photos and videos and extract emotions and events. This allows emotions and events to be extracted from visual information and incorporated into the record, making it possible to record more detailed memories.

[0052] The expression generation unit can use the emotion estimation function to analyze a user's emotions in real time and generate a record based on those emotions. For example, if a user inputs "I had fun today" in real time, the expression generation unit's generation AI can analyze the emotion on the spot and record it as "I had fun today. I really enjoyed spending time with my friends." The expression generation unit can also use the emotion estimation function to develop an algorithm for analyzing a user's emotions in real time and generating a record based on those emotions. For example, a machine learning model can be used to analyze emotions in real time and generate a record based on those emotions. The expression generation unit can also build a system for analyzing a user's emotions in real time and generating a record based on those emotions. For example, an emotion estimation algorithm can be used to analyze emotions in real time and generate a record based on those emotions. This allows for more accurate emotional expression by analyzing emotions in real time and generating a record based on those emotions.

[0053] The event analysis unit automatically extracts emotions and events from a user's social media posts and message history, and the generation AI can generate records based on these. For example, the event analysis unit analyzes a user's social media posts and extracts emotions and events. For example, if a user posts "I had a great time with my friends today," the unit records this as "I had a great time with my friends today." The event analysis unit can also analyze a user's message history and extract emotions and events. For example, it can extract information such as "I had fun today" from the message history and record this as "I had fun today." The event analysis unit can also develop algorithms to analyze a user's social media posts and message history and extract emotions and events. For example, it can use text analysis technology to analyze the content of social media posts and message history and extract emotions and events. This allows for the extraction of emotions and events from social media posts and message history and the generation of records, making it possible to record more detailed memories.

[0054] The expression generation unit can support recording of emotions and events in different languages, making it possible to accommodate international users. For example, if a user inputs "I had a great time today" in English, the expression generation unit's generation AI analyzes the emotion and records it as "I had a great time today." The expression generation unit can also develop algorithms to support recording of emotions and events in different languages. For example, multilingual natural language processing technology can be used to generate records of emotions and events in different languages. The expression generation unit can also build a system to support recording of emotions and events in different languages. For example, a generation AI that supports multiple languages ​​can be used to generate records of emotions and events in different languages. This allows for international users to be accommodated by supporting recording of emotions and events in different languages.

[0055] The expression generation unit can use the emotion estimation function to analyze the emotion a user feels when rereading a record and reedit the record based on that emotion. For example, when a user rereads a record, the expression generation unit uses the emotion estimation function to analyze the emotion and reedit the record. For example, if the emotion is strong, the expression generation unit can record, "It brings back happy memories." The expression generation unit can also use the emotion estimation function to analyze the emotion a user feels when rereading a record and develop an algorithm for reediting the record based on that emotion. For example, a machine learning model can be used to analyze the emotion and reedit the record based on that emotion. The expression generation unit can also build a system for analyzing the emotion a user feels when rereading a record and reediting the record based on that emotion. For example, an emotion estimation algorithm can be used to analyze the emotion and reedit the record based on that emotion. In this way, by analyzing the emotion when rereading a record and reediting the record based on that emotion, more accurate emotional expression is possible.

[0056] The event analysis unit can acquire information from a user's calendar or schedule app and automatically complete the context of events. For example, the event analysis unit acquires information such as "meeting in the morning, lunch in the afternoon" from the user's calendar, and the generation AI records it as "after the morning meeting, I enjoyed lunch with a colleague in the afternoon." The event analysis unit can also acquire information from a user's schedule app and automatically complete the context of events. For example, the event analysis unit acquires information such as "meeting in the morning, lunch in the afternoon" from the schedule app and records it as "after the morning meeting, I enjoyed lunch with a colleague in the afternoon." The event analysis unit can also acquire information from a user's calendar or schedule app and develop an algorithm to automatically complete the context of events. For example, the schedule analysis algorithm can analyze information from a calendar or schedule app and complete the context of events. This allows for more detailed memory recording by acquiring information from a calendar or schedule app and automatically completing the context of events.

[0057] The event analysis unit can use the emotion estimation function to analyze changes in the user's emotions before and after an event and reflect those changes in the record. For example, if the user inputs, "After visiting the museum, I relaxed at a cafe," the event analysis unit uses the emotion estimation function to analyze the changes in emotions and records the event as, "After being moved by the museum, I relaxed at a cafe." The event analysis unit can also use the emotion estimation function to develop an algorithm for analyzing changes in the user's emotions before and after an event. For example, a machine learning model can be used to analyze changes in emotions and reflect those changes in the record. The event analysis unit can also use the emotion estimation function to build a system for analyzing changes in the user's emotions before and after an event and reflecting those changes in the record. For example, an emotion estimation algorithm can be used to analyze changes in emotions and reflect those changes in the record. This allows for more detailed recording of memories by analyzing changes in emotions before and after an event and reflecting those changes in the record.

[0058] The event analysis unit can link the user's life log data to generate more detailed records. For example, the event analysis unit analyzes the user's step count data and reflects information such as "I walked a lot during my trip" in the record. For example, it might record "I walked a lot during my trip and enjoyed the beautiful scenery." The event analysis unit can also analyze the user's heart rate data and reflect changes in emotions in the record. For example, if the heart rate increases, it might record this as an "excited moment." The event analysis unit can also develop algorithms to analyze the user's life log data and reflect emotions and events in the record. For example, it might use a life log data analysis algorithm to analyze step count and heart rate data and reflect emotions and events in the record. By linking this with the life log data, it becomes possible to record more detailed memories.

[0059] The event analysis unit may support synchronization of previous and following events between different devices, allowing a user to provide information from multiple devices. For example, the event analysis unit may synchronize information provided by a user from a smartphone and a tablet, and record previous and following events. For example, "integrating photos taken on a smartphone with text entered on a tablet." The event analysis unit may also develop an algorithm for supporting synchronization of previous and following events between different devices. For example, an inter-device data synchronization algorithm may be used to integrate information provided from multiple devices. The event analysis unit may also build a system for supporting synchronization of previous and following events between different devices. For example, a cloud-based data synchronization system may be used to integrate information provided from multiple devices. This allows a user to provide information from multiple devices by supporting synchronization of previous and following events between different devices.

[0060] The event analysis unit can use the emotion estimation function to analyze the emotion a user feels when rereading a record and reedit the records of previous and following events based on that emotion. For example, when a user rereads a record, the event analysis unit can use the emotion estimation function to analyze the emotion and reedit the records of previous and following events. For example, if the user feels a strong positive emotion, the event analysis unit can emphasize happy events. The event analysis unit can also use the emotion estimation function to analyze the emotion a user feels when rereading a record and develop an algorithm for reediting the records of previous and following events based on that emotion. For example, a machine learning model can be used to analyze the emotion and reedit the records of previous and following events based on that emotion. The event analysis unit can also build a system for analyzing the emotion a user feels when rereading a record and reediting the records of previous and following events based on that emotion. For example, an emotion estimation algorithm can be used to analyze the emotion and reedit the records of previous and following events based on that emotion. In this way, by analyzing the emotion when rereading a record and reediting the records of previous and following events based on that emotion, more accurate emotional expression is possible.

[0061] The expression generation unit can refer to the user's past records and generate consistent expressions. For example, the expression generation unit refers to the user's past records, and the generation AI generates consistent expressions. For example, a new travel record is generated in the same style based on past travel records. The expression generation unit can also refer to the user's past records and develop an algorithm for generating consistent expressions. For example, the past records are retrieved from a database, and their contents are analyzed to generate consistent expressions. The expression generation unit can also build a system for referencing the user's past records and generating consistent expressions. For example, the past records are retrieved from a database, and their contents are analyzed to generate consistent expressions. In this way, by referring to past records and generating consistent expressions, it is possible to create records with a more unified look.

[0062] The expression generation unit enables the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, if the user inputs "fun," the generation AI generates rich expressions based on the keywords. For example, it records "I had a great time and felt fulfilled." The expression generation unit can also develop algorithms that enable the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, it can use text analysis technology to analyze keywords and phrases provided by the user and generate rich expressions based on those keywords and phrases. The expression generation unit can also build a system that enables the generation AI to generate rich expressions that include keywords and phrases provided by the user. For example, it can use a text analysis algorithm to analyze keywords and phrases provided by the user and generate rich expressions based on those keywords and phrases. This allows for the generation of rich expressions based on keywords and phrases provided by the user, making it possible to record more detailed memories.

[0063] The expression generation unit can use the emotion estimation function to generate expressions based on the user's emotions and provide appropriate expressions according to the intensity and type of emotion. For example, if the user inputs "I had fun," the expression generation unit can use the emotion estimation function to analyze the intensity of the emotion and record it as "The moment I truly enjoyed myself is coming back to me as vividly as if it were yesterday." The expression generation unit can also use the emotion estimation function to develop an algorithm for generating expressions based on the user's emotions. For example, a machine learning model can be used to analyze the intensity and type of emotion and generate appropriate expressions accordingly. The expression generation unit can also use the emotion estimation function to build a system for generating expressions based on the user's emotions. For example, an emotion estimation algorithm can be used to analyze the intensity and type of emotion and generate appropriate expressions accordingly. This allows for the emotion estimation function to generate expressions based on emotions, making it possible to record more detailed memories.

[0064] The expression generation unit can provide a function that can be customized to suit the user's preferences and style. For example, the expression generation unit allows the user to set a preferred expression style, and the generation AI generates a record that matches that style. For example, a concise record is generated for a user who likes simple expressions. The expression generation unit can also develop an algorithm to provide a function that can be customized to suit the user's preferences and style. For example, it generates customized expressions based on the user's settings and past preference data. The expression generation unit can also build a system to provide a function that can be customized to suit the user's preferences and style. For example, it generates customized expressions based on the user's settings and past preference data. This provides a function that can be customized to suit the user's preferences and style, making it possible to record more detailed memories.

[0065] The expression generation unit can support expressions of different genres (for example, poetry and stories) and enable users to generate records in a variety of formats. For example, if a user wants to generate a record in the form of poetry, the expression generation unit uses a generation AI to generate a record that matches the format of poetry. For example, memories of a fun trip can be recorded in the form of poetry. The expression generation unit can also develop algorithms to support expressions of different genres. For example, expressions corresponding to genres such as poetry, stories, and essays can be generated. The expression generation unit can also build a system to support expressions of different genres. For example, expressions corresponding to genres such as poetry, stories, and essays can be generated. In this way, by supporting expressions of different genres, users can generate records in a variety of formats.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] By analyzing the user's physical reactions without using emotion estimation functions, it is possible to reflect this in the records. For example, the user's heart rate and electrodermal activity can be analyzed and their state of stress or relaxation reflected in the records. If the user's heart rate increases, it can be recorded as "At this moment, my heart rate increased and I was excited." Also, if the electrodermal activity decreases, it can be recorded as "I was having a relaxed time." In this way, by analyzing the user's physical reactions and reflecting them in the records, it is possible to record more detailed memories.

[0068] The emotion estimation function can be used to analyze the emotions felt when a user rereads a record, and the record can be reedited based on those emotions. For example, when a user rereads a record, the emotion estimation function can be used to analyze the emotions and reedit the record. If the emotion is strong, it can be recorded as "bringing back happy memories." The emotion estimation function can also be used to analyze the emotions felt when a user rereads a record, and an algorithm can be developed to reedit the record based on those emotions. This allows for more accurate emotional expression by analyzing the emotions felt when rereading a record and reediting the record based on those emotions.

[0069] It is possible to analyze a user's behavioral patterns and reflect them in records without using emotion estimation functions. For example, it is possible to analyze a user's movement history and reflect the places visited and the route traveled in the records. It is possible to identify places that a user frequently visits and record "this place is a particularly favorite place." It is also possible to analyze a user's movement speed and length of stay and record "I spent a long time at this place." In this way, by analyzing a user's behavioral patterns and reflecting them in the records, it is possible to record more detailed memories.

[0070] Using the emotion estimation function, it is possible to generate expressions based on the user's emotions, providing appropriate expressions according to the intensity and type of emotion. For example, if a user inputs "I had fun," the generation AI will use the emotion estimation function to analyze the intensity of the emotion and record it as "The moment I truly enjoyed it comes back to me as vividly as if it were yesterday." The emotion estimation function can also be used to develop an algorithm for generating expressions based on the user's emotions. This will enable more detailed recording of memories by generating expressions based on emotions using the emotion estimation function.

[0071] Even without using the emotion estimation function, more detailed records can be generated by linking with the user's life log data. For example, by analyzing the user's step count data, information such as "I walked a lot during my trip" can be reflected in the record. For example, it can record "I walked a lot during my trip and enjoyed the beautiful scenery." It can also analyze the user's heart rate data and reflect changes in emotions in the record. For example, if the heart rate increases, it can record this as a "moment of excitement." By linking with life log data, it becomes possible to record more detailed memories.

[0072] The emotion estimation function can be used to suggest music based on the user's emotions and reflect that music in the recording. For example, if a user inputs "I had fun," the generative AI can use the emotion estimation function to analyze the emotion and suggest music that matches the happy mood. The emotion estimation function can also be used to develop an algorithm for suggesting music based on the user's emotions. This allows for more detailed recording of memories by suggesting music based on emotions using the emotion estimation function and reflecting that music in the recording.

[0073] Without using emotion estimation functions, emotions and events can be automatically extracted from a user's SNS posts and message history, and the generation AI can generate records based on these. For example, a user's SNS posts can be analyzed to extract emotions and events. For example, if a user posts "I had a great time with friends today," the AI ​​can record this as "I had a great time with friends today." It can also analyze a user's message history to extract emotions and events. This allows for the extraction of emotions and events from SNS posts and message history and the generation of records, making it possible to record memories in more detail.

[0074] The emotion estimation function can be used to add visual effects to recordings based on the user's emotions. For example, if a user types "I had fun," the generative AI can use the emotion estimation function to analyze their emotions and add visual effects to the recording that match their happy mood. The emotion estimation function can also be used to develop algorithms for adding visual effects based on the user's emotions. This allows for more detailed recording of memories by adding visual effects based on emotions to recordings using the emotion estimation function.

[0075] Without using emotion estimation functionality, it is possible to support synchronization of previous and next events between different devices of a user, allowing the user to provide information from multiple devices. For example, information provided by a user from a smartphone and a tablet can be synchronized to record previous and next events. For example, "integrating photos taken on a smartphone with text entered on a tablet." This allows the user to provide information from multiple devices by supporting synchronization of previous and next events between different devices.

[0076] The emotion estimation function can be used to provide advice based on the user's emotions and reflect that advice in the records. For example, if a user inputs "I'm tired today," the generative AI can use the emotion estimation function to analyze the emotion and provide the advice "Take a warm bath to relax." The emotion estimation function can also be used to develop an algorithm for providing advice based on the user's emotions. This allows for more detailed memory recording by providing advice based on emotions using the emotion estimation function and reflecting that advice in the records.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The emotion analysis unit analyzes the user's emotions. For example, the emotion analysis unit analyzes emotions from text entered by the user. The emotion analysis unit can also analyze the user's voice input and infer emotions from the tone and rhythm of the voice. The emotion analysis unit can also capture the user's facial expressions with a camera and analyze emotions using facial recognition technology. Step 2: The event analysis unit analyzes the preceding and following events based on the emotions analyzed by the emotion analysis unit. For example, the event analysis unit analyzes the preceding and following events from text information provided by the user. The event analysis unit can also analyze photos and videos provided by the user to extract emotions and events from visual information. The event analysis unit can also obtain information from the user's calendar or schedule app to automatically complete the context of events. Step 3: The expression generation unit generates an emotional record based on the preceding and following events analyzed by the event analysis unit. For example, the expression generation unit generates an emotional record based on the user's emotions and the events using a generation AI. The expression generation unit can also use an emotion estimation function to analyze the user's emotions in real time and generate a record based on those emotions. The expression generation unit can also generate rich expressions based on keywords and phrases provided by the user using a generation AI.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] 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.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] 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.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0138] 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.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 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 emotion analysis unit that analyzes the emotion of a user; an event analysis unit that analyzes preceding and following events based on the emotion analyzed by the emotion analysis unit; an expression generation unit that generates an emotional record based on the preceding and following events analyzed by the event analysis unit. A system characterized by:

2. The emotion analysis unit Analyzes the user's voice input, infers emotions from the tone and rhythm of the voice, and reflects them in the recording 2. The system of claim 1.

3. The event analysis unit Analyzes photos and videos provided by users, extracts emotions and events from visual information, and incorporates them into the records 2. The system of claim 1.

4. The expression generation unit Supports recording emotions and events in different languages, catering to international users 2. The system of claim 1.

5. The event analysis unit It automatically completes the context of events by pulling information from the user's calendar or scheduling app.

2. The system of claim 1.

6. The event analysis unit Analyzes the user's emotions when rereading the record and re-edits the record of previous and subsequent events based on those emotions.

2. The system of claim 1.

7. The expression generation unit Refer to the user's past records to generate a consistent representation 2. The system of claim 1.

8. The expression generation unit Analyze the emotions of users when rereading records and re-edit the expressions based on those emotions.

2. The system of claim 1.

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