System

Abstract

An object of a system according to an embodiment is to provide peripheral information according to a user's preference.SOLUTION: A system includes a reception unit, a route calculation unit, a collection unit, a learning unit, and a provision unit. The reception unit receives input of a departure place and a destination. The route calculator calculates a route from a current location to a destination based on the information received by the receiver. The collection unit collects surrounding information based on the route calculated by the route calculation unit. The learning unit learns the preference of the user based on the information collected by the collection unit. The providing unit provides recommendation information based on the information learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional navigation systems have not adequately provided surrounding information tailored to the user's preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide surrounding information that matches the preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a route calculation unit, a collection unit, a learning unit, and a providing unit. The reception unit receives input of a departure point and a destination. The route calculation unit calculates a route from a current location to the destination based on the information received by the reception unit. The collection unit collects surrounding information based on the route calculated by the route calculation unit. The learning unit learns user preferences based on the information collected by the collection unit. The providing unit provides recommended information based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide surrounding information that matches the preferences of the user. [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 navigation system according to an embodiment of the present invention provides recommended information on restaurants, shopping centers, and other locations tailored to a user's interests and preferences. The navigation system accepts input of a starting point and a destination, calculates a route from the user's current location to the destination, collects surrounding information, learns the user's preferences, and provides personalized information. For example, a user inputs a starting point and a destination. This information is input into the navigation system. The system then identifies the user's current location and calculates a route to the destination. At this time, information on nearby restaurants, shopping centers, and other locations is also collected. Furthermore, the system uses augmented reality glasses and a large-scale language model to learn the user's preferences and provide personalized information. For example, the system estimates the user's preferences based on data on restaurants and shopping centers the user has previously visited and provides optimal recommended information. This allows the user to obtain information tailored to the user's interests during navigation. This allows the navigation system to provide personalized information tailored to the user's interests and preferences. For example, if the user is interested in shopping centers, the system provides information on shopping centers along the route to the destination. Similarly, if the user is interested in restaurants, the system provides information on restaurants along the route to the destination. This allows the user to obtain information tailored to the user's interests during navigation.

[0029] A navigation system according to an embodiment includes a reception unit, a route calculation unit, a collection unit, a learning unit, and a providing unit. The reception unit receives input of a departure point and a destination. The departure point and destination may include, but are not limited to, an address, a place name, or GPS coordinates. The reception unit allows a user to input the departure point and destination using, for example, voice input or text input. The reception unit may also suggest an optimal input method based on the user's past input history. The route calculation unit calculates a route from a current location to a destination based on the information received by the reception unit. The route calculation unit calculates an optimal route based on criteria such as distance, time, and traffic conditions. The route calculation unit may also improve the accuracy of the route by referring to the user's past travel history. The collection unit collects surrounding information based on the route calculated by the route calculation unit. The surrounding information may include, for example, restaurants, shopping centers, and tourist attractions, but is not limited to these examples. The collection unit may also update the collected information, for example, taking real-time event information into account. The learning unit learns the user's preferences based on the information collected by the collection unit. The learning unit, for example, collects past behavioral data of the user and learns the user's preferences. The learning unit can also estimate the user's emotions and select learning data based on the estimated user emotions. The providing unit provides recommended information based on the information learned by the learning unit. The providing unit can provide information that matches the user's interests, for example. The providing unit can also estimate the user's emotions and adjust the display method of the recommended information based on the estimated user emotions. This allows the navigation system according to the embodiment to provide personalized information that matches the user's interests and preferences.

[0030] The learning unit can collect data on the user's past behavior and learn the user's preferences. Past behavior data includes, but is not limited to, visit history, purchase history, and the like. The learning unit can collect data on restaurants and shopping centers the user has visited in the past, for example, and learn the user's preferences. The learning unit can also analyze the user's past behavior patterns and optimize the learning algorithm. Furthermore, the learning unit can refer to the user's past behavior data and reflect feedback to improve the accuracy of learning. This makes it possible to provide more accurate recommended information based on the user's past behavior data.

[0031] The providing unit can provide recommended information to the user based on the information learned by the learning unit. Recommended information includes, but is not limited to, restaurant recommendations and tourist spot recommendations. The providing unit makes recommendations based on, for example, the user's preferences and current situation. The providing unit can also provide optimal information by referring to the user's past behavioral data. Furthermore, the providing unit can customize the information taking into account the user's current interests. This allows the user to receive optimal recommended information, thereby improving user satisfaction.

[0032] The collection unit can collect information about nearby restaurants or shopping centers. Information about nearby restaurants and shopping centers includes, but is not limited to, distance, ratings, types of cuisine, types of stores, and business hours. The collection unit can update the information about nearby restaurants and shopping centers based on, for example, real-time event information. The collection unit can also prioritize collection of highly relevant information based on the user's past visit history. Furthermore, the collection unit can customize the collection content taking into account the user's current interests. In this way, by collecting information about nearby restaurants, shopping centers, etc., it is possible to provide the user with useful information.

[0033] The route calculation unit can calculate a route from the current location to the destination. The route calculation unit calculates the optimal route based on criteria such as distance, time, and traffic conditions. The route calculation unit can propose the optimal route based on real-time traffic congestion information, for example. The route calculation unit can also propose the optimal route taking real-time weather information into consideration. Furthermore, the route calculation unit can improve the accuracy of the route by referring to the user's past travel history. In this way, the user's travel can be made more efficient by calculating the optimal route from the current location to the destination.

[0034] The providing unit can provide information that matches the user's interests. The user's interests include, but are not limited to, past behavioral data, survey results, and the like. The providing unit can provide optimal recommended information, for example, based on the user's past behavioral data. The providing unit can also customize information taking into account the user's current interests. Furthermore, the providing unit can improve the providing method by reflecting user feedback. This can improve user satisfaction by providing information that matches the user's interests.

[0035] The reception unit can analyze the user's past input history and suggest an input method. The past input history includes, but is not limited to, past search history, input patterns, and the like. For example, the reception unit can automatically display, as candidates, departure points and destinations that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest departure points and destinations to be used in a specific time period based on the user's past input history. This can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0036] The reception unit can provide input assistance based on the user's current situation and environment when inputting the departure point and destination. Examples of the current situation and environment include, but are not limited to, weather, traffic conditions, and ambient sounds. For example, if the user is using public transportation, the reception unit can automatically suggest the nearest station or bus stop as the departure point. Furthermore, if the user is driving a car, the reception unit can also set the departure point taking into account the location of a parking lot. Furthermore, if the user is walking, the reception unit can automatically acquire the user's current location and set it as the departure point. This allows for improved input accuracy by providing input assistance based on the user's current situation and environment.

[0037] The reception unit can provide various input methods, such as voice input and gesture input, when inputting the departure point and destination. The various input methods include, but are not limited to, voice input, gesture input, and touch input. For example, the reception unit can automatically set the departure point and destination when the user simply voice-inputs, for example, "from home to the station." The reception unit can also allow the user to easily set the departure point and destination by performing specific gestures on the smartphone screen. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to more intuitively set the departure point and destination. This provides various input methods, thereby improving user convenience.

[0038] When inputting a departure point and a destination, the reception unit can present highly relevant candidate locations in consideration of the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and Wi-Fi location information. For example, the reception unit can automatically display locations close to the user's current location as candidate locations. The reception unit can also suggest highly relevant candidate locations based on locations the user has visited in the past. Furthermore, the reception unit can also suggest intermediate locations between the user's current location and the destination as candidate locations. This can improve the efficiency of input by presenting highly relevant candidate locations in consideration of the user's geographical location information.

[0039] When the departure point and destination are input, the reception unit can analyze the user's social media activity and suggest related candidate locations. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related locations as candidate locations. Furthermore, the reception unit can suggest related locations based on the activity of the user's friends on social media. In this way, candidate locations that match the user's interests can be provided by suggesting related candidate locations based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting the departure point and destination. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can avoid input methods that the user has previously been dissatisfied with and suggest the most appropriate input method. In this way, customizing the input method by reflecting the user's past feedback can improve user convenience.

[0041] The route calculation unit can calculate a route taking into account traffic conditions and weather information. Traffic conditions include, but are not limited to, real-time traffic data and congestion information, for example. Weather information includes, but is not limited to, meteorological data and weather forecasts, for example. The route calculation unit proposes an optimal route based on, for example, real-time traffic congestion information. The route calculation unit can also propose an optimal route taking into account real-time weather information. Furthermore, the route calculation unit can propose an optimal route taking into account real-time public transportation operation status. In this way, by calculating an optimal route taking into account traffic conditions and weather information, the user's travel can be made more efficient.

[0042] When calculating a route, the route calculation unit can improve the accuracy of the route by referring to the user's past movement history. Past movement history includes, but is not limited to, for example, visit history and movement routes. The route calculation unit, for example, proposes an optimal route based on routes the user has used in the past. The route calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the route calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the accuracy of the route can be improved by referring to the user's past movement history.

[0043] When calculating a route, the route calculation unit can propose a route taking into account the user's current schedule. The current schedule includes, but is not limited to, calendar information, a timetable, and the like. For example, the route calculation unit can refer to the user's calendar information and propose an optimal route that matches the schedule. The route calculation unit can also propose a route with ample time based on the user's schedule. Furthermore, the route calculation unit can also propose the shortest route taking into account the user's schedule. This can improve the efficiency of the user's travel by proposing an optimal route taking into account the user's current schedule.

[0044] The route calculation unit can customize the route by taking into account the user's geographical preferences when calculating the route. Geographical preferences include, but are not limited to, for example, frequency of visits and ratings. For example, the route calculation unit prioritizes routes with scenic views that the user prefers. The route calculation unit can also propose routes that take into account places the user wants to avoid. Furthermore, the route calculation unit can propose routes that suit the user's preferences based on the user's past travel history. In this way, by customizing the route according to the user's geographical preferences, it is possible to provide an attractive route to the user.

[0045] When calculating a route, the route calculation unit can analyze the user's social media activity and suggest a related route. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the route calculation unit can suggest a route that passes through places where the user has checked in on social media. The route calculation unit can also analyze the content of the user's social media posts and suggest a related route. Furthermore, the route calculation unit can also suggest a related route by referring to the activity of the user's friends on social media. In this way, by suggesting a related route based on the user's social media activity, it is possible to provide a route that matches the user's interests.

[0046] The route calculation unit can improve the route calculation method by reflecting the user's past feedback when calculating a route. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the route calculation unit can preferentially suggest routes that the user has previously preferred. The route calculation unit can also improve the route calculation algorithm based on the user's past feedback. Furthermore, the route calculation unit can avoid routes that the user has previously dissatisfied with and suggest an optimal route. In this way, by improving the route calculation method by reflecting the user's past feedback, it is possible to provide an optimal route for the user.

[0047] When collecting surrounding information, the collection unit can update the collected content taking real-time event information into consideration. Examples of real-time event information include, but are not limited to, event calendars and social media posts. For example, the collection unit updates information about surrounding restaurants and shopping centers based on the real-time event information. The collection unit can also update the surrounding information taking real-time traffic information into consideration. Furthermore, the collection unit can update the surrounding information based on real-time weather information. By updating the collected content taking real-time event information into consideration, the latest information can be provided.

[0048] When collecting surrounding information, the collection unit can improve the accuracy of collection by referring to the user's past visit history. The past visit history includes, for example, visit frequency, stay time, etc., but is not limited to these examples. The collection unit, for example, collects surrounding information based on places the user has visited in the past. The collection unit can also prioritize collection of highly relevant information from the user's past visit history. Furthermore, the collection unit can analyze the user's past visit history and collect the most relevant information. In this way, by referring to the user's past visit history, the accuracy of collection can be improved.

[0049] When collecting peripheral information, the collection unit can customize the collection content by taking into account the user's current interests. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the collection unit prioritizes collecting information about restaurants in which the user is currently interested. The collection unit can also prioritize collecting information about shopping centers in which the user is currently interested. Furthermore, the collection unit can collect highly relevant information based on the user's current interests. In this way, by customizing the collection content by taking into account the user's current interests, it is possible to provide useful information to the user.

[0050] When collecting surrounding information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, the collection unit prioritizes collecting information about locations close to the user's current location. The collection unit can also collect highly relevant information based on the user's past visit history. Furthermore, the collection unit can prioritize collecting information about intermediate points between the user's current location and the destination. In this way, by prioritizing the collection of highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user.

[0051] When collecting peripheral information, the collection unit can analyze the user's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can also collect information about locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, by collecting related information based on the user's social media activities, it is possible to provide useful information to the user.

[0052] When collecting peripheral information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the collection unit preferentially collects information that the user has previously liked. The collection unit can also improve the collection algorithm based on the user's past feedback. Furthermore, the collection unit can avoid information that the user has previously dissatisfied with and collect optimal information. In this way, by customizing the collection method by reflecting the user's past feedback, it is possible to provide useful information to the user.

[0053] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, past training data and evaluation data. The learning unit, for example, selects an optimal learning algorithm based on the past learning data. The learning unit can also adjust parameters to improve learning accuracy from the past learning data. Furthermore, the learning unit can analyze the past learning data and identify areas for improvement in the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to the past learning data.

[0054] During learning, the learning unit can analyze the user's past behavioral data to improve the accuracy of the learning. Past behavioral data includes, but is not limited to, for example, visit history and purchase history. The learning unit improves the accuracy of the learning, for example, based on the user's past behavioral data. The learning unit can also analyze the user's past behavioral patterns and optimize the learning algorithm. Furthermore, the learning unit can refer to the user's past behavioral data and reflect feedback to improve the accuracy of the learning. In this way, the accuracy of the learning can be improved by analyzing the user's past behavioral data.

[0055] The learning unit can customize the learning content by taking into account the user's current interests during learning. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the learning unit prioritizes learning data related to topics in which the user is currently interested. The learning unit can also customize the learning content based on the user's current interests. Furthermore, the learning unit can select learning data by taking into account the user's current interests. In this way, customizing the learning content by taking into account the user's current interests can improve the accuracy of learning.

[0056] During learning, the learning unit can select learning data taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, the learning unit prioritizes learning data related to the user's current location. The learning unit can also learn highly relevant data based on the user's past visit history. Furthermore, the learning unit can also learn data related to locations between the user's current location and the destination. In this way, by selecting learning data taking into consideration the user's geographical location information, the accuracy of learning can be improved.

[0057] During learning, the learning unit can analyze the user's social media activity and incorporate related data into the learning. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The learning unit can, for example, learn data related to the locations where the user checked in on social media. The learning unit can also analyze the content of the user's social media posts and learn related data. Furthermore, the learning unit can learn related data by referring to the activities of the user's friends on social media. In this way, by incorporating related data based on the user's social media activity into the learning, the accuracy of the learning can be improved.

[0058] During learning, the learning unit can improve the learning algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments, for example. The learning unit improves the learning algorithm, for example, based on the user's past feedback. The learning unit can also prioritize learning data that the user has previously preferred. Furthermore, the learning unit can also improve the accuracy of learning by reflecting the user's past feedback. In this way, the accuracy of learning can be improved by improving the learning algorithm by reflecting the user's past feedback.

[0059] When providing recommended information, the providing unit can provide optimal information by referring to the user's past behavioral data. Past behavioral data includes, but is not limited to, for example, visit history and purchase history. The providing unit can provide optimal recommended information, for example, based on places the user has visited in the past. The providing unit can also prioritize providing highly relevant information from the user's past behavioral data. Furthermore, the providing unit can analyze the user's past behavioral data and provide the most relevant information. This makes it possible to provide optimal information based on the user's past behavioral data, thereby providing information that is useful to the user.

[0060] When providing recommended information, the providing unit can customize the information taking into account the user's current interests. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the providing unit can prioritize providing information related to topics in which the user is currently interested. The providing unit can also customize the recommended information based on the user's current interests. Furthermore, the providing unit can provide highly relevant information taking into account the user's current interests. In this way, by customizing the information taking into account the user's current interests, it is possible to provide useful information to the user.

[0061] The providing unit can improve the method of providing recommended information by reflecting user feedback. Feedback includes, but is not limited to, user ratings and comments, for example. The providing unit can improve the method of providing recommended information based on, for example, the user's past feedback. The providing unit can also provide information that the user has previously preferred preferentially. Furthermore, the providing unit can improve the accuracy of the recommended information by reflecting the user's past feedback. In this way, by improving the method of providing recommended information by reflecting the user's feedback, it is possible to provide information that is useful to the user.

[0062] When providing recommended information, the providing unit can provide optimal information by taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and Wi-Fi location information. For example, the providing unit can provide information about locations close to the user's current location with priority. The providing unit can also provide highly relevant information based on the user's past visit history. Furthermore, the providing unit can also provide information about intermediate points between the user's current location and the destination with priority. In this way, by providing optimal information by taking into consideration the user's geographical location information, it is possible to provide information that is useful to the user.

[0063] When providing recommended information, the providing unit can analyze the user's social media activity and provide related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can also provide, for example, information on locations where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, by providing related information based on the user's social media activity, it is possible to provide useful information to the user.

[0064] When providing recommended information, the providing unit can customize the content of the information to be provided by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and the like. The providing unit customizes the content of the recommended information, for example, based on the user's past feedback. The providing unit can also provide information that the user has previously preferred with priority. Furthermore, the providing unit can improve the accuracy of the recommended information by reflecting the user's past feedback. In this way, by customizing the content of the information to be provided by reflecting the user's past feedback, it is possible to provide information that is useful to the user.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The navigation system may further include a schedule management unit that manages the user's schedule. The schedule management unit refers to the user's calendar information and planner, and proposes the optimal route taking into consideration travel time and arrival time. For example, if the user is likely to be late for a meeting, the schedule management unit can prioritize proposing the shortest route. Also, if the user has ample time to travel, the schedule management unit can propose a scenic route or a route that passes through tourist spots. Furthermore, the schedule management unit can suggest rest stops and meal times based on the user's schedule. This allows the navigation system to provide personalized information tailored to the user's schedule.

[0067] The navigation system may further include a driving style learning unit that learns the user's driving style. The driving style learning unit analyzes the user's driving patterns, speed, frequency of braking, and the like to learn the user's driving style. For example, if the user frequently brakes suddenly, the driving style learning unit can provide safe driving advice. Also, if the user prefers to use expressways, the driving style learning unit can suggest routes that prioritize expressways. Furthermore, the driving style learning unit can suggest fuel-efficient driving methods based on the user's driving style. This allows the navigation system to provide personalized information tailored to the user's driving style.

[0068] The navigation system may further include a travel history analysis unit that analyzes the user's travel history. The travel history analysis unit collects the user's past travel data and analyzes the user's travel preferences and patterns. For example, it can suggest travel destinations that suit the user's preferences based on data on tourist spots and accommodations that the user has visited in the past. Also, if the user prefers to travel during a particular season, the travel history analysis unit can suggest travel destinations that suit that season. Furthermore, the travel history analysis unit can suggest optimal travel destinations depending on the user's travel purpose (e.g., business, leisure). This allows the navigation system to provide personalized information based on the user's travel history.

[0069] The navigation system may further include an exercise history analysis unit that analyzes the user's exercise history. The exercise history analysis unit collects the user's past exercise data and analyzes the user's exercise preferences and patterns. For example, if the user likes a particular sport, the exercise history analysis unit can suggest places where the user can enjoy that sport. Also, if the user exercises regularly, the exercise history analysis unit can suggest the timing and location of the exercise. Furthermore, the exercise history analysis unit can suggest the optimal exercise method depending on the user's exercise purpose (e.g., dieting, maintaining health). This allows the navigation system to provide personalized information based on the user's exercise history.

[0070] The navigation system may further include a sleep management unit that monitors the user's sleep patterns. The sleep management unit collects the user's sleep data and analyzes the user's sleep patterns. For example, if the user is not getting enough sleep, the sleep management unit may suggest places or activities to relax. Also, if the user tends to sleep better during certain times of the day, the sleep management unit may suggest a schedule tailored to those times. Furthermore, the sleep management unit may provide advice to improve the user's sleep quality. This allows the navigation system to provide personalized information based on the user's sleep patterns.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit accepts input of the departure point and destination. The departure point and destination can include addresses, place names, GPS coordinates, etc. The user can enter the departure point and destination using voice input or text input. The reception unit can also suggest the optimal input method based on the user's past input history. Step 2: The route calculation unit calculates the route from the current location to the destination based on the information received by the reception unit. The route calculation unit calculates the optimal route based on criteria such as distance, time, and traffic conditions. It can also improve the accuracy of the route by referring to the user's past travel history. Step 3: The collection unit collects surrounding information based on the route calculated by the route calculation unit. The surrounding information includes restaurants, shopping centers, tourist attractions, etc. The collection unit can also update the collected information taking into account real-time event information. Step 4: The learning unit learns the user's preferences based on the information collected by the collection unit. The learning unit collects the user's past behavioral data and learns the user's preferences. It can also estimate the user's emotions and select learning data based on the estimated user emotions. Step 5: The providing unit provides recommended information based on the information learned by the learning unit. The providing unit provides information that matches the user's interests. The providing unit can also estimate the user's emotions and adjust the display method of the recommended information based on the estimated user emotions.

[0073] (Example 2) A navigation system according to an embodiment of the present invention provides recommended information on restaurants, shopping centers, and other locations tailored to a user's interests and preferences. The navigation system accepts input of a starting point and a destination, calculates a route from the user's current location to the destination, collects surrounding information, learns the user's preferences, and provides personalized information. For example, a user inputs a starting point and a destination. This information is input into the navigation system. The system then identifies the user's current location and calculates a route to the destination. At this time, information on nearby restaurants, shopping centers, and other locations is also collected. Furthermore, the system uses augmented reality glasses and a large-scale language model to learn the user's preferences and provide personalized information. For example, the system estimates the user's preferences based on data on restaurants and shopping centers the user has previously visited and provides optimal recommended information. This allows the user to obtain information tailored to the user's interests during navigation. This allows the navigation system to provide personalized information tailored to the user's interests and preferences. For example, if the user is interested in shopping centers, the system provides information on shopping centers along the route to the destination. Similarly, if the user is interested in restaurants, the system provides information on restaurants along the route to the destination. This allows the user to obtain information tailored to the user's interests during navigation.

[0074] A navigation system according to an embodiment includes a reception unit, a route calculation unit, a collection unit, a learning unit, and a providing unit. The reception unit receives input of a departure point and a destination. The departure point and destination may include, but are not limited to, an address, a place name, or GPS coordinates. The reception unit allows a user to input the departure point and destination using, for example, voice input or text input. The reception unit may also suggest an optimal input method based on the user's past input history. The route calculation unit calculates a route from a current location to a destination based on the information received by the reception unit. The route calculation unit calculates an optimal route based on criteria such as distance, time, and traffic conditions. The route calculation unit may also improve the accuracy of the route by referring to the user's past travel history. The collection unit collects surrounding information based on the route calculated by the route calculation unit. The surrounding information may include, for example, restaurants, shopping centers, and tourist attractions, but is not limited to these examples. The collection unit may also update the collected information, for example, taking real-time event information into account. The learning unit learns the user's preferences based on the information collected by the collection unit. The learning unit, for example, collects past behavioral data of the user and learns the user's preferences. The learning unit can also estimate the user's emotions and select learning data based on the estimated user emotions. The providing unit provides recommended information based on the information learned by the learning unit. The providing unit can provide information that matches the user's interests, for example. The providing unit can also estimate the user's emotions and adjust the display method of the recommended information based on the estimated user emotions. This allows the navigation system according to the embodiment to provide personalized information that matches the user's interests and preferences.

[0075] The learning unit can collect data on the user's past behavior and learn the user's preferences. Past behavior data includes, but is not limited to, visit history, purchase history, and the like. The learning unit can collect data on restaurants and shopping centers the user has visited in the past, for example, and learn the user's preferences. The learning unit can also analyze the user's past behavior patterns and optimize the learning algorithm. Furthermore, the learning unit can refer to the user's past behavior data and reflect feedback to improve the accuracy of learning. This makes it possible to provide more accurate recommended information based on the user's past behavior data.

[0076] The providing unit can provide recommended information to the user based on the information learned by the learning unit. Recommended information includes, but is not limited to, restaurant recommendations and tourist spot recommendations. The providing unit makes recommendations based on, for example, the user's preferences and current situation. The providing unit can also provide optimal information by referring to the user's past behavioral data. Furthermore, the providing unit can customize the information taking into account the user's current interests. This allows the user to receive optimal recommended information, thereby improving user satisfaction.

[0077] The collection unit can collect information about nearby restaurants or shopping centers. Information about nearby restaurants and shopping centers includes, but is not limited to, distance, ratings, types of cuisine, types of stores, and business hours. The collection unit can update the information about nearby restaurants and shopping centers based on, for example, real-time event information. The collection unit can also prioritize collection of highly relevant information based on the user's past visit history. Furthermore, the collection unit can customize the collection content taking into account the user's current interests. In this way, by collecting information about nearby restaurants, shopping centers, etc., it is possible to provide the user with useful information.

[0078] The route calculation unit can calculate a route from the current location to the destination. The route calculation unit calculates the optimal route based on criteria such as distance, time, and traffic conditions. The route calculation unit can propose the optimal route based on real-time traffic congestion information, for example. The route calculation unit can also propose the optimal route taking real-time weather information into consideration. Furthermore, the route calculation unit can improve the accuracy of the route by referring to the user's past travel history. In this way, the user's travel can be made more efficient by calculating the optimal route from the current location to the destination.

[0079] The providing unit can provide information that matches the user's interests. The user's interests include, but are not limited to, past behavioral data, survey results, and the like. The providing unit can provide optimal recommended information, for example, based on the user's past behavioral data. The providing unit can also customize information taking into account the user's current interests. Furthermore, the providing unit can improve the providing method by reflecting user feedback. This can improve user satisfaction by providing information that matches the user's interests.

[0080] The reception unit can estimate the user's emotions and adjust the input method for the departure point and destination based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the departure point and destination. This can reduce the user's stress by adjusting the input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The reception unit can analyze the user's past input history and suggest an input method. The past input history includes, but is not limited to, past search history, input patterns, and the like. For example, the reception unit can automatically display, as candidates, departure points and destinations that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest departure points and destinations to be used in a specific time period based on the user's past input history. This can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0082] The reception unit can provide input assistance based on the user's current situation and environment when inputting the departure point and destination. Examples of the current situation and environment include, but are not limited to, weather, traffic conditions, and ambient sounds. For example, if the user is using public transportation, the reception unit can automatically suggest the nearest station or bus stop as the departure point. Furthermore, if the user is driving a car, the reception unit can also set the departure point taking into account the location of a parking lot. Furthermore, if the user is walking, the reception unit can automatically acquire the user's current location and set it as the departure point. This allows for improved input accuracy by providing input assistance based on the user's current situation and environment.

[0083] The reception unit can provide various input methods, such as voice input and gesture input, when inputting the departure point and destination. The various input methods include, but are not limited to, voice input, gesture input, and touch input. For example, the reception unit can automatically set the departure point and destination when the user simply voice-inputs, for example, "from home to the station." The reception unit can also allow the user to easily set the departure point and destination by performing specific gestures on the smartphone screen. Furthermore, the reception unit can also allow the user to combine voice input and gesture input to more intuitively set the departure point and destination. This provides various input methods, thereby improving user convenience.

[0084] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is stressed, the reception unit can prioritize input of important information and postpone input of detailed information. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed information and provide customizable options. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input only the most important information and quickly start navigation. Thus, by prioritizing input content according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When inputting a departure point and a destination, the reception unit can present highly relevant candidate locations in consideration of the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and Wi-Fi location information. For example, the reception unit can automatically display locations close to the user's current location as candidate locations. The reception unit can also suggest highly relevant candidate locations based on locations the user has visited in the past. Furthermore, the reception unit can also suggest intermediate locations between the user's current location and the destination as candidate locations. This can improve the efficiency of input by presenting highly relevant candidate locations in consideration of the user's geographical location information.

[0086] When the departure point and destination are input, the reception unit can analyze the user's social media activity and suggest related candidate locations. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related locations as candidate locations. Furthermore, the reception unit can suggest related locations based on the activity of the user's friends on social media. In this way, candidate locations that match the user's interests can be provided by suggesting related candidate locations based on the user's social media activity.

[0087] The reception unit can customize the input method by reflecting the user's past feedback when inputting the departure point and destination. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can avoid input methods that the user has previously been dissatisfied with and suggest the most appropriate input method. In this way, customizing the input method by reflecting the user's past feedback can improve user convenience.

[0088] The route calculation unit can estimate the user's emotions and adjust the route calculation criteria based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is relaxed, the route calculation unit can prioritize a scenic route. Furthermore, if the user is in a hurry, the route calculation unit can prioritize the shortest route. Furthermore, if the user is stressed, the route calculation unit can prioritize a route that avoids crowded areas. By adjusting the route calculation criteria according to the user's emotions, the optimal route can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The route calculation unit can calculate a route taking into account traffic conditions and weather information. Traffic conditions include, but are not limited to, real-time traffic data and congestion information, for example. Weather information includes, but is not limited to, meteorological data and weather forecasts, for example. The route calculation unit proposes an optimal route based on, for example, real-time traffic congestion information. The route calculation unit can also propose an optimal route taking into account real-time weather information. Furthermore, the route calculation unit can propose an optimal route taking into account real-time public transportation operation status. In this way, by calculating an optimal route taking into account traffic conditions and weather information, the user's travel can be made more efficient.

[0090] When calculating a route, the route calculation unit can improve the accuracy of the route by referring to the user's past movement history. Past movement history includes, but is not limited to, for example, visit history and movement routes. The route calculation unit, for example, proposes an optimal route based on routes the user has used in the past. The route calculation unit can also propose a route that avoids congestion based on the user's past movement history. Furthermore, the route calculation unit can analyze the user's past movement history and propose the most efficient route. In this way, the accuracy of the route can be improved by referring to the user's past movement history.

[0091] When calculating a route, the route calculation unit can propose a route taking into account the user's current schedule. The current schedule includes, but is not limited to, calendar information, a timetable, and the like. For example, the route calculation unit can refer to the user's calendar information and propose an optimal route that matches the schedule. The route calculation unit can also propose a route with ample time based on the user's schedule. Furthermore, the route calculation unit can also propose the shortest route taking into account the user's schedule. This can improve the efficiency of the user's travel by proposing an optimal route taking into account the user's current schedule.

[0092] The route calculation unit can estimate the user's emotions and adjust the route display method based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is relaxed, the route calculation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the route calculation unit can provide a display method that focuses on the main points. Furthermore, if the user is stressed, the route calculation unit can provide a simple, highly visible display method. This allows the route display method to be adjusted according to the user's emotions, providing a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The route calculation unit can customize the route by taking into account the user's geographical preferences when calculating the route. Geographical preferences include, but are not limited to, for example, frequency of visits and ratings. For example, the route calculation unit prioritizes routes with scenic views that the user prefers. The route calculation unit can also propose routes that take into account places the user wants to avoid. Furthermore, the route calculation unit can propose routes that suit the user's preferences based on the user's past travel history. In this way, by customizing the route according to the user's geographical preferences, it is possible to provide an attractive route to the user.

[0094] When calculating a route, the route calculation unit can analyze the user's social media activity and suggest a related route. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the route calculation unit can suggest a route that passes through places where the user has checked in on social media. The route calculation unit can also analyze the content of the user's social media posts and suggest a related route. Furthermore, the route calculation unit can also suggest a related route by referring to the activity of the user's friends on social media. In this way, by suggesting a related route based on the user's social media activity, it is possible to provide a route that matches the user's interests.

[0095] The route calculation unit can improve the route calculation method by reflecting the user's past feedback when calculating a route. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the route calculation unit can preferentially suggest routes that the user has previously preferred. The route calculation unit can also improve the route calculation algorithm based on the user's past feedback. Furthermore, the route calculation unit can avoid routes that the user has previously dissatisfied with and suggest an optimal route. In this way, by improving the route calculation method by reflecting the user's past feedback, it is possible to provide an optimal route for the user.

[0096] The collection unit can estimate the user's emotions and adjust the collection range of surrounding information based on the estimated user emotions. The collection range of surrounding information includes, but is not limited to, distance, category, and the like. For example, when the user is relaxed, the collection unit can collect information from a wide range. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting nearby information. Furthermore, when the user is feeling stressed, the collection unit can collect information on places to avoid crowds. In this way, by adjusting the collection range of surrounding information according to the user's emotions, it is possible to provide optimal information for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] When collecting surrounding information, the collection unit can update the collected content taking real-time event information into consideration. Examples of real-time event information include, but are not limited to, event calendars and social media posts. For example, the collection unit updates information about surrounding restaurants and shopping centers based on the real-time event information. The collection unit can also update the surrounding information taking real-time traffic information into consideration. Furthermore, the collection unit can update the surrounding information based on real-time weather information. By updating the collected content taking real-time event information into consideration, the latest information can be provided.

[0098] When collecting surrounding information, the collection unit can improve the accuracy of collection by referring to the user's past visit history. The past visit history includes, for example, visit frequency, stay time, etc., but is not limited to these examples. The collection unit, for example, collects surrounding information based on places the user has visited in the past. The collection unit can also prioritize collection of highly relevant information from the user's past visit history. Furthermore, the collection unit can analyze the user's past visit history and collect the most relevant information. In this way, by referring to the user's past visit history, the accuracy of collection can be improved.

[0099] When collecting peripheral information, the collection unit can customize the collection content by taking into account the user's current interests. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the collection unit prioritizes collecting information about restaurants in which the user is currently interested. The collection unit can also prioritize collecting information about shopping centers in which the user is currently interested. Furthermore, the collection unit can collect highly relevant information based on the user's current interests. In this way, by customizing the collection content by taking into account the user's current interests, it is possible to provide useful information to the user.

[0100] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. Information priorities include, but are not limited to, importance and urgency. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting important information. Furthermore, when the user is stressed, the collection unit can prioritize collecting information to avoid crowds. Thus, by determining the priority of information to be collected according to the user's emotions, it is possible to prioritize collecting information that is important to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] When collecting surrounding information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, the collection unit prioritizes collecting information about locations close to the user's current location. The collection unit can also collect highly relevant information based on the user's past visit history. Furthermore, the collection unit can prioritize collecting information about intermediate points between the user's current location and the destination. In this way, by prioritizing the collection of highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user.

[0102] When collecting peripheral information, the collection unit can analyze the user's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of likes, and the number of followers. The collection unit can also collect information about locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, by collecting related information based on the user's social media activities, it is possible to provide useful information to the user.

[0103] When collecting peripheral information, the collection unit can customize the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and the like. For example, the collection unit preferentially collects information that the user has previously liked. The collection unit can also improve the collection algorithm based on the user's past feedback. Furthermore, the collection unit can avoid information that the user has previously dissatisfied with and collect optimal information. In this way, by customizing the collection method by reflecting the user's past feedback, it is possible to provide useful information to the user.

[0104] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The selection of training data includes, but is not limited to, the type of data and the quality of data. For example, when the user is relaxed, the learning unit uses detailed data for training. Furthermore, when the user is in a hurry, the learning unit can prioritize important data for training. Furthermore, when the user is stressed, the learning unit can use data that avoids crowds for training. This allows for improved learning accuracy by selecting training data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, past training data and evaluation data. The learning unit, for example, selects an optimal learning algorithm based on the past learning data. The learning unit can also adjust parameters to improve learning accuracy from the past learning data. Furthermore, the learning unit can analyze the past learning data and identify areas for improvement in the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to the past learning data.

[0106] During learning, the learning unit can analyze the user's past behavioral data to improve the accuracy of the learning. Past behavioral data includes, but is not limited to, for example, visit history and purchase history. The learning unit improves the accuracy of the learning, for example, based on the user's past behavioral data. The learning unit can also analyze the user's past behavioral patterns and optimize the learning algorithm. Furthermore, the learning unit can refer to the user's past behavioral data and reflect feedback to improve the accuracy of the learning. In this way, the accuracy of the learning can be improved by analyzing the user's past behavioral data.

[0107] The learning unit can customize the learning content by taking into account the user's current interests during learning. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the learning unit prioritizes learning data related to topics in which the user is currently interested. The learning unit can also customize the learning content based on the user's current interests. Furthermore, the learning unit can select learning data by taking into account the user's current interests. In this way, customizing the learning content by taking into account the user's current interests can improve the accuracy of learning.

[0108] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. Examples of the learning frequency include, but are not limited to, the frequency of data updates and the frequency of user use. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is stressed. In this way, by adjusting the frequency of learning according to the user's emotions, learning efficiency can be improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] During learning, the learning unit can select learning data taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, the learning unit prioritizes learning data related to the user's current location. The learning unit can also learn highly relevant data based on the user's past visit history. Furthermore, the learning unit can also learn data related to locations between the user's current location and the destination. In this way, by selecting learning data taking into consideration the user's geographical location information, the accuracy of learning can be improved.

[0110] During learning, the learning unit can analyze the user's social media activity and incorporate related data into the learning. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The learning unit can, for example, learn data related to the locations where the user checked in on social media. The learning unit can also analyze the content of the user's social media posts and learn related data. Furthermore, the learning unit can learn related data by referring to the activities of the user's friends on social media. In this way, by incorporating related data based on the user's social media activity into the learning, the accuracy of the learning can be improved.

[0111] During learning, the learning unit can improve the learning algorithm by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings and comments, for example. The learning unit improves the learning algorithm, for example, based on the user's past feedback. The learning unit can also prioritize learning data that the user has previously preferred. Furthermore, the learning unit can also improve the accuracy of learning by reflecting the user's past feedback. In this way, the accuracy of learning can be improved by improving the learning algorithm by reflecting the user's past feedback.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the recommended information based on the estimated user emotions. Examples of display methods for the recommended information include, but are not limited to, map display, text display, and audio guidance. For example, when the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. Furthermore, when the user is stressed, the providing unit can also provide a simple, highly visible display method. This allows the display method for the recommended information to be adjusted according to the user's emotions, thereby providing a display that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] When providing recommended information, the providing unit can provide optimal information by referring to the user's past behavioral data. Past behavioral data includes, but is not limited to, for example, visit history and purchase history. The providing unit can provide optimal recommended information, for example, based on places the user has visited in the past. The providing unit can also prioritize providing highly relevant information from the user's past behavioral data. Furthermore, the providing unit can analyze the user's past behavioral data and provide the most relevant information. This makes it possible to provide optimal information based on the user's past behavioral data, thereby providing information that is useful to the user.

[0114] When providing recommended information, the providing unit can customize the information taking into account the user's current interests. Current interests include, but are not limited to, search history, social media posts, and the like. For example, the providing unit can prioritize providing information related to topics in which the user is currently interested. The providing unit can also customize the recommended information based on the user's current interests. Furthermore, the providing unit can provide highly relevant information taking into account the user's current interests. In this way, by customizing the information taking into account the user's current interests, it is possible to provide useful information to the user.

[0115] The providing unit can improve the method of providing recommended information by reflecting user feedback. Feedback includes, but is not limited to, user ratings and comments, for example. The providing unit can improve the method of providing recommended information based on, for example, the user's past feedback. The providing unit can also provide information that the user has previously preferred preferentially. Furthermore, the providing unit can improve the accuracy of the recommended information by reflecting the user's past feedback. In this way, by improving the method of providing recommended information by reflecting the user's feedback, it is possible to provide information that is useful to the user.

[0116] The providing unit can estimate the user's emotions and determine the priority of recommended information based on the estimated user emotions. Examples of the priority of recommended information include, but are not limited to, importance and urgency. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing important information. Furthermore, when the user is stressed, the providing unit can prioritize providing information to avoid crowds. Thus, by determining the priority of recommended information according to the user's emotions, it is possible to prioritize providing information that is important to the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] When providing recommended information, the providing unit can provide optimal information by taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and Wi-Fi location information. For example, the providing unit can provide information about locations close to the user's current location with priority. The providing unit can also provide highly relevant information based on the user's past visit history. Furthermore, the providing unit can also provide information about intermediate points between the user's current location and the destination with priority. In this way, by providing optimal information by taking into consideration the user's geographical location information, it is possible to provide information that is useful to the user.

[0118] When providing recommended information, the providing unit can analyze the user's social media activity and provide related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can also provide, for example, information on locations where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can also provide related information by referring to the activities of the user's friends on social media. In this way, by providing related information based on the user's social media activity, it is possible to provide useful information to the user.

[0119] When providing recommended information, the providing unit can customize the content of the information to be provided by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and the like. The providing unit customizes the content of the recommended information, for example, based on the user's past feedback. The providing unit can also provide information that the user has previously preferred with priority. Furthermore, the providing unit can improve the accuracy of the recommended information by reflecting the user's past feedback. In this way, by customizing the content of the information to be provided by reflecting the user's past feedback, it is possible to provide information that is useful to the user. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, route calculation unit, collection unit, learning unit, and providing 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 is realized by the reception device 38 of the smart device 14, allowing the user to input a starting point and a destination using voice input or text input. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route from a current location to a destination. The collection unit collects surrounding information using, for example, the camera 42 or the communication I / F 44 of the smart device 14. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences. The providing unit is realized, for example, by the output device 40 of the smart device 14, and provides personalized information to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, route calculation unit, collection unit, learning unit, and providing 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 is realized by the microphone 238 of the smart glasses 214, allowing the user to input a starting point and a destination using voice input. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route from a current location to a destination. The collection unit collects surrounding information using, for example, the camera 42 and the communication I / F 44 of the smart glasses 214. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences. The providing unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides personalized information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, route calculation unit, collection unit, learning unit, and providing unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, allowing the user to input a starting point and a destination using voice input. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route from the current location to the destination. The collection unit collects surrounding information using, for example, the camera 42 and the communication I / F 44 of the headset terminal 314. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences. The providing unit is realized, for example, by the display 343 of the headset terminal 314, and provides personalized information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, route calculation unit, collection unit, learning unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input a starting point and a destination using voice input. The route calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route from a current location to a destination. The collection unit collects surrounding information using, for example, the camera 42 and the communication I / F 44 of the robot 414. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and learns the user's preferences. The providing unit is realized, for example, by the speaker 240 of the robot 414, and provides personalized information to the user.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, blood pressure, and stress level, and evaluates the user's health condition based on this data. For example, if the user indicates a high stress level, the health management unit may recommend places and activities where the user can relax. Also, if the user's heart rate is high, the health management unit may advise the user to refrain from exercising. Furthermore, the health management unit may suggest meals and timing of rest based on the user's health condition. This allows the navigation system to provide personalized information that takes the user's health condition into consideration.

[0122] The navigation system may further include a schedule management unit that manages the user's schedule. The schedule management unit refers to the user's calendar information and planner, and proposes the optimal route taking into consideration travel time and arrival time. For example, if the user is likely to be late for a meeting, the schedule management unit can prioritize proposing the shortest route. Also, if the user has ample time to travel, the schedule management unit can propose a scenic route or a route that passes through tourist spots. Furthermore, the schedule management unit can suggest rest stops and meal times based on the user's schedule. This allows the navigation system to provide personalized information tailored to the user's schedule.

[0123] The navigation system may further include a music recommendation unit that recommends music based on the user's music preferences. The music recommendation unit recommends music that matches the user's preferences based on the user's past playback history and ratings. For example, if the user wants to relax, the music recommendation unit can recommend relaxing music. Also, if the user wants to concentrate while driving, the music recommendation unit can recommend music that will help improve concentration. Furthermore, the music recommendation unit can recommend optimal music depending on the user's current emotions and situation. This allows the navigation system to provide personalized information tailored to the user's music preferences.

[0124] The navigation system may further include a driving style learning unit that learns the user's driving style. The driving style learning unit analyzes the user's driving patterns, speed, frequency of braking, and the like to learn the user's driving style. For example, if the user frequently brakes suddenly, the driving style learning unit can provide safe driving advice. Also, if the user prefers to use expressways, the driving style learning unit can suggest routes that prioritize expressways. Furthermore, the driving style learning unit can suggest fuel-efficient driving methods based on the user's driving style. This allows the navigation system to provide personalized information tailored to the user's driving style.

[0125] The navigation system may further include a travel history analysis unit that analyzes the user's travel history. The travel history analysis unit collects the user's past travel data and analyzes the user's travel preferences and patterns. For example, it can suggest travel destinations that suit the user's preferences based on data on tourist spots and accommodations that the user has visited in the past. Also, if the user prefers to travel during a particular season, the travel history analysis unit can suggest travel destinations that suit that season. Furthermore, the travel history analysis unit can suggest optimal travel destinations depending on the user's travel purpose (e.g., business, leisure). This allows the navigation system to provide personalized information based on the user's travel history.

[0126] The navigation system may further include a food learning unit that learns the user's food preferences. The food learning unit learns the user's food preferences based on the user's past dining history and ratings. For example, if the user likes a particular dish, the food learning unit can preferentially suggest restaurants that serve that dish. Also, if the user is health-conscious, the food learning unit can suggest restaurants that offer healthy menus. Furthermore, the food learning unit can suggest optimal meals based on the user's current emotions and situation. This allows the navigation system to provide personalized information tailored to the user's food preferences.

[0127] The navigation system may further include an exercise history analysis unit that analyzes the user's exercise history. The exercise history analysis unit collects the user's past exercise data and analyzes the user's exercise preferences and patterns. For example, if the user likes a particular sport, the exercise history analysis unit can suggest places where the user can enjoy that sport. Also, if the user exercises regularly, the exercise history analysis unit can suggest the timing and location of the exercise. Furthermore, the exercise history analysis unit can suggest the optimal exercise method depending on the user's exercise purpose (e.g., dieting, maintaining health). This allows the navigation system to provide personalized information based on the user's exercise history.

[0128] The navigation system may further include a reading history analysis unit that analyzes the user's reading history. The reading history analysis unit collects the user's past reading data and analyzes the user's reading preferences and patterns. For example, if the user likes books of a particular genre, the reading history analysis unit can suggest bookstores and libraries that offer books of that genre. Also, if the user likes a particular author, the reading history analysis unit can provide information on new releases by that author. Furthermore, the reading history analysis unit can suggest the optimal reading environment based on the user's current emotions and situation. This allows the navigation system to provide personalized information based on the user's reading history.

[0129] The navigation system may further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit learns the user's hobbies and interests based on the user's past behavioral data and evaluations. For example, if the user likes a particular activity, the hobby learning unit can suggest places where the user can enjoy that activity. Also, if the user is interested in a particular event, the hobby learning unit can provide information about that event. Furthermore, the hobby learning unit can suggest optimal hobbies and activities based on the user's current emotions and situation. This allows the navigation system to provide personalized information tailored to the user's hobbies and interests.

[0130] The navigation system may further include a sleep management unit that monitors the user's sleep patterns. The sleep management unit collects the user's sleep data and analyzes the user's sleep patterns. For example, if the user is not getting enough sleep, the sleep management unit may suggest places or activities to relax. Also, if the user tends to sleep better during certain times of the day, the sleep management unit may suggest a schedule tailored to those times. Furthermore, the sleep management unit may provide advice to improve the user's sleep quality. This allows the navigation system to provide personalized information based on the user's sleep patterns.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit accepts input of the departure point and destination. The departure point and destination can include addresses, place names, GPS coordinates, etc. The user can enter the departure point and destination using voice input or text input. The reception unit can also suggest the optimal input method based on the user's past input history. Step 2: The route calculation unit calculates the route from the current location to the destination based on the information received by the reception unit. The route calculation unit calculates the optimal route based on criteria such as distance, time, and traffic conditions. It can also improve the accuracy of the route by referring to the user's past travel history. Step 3: The collection unit collects surrounding information based on the route calculated by the route calculation unit. The surrounding information includes restaurants, shopping centers, tourist attractions, etc. The collection unit can also update the collected information taking into account real-time event information. Step 4: The learning unit learns the user's preferences based on the information collected by the collection unit. The learning unit collects the user's past behavioral data and learns the user's preferences. It can also estimate the user's emotions and select learning data based on the estimated user emotions. Step 5: The providing unit provides recommended information based on the information learned by the learning unit. The providing unit provides information that matches the user's interests. The providing unit can also estimate the user's emotions and adjust the display method of the recommended information based on the estimated user emotions.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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, in order to avoid confusion and to 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 input of a departure point and a destination; a route calculation unit that calculates a route from a current location to a destination based on the information received by the reception unit; a collection unit that collects surrounding information based on the route calculated by the route calculation unit; a learning unit that learns user preferences based on the information collected by the collecting unit; a providing unit that provides recommended information based on the information learned by the learning unit. A system characterized by:

2. The learning unit Collects user past behavior data and learns user preferences 2. The system of claim 1.

3. The providing unit Providing recommended information to a user based on the information learned by the learning unit 2. The system of claim 1.

4. The collecting unit Gather information about nearby restaurants or shopping centers 2. The system of claim 1.

5. The route calculation unit Calculate the route from your current location to your destination 2. The system of claim 1.

6. The providing unit Providing information that matches your interests 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the input method for departure and destination based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

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