system

Abstract

An object of a system according to an embodiment is to perform navigation in consideration of hobbies and preferences of a plurality of persons and movement restrictions.SOLUTION: A system includes a collection unit, a generation unit, a fusion unit, and a navigation unit. The collection unit collects hobbies and preferences or movement restrictions of each user. The generation unit generates an LLM personalized to each user based on the data collected by the collection unit. The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The navigation unit performs navigation based on the new LLM generated by the fusion 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 technologies have had the problem that it is difficult to provide navigation that takes into account the hobbies, preferences, and travel restrictions of each user when multiple people are traveling together.

[0005] The system according to the embodiment aims to perform navigation that takes into consideration the hobbies, preferences, and travel restrictions of multiple people. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a fusion unit, and a navigation unit. The collection unit collects each user's hobbies, preferences, or travel constraints. The generation unit generates an LLM personalized for each user based on the data collected by the collection unit. The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The navigation unit performs navigation based on the new LLM generated by the fusion unit. [Effects of the Invention]

[0007] The system according to the embodiment can perform navigation taking into consideration the hobbies, preferences, and travel restrictions of multiple people. [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 collects each user's hobbies, preferences, and travel constraints, generates a personalized LLM using a generation AI, combines them to generate a new LLM, and performs navigation based on the LLM. The navigation system collects each user's hobbies, preferences, and travel constraints, generates a personalized LLM using a generation AI, combines them to generate a new LLM, and performs navigation based on the LLM. For example, the navigation system collects each user's hobbies, preferences, and travel constraints. For example, information such as User A's preference for tourist spots and User B's preference for shopping is collected. This data is acquired from each user's smartphone or wearable device. Next, the navigation system generates a personalized LLM for each user based on the collected data. The generated LLM provides navigation that takes into account each user's hobbies, preferences, and travel constraints. The navigation system then combines the personalized LLMs for each user to generate a new LLM. This new LLM integrates the hobbies, preferences, and travel constraints of multiple users to propose an optimal route. For example, it generates a route that includes a good balance of tourist spots and shopping areas. Finally, the navigation system performs navigation based on the new LLM. Users can receive navigation via smartphones or wearable devices. This facilitates the movement of multiple people and realizes a next-generation navigation system. For example, on family or group trips, it is possible to provide a travel plan that satisfies everyone by proposing a route that takes into account everyone's hobbies, preferences, and travel constraints. This facilitates the movement of multiple people and realizes a next-generation navigation system. For example, on family or group trips, it is possible to provide a travel plan that satisfies everyone by proposing a route that takes into account everyone's hobbies, preferences, and travel constraints.

[0029] A navigation system according to an embodiment includes a collection unit, a generation unit, a fusion unit, and a navigation unit. The collection unit collects each user's hobbies, preferences, or travel constraints. The collection unit collects data from, for example, each user's smartphone or wearable device. The collection unit collects information, such as user A's preference for tourist spots and user B's preference for shopping. The generation unit generates an LLM personalized for each user based on the data collected by the collection unit. The generation unit generates an LLM that takes into account each user's hobbies, preferences, and travel constraints, for example. The generation unit can also use a generation AI to generate an LLM that takes into account each user's hobbies, preferences, and travel constraints. The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The fusion unit can fuse multiple LLMs to generate a new LLM, for example. The fusion unit can also use a generation AI to fuse multiple LLMs to generate a new LLM. The navigation unit performs navigation based on the new LLM generated by the fusion unit. The navigation unit performs navigation based on the new LLM, for example. The navigation unit can also perform navigation based on the new LLM using a generating AI. This enables the navigation system according to the embodiment to perform navigation that takes into account the hobbies, preferences, and travel restrictions of multiple people.

[0030] The collection unit can collect data from each user's smartphone or wearable device. For example, the collection unit collects data from each user's smartphone. The collection unit can also collect data from each user's wearable device. For example, the collection unit collects data from devices such as smartwatches and fitness trackers. This allows for more accurate information to be obtained by collecting data directly from each user's device. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartphone or wearable device into a generation AI and have the generation AI analyze the data.

[0031] The generation unit can generate an LLM that takes into account each user's hobbies, preferences, or travel constraints. The generation unit, for example, generates an LLM that takes into account each user's hobbies, preferences, or travel constraints. The generation unit can also generate an LLM that takes into account each user's travel constraints, for example. The generation unit generates an LLM based on, for example, data weighting and priority settings. This allows for generating an LLM optimized for each user, thereby meeting individual needs. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input data regarding each user's hobbies, preferences, and travel constraints into the generation AI, and have the generation AI generate a personalized LLM.

[0032] The fusion unit can fuse multiple LLMs to generate a new LLM. For example, the fusion unit fuses multiple LLMs based on the details of the data integration method and algorithm. For example, the fusion unit generates a new LLM based on the details of the data integration method and algorithm. This makes it possible to generate an LLM that integrates the hobbies, preferences, and movement constraints of multiple people. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input multiple LLMs into the generation AI and cause the generation AI to generate a new LLM.

[0033] The navigation unit can perform navigation based on the new LLM. The navigation unit, for example, provides route guidance and suggests destinations based on the new LLM. The navigation unit, for example, provides navigation based on route guidance and suggests destinations. This enables navigation that takes into account the needs of multiple people. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input the new LLM into the generation AI and have the generation AI execute navigation.

[0034] The navigation unit can update data in real time and reflect user feedback. The navigation unit updates data in real time based on, for example, the frequency of data acquisition and the timing of updates. The navigation unit reflects user feedback based on, for example, the method of collecting feedback and the timing of reflection. This provides more appropriate navigation by updating data in real time and reflecting feedback. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input data acquired in real time into the generation AI and have the generation AI update the data and reflect the feedback.

[0035] The collection unit can analyze the user's past movement history and select an efficient data collection method. The collection unit, for example, determines data collection priorities based on places the user has frequently visited in the past. The collection unit, for example, analyzes the user's past movement patterns and sets an efficient data collection route. The collection unit, for example, takes into account places the user has avoided in the past and excludes them from data collection targets. This enables efficient data collection by selecting a data collection method based on the past movement history. Some or all of the above-mentioned processing in the collection unit can be performed using a generation AI. For example, the collection unit can input the user's past movement history data into the generation AI and have the generation AI select an efficient data collection method.

[0036] When collecting data, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is currently sightseeing, the collection unit prioritizes collecting data related to tourist spots. For example, if the user is shopping, the collection unit prioritizes collecting store information and sale information. For example, if the user is traveling, the collection unit prioritizes collecting traffic information and route information. This makes it possible to collect data according to the user's current activity status and areas of interest. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input data related to the user's current activity status and areas of interest into the generation AI and have the generation AI filter the data.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. For example, if the user is using text input, the collection unit prioritizes collecting text data. For example, if the user is using image input, the collection unit prioritizes collecting image data. This enables optimal data collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is traveling, the collection unit prioritizes collecting data related to the area to which the user is traveling. For example, when the user is in a tourist destination, the collection unit prioritizes collecting data related to that tourist destination. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0039] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to the locations where the user checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and have the generation AI collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the priority of data collection based on feedback provided by the user in the past. For example, the collection unit preferentially uses a specific data collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and suggests an optimal data collection method. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] When generating an LLM, the generation unit can adjust the generation algorithm taking into account changes in the user's interests and preferences. For example, if the user's interests and preferences change, the generation unit generates an LLM that reflects those changes. For example, the generation unit detects changes in the user's interests and preferences in real time and adjusts the generation algorithm. For example, the generation unit analyzes changes in the user's interests and preferences and generates an optimal LLM. This allows a more appropriate LLM to be generated by optimizing the generation algorithm in response to changes in the user's interests and preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's interest and preference data into the generation AI and have the generation AI adjust the generation algorithm.

[0042] When generating an LLM, the generation unit can improve the accuracy of generation by referring to the user's past movement patterns. The generation unit generates an optimal LLM, for example, based on the user's past movement patterns. The generation unit, for example, analyzes the user's past movement patterns and optimizes the generation algorithm. The generation unit, for example, improves the accuracy of generation by referring to the user's past movement patterns. This makes it possible to improve the accuracy of generation based on the user's past movement patterns. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past movement pattern data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] When generating an LLM, the generation unit can customize the generated content based on the user's current activity status. For example, if the user is sightseeing, the generation unit generates an LLM including information about tourist spots. For example, if the user is shopping, the generation unit generates an LLM including store information and sale information. For example, if the user is traveling, the generation unit generates an LLM including traffic information and route information. This makes it possible to customize the generated content according to the user's current activity status. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current activity status data into the generation AI and have the generation AI customize the generated content.

[0044] When generating an LLM, the generation unit can adjust the generated content taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit generates an LLM including information related to that area. For example, if the user is traveling, the generation unit generates an LLM including information related to the area to which the user is traveling. For example, if the user is in a tourist destination, the generation unit generates an LLM including information related to the tourist destination. This makes it possible to optimize the generated content based on the user's geographical location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI adjust the generated content.

[0045] The generation unit can analyze the user's social media activity and reflect related information when generating the LLM. For example, the generation unit generates an LLM including information about places where the user checked in on social media. For example, the generation unit analyzes the content of the user's social media posts and generates an LLM including related information. For example, the generation unit generates an LLM including related information by referring to the activities of the user's friends on social media. This makes it possible to reflect related information based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to reflect the related information.

[0046] When generating an LLM, the generation unit can adjust the generation algorithm by reflecting the user's past feedback. The generation unit adjusts the generation algorithm, for example, based on feedback provided by the user in the past. The generation unit, for example, preferentially uses a specific generation method based on the user's past feedback. The generation unit, for example, analyzes the user's past feedback and proposes an optimal generation algorithm. This makes it possible to adjust the generation algorithm based on the user's past feedback. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the generation algorithm.

[0047] When fusing LLMs, the fusion unit can adjust the fusion algorithm taking into account the balance of each user's interests and preferences. For example, the fusion unit fuses LLMs that evenly reflect each user's interests and preferences. For example, the fusion unit detects the balance of each user's interests and preferences in real time and adjusts the fusion algorithm. For example, the fusion unit analyzes the balance of each user's interests and preferences and fuses the optimal LLM. This optimizes the fusion algorithm according to the balance of each user's interests and preferences, resulting in a more appropriate LLM fusion. Some or all of the above-described processing in the fusion unit can be performed using a generation AI. For example, the fusion unit can input each user's interest and preference data into the generation AI and have the generation AI adjust the fusion algorithm.

[0048] During LLM fusion, the fusion unit can improve the accuracy of fusion by referring to each user's past movement patterns. The fusion unit, for example, fuses the optimal LLM based on each user's past movement patterns. The fusion unit, for example, analyzes each user's past movement patterns and optimizes the fusion algorithm. The fusion unit, for example, improves the accuracy of fusion by referring to each user's past movement patterns. This makes it possible to improve the accuracy of fusion based on each user's past movement patterns. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's past movement pattern data into the generation AI and cause the generation AI to improve the accuracy of fusion.

[0049] During LLM fusion, the fusion unit can customize the fusion content based on each user's current activity status. For example, if each user is sightseeing, the fusion unit fuses LLMs containing information about tourist spots. For example, if each user is shopping, the fusion unit fuses LLMs containing store information and sale information. For example, if each user is traveling, the fusion unit fuses LLMs containing traffic information and route information. This makes it possible to customize the fusion content according to each user's current activity status. Some or all of the above-described processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's current activity status data into the generation AI and have the generation AI customize the fusion content.

[0050] During LLM fusion, the fusion unit can adjust the fusion content taking into account each user's geographic location information. For example, if each user is in a specific area, the fusion unit fuses LLMs containing information related to that area. For example, if each user is traveling, the fusion unit fuses LLMs containing information related to the destination area. For example, if each user is in a tourist destination, the fusion unit fuses LLMs containing information related to the tourist destination. This enables optimization of the fusion content based on each user's geographic location information. Some or all of the above-described processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's geographic location information into the generation AI and have the generation AI adjust the fusion content.

[0051] During LLM fusion, the fusion unit can analyze each user's social media activity and reflect related information. For example, the fusion unit fuses LLMs containing information about the locations where each user checked in on social media. For example, the fusion unit analyzes each user's social media posts and fuses LLMs containing related information. For example, the fusion unit fuses LLMs containing related information by referring to the activities of each user's friends on social media. This makes it possible to reflect related information based on each user's social media activity. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's social media activity data into the generation AI and cause the generation AI to reflect related information.

[0052] During LLM fusion, the fusion unit can adjust the fusion algorithm by reflecting each user's past feedback. For example, the fusion unit adjusts the fusion algorithm based on feedback provided by each user in the past. For example, the fusion unit preferentially uses a specific fusion method based on each user's past feedback. For example, the fusion unit analyzes each user's past feedback and proposes an optimal fusion algorithm. This makes it possible to adjust the fusion algorithm based on each user's past feedback. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's past feedback data into the generation AI and have the generation AI adjust the fusion algorithm.

[0053] During navigation, the navigation unit can adjust the route taking into account the balance of each user's hobbies and preferences. For example, the navigation unit proposes a route that evenly reflects each user's hobbies and preferences. For example, the navigation unit detects the balance of each user's hobbies and preferences in real time and adjusts the route. For example, the navigation unit analyzes the balance of each user's hobbies and preferences and proposes an optimal route. This makes it possible to optimize the route according to the balance of each user's hobbies and preferences. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's hobbies and preference data into the generation AI and have the generation AI adjust the route.

[0054] During navigation, the navigation unit can improve the accuracy of the route by referring to each user's past movement patterns. The navigation unit, for example, proposes an optimal route based on each user's past movement patterns. The navigation unit, for example, analyzes each user's past movement patterns to improve the accuracy of the route. The navigation unit, for example, improves the accuracy of the route by referring to each user's past movement patterns. This makes it possible to improve the accuracy of the route based on each user's past movement patterns. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's past movement pattern data into the generation AI and have the generation AI improve the accuracy of the route.

[0055] During navigation, the navigation unit can customize a route based on each user's current activity status. For example, if each user is sightseeing, the navigation unit proposes a route that includes tourist attractions. For example, if each user is shopping, the navigation unit proposes a route that includes store information and sale information. For example, if each user is traveling, the navigation unit proposes a route that includes traffic information and route information. This makes it possible to customize a route according to each user's current activity status. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's current activity status data into the generation AI and have the generation AI customize the route.

[0056] During navigation, the navigation unit can adjust the route taking into account the geographical location information of each user. For example, when each user is in a specific area, the navigation unit proposes a route including information related to that area. For example, when each user is traveling, the navigation unit proposes a route including information related to the destination area. For example, when each user is in a tourist spot, the navigation unit proposes a route including information related to the tourist spot. This makes it possible to optimize the route based on the geographical location information of each user. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input the geographical location information of each user into the generation AI and have the generation AI adjust the route.

[0057] The navigation unit can analyze each user's social media activity and reflect related information during navigation. For example, the navigation unit proposes a route including information about places where each user has checked in on social media. For example, the navigation unit analyzes each user's social media posts and proposes a route including related information. For example, the navigation unit proposes a route including related information by referring to the activities of each user's friends on social media. This makes it possible to reflect related information based on each user's social media activity. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's social media activity data into the generation AI and have the generation AI reflect the related information.

[0058] During navigation, the navigation unit can adjust the route by reflecting each user's past feedback. For example, the navigation unit adjusts the route based on feedback provided by each user in the past. For example, the navigation unit preferentially uses a specific route based on each user's past feedback. For example, the navigation unit analyzes each user's past feedback and proposes an optimal route. This makes it possible to adjust the route based on each user's past feedback. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit inputs each user's past feedback data into the generation AI and causes the generation AI to adjust the route.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The navigation system can further include a health monitoring unit that monitors the user's health and adjusts navigation based on the user's health status. For example, if the user feels tired, it can suggest rest spots. If the user is not getting enough exercise, it can suggest routes to increase walking distance. If the user has a specific health problem, it can provide a route that takes that problem into consideration. This enables navigation that is tailored to the user's health status, resulting in healthier travel.

[0061] The navigation system may further include a travel history analysis unit that analyzes the user's past travel history and customizes navigation based on the past travel history. For example, the system may suggest a route that revisits places the user has previously visited, provide a route that excludes places the user has previously avoided, or generate a route that prioritizes places that the user has previously rated highly. This provides personalized navigation based on the user's past travel history.

[0062] The navigation system may further include a weather information acquisition unit that acquires real-time weather information from the user and adjusts navigation based on the weather information. For example, if it is raining, indoor tourist spots may be suggested. If it is sunny, a route including outdoor activities may be provided. If bad weather is predicted, a route that takes the weather into consideration may be generated. This allows for flexible navigation based on weather information.

[0063] The navigation system may further include a transportation consideration unit that considers the user's transportation mode and adjusts navigation based on the mode of transportation. For example, for a user using public transportation, a route that takes bus and train timetables into consideration may be proposed. For a user using bicycles, a route that includes bicycle-only lanes may be provided. For a user traveling on foot, a pedestrian-friendly route may be generated. This provides optimal navigation according to the user's transportation mode.

[0064] The navigation system may further include a language setting consideration unit that considers the user's language setting and provides navigation based on the language setting. For example, if the user speaks English, navigation in English is provided. If the user speaks Japanese, navigation in Japanese is provided. If multilingual support is required, navigation in multiple languages ​​is provided. This enables navigation according to the user's language setting.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects each user's hobbies, preferences, or travel constraints. For example, the collection unit collects data from each user's smartphone or wearable device. For example, the collection unit collects information such as User A's preference for tourist spots and User B's preference for shopping. Step 2: The generation unit generates an LLM personalized for each user based on the data collected by the collection unit. The generation unit generates an LLM that takes into account, for example, each user's hobbies, preferences, and travel constraints. The generation unit can also use a generation AI to generate an LLM that takes into account each user's hobbies, preferences, and travel constraints. Step 3: The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The fusion unit, for example, fuses multiple LLMs to generate a new LLM. The fusion unit can also use the generation AI to fuse multiple LLMs to generate a new LLM. Step 4: The navigation unit performs navigation based on the new LLM generated by the fusion unit. The navigation unit performs navigation based on the new LLM, for example. The navigation unit can also perform navigation based on the new LLM using the generation AI.

[0067] (Example 2) A navigation system according to an embodiment of the present invention collects each user's hobbies, preferences, and travel constraints, generates a personalized LLM using a generation AI, combines them to generate a new LLM, and performs navigation based on the LLM. The navigation system collects each user's hobbies, preferences, and travel constraints, generates a personalized LLM using a generation AI, combines them to generate a new LLM, and performs navigation based on the LLM. For example, the navigation system collects each user's hobbies, preferences, and travel constraints. For example, information such as User A's preference for tourist spots and User B's preference for shopping is collected. This data is acquired from each user's smartphone or wearable device. Next, the navigation system generates a personalized LLM for each user based on the collected data. The generated LLM provides navigation that takes into account each user's hobbies, preferences, and travel constraints. The navigation system then combines the personalized LLMs for each user to generate a new LLM. This new LLM integrates the hobbies, preferences, and travel constraints of multiple users to propose an optimal route. For example, it generates a route that includes a good balance of tourist spots and shopping areas. Finally, the navigation system performs navigation based on the new LLM. Users can receive navigation via smartphones or wearable devices. This facilitates the movement of multiple people and realizes a next-generation navigation system. For example, on family or group trips, it is possible to provide a travel plan that satisfies everyone by proposing a route that takes into account everyone's hobbies, preferences, and travel constraints. This facilitates the movement of multiple people and realizes a next-generation navigation system. For example, on family or group trips, it is possible to provide a travel plan that satisfies everyone by proposing a route that takes into account everyone's hobbies, preferences, and travel constraints.

[0068] A navigation system according to an embodiment includes a collection unit, a generation unit, a fusion unit, and a navigation unit. The collection unit collects each user's hobbies, preferences, or travel constraints. The collection unit collects data from, for example, each user's smartphone or wearable device. The collection unit collects information, such as user A's preference for tourist spots and user B's preference for shopping. The generation unit generates an LLM personalized for each user based on the data collected by the collection unit. The generation unit generates an LLM that takes into account each user's hobbies, preferences, and travel constraints, for example. The generation unit can also use a generation AI to generate an LLM that takes into account each user's hobbies, preferences, and travel constraints. The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The fusion unit can fuse multiple LLMs to generate a new LLM, for example. The fusion unit can also use a generation AI to fuse multiple LLMs to generate a new LLM. The navigation unit performs navigation based on the new LLM generated by the fusion unit. The navigation unit performs navigation based on the new LLM, for example. The navigation unit can also perform navigation based on the new LLM using a generating AI. This enables the navigation system according to the embodiment to perform navigation that takes into account the hobbies, preferences, and travel restrictions of multiple people.

[0069] The collection unit can collect data from each user's smartphone or wearable device. For example, the collection unit collects data from each user's smartphone. The collection unit can also collect data from each user's wearable device. For example, the collection unit collects data from devices such as smartwatches and fitness trackers. This allows for more accurate information to be obtained by collecting data directly from each user's device. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartphone or wearable device into a generation AI and have the generation AI analyze the data.

[0070] The generation unit can generate an LLM that takes into account each user's hobbies, preferences, or travel constraints. The generation unit, for example, generates an LLM that takes into account each user's hobbies, preferences, or travel constraints. The generation unit can also generate an LLM that takes into account each user's travel constraints, for example. The generation unit generates an LLM based on, for example, data weighting and priority settings. This allows for generating an LLM optimized for each user, thereby meeting individual needs. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input data regarding each user's hobbies, preferences, and travel constraints into the generation AI, and have the generation AI generate a personalized LLM.

[0071] The fusion unit can fuse multiple LLMs to generate a new LLM. For example, the fusion unit fuses multiple LLMs based on the details of the data integration method and algorithm. For example, the fusion unit generates a new LLM based on the details of the data integration method and algorithm. This makes it possible to generate an LLM that integrates the hobbies, preferences, and movement constraints of multiple people. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input multiple LLMs into the generation AI and cause the generation AI to generate a new LLM.

[0072] The navigation unit can perform navigation based on the new LLM. The navigation unit, for example, provides route guidance and suggests destinations based on the new LLM. The navigation unit, for example, provides navigation based on route guidance and suggests destinations. This enables navigation that takes into account the needs of multiple people. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input the new LLM into the generation AI and have the generation AI execute navigation.

[0073] The navigation unit can update data in real time and reflect user feedback. The navigation unit updates data in real time based on, for example, the frequency of data acquisition and the timing of updates. The navigation unit reflects user feedback based on, for example, the method of collecting feedback and the timing of reflection. This provides more appropriate navigation by updating data in real time and reflecting feedback. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input data acquired in real time into the generation AI and have the generation AI update the data and reflect the feedback.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects data frequently to obtain detailed information. For example, when the user is stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, when the user is in a hurry, the collection unit prioritizes collecting only important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can analyze the user's past movement history and select an efficient data collection method. The collection unit, for example, determines data collection priorities based on places the user has frequently visited in the past. The collection unit, for example, analyzes the user's past movement patterns and sets an efficient data collection route. The collection unit, for example, takes into account places the user has avoided in the past and excludes them from data collection targets. This enables efficient data collection by selecting a data collection method based on the past movement history. Some or all of the above-mentioned processing in the collection unit can be performed using a generation AI. For example, the collection unit can input the user's past movement history data into the generation AI and have the generation AI select an efficient data collection method.

[0076] When collecting data, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is currently sightseeing, the collection unit prioritizes collecting data related to tourist spots. For example, if the user is shopping, the collection unit prioritizes collecting store information and sale information. For example, if the user is traveling, the collection unit prioritizes collecting traffic information and route information. This makes it possible to collect data according to the user's current activity status and areas of interest. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input data related to the user's current activity status and areas of interest into the generation AI and have the generation AI filter the data.

[0077] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. For example, if the user is using text input, the collection unit prioritizes collecting text data. For example, if the user is using image input, the collection unit prioritizes collecting image data. This enables optimal data collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting entertainment-related data. For example, if the user is relaxed, the collection unit prioritizes collecting relaxation-related data. For example, if the user is stressed, the collection unit prioritizes collecting data that is useful for stress reduction. This enables more appropriate data collection by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is traveling, the collection unit prioritizes collecting data related to the area to which the user is traveling. For example, when the user is in a tourist destination, the collection unit prioritizes collecting data related to that tourist destination. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0080] During data collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit collects data related to the locations where the user checked in on social media. For example, the collection unit analyzes the content of the user's social media posts and collects related data. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and have the generation AI collect related data.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the priority of data collection based on feedback provided by the user in the past. For example, the collection unit preferentially uses a specific data collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and suggests an optimal data collection method. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit is performed using a generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0082] The generation unit can estimate the user's emotions and adjust the LLM generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates an LLM that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit generates an LLM that emphasizes the shortest route. For example, if the user is excited, the generation unit generates an LLM that adds a visually stimulating effect. This allows for the generation of a more appropriate LLM by adjusting the LLM generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., an LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the LLM generation method.

[0083] When generating an LLM, the generation unit can adjust the generation algorithm taking into account changes in the user's interests and preferences. For example, if the user's interests and preferences change, the generation unit generates an LLM that reflects those changes. For example, the generation unit detects changes in the user's interests and preferences in real time and adjusts the generation algorithm. For example, the generation unit analyzes changes in the user's interests and preferences and generates an optimal LLM. This allows a more appropriate LLM to be generated by optimizing the generation algorithm in response to changes in the user's interests and preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's interest and preference data into the generation AI and have the generation AI adjust the generation algorithm.

[0084] When generating an LLM, the generation unit can improve the accuracy of generation by referring to the user's past movement patterns. The generation unit generates an optimal LLM, for example, based on the user's past movement patterns. The generation unit, for example, analyzes the user's past movement patterns and optimizes the generation algorithm. The generation unit, for example, improves the accuracy of generation by referring to the user's past movement patterns. This makes it possible to improve the accuracy of generation based on the user's past movement patterns. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past movement pattern data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0085] When generating an LLM, the generation unit can customize the generated content based on the user's current activity status. For example, if the user is sightseeing, the generation unit generates an LLM including information about tourist spots. For example, if the user is shopping, the generation unit generates an LLM including store information and sale information. For example, if the user is traveling, the generation unit generates an LLM including traffic information and route information. This makes it possible to customize the generated content according to the user's current activity status. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current activity status data into the generation AI and have the generation AI customize the generated content.

[0086] The generation unit can estimate the user's emotions and adjust the frequency of LLM generation based on the estimated user's emotions. For example, if the user is relaxed, the generation unit increases the frequency of LLM generation. For example, if the user is stressed, the generation unit decreases the frequency of LLM generation. For example, if the user is in a hurry, the generation unit frequently generates LLMs containing only important information. This allows for the generation of more appropriate LLMs by adjusting the LLM generation frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., an LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the LLM generation frequency.

[0087] When generating an LLM, the generation unit can adjust the generated content taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit generates an LLM including information related to that area. For example, if the user is traveling, the generation unit generates an LLM including information related to the area to which the user is traveling. For example, if the user is in a tourist destination, the generation unit generates an LLM including information related to the tourist destination. This makes it possible to optimize the generated content based on the user's geographical location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI adjust the generated content.

[0088] The generation unit can analyze the user's social media activity and reflect related information when generating the LLM. For example, the generation unit generates an LLM including information about places where the user checked in on social media. For example, the generation unit analyzes the content of the user's social media posts and generates an LLM including related information. For example, the generation unit generates an LLM including related information by referring to the activities of the user's friends on social media. This makes it possible to reflect related information based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to reflect the related information.

[0089] When generating an LLM, the generation unit can adjust the generation algorithm by reflecting the user's past feedback. The generation unit adjusts the generation algorithm, for example, based on feedback provided by the user in the past. The generation unit, for example, preferentially uses a specific generation method based on the user's past feedback. The generation unit, for example, analyzes the user's past feedback and proposes an optimal generation algorithm. This makes it possible to adjust the generation algorithm based on the user's past feedback. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and have the generation AI adjust the generation algorithm.

[0090] The fusion unit can estimate the user's emotions and adjust the LLM fusion method based on the estimated user's emotions. For example, if the user is relaxed, the fusion unit fuses LLMs that proceed at a leisurely pace. For example, if the user is in a hurry, the fusion unit fuses LLMs that emphasize the shortest route. For example, if the user is excited, the fusion unit fuses LLMs that add visually stimulating effects. This adjusts the LLM fusion method according to the user's emotions, resulting in a more appropriate LLM fusion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the fusion unit is performed using the generation AI. For example, the fusion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the LLM fusion method.

[0091] When fusing LLMs, the fusion unit can adjust the fusion algorithm taking into account the balance of each user's interests and preferences. For example, the fusion unit fuses LLMs that evenly reflect each user's interests and preferences. For example, the fusion unit detects the balance of each user's interests and preferences in real time and adjusts the fusion algorithm. For example, the fusion unit analyzes the balance of each user's interests and preferences and fuses the optimal LLM. This optimizes the fusion algorithm according to the balance of each user's interests and preferences, resulting in a more appropriate LLM fusion. Some or all of the above-described processing in the fusion unit can be performed using a generation AI. For example, the fusion unit can input each user's interest and preference data into the generation AI and have the generation AI adjust the fusion algorithm.

[0092] During LLM fusion, the fusion unit can improve the accuracy of fusion by referring to each user's past movement patterns. The fusion unit, for example, fuses the optimal LLM based on each user's past movement patterns. The fusion unit, for example, analyzes each user's past movement patterns and optimizes the fusion algorithm. The fusion unit, for example, improves the accuracy of fusion by referring to each user's past movement patterns. This makes it possible to improve the accuracy of fusion based on each user's past movement patterns. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's past movement pattern data into the generation AI and cause the generation AI to improve the accuracy of fusion.

[0093] During LLM fusion, the fusion unit can customize the fusion content based on each user's current activity status. For example, if each user is sightseeing, the fusion unit fuses LLMs containing information about tourist spots. For example, if each user is shopping, the fusion unit fuses LLMs containing store information and sale information. For example, if each user is traveling, the fusion unit fuses LLMs containing traffic information and route information. This makes it possible to customize the fusion content according to each user's current activity status. Some or all of the above-described processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's current activity status data into the generation AI and have the generation AI customize the fusion content.

[0094] The fusion unit can estimate the user's emotions and adjust the fusion frequency of the LLMs based on the estimated user's emotions. For example, if the user is relaxed, the fusion unit increases the fusion frequency of the LLMs. For example, if the user is stressed, the fusion unit decreases the fusion frequency of the LLMs. For example, if the user is in a hurry, the fusion unit frequently fuses LLMs containing only important information. This adjusts the LLM fusion frequency according to the user's emotions, resulting in more appropriate LLMs being fused. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., an LLM) or a multimodal generation AI. Some or all of the above-described processing in the fusion unit is performed using the generation AI. For example, the fusion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the fusion frequency of the LLMs.

[0095] During LLM fusion, the fusion unit can adjust the fusion content taking into account each user's geographic location information. For example, if each user is in a specific area, the fusion unit fuses LLMs containing information related to that area. For example, if each user is traveling, the fusion unit fuses LLMs containing information related to the destination area. For example, if each user is in a tourist destination, the fusion unit fuses LLMs containing information related to the tourist destination. This enables optimization of the fusion content based on each user's geographic location information. Some or all of the above-described processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's geographic location information into the generation AI and have the generation AI adjust the fusion content.

[0096] During LLM fusion, the fusion unit can analyze each user's social media activity and reflect related information. For example, the fusion unit fuses LLMs containing information about the locations where each user checked in on social media. For example, the fusion unit analyzes each user's social media posts and fuses LLMs containing related information. For example, the fusion unit fuses LLMs containing related information by referring to the activities of each user's friends on social media. This makes it possible to reflect related information based on each user's social media activity. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's social media activity data into the generation AI and cause the generation AI to reflect related information.

[0097] During LLM fusion, the fusion unit can adjust the fusion algorithm by reflecting each user's past feedback. For example, the fusion unit adjusts the fusion algorithm based on feedback provided by each user in the past. For example, the fusion unit preferentially uses a specific fusion method based on each user's past feedback. For example, the fusion unit analyzes each user's past feedback and proposes an optimal fusion algorithm. This makes it possible to adjust the fusion algorithm based on each user's past feedback. Some or all of the above-mentioned processing in the fusion unit is performed using a generation AI. For example, the fusion unit can input each user's past feedback data into the generation AI and have the generation AI adjust the fusion algorithm.

[0098] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated user emotions. For example, if the user is nervous, the navigation unit provides a simple, highly visible navigation method. For example, if the user is relaxed, the navigation unit provides a navigation method that includes detailed information. For example, if the user is in a hurry, the navigation unit provides a navigation method that focuses on the main points. This allows for more appropriate navigation by adjusting the navigation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit is performed using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the navigation method.

[0099] During navigation, the navigation unit can adjust the route taking into account the balance of each user's hobbies and preferences. For example, the navigation unit proposes a route that evenly reflects each user's hobbies and preferences. For example, the navigation unit detects the balance of each user's hobbies and preferences in real time and adjusts the route. For example, the navigation unit analyzes the balance of each user's hobbies and preferences and proposes an optimal route. This makes it possible to optimize the route according to the balance of each user's hobbies and preferences. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's hobbies and preference data into the generation AI and have the generation AI adjust the route.

[0100] During navigation, the navigation unit can improve the accuracy of the route by referring to each user's past movement patterns. The navigation unit, for example, proposes an optimal route based on each user's past movement patterns. The navigation unit, for example, analyzes each user's past movement patterns to improve the accuracy of the route. The navigation unit, for example, improves the accuracy of the route by referring to each user's past movement patterns. This makes it possible to improve the accuracy of the route based on each user's past movement patterns. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's past movement pattern data into the generation AI and have the generation AI improve the accuracy of the route.

[0101] During navigation, the navigation unit can customize a route based on each user's current activity status. For example, if each user is sightseeing, the navigation unit proposes a route that includes tourist attractions. For example, if each user is shopping, the navigation unit proposes a route that includes store information and sale information. For example, if each user is traveling, the navigation unit proposes a route that includes traffic information and route information. This makes it possible to customize a route according to each user's current activity status. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's current activity status data into the generation AI and have the generation AI customize the route.

[0102] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. For example, if the user is nervous, the navigation unit prioritizes providing important information. For example, if the user is relaxed, the navigation unit prioritizes providing detailed information. For example, if the user is in a hurry, the navigation unit prioritizes providing information that covers the main points. This allows for more appropriate navigation by determining navigation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the navigation unit is performed using the generation AI. For example, the navigation unit can input user emotion data into the generation AI and have the generation AI determine the navigation priorities.

[0103] During navigation, the navigation unit can adjust the route taking into account the geographical location information of each user. For example, when each user is in a specific area, the navigation unit proposes a route including information related to that area. For example, when each user is traveling, the navigation unit proposes a route including information related to the destination area. For example, when each user is in a tourist spot, the navigation unit proposes a route including information related to the tourist spot. This makes it possible to optimize the route based on the geographical location information of each user. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input the geographical location information of each user into the generation AI and have the generation AI adjust the route.

[0104] The navigation unit can analyze each user's social media activity and reflect related information during navigation. For example, the navigation unit proposes a route including information about places where each user has checked in on social media. For example, the navigation unit analyzes each user's social media posts and proposes a route including related information. For example, the navigation unit proposes a route including related information by referring to the activities of each user's friends on social media. This makes it possible to reflect related information based on each user's social media activity. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit can input each user's social media activity data into the generation AI and have the generation AI reflect the related information.

[0105] During navigation, the navigation unit can adjust the route by reflecting each user's past feedback. For example, the navigation unit adjusts the route based on feedback provided by each user in the past. For example, the navigation unit preferentially uses a specific route based on each user's past feedback. For example, the navigation unit analyzes each user's past feedback and proposes an optimal route. This makes it possible to adjust the route based on each user's past feedback. Some or all of the above-mentioned processing in the navigation unit is performed using a generation AI. For example, the navigation unit inputs each user's past feedback data into the generation AI and causes the generation AI to adjust the route. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, fusion unit, and navigation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects each user's hobbies, preferences, and travel constraints using the camera 42 and microphone 38B of the smart device 14. The generation unit generates a personalized LLM based on the collected data using the specific processing unit 290 of the data processing device 12. The fusion unit fuses the generated LLMs using the specific processing unit 290 of the data processing device 12 to generate a new LLM. The navigation unit performs navigation based on the new LLM using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, fusion unit, and navigation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects each user's hobbies, preferences, and movement constraints using the camera 42 and microphone 238 of the smart glasses 214. The generation unit generates a personalized LLM based on the collected data using the specific processing unit 290 of the data processing device 12. The fusion unit fuses the generated LLMs using the specific processing unit 290 of the data processing device 12 to generate a new LLM. The navigation unit performs navigation based on the new LLM using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, fusion unit, and navigation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects each user's hobbies, preferences, and movement constraints using the camera 42 and microphone 238 of the headset type terminal 314. The generation unit generates a personalized LLM based on the collected data using the specific processing unit 290 of the data processing device 12. The fusion unit fuses the generated LLMs using the specific processing unit 290 of the data processing device 12 to generate a new LLM. The navigation unit performs navigation based on the new LLM using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, fusion unit, and navigation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects each user's hobbies, preferences, and movement constraints using the camera 42 and microphone 238 of the robot 414. The generation unit generates a personalized LLM based on the collected data using the specific processing unit 290 of the data processing device 12. The fusion unit fuses the generated LLMs using the specific processing unit 290 of the data processing device 12 to generate a new LLM. The navigation unit performs navigation based on the new LLM using the control unit 46A of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The navigation system can further include a health monitoring unit that monitors the user's health and adjusts navigation based on the user's health status. For example, if the user feels tired, it can suggest rest spots. If the user is not getting enough exercise, it can suggest routes to increase walking distance. If the user has a specific health problem, it can provide a route that takes that problem into consideration. This enables navigation that is tailored to the user's health status, resulting in healthier travel.

[0108] The navigation system may further include a travel history analysis unit that analyzes the user's past travel history and customizes navigation based on the past travel history. For example, the system may suggest a route that revisits places the user has previously visited, provide a route that excludes places the user has previously avoided, or generate a route that prioritizes places that the user has previously rated highly. This provides personalized navigation based on the user's past travel history.

[0109] The navigation system may further include a weather information acquisition unit that acquires real-time weather information from the user and adjusts navigation based on the weather information. For example, if it is raining, indoor tourist spots may be suggested. If it is sunny, a route including outdoor activities may be provided. If bad weather is predicted, a route that takes the weather into consideration may be generated. This allows for flexible navigation based on weather information.

[0110] The navigation system may further include a transportation consideration unit that considers the user's transportation mode and adjusts navigation based on the mode of transportation. For example, for a user using public transportation, a route that takes bus and train timetables into consideration may be proposed. For a user using bicycles, a route that includes bicycle-only lanes may be provided. For a user traveling on foot, a pedestrian-friendly route may be generated. This provides optimal navigation according to the user's transportation mode.

[0111] The navigation system may further include a language setting consideration unit that considers the user's language setting and provides navigation based on the language setting. For example, if the user speaks English, navigation in English is provided. If the user speaks Japanese, navigation in Japanese is provided. If multilingual support is required, navigation in multiple languages ​​is provided. This enables navigation according to the user's language setting.

[0112] The navigation system can further estimate the user's emotions and adjust the tone of the navigation based on the estimated user's emotions. For example, if the user is relaxed, the navigation system provides navigation in a calm tone. If the user is nervous, the navigation system provides navigation in a reassuring tone. If the user is excited, the navigation system provides navigation in a lively tone. This makes it possible to adjust the tone of the navigation according to the user's emotions.

[0113] The navigation system can further estimate the user's emotions and adjust the level of navigation detail based on the estimated user's emotions. For example, if the user is relaxed, detailed navigation information is provided. If the user is in a hurry, concise navigation information that focuses on the main points is provided. If the user is stressed, information to reduce stress is provided as a priority. This makes it possible to adjust the level of navigation detail according to the user's emotions.

[0114] The navigation system can further estimate the user's emotions and adjust the frequency of navigation based on the estimated user's emotions. For example, if the user is relaxed, navigation information is provided more frequently. If the user is stressed, navigation information is provided less frequently. If the user is in a hurry, important information is provided more frequently. This makes it possible to adjust the frequency of navigation according to the user's emotions.

[0115] The navigation system can further estimate the user's emotions and adjust the navigation route based on the estimated user's emotions. For example, if the user is relaxed, a scenic route is suggested. If the user is in a hurry, the shortest route is provided. If the user is stressed, a quiet route is suggested. This makes it possible to adjust the navigation route according to the user's emotions.

[0116] The navigation system can further estimate the user's emotions and adjust the navigation voice guidance based on the estimated user's emotions. For example, if the user is relaxed, the navigation system can provide guidance in a calm voice. If the user is nervous, the navigation system can provide guidance in a reassuring voice. If the user is excited, the navigation system can provide guidance in a lively voice. This makes it possible to adjust the navigation voice guidance according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects each user's hobbies, preferences, or travel constraints. For example, the collection unit collects data from each user's smartphone or wearable device. For example, the collection unit collects information such as User A's preference for tourist spots and User B's preference for shopping. Step 2: The generation unit generates an LLM personalized for each user based on the data collected by the collection unit. The generation unit generates an LLM that takes into account, for example, each user's hobbies, preferences, and travel constraints. The generation unit can also use a generation AI to generate an LLM that takes into account each user's hobbies, preferences, and travel constraints. Step 3: The fusion unit fuses the LLMs generated by the generation unit to generate a new LLM. The fusion unit, for example, fuses multiple LLMs to generate a new LLM. The fusion unit can also use the generation AI to fuse multiple LLMs to generate a new LLM. Step 4: The navigation unit performs navigation based on the new LLM generated by the fusion unit. The navigation unit performs navigation based on the new LLM, for example. The navigation unit can also perform navigation based on the new LLM using the generation AI.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects the hobbies, preferences, or travel restrictions of each user; a generating unit that generates a personalized LLM for each user based on the data collected by the collecting unit; a fusion unit that fuses the LLMs generated by the generation unit to generate a new LLM; a navigation unit that performs navigation based on the new LLM generated by the fusion unit. A system characterized by:

2. The collecting unit Collect data from each user's smartphone or wearable device 2. The system of claim 1.

3. The generation unit Generate LLMs that consider each user's preferences or travel constraints 2. The system of claim 1.

4. The fusion portion is Fusing multiple LLMs to generate a new LLM 2. The system of claim 1.

5. The navigation unit Navigation based on the new LLM 2. The system of claim 1.

6. The navigation unit Update data in real time to reflect user feedback 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past movement history and select an efficient data collection method 2. The system of claim 1.

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