system

Abstract

The system according to the embodiment aims to analyze emotions through speech and provide information based on the emotions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an emotion analysis unit, an information search unit, and a provision unit. The reception unit receives voice. The analysis unit analyzes the voice received by the reception unit. The emotion analysis unit processes the emotion analyzed by the analysis unit. The information search unit searches for information based on the emotion analyzed by the emotion analysis unit. The provision unit provides the information searched for by the information search unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately analyzed emotions through speech and provided information based on those emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze emotions through speech and provide information based on the emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an emotion analysis unit, an information search unit, and a provision unit. The reception unit receives voice. The analysis unit analyzes the voice received by the reception unit. The emotion analysis unit processes the emotion analyzed by the analysis unit. The information search unit searches for information based on the emotion analyzed by the emotion analysis unit. The provision unit provides the information searched for by the information search unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze emotions through speech and provide information based on the emotions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cycling experience enhancement system according to an embodiment of the present invention combines a generative AI with voice technology to enhance cycling experiences. In this system, when a user expresses emotions or interests through speech, the generative AI analyzes the speech and provides relevant information. For example, if a user says, "This view is amazing," the generative AI analyzes the emotion and provides detailed information about the scenery, its history, and geographical features. Similarly, if a user says, "I'd like to know more about this place," the generative AI analyzes the request and searches for and provides relevant information. This system enriches and enhances the cycling experience. For example, when a user expresses emotions or interests through speech, they do not need to use specific keywords or phrases; they can express them in natural language. Examples include, "This view is amazing" and "I'd like to know more about this place." Next, the generative AI analyzes the input speech. The generative AI understands the content of the speech and analyzes emotions and requests. For example, it analyzes the word "amazing" for positive emotions and interprets the request, "I want to know more." Based on the analysis results, the generative AI provides relevant information, such as detailed information about the scenery, its history, and geographical features. The system also searches for and provides relevant information based on the user's requests. This can include information about specific locations and details about tourist attractions. This makes the cycling experience richer and more fulfilling. Users can obtain information by voice without using their hands, ensuring safety while cycling. Furthermore, the generative AI can analyze the user's emotions and requests to provide information tailored to the user's interests. For example, if a user is impressed by a particular view, detailed information about that view can be provided to further deepen the user's experience. This allows the cycling experience expansion system to expand the cycling experience by analyzing the user's voice and providing relevant information based on their emotions and requests.

[0029] The cycling experience expansion system according to the embodiment includes a reception unit, an analysis unit, an emotion analysis unit, an information search unit, and a provision unit. The reception unit receives a user's voice. The user's voice includes, but is not limited to, natural language speech and command speech. For example, the reception unit receives voice generated by the user while cycling using a microphone. The reception unit can also receive voice generated by the user via a smartphone or wearable device. The analysis unit analyzes the voice received by the reception unit. For example, the analysis unit converts the voice into text data using speech recognition technology. The analysis unit can also analyze the content of the voice using an emotion analysis algorithm to understand emotions and requests. For example, the analysis unit converts the content of the user's speech into text data using speech recognition technology and performs emotion analysis based on the text data. The emotion analysis unit analyzes emotions from the voice analyzed by the analysis unit. For example, the emotion analysis unit analyzes emotions from the voice using a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze emotions in speech with high accuracy. For example, the emotion analysis unit uses the generation AI to classify emotions in speech into positive, negative, neutral, etc. The information search unit searches for related information based on the emotions and requests analyzed by the emotion analysis unit. The information search unit searches for related information, for example, using the generation AI. The generation AI can search for information based on a large database and provide information according to a user's request. For example, the information search unit uses the generation AI to search for information about a specific location or details about tourist attractions. The provision unit provides the user with the information searched by the information search unit. The provision unit provides the user with, for example, audio information, text information, image information, etc. The provision unit can also display information on the user's device. For example, the provision unit displays information on a smartphone screen or reads out information aloud. As a result, the cycling experience enhancement system according to the embodiment can enhance the cycling experience by analyzing the user's speech and providing related information based on the user's emotions and requests.

[0030] The reception unit can receive the user's voice. For example, the reception unit receives the user's voice while cycling using a microphone. For example, the reception unit can also receive the user's voice through a smartphone or wearable device. By receiving the user's voice, the system can receive the user's input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when receiving the user's voice, the reception unit can convert the voice into text data using voice recognition technology.

[0031] The analysis unit can analyze the content of the voice and understand the emotions and requests. The analysis unit can convert the voice into text data, for example, using voice recognition technology. For example, the analysis unit can also analyze the content of the voice and understand the emotions and requests using an emotion analysis algorithm. In this way, by analyzing the content of the voice, it is possible to understand the emotions and requests of the user. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can convert the content of the user's utterance into text data using voice recognition technology, and perform emotion analysis based on the text data.

[0032] The emotion analysis unit can analyze emotions from the analyzed voice. The emotion analysis unit, for example, uses a generation AI to analyze the emotions of the voice. For example, the emotion analysis unit uses a generation AI to classify the emotions of the voice into positive, negative, neutral, etc. This makes it possible to understand the user's emotions by analyzing the emotions from the analyzed voice. Some or all of the above-mentioned processing in the emotion analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can analyze the emotions of the voice with high accuracy using a generation AI.

[0033] The information search unit can search for related information based on the analyzed emotions or requests. The information search unit, for example, uses a generation AI to search for related information. For example, the information search unit uses a generation AI to search for information about a specific location or details about tourist attractions. This makes it possible to provide information that meets the user's needs by searching for related information based on the analyzed emotions or requests. Some or all of the above-mentioned processing in the information search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the information search unit can use a generation AI to search for information based on a large database and provide information that meets the user's requests.

[0034] The providing unit can provide the searched information to the user. The providing unit can provide, for example, audio information, text information, image information, etc. to the user. For example, the providing unit can display information on a smartphone screen or read the information aloud. In this way, the searched information can be provided to the user, allowing the user to obtain the information they need. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can display information on the user's device.

[0035] The reception unit can analyze the user's past voice input history and select an appropriate reception method. The reception unit, for example, prioritizes reception of phrases that the user has frequently used in the past. For example, the reception unit can analyze the user's past voice input patterns and set an optimal reception timing. The reception unit can also prioritize reception of voice input methods (voice commands, natural language, etc.) that the user has used in the past. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past voice input history and select an optimal reception method.

[0036] The reception unit can filter the user's current environmental sounds to remove noise when receiving the voice. For example, the reception unit can filter wind noise during cycling to improve the clarity of the voice. For example, the reception unit can remove surrounding traffic noise to receive the user's voice clearly. The reception unit can also remove unnecessary noise while leaving a moderate amount of natural environmental sounds. In this way, the clarity of the voice can be improved by filtering the user's current environmental sounds to remove noise. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can filter the user's current environmental sounds to remove noise when receiving the voice.

[0037] When receiving voice, the reception unit can prioritize receiving highly relevant voice in consideration of the user's geographical location information. For example, if the user is in a tourist spot, the reception unit can prioritize receiving voice related to that location. For example, if the user is in a natural park, the reception unit can also prioritize receiving voice related to nature. Furthermore, if the user is in an urban area, the reception unit can also prioritize receiving voice related to the city. In this way, highly relevant voice can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can prioritize receiving highly relevant voice in consideration of the user's geographical location information.

[0038] When receiving the voice, the reception unit can analyze the user's social media activity and receive related voice. For example, the reception unit can preferentially receive voice related to places shared by the user on social media. For example, the reception unit can also preferentially receive voice related to topics in which the user has shown interest on social media. The reception unit can also preferentially receive voice related to topics in which the user's social media followers are interested. In this way, by analyzing the user's social media activity, related voice can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's social media activity and receive related voice.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio. For example, the analysis unit analyzes audio containing important information in detail. For example, the analysis unit can also analyze audio containing general information simply. The analysis unit can also dynamically adjust the level of detail of the analysis in response to a user request. This makes it possible to provide necessary information with an appropriate level of detail by adjusting the level of detail of the analysis based on the importance of the audio. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio. For example, the analysis unit applies a natural language processing algorithm to audio related to nature. For example, the analysis unit can apply an algorithm that references a history database to audio related to history. Furthermore, the analysis unit can apply an algorithm that references a geographic information system to audio related to geography. In this way, by applying different analysis algorithms depending on the category of audio, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can apply different analysis algorithms depending on the category of audio.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the audio. The analysis unit, for example, prioritizes analysis of the most recent audio. For example, the analysis unit can also prioritize analysis of audio from a time period specified by the user. The analysis unit can also prioritize analysis of audio related to a specific event. In this way, by determining the priority of analysis based on the time of submission of the audio, it is possible to prioritize analysis of the most recent information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the priority of analysis based on the time of submission of the audio.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. For example, the analysis unit prioritizes analysis of audio related to the user's current interests. For example, the analysis unit can prioritize analysis of audio with high relevance based on the user's past search history. The analysis unit can also prioritize analysis of audio with high relevance based on the user's social media activity. In this way, by adjusting the order of analysis based on the relevance of the audio, highly relevant information can be analyzed with high relevance. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the audio.

[0043] The emotion analysis unit can improve the accuracy of emotion analysis by taking into account the interrelationships between voices during emotion analysis. The emotion analysis unit, for example, analyzes the associations between multiple voices and tracks changes in emotions. For example, the emotion analysis unit can also check the consistency of emotions by referring to the user's past voice history. The emotion analysis unit can also improve the accuracy of emotion analysis by taking into account the context of the voice. In this way, the accuracy of emotion analysis can be improved by taking into account the interrelationships between voices. Some or all of the above-mentioned processing in the emotion analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can improve the accuracy of emotion analysis by taking into account the interrelationships between voices during emotion analysis.

[0044] The emotion analysis unit can perform emotion analysis while taking into account attribute information of the person submitting the audio. The emotion analysis unit can perform emotion analysis while taking into account, for example, the age and gender of the person submitting the audio. For example, the emotion analysis unit can also perform emotion analysis while referring to the submitter's past emotional history. The emotion analysis unit can also perform emotion analysis while taking into account the submitter's cultural background. In this way, more appropriate emotion analysis can be performed by taking into account the attribute information of the person submitting the audio. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the emotion analysis unit can perform emotion analysis while taking into account attribute information of the person submitting the audio.

[0045] The emotion analysis unit can perform emotion analysis taking into account the geographical distribution of audio. For example, the emotion analysis unit prioritizes analysis of audio related to a specific region. For example, the emotion analysis unit can also prioritize analysis of audio related to geographically nearby locations. The emotion analysis unit can also perform emotion analysis taking into account geographical trends. This allows for more appropriate emotion analysis by taking into account the geographical distribution of audio. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the emotion analysis unit can perform emotion analysis taking into account the geographical distribution of audio.

[0046] The emotion analysis unit can improve the accuracy of emotion analysis by referring to literature related to the audio during emotion analysis. The emotion analysis unit, for example, performs emotion analysis by referring to related academic papers. For example, the emotion analysis unit can also perform emotion analysis by referring to related news articles. The emotion analysis unit can also perform emotion analysis by referring to related books. In this way, by referring to literature related to the audio, the accuracy of emotion analysis can be improved. Some or all of the above-mentioned processing in the emotion analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can improve the accuracy of emotion analysis by referring to literature related to the audio during emotion analysis.

[0047] The information search unit can select the optimal information search method by analyzing the user's past search history when searching for information. The information search unit, for example, searches for related information based on keywords that the user has previously searched for. For example, the information search unit can select the optimal search algorithm from the user's past search history. The information search unit can also analyze the user's past search patterns and suggest the optimal information search method. In this way, the optimal information search method can be selected by analyzing the user's past search history. Some or all of the above-mentioned processing in the information search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the information search unit can select the optimal information search method by analyzing the user's past search history when searching for information.

[0048] The information search unit can customize the information search means based on the user's current interests during information search. For example, the information search unit prioritizes searching for information related to topics in which the user is currently interested. For example, the information search unit can customize search results based on the user's current interests. The information search unit can also adjust the search algorithm based on the user's current interests. This allows for customizing the information search means based on the user's current interests, thereby providing more appropriate information. Some or all of the above-described processing in the information search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the information search unit can customize the information search means based on the user's current interests during information search.

[0049] The information search unit can select an appropriate information search method by taking into account the user's geographical location information when searching for information. The information search unit, for example, prioritizes searching for information related to the user's current location. For example, the information search unit can also search for highly relevant information based on the user's geographical location information. The information search unit can also customize search results by taking into account the user's geographical location information. This makes it possible to prioritize searching for highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the information search unit may be performed using, or without, a generation AI, for example. For example, the information search unit can select an appropriate information search method by taking into account the user's geographical location information when searching for information.

[0050] The information search unit can analyze the user's social media activity and suggest information search methods during information searches. For example, the information search unit prioritizes searches for information related to topics shared by the user on social media. For example, the information search unit can search for highly relevant information based on the user's social media activity. The information search unit can also analyze the user's social media activity and suggest an optimal information search method. This makes it possible to prioritize the search for highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the information search unit may be performed using, or without, a generation AI, for example. For example, the information search unit can analyze the user's social media activity and suggest information search methods during information searches.

[0051] The providing unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information. The providing unit provides highly relevant information, for example, based on information previously acquired by the user. For example, the providing unit can select the optimal information provision method by referring to the user's past information acquisition history. The providing unit can also analyze the user's past information acquisition patterns and suggest the optimal information provision method. In this way, the optimal information provision method can be selected by referring to the user's past information acquisition history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information.

[0052] The providing unit can customize the means of providing information based on the user's current situation when providing information. The providing unit, for example, provides information related to a topic in which the user is currently interested. For example, the providing unit can customize the means of providing information based on the user's current situation. The providing unit can also adjust the method of providing information based on the user's current situation. In this way, by customizing the means of providing information based on the user's current situation, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means of providing information based on the user's current situation when providing information.

[0053] The providing unit can select the optimal information providing method by taking into account the user's geographical location information when providing information. The providing unit, for example, can prioritize providing information related to the user's current location. For example, the providing unit can also provide highly relevant information based on the user's geographical location information. The providing unit can also adjust the information providing method by taking into account the user's geographical location information. In this way, by taking into account the user's geographical location information, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the optimal information providing method by taking into account the user's geographical location information when providing information.

[0054] The providing unit can analyze the user's social media activity and suggest a means of providing the information when providing the information. For example, the providing unit can prioritize providing information related to topics shared by the user on social media. For example, the providing unit can also provide highly relevant information based on the user's social media activity. The providing unit can also analyze the user's social media activity and suggest an optimal information provision method. In this way, by analyzing the user's social media activity, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's social media activity and suggest a means of providing the information when providing the information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The cycling experience augmentation system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric information, such as the user's heart rate, respiratory rate, and body temperature, in real time and transmits it to the analysis unit. The analysis unit evaluates the user's health condition based on this biometric information and, if necessary, provides the user with a notification via the provision unit to encourage them to take a break. For example, if the user's heart rate becomes too high, the analysis unit may notify the user to take a break. Also, if the user's respiratory rate increases rapidly, the analysis unit may advise the user to slow down. This allows the system to monitor the user's health condition and encourage them to take a break or adjust their pace at the appropriate time, thereby providing a safe and comfortable cycling experience.

[0057] The cycling experience enhancement system may further include a weather information unit that provides real-time weather information based on the user's location information. The weather information unit obtains weather information for the user's current location and notifies the user through the providing unit. For example, if the user is heading to an area where rain is likely to fall, the weather information unit may send a notification urging the user to prepare rain gear. Also, if strong winds are predicted, the weather information unit may warn the user. Furthermore, the weather information unit may provide a weather forecast along the user's route, which can be used as reference information when planning a cycling trip. This allows the user to more easily adapt to changes in the weather and enjoy safe and comfortable cycling.

[0058] The cycling experience enhancement system may further include a performance evaluation unit that evaluates the user's cycling performance. The performance evaluation unit acquires data such as the user's speed, distance, and calories burned, and transmits it to the analysis unit. The analysis unit evaluates the user's performance based on this data and provides feedback through the provision unit. For example, if the user achieves a target distance, the analysis unit can send the user a notification of achievement. It can also display the user's progress toward the goal set by the user in real time. It can also compare the user's performance with past performance data and provide suggestions and advice on areas for improvement. This allows the user to understand their own performance and enjoy cycling while increasing their motivation.

[0059] The cycling experience augmentation system can further include a safety management unit that ensures the user's safety while cycling. The safety management unit predicts danger and provides warnings based on the user's location information and surrounding environmental information. For example, if the user approaches a busy road, the safety management unit can send a warning to warn the user. Also, if a sudden change in weather is predicted, the safety management unit can send a notification to the user urging them to evacuate. Furthermore, the safety management unit can automatically send a notification to emergency contacts if the user falls or if abnormal movement is detected. This ensures the user's safety and allows them to enjoy cycling with peace of mind.

[0060] The cycling experience enhancement system may further include an energy replenishment support unit that supports the user in replenishing energy while cycling. The energy replenishment support unit analyzes the user's calorie consumption and current energy level and suggests appropriate timing for replenishing energy. For example, if the user has been cycling for a long time, the energy replenishment support unit may send a notification encouraging the user to take a break and replenish energy. Also, if the user approaches a specific energy replenishment point, the energy replenishment support unit may suggest replenishment at that location. Furthermore, the energy replenishment support unit may suggest appropriate energy replenishment foods based on the user's dietary history and preferences. This allows the user to replenish energy at the appropriate time and enjoy cycling while maintaining sustainable performance.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives the user's voice. The user's voice may include, but is not limited to, natural language speech and command speech. For example, the reception unit receives the user's voice while cycling using a microphone. The reception unit may also receive the user's voice via a smartphone or wearable device. Step 2: The analysis unit analyzes the voice received by the reception unit. The analysis unit converts the voice into text data using, for example, voice recognition technology. The analysis unit can also analyze the content of the voice using an emotion analysis algorithm to understand emotions and requests. For example, the analysis unit converts the content of the user's utterance into text data using voice recognition technology, and performs emotion analysis based on the text data. Step 3: The emotion analysis unit analyzes emotions from the voice analyzed by the analysis unit. The emotion analysis unit analyzes the emotions of the voice using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the emotions of the voice with high accuracy. For example, the emotion analysis unit uses the generation AI to classify the emotions of the voice into positive, negative, neutral, etc. Step 4: The information search unit searches for relevant information based on the emotions and requests analyzed by the emotion analysis unit. The information search unit searches for relevant information using, for example, a generation AI. The generation AI can search for information based on a large database and provide information that meets the user's request. For example, the information search unit uses a generation AI to search for information about a specific location or details about tourist attractions. Step 5: The providing unit provides the information searched by the information searching unit to the user. The providing unit provides, for example, audio information, text information, image information, etc. to the user. The providing unit can also display the information on the user's device. For example, the providing unit displays the information on the screen of a smartphone or reads the information aloud.

[0063] (Example 2) A cycling experience enhancement system according to an embodiment of the present invention combines a generative AI with voice technology to enhance cycling experiences. In this system, when a user expresses emotions or interests through speech, the generative AI analyzes the speech and provides relevant information. For example, if a user says, "This view is amazing," the generative AI analyzes the emotion and provides detailed information about the scenery, its history, and geographical features. Similarly, if a user says, "I'd like to know more about this place," the generative AI analyzes the request and searches for and provides relevant information. This system enriches and enhances the cycling experience. For example, when a user expresses emotions or interests through speech, they do not need to use specific keywords or phrases; they can express them in natural language. Examples include, "This view is amazing" and "I'd like to know more about this place." Next, the generative AI analyzes the input speech. The generative AI understands the content of the speech and analyzes emotions and requests. For example, it analyzes the word "amazing" for positive emotions and interprets the request, "I want to know more." Based on the analysis results, the generative AI provides relevant information, such as detailed information about the scenery, its history, and geographical features. The system also searches for and provides relevant information based on the user's requests. This can include information about specific locations and details about tourist attractions. This makes the cycling experience richer and more fulfilling. Users can obtain information by voice without using their hands, ensuring safety while cycling. Furthermore, the generative AI can analyze the user's emotions and requests to provide information tailored to the user's interests. For example, if a user is impressed by a particular view, detailed information about that view can be provided to further deepen the user's experience. This allows the cycling experience expansion system to expand the cycling experience by analyzing the user's voice and providing relevant information based on their emotions and requests.

[0064] The cycling experience expansion system according to the embodiment includes a reception unit, an analysis unit, an emotion analysis unit, an information search unit, and a provision unit. The reception unit receives a user's voice. The user's voice includes, but is not limited to, natural language speech and command speech. For example, the reception unit receives voice generated by the user while cycling using a microphone. The reception unit can also receive voice generated by the user via a smartphone or wearable device. The analysis unit analyzes the voice received by the reception unit. For example, the analysis unit converts the voice into text data using speech recognition technology. The analysis unit can also analyze the content of the voice using an emotion analysis algorithm to understand emotions and requests. For example, the analysis unit converts the content of the user's speech into text data using speech recognition technology and performs emotion analysis based on the text data. The emotion analysis unit analyzes emotions from the voice analyzed by the analysis unit. For example, the emotion analysis unit analyzes emotions from the voice using a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze emotions in speech with high accuracy. For example, the emotion analysis unit uses the generation AI to classify emotions in speech into positive, negative, neutral, etc. The information search unit searches for related information based on the emotions and requests analyzed by the emotion analysis unit. The information search unit searches for related information, for example, using the generation AI. The generation AI can search for information based on a large database and provide information according to a user's request. For example, the information search unit uses the generation AI to search for information about a specific location or details about tourist attractions. The provision unit provides the user with the information searched by the information search unit. The provision unit provides the user with, for example, audio information, text information, image information, etc. The provision unit can also display information on the user's device. For example, the provision unit displays information on a smartphone screen or reads out information aloud. As a result, the cycling experience enhancement system according to the embodiment can enhance the cycling experience by analyzing the user's speech and providing related information based on the user's emotions and requests.

[0065] The reception unit can receive the user's voice. For example, the reception unit receives the user's voice while cycling using a microphone. For example, the reception unit can also receive the user's voice through a smartphone or wearable device. By receiving the user's voice, the system can receive the user's input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when receiving the user's voice, the reception unit can convert the voice into text data using voice recognition technology.

[0066] The analysis unit can analyze the content of the voice and understand the emotions and requests. The analysis unit can convert the voice into text data, for example, using voice recognition technology. For example, the analysis unit can also analyze the content of the voice and understand the emotions and requests using an emotion analysis algorithm. In this way, by analyzing the content of the voice, it is possible to understand the emotions and requests of the user. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can convert the content of the user's utterance into text data using voice recognition technology, and perform emotion analysis based on the text data.

[0067] The emotion analysis unit can analyze emotions from the analyzed voice. The emotion analysis unit, for example, uses a generation AI to analyze the emotions of the voice. For example, the emotion analysis unit uses a generation AI to classify the emotions of the voice into positive, negative, neutral, etc. This makes it possible to understand the user's emotions by analyzing the emotions from the analyzed voice. Some or all of the above-mentioned processing in the emotion analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can analyze the emotions of the voice with high accuracy using a generation AI.

[0068] The information search unit can search for related information based on the analyzed emotions or requests. The information search unit, for example, uses a generation AI to search for related information. For example, the information search unit uses a generation AI to search for information about a specific location or details about tourist attractions. This makes it possible to provide information that meets the user's needs by searching for related information based on the analyzed emotions or requests. Some or all of the above-mentioned processing in the information search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the information search unit can use a generation AI to search for information based on a large database and provide information that meets the user's requests.

[0069] The providing unit can provide the searched information to the user. The providing unit can provide, for example, audio information, text information, image information, etc. to the user. For example, the providing unit can display information on a smartphone screen or read the information aloud. In this way, the searched information can be provided to the user, allowing the user to obtain the information they need. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can display information on the user's device.

[0070] The reception unit can estimate the user's emotion and adjust the timing of receiving the voice based on the estimated user's emotion. For example, if the user is excited, the reception unit immediately receives the voice and starts analysis. For example, if the user is relaxed, the reception unit can receive the voice with a slight delay. Furthermore, if the user is tired, the reception unit can temporarily stop receiving the voice and encourage the user to take a break. This allows the voice to be received at a more appropriate time by adjusting the timing of receiving the voice based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can estimate the user's emotion and adjust the timing of receiving the voice based on the estimated emotion.

[0071] The reception unit can analyze the user's past voice input history and select an appropriate reception method. The reception unit, for example, prioritizes reception of phrases that the user has frequently used in the past. For example, the reception unit can analyze the user's past voice input patterns and set an optimal reception timing. The reception unit can also prioritize reception of voice input methods (voice commands, natural language, etc.) that the user has used in the past. In this way, the optimal reception method can be selected by analyzing the user's past voice input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past voice input history and select an optimal reception method.

[0072] The reception unit can filter the user's current environmental sounds to remove noise when receiving the voice. For example, the reception unit can filter wind noise during cycling to improve the clarity of the voice. For example, the reception unit can remove surrounding traffic noise to receive the user's voice clearly. The reception unit can also remove unnecessary noise while leaving a moderate amount of natural environmental sounds. In this way, the clarity of the voice can be improved by filtering the user's current environmental sounds to remove noise. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can filter the user's current environmental sounds to remove noise when receiving the voice.

[0073] The reception unit can estimate the user's emotion and determine the priority of speech to be received based on the estimated user's emotion. For example, if the user is excited, the reception unit can preferentially receive that speech. For example, if the user is relaxed, the reception unit can postpone receiving other speech. Furthermore, if the user is tired, the reception unit can preferentially receive only important speech. In this way, by determining the priority of speech based on the user's emotion, important speech can be preferentially received. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can estimate the user's emotion and determine the priority of speech based on the estimated emotion.

[0074] When receiving voice, the reception unit can prioritize receiving highly relevant voice in consideration of the user's geographical location information. For example, if the user is in a tourist spot, the reception unit can prioritize receiving voice related to that location. For example, if the user is in a natural park, the reception unit can also prioritize receiving voice related to nature. Furthermore, if the user is in an urban area, the reception unit can also prioritize receiving voice related to the city. In this way, highly relevant voice can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can prioritize receiving highly relevant voice in consideration of the user's geographical location information.

[0075] When receiving the voice, the reception unit can analyze the user's social media activity and receive related voice. For example, the reception unit can preferentially receive voice related to places shared by the user on social media. For example, the reception unit can also preferentially receive voice related to topics in which the user has shown interest on social media. The reception unit can also preferentially receive voice related to topics in which the user's social media followers are interested. In this way, by analyzing the user's social media activity, related voice can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's social media activity and receive related voice.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis expression method based on the estimated user's emotion. For example, if the user is excited, the analysis unit uses an expression method that emphasizes the emotion. For example, if the user is relaxed, the analysis unit can use a calm expression method. Furthermore, if the user is tired, the analysis unit can use a concise and easy-to-understand expression method. By adjusting the analysis expression method based on the user's emotion, a more appropriate expression method can be used. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can estimate the user's emotion and adjust the analysis expression method based on the estimated emotion.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio. For example, the analysis unit analyzes audio containing important information in detail. For example, the analysis unit can also analyze audio containing general information simply. The analysis unit can also dynamically adjust the level of detail of the analysis in response to a user request. This makes it possible to provide necessary information with an appropriate level of detail by adjusting the level of detail of the analysis based on the importance of the audio. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio. For example, the analysis unit applies a natural language processing algorithm to audio related to nature. For example, the analysis unit can apply an algorithm that references a history database to audio related to history. Furthermore, the analysis unit can apply an algorithm that references a geographic information system to audio related to geography. In this way, by applying different analysis algorithms depending on the category of audio, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can apply different analysis algorithms depending on the category of audio.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit performs a detailed analysis. For example, if the user is relaxed, the analysis unit can perform an appropriate length of analysis. Furthermore, if the user is tired, the analysis unit can perform a short, concise analysis. This allows for a more appropriate length of analysis by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the audio. The analysis unit, for example, prioritizes analysis of the most recent audio. For example, the analysis unit can also prioritize analysis of audio from a time period specified by the user. The analysis unit can also prioritize analysis of audio related to a specific event. In this way, by determining the priority of analysis based on the time of submission of the audio, it is possible to prioritize analysis of the most recent information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can determine the priority of analysis based on the time of submission of the audio.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. For example, the analysis unit prioritizes analysis of audio related to the user's current interests. For example, the analysis unit can prioritize analysis of audio with high relevance based on the user's past search history. The analysis unit can also prioritize analysis of audio with high relevance based on the user's social media activity. In this way, by adjusting the order of analysis based on the relevance of the audio, highly relevant information can be analyzed with high relevance. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can adjust the order of analysis based on the relevance of the audio.

[0082] The emotion analysis unit can estimate the user's emotion and adjust the emotion analysis criteria based on the estimated user's emotion. For example, if the user is excited, the emotion analysis unit can use criteria that emphasize positive emotions. For example, if the user is relaxed, the emotion analysis unit can use criteria that emphasize neutral emotions. Furthermore, if the user is tired, the emotion analysis unit can use criteria that reduce negative emotions. This allows for more appropriate emotion analysis by adjusting the emotion analysis criteria based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the emotion analysis unit can estimate the user's emotion and adjust the emotion analysis criteria based on the estimated emotion.

[0083] The emotion analysis unit can improve the accuracy of emotion analysis by taking into account the interrelationships between voices during emotion analysis. The emotion analysis unit, for example, analyzes the associations between multiple voices and tracks changes in emotions. For example, the emotion analysis unit can also check the consistency of emotions by referring to the user's past voice history. The emotion analysis unit can also improve the accuracy of emotion analysis by taking into account the context of the voice. In this way, the accuracy of emotion analysis can be improved by taking into account the interrelationships between voices. Some or all of the above-mentioned processing in the emotion analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can improve the accuracy of emotion analysis by taking into account the interrelationships between voices during emotion analysis.

[0084] The emotion analysis unit can perform emotion analysis while taking into account attribute information of the person submitting the audio. The emotion analysis unit can perform emotion analysis while taking into account, for example, the age and gender of the person submitting the audio. For example, the emotion analysis unit can also perform emotion analysis while referring to the submitter's past emotional history. The emotion analysis unit can also perform emotion analysis while taking into account the submitter's cultural background. In this way, more appropriate emotion analysis can be performed by taking into account the attribute information of the person submitting the audio. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the emotion analysis unit can perform emotion analysis while taking into account attribute information of the person submitting the audio.

[0085] The emotion analysis unit can estimate the user's emotion and adjust the order in which the emotion analysis results are displayed based on the estimated user emotion. The emotion analysis unit, for example, prioritizes displaying positive emotions. For example, the emotion analysis unit can also prioritize displaying results related to the user's current emotion. The emotion analysis unit can also prioritize displaying highly relevant results based on the user's past emotion history. This allows the results to be displayed in a more appropriate order by adjusting the order in which the emotion analysis results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the emotion analysis unit can estimate the user's emotion and adjust the order in which the emotion analysis results are displayed based on the estimated emotion.

[0086] The emotion analysis unit can perform emotion analysis taking into account the geographical distribution of audio. For example, the emotion analysis unit prioritizes analysis of audio related to a specific region. For example, the emotion analysis unit can also prioritize analysis of audio related to geographically nearby locations. The emotion analysis unit can also perform emotion analysis taking into account geographical trends. This allows for more appropriate emotion analysis by taking into account the geographical distribution of audio. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the emotion analysis unit can perform emotion analysis taking into account the geographical distribution of audio.

[0087] The emotion analysis unit can improve the accuracy of emotion analysis by referring to literature related to the audio during emotion analysis. The emotion analysis unit, for example, performs emotion analysis by referring to related academic papers. For example, the emotion analysis unit can also perform emotion analysis by referring to related news articles. The emotion analysis unit can also perform emotion analysis by referring to related books. In this way, by referring to literature related to the audio, the accuracy of emotion analysis can be improved. Some or all of the above-mentioned processing in the emotion analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the emotion analysis unit can improve the accuracy of emotion analysis by referring to literature related to the audio during emotion analysis.

[0088] The information search unit can estimate the user's emotions and adjust the information search method based on the estimated user's emotions. For example, if the user is excited, the information search unit can prioritize searching for detailed information. For example, if the user is relaxed, the information search unit can prioritize searching for general information. Furthermore, if the user is tired, the information search unit can prioritize searching for concise information. This allows more appropriate information to be searched by adjusting the information search method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information search unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the information search unit can estimate the user's emotions and adjust the information search method based on the estimated emotions.

[0089] The information search unit can select the optimal information search method by analyzing the user's past search history when searching for information. The information search unit, for example, searches for related information based on keywords that the user has previously searched for. For example, the information search unit can select the optimal search algorithm from the user's past search history. The information search unit can also analyze the user's past search patterns and suggest the optimal information search method. In this way, the optimal information search method can be selected by analyzing the user's past search history. Some or all of the above-mentioned processing in the information search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the information search unit can select the optimal information search method by analyzing the user's past search history when searching for information.

[0090] The information search unit can customize the information search means based on the user's current interests during information search. For example, the information search unit prioritizes searching for information related to topics in which the user is currently interested. For example, the information search unit can customize search results based on the user's current interests. The information search unit can also adjust the search algorithm based on the user's current interests. This allows for customizing the information search means based on the user's current interests, thereby providing more appropriate information. Some or all of the above-described processing in the information search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the information search unit can customize the information search means based on the user's current interests during information search.

[0091] The information search unit can estimate the user's emotions and determine information search priorities based on the estimated user emotions. For example, if the user is excited, the information search unit can prioritize searching for detailed information. For example, if the user is relaxed, the information search unit can prioritize searching for general information. Furthermore, if the user is tired, the information search unit can prioritize searching for concise information. This allows more important information to be searched preferentially by determining information search priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information search unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the information search unit can estimate the user's emotions and determine information search priorities based on the estimated emotions.

[0092] The information search unit can select an appropriate information search method by taking into account the user's geographical location information when searching for information. The information search unit, for example, prioritizes searching for information related to the user's current location. For example, the information search unit can also search for highly relevant information based on the user's geographical location information. The information search unit can also customize search results by taking into account the user's geographical location information. This makes it possible to prioritize searching for highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the information search unit may be performed using, or without, a generation AI, for example. For example, the information search unit can select an appropriate information search method by taking into account the user's geographical location information when searching for information.

[0093] The information search unit can analyze the user's social media activity and suggest information search methods during information searches. For example, the information search unit prioritizes searches for information related to topics shared by the user on social media. For example, the information search unit can search for highly relevant information based on the user's social media activity. The information search unit can also analyze the user's social media activity and suggest an optimal information search method. This makes it possible to prioritize the search for highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing in the information search unit may be performed using, or without, a generation AI, for example. For example, the information search unit can analyze the user's social media activity and suggest information search methods during information searches.

[0094] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. For example, if the user is excited, the providing unit can provide detailed information. For example, if the user is relaxed, the providing unit can provide general information. Furthermore, if the user is tired, the providing unit can provide concise information. This allows the method of providing information to be adjusted based on the user's emotion, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated emotion.

[0095] The providing unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information. The providing unit provides highly relevant information, for example, based on information previously acquired by the user. For example, the providing unit can select the optimal information provision method by referring to the user's past information acquisition history. The providing unit can also analyze the user's past information acquisition patterns and suggest the optimal information provision method. In this way, the optimal information provision method can be selected by referring to the user's past information acquisition history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information.

[0096] The providing unit can customize the means of providing information based on the user's current situation when providing information. The providing unit, for example, provides information related to a topic in which the user is currently interested. For example, the providing unit can customize the means of providing information based on the user's current situation. The providing unit can also adjust the method of providing information based on the user's current situation. In this way, by customizing the means of providing information based on the user's current situation, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the means of providing information based on the user's current situation when providing information.

[0097] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, if the user is excited, the providing unit can prioritize providing detailed information. For example, if the user is relaxed, the providing unit can prioritize providing general information. Furthermore, if the user is tired, the providing unit can prioritize providing concise information. This allows for determining the priority of information provision based on the user's emotions, thereby enabling more important information to be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated emotions.

[0098] The providing unit can select the optimal information providing method by taking into account the user's geographical location information when providing information. The providing unit, for example, can prioritize providing information related to the user's current location. For example, the providing unit can also provide highly relevant information based on the user's geographical location information. The providing unit can also adjust the information providing method by taking into account the user's geographical location information. In this way, by taking into account the user's geographical location information, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select the optimal information providing method by taking into account the user's geographical location information when providing information.

[0099] The providing unit can analyze the user's social media activity and suggest a means of providing the information when providing the information. For example, the providing unit can prioritize providing information related to topics shared by the user on social media. For example, the providing unit can also provide highly relevant information based on the user's social media activity. The providing unit can also analyze the user's social media activity and suggest an optimal information provision method. In this way, by analyzing the user's social media activity, highly relevant information can be prioritized. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's social media activity and suggest a means of providing the information when providing the information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, emotion analysis unit, information search unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's voice using the microphone 38B of the smart device 14. The analysis unit converts the voice into text data using the specific processing unit 290 of the data processing device 12, and the emotion analysis unit analyzes the emotion of the voice using a generation AI. The information search unit searches for related information using the specific processing unit 290 of the data processing device 12, and the provision unit provides the information to the user using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, emotion analysis unit, information search unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice using the microphone 238 of the smart glasses 214. The analysis unit converts the voice into text data using the specific processing unit 290 of the data processing device 12, and the emotion analysis unit analyzes the emotion of the voice using a generative AI. The information search unit searches for related information using the specific processing unit 290 of the data processing device 12, and the provision unit provides the information to the user using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, emotion analysis unit, information search unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives the user's voice using the microphone 238 of the headset-type terminal 314. The analysis unit converts the voice into text data using the specific processing unit 290 of the data processing device 12, and the emotion analysis unit analyzes the emotion of the voice using a generation AI. The information search unit searches for related information using the specific processing unit 290 of the data processing device 12, and the provision unit provides the information to the user using the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, emotion analysis unit, information search unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's voice using the microphone 238 of the robot 414. The analysis unit converts the voice into text data using the specific processing unit 290 of the data processing device 12, and the emotion analysis unit analyzes the emotion of the voice using a generative AI. The information search unit searches for related information using the specific processing unit 290 of the data processing device 12, and the provision unit provides the information to the user using the control unit 46A of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The cycling experience augmentation system may further include a health management unit that monitors the user's health condition. The health management unit acquires biometric information, such as the user's heart rate, respiratory rate, and body temperature, in real time and transmits it to the analysis unit. The analysis unit evaluates the user's health condition based on this biometric information and, if necessary, provides the user with a notification via the provision unit to encourage them to take a break. For example, if the user's heart rate becomes too high, the analysis unit may notify the user to take a break. Also, if the user's respiratory rate increases rapidly, the analysis unit may advise the user to slow down. This allows the system to monitor the user's health condition and encourage them to take a break or adjust their pace at the appropriate time, thereby providing a safe and comfortable cycling experience.

[0102] The cycling experience enhancement system may further include a music provider that provides music based on the user's preferences. The music provider selects appropriate music based on the user's past music playback history and current emotional state, and plays it to the user through the provider. For example, if the user is relaxed, calm music may be played. If the user is excited, energetic music may be played. Furthermore, if the user prefers a particular artist or genre, music tailored to that preference may be provided. This makes it possible to provide music tailored to the user's emotions and preferences, making the cycling experience even richer and more enjoyable.

[0103] The cycling experience enhancement system may further include a weather information unit that provides real-time weather information based on the user's location information. The weather information unit obtains weather information for the user's current location and notifies the user through the providing unit. For example, if the user is heading to an area where rain is likely to fall, the weather information unit may send a notification urging the user to prepare rain gear. Also, if strong winds are predicted, the weather information unit may warn the user. Furthermore, the weather information unit may provide a weather forecast along the user's route, which can be used as reference information when planning a cycling trip. This allows the user to more easily adapt to changes in the weather and enjoy safe and comfortable cycling.

[0104] The cycling experience enhancement system may further include a performance evaluation unit that evaluates the user's cycling performance. The performance evaluation unit acquires data such as the user's speed, distance, and calories burned, and transmits it to the analysis unit. The analysis unit evaluates the user's performance based on this data and provides feedback through the provision unit. For example, if the user achieves a target distance, the analysis unit can send the user a notification of achievement. It can also display the user's progress toward the goal set by the user in real time. It can also compare the user's performance with past performance data and provide suggestions and advice on areas for improvement. This allows the user to understand their own performance and enjoy cycling while increasing their motivation.

[0105] The cycling experience enhancement system can further include a route suggestion unit that estimates the user's emotions and suggests a cycling route based on the estimated emotions. The route suggestion unit suggests an optimal cycling route based on the user's current emotional state and past route history. For example, if the user wants to relax, a route rich in nature can be suggested. Also, if the user is looking for an exciting experience, a challenging route can be suggested. Furthermore, if the user wants to visit a specific tourist spot, a route that includes that spot can be suggested. In this way, a more fulfilling cycling experience can be provided by suggesting cycling routes that match the user's emotions and interests.

[0106] The cycling experience enhancement system may further include a photography support unit that supports the user in taking photos while cycling. The photography support unit suggests optimal photo spots and timing based on the user's location information and emotional state. For example, if the user is impressed by a beautiful view, the photography support unit may suggest taking a photo of that view. It may also notify the user when they arrive at a specific tourist spot when it's time to take a photo. Furthermore, the photography support unit may analyze the user's past photography history and suggest unphotographed spots. This allows the user to capture beautiful moments while cycling and preserve memories.

[0107] The cycling experience enhancement system may further include a communication support unit that supports communication while the user is cycling. The communication support unit analyzes the user's voice input and provides appropriate responses and information. For example, when the user is conversing with another cyclist, the communication support unit can analyze the content of the conversation and provide relevant information. When the user asks a question, the communication support unit can also provide an answer to the question. Furthermore, the communication support unit can analyze the user's emotional state and respond with an appropriate tone or expression. This allows the user to enjoy smooth communication while cycling.

[0108] The cycling experience augmentation system can further include a safety management unit that ensures the user's safety while cycling. The safety management unit predicts danger and provides warnings based on the user's location information and surrounding environmental information. For example, if the user approaches a busy road, the safety management unit can send a warning to warn the user. Also, if a sudden change in weather is predicted, the safety management unit can send a notification to the user urging them to evacuate. Furthermore, the safety management unit can automatically send a notification to emergency contacts if the user falls or if abnormal movement is detected. This ensures the user's safety and allows them to enjoy cycling with peace of mind.

[0109] The cycling experience enhancement system may further include an energy replenishment support unit that supports the user in replenishing energy while cycling. The energy replenishment support unit analyzes the user's calorie consumption and current energy level and suggests appropriate timing for replenishing energy. For example, if the user has been cycling for a long time, the energy replenishment support unit may send a notification encouraging the user to take a break and replenish energy. Also, if the user approaches a specific energy replenishment point, the energy replenishment support unit may suggest replenishment at that location. Furthermore, the energy replenishment support unit may suggest appropriate energy replenishment foods based on the user's dietary history and preferences. This allows the user to replenish energy at the appropriate time and enjoy cycling while maintaining sustainable performance.

[0110] The cycling experience enhancement system may further include an exercise suggestion unit that estimates the user's emotions and suggests exercises to perform while cycling based on the estimated emotions. The exercise suggestion unit analyzes the user's current emotional state and physical condition and suggests appropriate exercises. For example, if the user wants to relax, it may suggest light stretching or relaxation exercises. Alternatively, if the user wants an energetic experience, it may suggest interval training or high-intensity exercises. Furthermore, if the user has specific fitness goals, it may provide an exercise plan tailored to those goals. This allows for a more effective and enjoyable cycling experience by suggesting exercises tailored to the user's emotions and physical condition.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives the user's voice. The user's voice may include, but is not limited to, natural language speech and command speech. For example, the reception unit receives the user's voice while cycling using a microphone. The reception unit may also receive the user's voice via a smartphone or wearable device. Step 2: The analysis unit analyzes the voice received by the reception unit. The analysis unit converts the voice into text data using, for example, voice recognition technology. The analysis unit can also analyze the content of the voice using an emotion analysis algorithm to understand emotions and requests. For example, the analysis unit converts the content of the user's utterance into text data using voice recognition technology, and performs emotion analysis based on the text data. Step 3: The emotion analysis unit analyzes emotions from the voice analyzed by the analysis unit. The emotion analysis unit analyzes the emotions of the voice using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze the emotions of the voice with high accuracy. For example, the emotion analysis unit uses the generation AI to classify the emotions of the voice into positive, negative, neutral, etc. Step 4: The information search unit searches for relevant information based on the emotions and requests analyzed by the emotion analysis unit. The information search unit searches for relevant information using, for example, a generation AI. The generation AI can search for information based on a large database and provide information that meets the user's request. For example, the information search unit uses a generation AI to search for information about a specific location or details about tourist attractions. Step 5: The providing unit provides the information searched by the information searching unit to the user. The providing unit provides, for example, audio information, text information, image information, etc. to the user. The providing unit can also display the information on the user's device. For example, the providing unit displays the information on the screen of a smartphone or reads the information aloud.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice; an analysis unit that analyzes the voice received by the reception unit; an emotion analysis unit that processes the emotion analyzed by the analysis unit; an information search unit that searches for information based on the emotion analyzed by the emotion analysis unit; a providing unit that provides the information searched by the information search unit. A system characterized by:

2. The reception unit Accepts user voice 2. The system of claim 1.

3. The analysis unit Analyzes voice content to understand emotions and requests 2. The system of claim 1.

4. The emotion analysis unit Analyzing emotions from analyzed voice 2. The system of claim 1.

5. The information search unit Find relevant information based on analyzed sentiment or requests 2. The system of claim 1.

6. The providing unit Providing the searched information to the user 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of voice reception based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice input history and select the appropriate reception method 2. The system of claim 1.

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