System

Abstract

An object of a system according to an embodiment is to generate a going-out route on the basis of a desire of a user and effectively present information of a store that has paid an advertisement fee.SOLUTION: A system according to an embodiment includes a route generation unit, an information providing unit, and an advertisement management unit. The route generation unit generates a going-out route based on a desire of a user. The information providing unit provides information on the route generated by the route generation unit. The advertisement management part presents the information of the store having paid the advertisement cost at a high rank.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to automatically generate outing routes based on the user's wishes, and there is a lack of means to effectively present information about stores that have paid advertising fees.

[0005] The system according to the embodiment aims to generate an outing route based on the user's wishes and effectively present information about stores for which the user has paid advertising fees. [Means for solving the problem]

[0006] The system according to the embodiment includes a route generation unit, an information provision unit, and an advertisement management unit. The route generation unit generates an outing route based on the user's wishes. The information provision unit provides information about the route generated by the route generation unit. The advertisement management unit presents information about stores that have paid advertising fees at a higher level. [Effects of the Invention]

[0007] The system according to the embodiment can generate an outing route based on the user's wishes and effectively present information about stores for which the user has paid advertising fees. [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) The outing route creation system according to an embodiment of the present invention is a system in which a generation AI automatically creates an outing route that meets the user's wishes, provides the information, and displays information on stores that have paid advertising fees at a higher level. As a result, the outing route creation system can improve user convenience by proposing the optimal route based on the user's wishes, providing the information, and displaying information on stores that have paid advertising fees at a higher level.

[0029] An outing route creation system according to an embodiment includes a route generation unit, an information provision unit, and an advertisement management unit. The route generation unit generates an outing route based on a user's preferences. For example, the generation AI generates a route based on information such as the user's desired tourist spots and shops, ways to kill time before an event, and hidden spots with fewer crowds. The generation AI can also suggest an optimal route based on user input. The information provision unit provides information about the route generated by the route generation unit. For example, the generation AI provides detailed information about tourist spots and shops, their business hours, and access methods. The generation AI can also provide information such as photos and reviews of places the user will visit. The advertisement management unit presents information about stores that have paid advertising fees at a higher level. For example, the generation AI prioritizes displaying information about stores that have paid advertising fees and provides coupons and special offer information. The generation AI can also analyze ratings and reviews of stores that have paid advertising fees to provide reliable information. As a result, the outing route creation system according to the embodiment can improve user convenience by proposing optimal routes based on the user's wishes, providing information, and displaying information about stores that have paid advertising fees at the top of the list.

[0030] The route generation unit can learn the user's past behavioral history and preferences to generate a more personalized route. For example, the route generation unit collects the user's past visit history and evaluation data, and the generation AI proposes a personalized route based on that. For example, a new route that suits the user's preferences is generated based on the evaluations of tourist spots and shops visited in the past. The route generation unit also collects surveys and feedback to learn the user's preferences, and the generation AI proposes the optimal route based on that data. For example, the route generation unit customizes the route based on the user's favorite foods and activities of interest. The route generation unit also connects with the user's social media accounts and analyzes data on posts and places that the user has "liked," allowing the generation AI to propose a personalized route. For example, the generation AI generates a route that includes spots of interest based on photos and comments the user has shared on social media. This allows the system to learn the user's past behavioral history and preferences and generate a more personalized route.

[0031] The route generation unit reflects weather and traffic conditions in real time and can dynamically update the optimal route. For example, the route generation unit collects weather data in real time, and the generation AI dynamically updates the route based on that data. For example, if it looks like it might rain, the unit will propose a route that prioritizes indoor tourist spots. The route generation unit also analyzes traffic conditions in real time, and the generation AI proposes the optimal route that avoids congestion and delays. For example, it generates a route that allows for smooth travel while avoiding areas with heavy traffic. The route generation unit also integrates weather and traffic condition data, and the generation AI updates the optimal route in real time. For example, if the weather worsens or traffic congestion occurs, the unit will immediately propose an alternative route. This allows the optimal route to be dynamically updated, reflecting weather and traffic conditions in real time.

[0032] The information providing unit can suggest photo spots at places visited by the user and encourage sharing on social media. For example, the generation AI of the information providing unit suggests photo spots at tourist attractions and shops and creates a route that is easy for the user to share on social media. For example, it suggests a route that includes Instagrammable spots. The information providing unit also notifies the user of photo spots at places visited by the user in real time and encourages sharing on social media. For example, it generates a route that includes beautiful scenery at tourist attractions and unique artworks. The information providing unit also suggests photo spots that match the user's preferences and encourages sharing on social media. For example, it creates a route that includes spots that match the user's favorite theme or style. This allows the generation AI to suggest photo spots at places visited by the user and encourage sharing on social media.

[0033] The information provision unit can provide an audio guide along the route and explain the history and background of tourist spots. For example, the generation AI of the information provision unit provides an audio guide along the route and explains the history and background of tourist spots. For example, the audio guide is automatically played when the user arrives at a tourist spot. The information provision unit also provides an audio guide tailored to the user's preferences and explains detailed information about the tourist spots. For example, an audio guide including detailed historical commentary is provided for a user who loves history. The information provision unit also updates the audio guide in real time using the generation AI to provide information according to the places the user visits. For example, an audio guide including the latest information on tourist spots and event information is provided. This makes it possible to provide an audio guide along the route and explain the history and background of tourist spots.

[0034] The advertising management unit analyzes the ratings and reviews of businesses that have paid advertising fees and can provide highly reliable information. For example, the advertising management unit analyzes the ratings and reviews of businesses that have paid advertising fees using a generation AI and provides highly reliable information. For example, it evaluates the reliability of businesses based on highly rated reviews. The advertising management unit also analyzes the ratings and reviews of businesses that have paid advertising fees using a generation AI and provides optimal information based on the user's preferences. For example, it prioritizes suggestions of businesses that interest the user. The advertising management unit also updates the ratings and reviews of businesses that have paid advertising fees using a generation AI in real time and provides the user with the latest information. For example, it immediately reflects newly added reviews in suggestions. This makes it possible to analyze the ratings and reviews of businesses that have paid advertising fees and provide highly reliable information.

[0035] The advertising management unit can suggest optimal coupons based on a user's past purchasing history. In the advertising management unit, for example, the generation AI analyzes a user's past purchasing history and suggests optimal coupons based on that. For example, coupons related to products or services purchased in the past are provided. The advertising management unit also updates the optimal coupons in real time based on the user's purchasing history, providing the user with the latest information. For example, newly added coupons are immediately reflected in the suggestions. The advertising management unit also analyzes a user's purchasing history using the generation AI to suggest coupons that match the user's preferences. For example, coupons related to products or services in which the user is interested are provided. This makes it possible to suggest optimal coupons based on the user's past purchasing history.

[0036] The advertising management unit can provide information about special events and campaigns held by stores that have paid advertising fees. For example, the advertising management unit provides information about special events and campaigns held by stores that have paid advertising fees, and makes suggestions that will interest the user. For example, it makes suggestions that include limited-time events and special sales information. The advertising management unit also analyzes information about special events and campaigns held by stores that have paid advertising fees, based on the user's preferences, and provides optimal information. For example, it prioritizes suggestions about events that interest the user. The advertising management unit also updates information about special events and campaigns held by stores that have paid advertising fees in real time, providing the user with the latest information. For example, it immediately reflects newly added event information in suggestions. This makes it possible to provide information about special events and campaigns held by stores that have paid advertising fees.

[0037] The advertising management unit tracks coupon usage in real time and can propose effective customer acquisition strategies. In the advertising management unit, for example, the generation AI tracks coupon usage in real time and proposes effective customer acquisition strategies. For example, it proposes the optimal method of attracting customers based on the coupon usage rate. In addition, the advertising management unit has the generation AI propose effective customer acquisition strategies based on users' coupon usage data. For example, it provides coupons with high usage rates preferentially. In addition, the advertising management unit has the generation AI update coupon usage status in real time and provide users with the latest information. For example, it immediately reflects the usage status of newly added coupons in its proposals. This makes it possible to track coupon usage in real time and propose effective customer acquisition strategies.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The route generation unit can also monitor the user's health condition and suggest routes that take health into consideration. For example, it can use a pedometer or heart rate monitor to understand the user's exercise volume and suggest routes that allow for moderate exercise. It can also generate routes that include rest spots and places to hydrate based on the user's health condition. Furthermore, the generation AI can suggest restaurants where healthy meals are served based on the user's health data. This makes it possible to provide routes that take the user's health condition into consideration.

[0040] The route generation unit can also suggest routes along specific themes based on the user's hobbies and interests. For example, a route that visits art museums and galleries can be suggested to a user who loves art. A route that includes historical landmarks and museums can also be generated for a user who loves history. Furthermore, a route that visits restaurants and cafes where you can enjoy local specialties can be suggested to a user who enjoys eating out. This makes it possible to provide themed routes that match the user's hobbies and interests.

[0041] The route generation unit can also propose a route that suits the user's budget. For example, it can propose a route that includes tourist spots that can be enjoyed on a budget and free events. It can also generate a route that includes reasonably priced restaurants and cafes according to the user's budget. It can also provide discount coupons and special offer information according to the user's budget. This makes it possible to provide the optimal route according to the user's budget.

[0042] The information providing unit can also introduce the local culture and traditions of the places the user visits. For example, it can provide information about local festivals and events, allowing the user to experience the local culture. It can also introduce local traditional crafts and specialties, allowing the user to gain a deeper understanding of the charms of the area. It can also provide information about local history and legends, allowing the user to learn the background of the places they visit. This makes it possible to introduce the local culture and traditions of the places the user visits.

[0043] The information providing unit can also promote interactions with local people in the places the user visits. For example, it can collaborate with local guides and volunteers to provide users with opportunities to interact directly with local people. It can also provide information on local community events and workshops, allowing users to interact with people in the area. It can also provide information on hidden attractions and recommended spots by locals, allowing users to enjoy the area from a local perspective. This can promote interactions with local people in the places the user visits.

[0044] The advertising management department can also provide samples or free samples of related products based on the user's purchasing history. For example, it can provide samples of new products related to products previously purchased. The generation AI can also suggest the most suitable free samples based on the user's purchasing history. It can also provide free samples of specific brands or products based on the user's preferences. This makes it possible to provide samples or free samples of related products based on the user's purchasing history.

[0045] The advertising management department can also provide information about fan events and limited sales for specific brands and products based on the user's purchasing history. For example, it can provide information about fan events for brands that the user has previously purchased. The generation AI can also suggest optimal limited sales information based on the user's purchasing history. Furthermore, it can also provide information about special events for specific brands and products based on the user's preferences. This makes it possible to provide information about fan events and limited sales for specific brands and products based on the user's purchasing history.

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

[0047] Step 1: The route generation unit generates an outing route based on the user's preferences. For example, the generation AI generates a route based on information such as the user's desired tourist spots and shops, ways to kill time before an event, and hidden spots with fewer crowds. The generation AI can also suggest optimal routes based on the user's input. Step 2: The information provision unit provides information about the route generated by the route generation unit. For example, the generation AI can provide detailed information about tourist attractions and shops, their opening hours, and how to get there. The generation AI can also provide information such as photos and reviews of the places the user will visit. Step 3: The advertising management department presents information about stores that have paid advertising fees at the top. For example, the generation AI prioritizes displaying information about stores that have paid advertising fees and provides coupons and special offers. The generation AI can also analyze the ratings and reviews of stores that have paid advertising fees to provide highly reliable information.

[0048] (Example 2) The outing route creation system according to an embodiment of the present invention is a system in which a generation AI automatically creates an outing route that meets the user's wishes, provides the information, and displays information on stores that have paid advertising fees at a higher level. As a result, the outing route creation system can improve user convenience by proposing the optimal route based on the user's wishes, providing the information, and displaying information on stores that have paid advertising fees at a higher level.

[0049] An outing route creation system according to an embodiment includes a route generation unit, an information provision unit, and an advertisement management unit. The route generation unit generates an outing route based on a user's preferences. For example, the generation AI generates a route based on information such as the user's desired tourist spots and shops, ways to kill time before an event, and hidden spots with fewer crowds. The generation AI can also suggest an optimal route based on user input. The information provision unit provides information about the route generated by the route generation unit. For example, the generation AI provides detailed information about tourist spots and shops, their business hours, and access methods. The generation AI can also provide information such as photos and reviews of places the user will visit. The advertisement management unit presents information about stores that have paid advertising fees at a higher level. For example, the generation AI prioritizes displaying information about stores that have paid advertising fees and provides coupons and special offer information. The generation AI can also analyze ratings and reviews of stores that have paid advertising fees to provide reliable information. As a result, the outing route creation system according to the embodiment can improve user convenience by proposing optimal routes based on the user's wishes, providing information, and displaying information about stores that have paid advertising fees at the top of the list.

[0050] The route generation unit can learn the user's past behavioral history and preferences to generate a more personalized route. For example, the route generation unit collects the user's past visit history and evaluation data, and the generation AI proposes a personalized route based on that. For example, a new route that suits the user's preferences is generated based on the evaluations of tourist spots and shops visited in the past. The route generation unit also collects surveys and feedback to learn the user's preferences, and the generation AI proposes the optimal route based on that data. For example, the route generation unit customizes the route based on the user's favorite foods and activities of interest. The route generation unit also connects with the user's social media accounts and analyzes data on posts and places that the user has "liked," allowing the generation AI to propose a personalized route. For example, the generation AI generates a route that includes spots of interest based on photos and comments the user has shared on social media. This allows the system to learn the user's past behavioral history and preferences and generate a more personalized route.

[0051] The route generation unit reflects weather and traffic conditions in real time and can dynamically update the optimal route. For example, the route generation unit collects weather data in real time, and the generation AI dynamically updates the route based on that data. For example, if it looks like it might rain, the unit will propose a route that prioritizes indoor tourist spots. The route generation unit also analyzes traffic conditions in real time, and the generation AI proposes the optimal route that avoids congestion and delays. For example, it generates a route that allows for smooth travel while avoiding areas with heavy traffic. The route generation unit also integrates weather and traffic condition data, and the generation AI updates the optimal route in real time. For example, if the weather worsens or traffic congestion occurs, the unit will immediately propose an alternative route. This allows the optimal route to be dynamically updated, reflecting weather and traffic conditions in real time.

[0052] The route generation unit can use the emotion estimation function to suggest a route that matches the user's current mood. For example, the route generation unit analyzes the user's current mood using the emotion estimation function, and the generation AI suggests the optimal route based on the results. For example, for a user who wants to relax, the route generation unit suggests a route that includes quiet cafes and parks. The route generation unit also uses the emotion estimation function to suggest activities that match the user's mood. For example, for a user who is feeling active, the route generation unit generates a route that includes sports facilities and activities. The route generation unit also collects the user's emotion data in real time, and the generation AI dynamically updates the route based on that data. For example, if the user is tired, the generation AI suggests a route that includes many rest spots. This makes it possible to suggest a route that matches the user's current mood.

[0053] The information providing unit can suggest photo spots at places visited by the user and encourage sharing on social media. For example, the generation AI of the information providing unit suggests photo spots at tourist attractions and shops and creates a route that is easy for the user to share on social media. For example, it suggests a route that includes Instagrammable spots. The information providing unit also notifies the user of photo spots at places visited by the user in real time and encourages sharing on social media. For example, it generates a route that includes beautiful scenery at tourist attractions and unique artworks. The information providing unit also suggests photo spots that match the user's preferences and encourages sharing on social media. For example, it creates a route that includes spots that match the user's favorite theme or style. This allows the generation AI to suggest photo spots at places visited by the user and encourage sharing on social media.

[0054] The information provision unit can provide an audio guide along the route and explain the history and background of tourist spots. For example, the generation AI of the information provision unit provides an audio guide along the route and explains the history and background of tourist spots. For example, the audio guide is automatically played when the user arrives at a tourist spot. The information provision unit also provides an audio guide tailored to the user's preferences and explains detailed information about the tourist spots. For example, an audio guide including detailed historical commentary is provided for a user who loves history. The information provision unit also updates the audio guide in real time using the generation AI to provide information according to the places the user visits. For example, an audio guide including the latest information on tourist spots and event information is provided. This makes it possible to provide an audio guide along the route and explain the history and background of tourist spots.

[0055] The information providing unit can use the emotion estimation function to collect emotional responses from places visited by the user and reflect them in the generation of the next route. For example, the information providing unit uses the emotion estimation function to collect emotional responses from places visited by the user and reflect them in the generation of the next route. For example, places that the user enjoyed are prioritized to be included in the next route. Furthermore, the information providing unit allows the generation AI to optimize the next route based on the user's emotional response data. For example, the information providing unit suggests a route that includes places with many positive emotional responses. Furthermore, the information providing unit uses the emotion estimation function to collect the user's emotional data in real time and utilize it in the generation of the next route. For example, places where the user felt relaxed are included in the next route. In this way, emotional responses from places visited by the user can be collected and reflected in the generation of the next route.

[0056] The advertising management unit analyzes the ratings and reviews of businesses that have paid advertising fees and can provide highly reliable information. For example, the advertising management unit analyzes the ratings and reviews of businesses that have paid advertising fees using a generation AI and provides highly reliable information. For example, it evaluates the reliability of businesses based on highly rated reviews. The advertising management unit also analyzes the ratings and reviews of businesses that have paid advertising fees using a generation AI and provides optimal information based on the user's preferences. For example, it prioritizes suggestions of businesses that interest the user. The advertising management unit also updates the ratings and reviews of businesses that have paid advertising fees using a generation AI in real time and provides the user with the latest information. For example, it immediately reflects newly added reviews in suggestions. This makes it possible to analyze the ratings and reviews of businesses that have paid advertising fees and provide highly reliable information.

[0057] The advertising management unit can suggest optimal coupons based on a user's past purchasing history. In the advertising management unit, for example, the generation AI analyzes a user's past purchasing history and suggests optimal coupons based on that. For example, coupons related to products or services purchased in the past are provided. The advertising management unit also updates the optimal coupons in real time based on the user's purchasing history, providing the user with the latest information. For example, newly added coupons are immediately reflected in the suggestions. The advertising management unit also analyzes a user's purchasing history using the generation AI to suggest coupons that match the user's preferences. For example, coupons related to products or services in which the user is interested are provided. This makes it possible to suggest optimal coupons based on the user's past purchasing history.

[0058] The advertising management unit can use the emotion estimation function to provide optimal coupons based on the emotions a user feels toward a proposed coupon. For example, the advertising management unit uses the emotion estimation function to analyze the emotions a user feels toward a proposed coupon and provide optimal coupons based on that. For example, it prioritizes suggesting coupons that evoke positive emotions. The advertising management unit also uses a generation AI to suggest optimal coupons based on the user's emotion data. For example, it provides coupons related to products or services in which the user is interested. The advertising management unit also uses the emotion estimation function to collect user emotion data in real time and dynamically update coupons based on that data. For example, it includes coupons that evoke positive emotions in the next proposal. This allows optimal coupons to be provided based on the user's emotions toward the proposed coupon.

[0059] The advertising management unit can provide information about special events and campaigns held by stores that have paid advertising fees. For example, the advertising management unit provides information about special events and campaigns held by stores that have paid advertising fees, and makes suggestions that will interest the user. For example, it makes suggestions that include limited-time events and special sales information. The advertising management unit also analyzes information about special events and campaigns held by stores that have paid advertising fees, based on the user's preferences, and provides optimal information. For example, it prioritizes suggestions about events that interest the user. The advertising management unit also updates information about special events and campaigns held by stores that have paid advertising fees in real time, providing the user with the latest information. For example, it immediately reflects newly added event information in suggestions. This makes it possible to provide information about special events and campaigns held by stores that have paid advertising fees.

[0060] The advertising management unit tracks coupon usage in real time and can propose effective customer acquisition strategies. In the advertising management unit, for example, the generation AI tracks coupon usage in real time and proposes effective customer acquisition strategies. For example, it proposes the optimal method of attracting customers based on the coupon usage rate. In addition, the advertising management unit has the generation AI propose effective customer acquisition strategies based on users' coupon usage data. For example, it provides coupons with high usage rates preferentially. In addition, the advertising management unit has the generation AI update coupon usage status in real time and provide users with the latest information. For example, it immediately reflects the usage status of newly added coupons in its proposals. This makes it possible to track coupon usage in real time and propose effective customer acquisition strategies.

[0061] The advertising management unit can use the emotion estimation function to collect the emotions that users have toward proposed coupons and reflect them in the next proposal. For example, the advertising management unit uses the emotion estimation function to collect the emotions that users have toward proposed coupons and reflect them in the next proposal. For example, coupons that the user has positive emotions about are preferentially included in the next proposal. In addition, the advertising management unit uses a generation AI to optimize the next coupon proposal based on the user's emotional response data. For example, the proposal includes coupons that have a high number of positive emotional responses. In addition, the advertising management unit uses the emotion estimation function to collect the user's emotional data in real time and utilize it in the next coupon proposal. For example, places where the user found relaxing are included in the next proposal. In this way, the emotions that the user has toward proposed coupons can be collected and reflected in the next proposal.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The route generation unit can also monitor the user's health condition and suggest routes that take health into consideration. For example, it can use a pedometer or heart rate monitor to understand the user's exercise volume and suggest routes that allow for moderate exercise. It can also generate routes that include rest spots and places to hydrate based on the user's health condition. Furthermore, the generation AI can suggest restaurants where healthy meals are served based on the user's health data. This makes it possible to provide routes that take the user's health condition into consideration.

[0064] The route generation unit can also suggest routes along specific themes based on the user's hobbies and interests. For example, a route that visits art museums and galleries can be suggested to a user who loves art. A route that includes historical landmarks and museums can also be generated for a user who loves history. Furthermore, a route that visits restaurants and cafes where you can enjoy local specialties can be suggested to a user who enjoys eating out. This makes it possible to provide themed routes that match the user's hobbies and interests.

[0065] The route generation unit can also propose a route that suits the user's budget. For example, it can propose a route that includes tourist spots that can be enjoyed on a budget and free events. It can also generate a route that includes reasonably priced restaurants and cafes according to the user's budget. It can also provide discount coupons and special offer information according to the user's budget. This makes it possible to provide the optimal route according to the user's budget.

[0066] The route generation unit can also use the user's emotion estimation function to suggest a relaxing route to reduce the user's stress level. For example, it can suggest a route that includes a park or forest bathing spot where you can relax in nature. It can also generate a route that includes relaxing places such as quiet cafes and spas. Furthermore, it can suggest activities that have a high relaxing effect based on the user's emotion data. This makes it possible to provide a relaxing route to reduce the user's stress level.

[0067] The information providing unit can also introduce the local culture and traditions of the places the user visits. For example, it can provide information about local festivals and events, allowing the user to experience the local culture. It can also introduce local traditional crafts and specialties, allowing the user to gain a deeper understanding of the charms of the area. It can also provide information about local history and legends, allowing the user to learn the background of the places they visit. This makes it possible to introduce the local culture and traditions of the places the user visits.

[0068] The information providing unit can also promote interactions with local people in the places the user visits. For example, it can collaborate with local guides and volunteers to provide users with opportunities to interact directly with local people. It can also provide information on local community events and workshops, allowing users to interact with people in the area. It can also provide information on hidden attractions and recommended spots by locals, allowing users to enjoy the area from a local perspective. This can promote interactions with local people in the places the user visits.

[0069] The information provision unit can also use the emotion estimation function to suggest recommended activities for the next visit based on the user's emotional reactions at places they have visited. For example, activities that the user enjoyed can be included in the next route. The generation AI can also suggest new activities for the next visit based on the user's emotional data. Furthermore, it can also include places where the user found relaxation in the next route. This makes it possible to suggest recommended activities for the next visit based on the user's emotional reactions at places they have visited.

[0070] The advertising management department can also provide samples or free samples of related products based on the user's purchasing history. For example, it can provide samples of new products related to products previously purchased. The generation AI can also suggest the most suitable free samples based on the user's purchasing history. It can also provide free samples of specific brands or products based on the user's preferences. This makes it possible to provide samples or free samples of related products based on the user's purchasing history.

[0071] The advertising management department can also provide information about fan events and limited sales for specific brands and products based on the user's purchasing history. For example, it can provide information about fan events for brands that the user has previously purchased. The generation AI can also suggest optimal limited sales information based on the user's purchasing history. Furthermore, it can also provide information about special events for specific brands and products based on the user's preferences. This makes it possible to provide information about fan events and limited sales for specific brands and products based on the user's purchasing history.

[0072] The advertising management unit can also use the emotion estimation function to optimize the next coupon proposal based on the emotion the user feels toward the proposed coupon. For example, coupons that evoke positive emotions from the user can be included in the next proposal. The generation AI can also optimize the next coupon proposal based on the user's emotion data. Furthermore, it can also include places where the user found relaxing in the next coupon proposal. This makes it possible to optimize the next coupon proposal based on the emotion the user feels toward the proposed coupon.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The route generation unit generates an outing route based on the user's preferences. For example, the generation AI generates a route based on information such as the user's desired tourist spots and shops, ways to kill time before an event, and hidden spots with fewer crowds. The generation AI can also suggest optimal routes based on the user's input. Step 2: The information provision unit provides information about the route generated by the route generation unit. For example, the generation AI can provide detailed information about tourist attractions and shops, their opening hours, and how to get there. The generation AI can also provide information such as photos and reviews of the places the user will visit. Step 3: The advertising management department presents information about stores that have paid advertising fees at the top. For example, the generation AI prioritizes displaying information about stores that have paid advertising fees and provides coupons and special offers. The generation AI can also analyze the ratings and reviews of stores that have paid advertising fees to provide highly reliable information.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0119] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0142] 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 route generation unit that generates an outing route based on the user's wishes; an information providing unit that provides information about the route generated by the route generating unit; An advertising management unit that displays information on stores that have paid advertising fees at a higher level. A system characterized by:

2. The route generation unit Learn the user's past behavioral history and preferences to generate a more personalized route 2. The system of claim 1.

3. The route generation unit Dynamically update the best route based on real-time weather and traffic conditions 2. The system of claim 1.

4. The route generation unit Suggest a route that matches the user's current mood 2. The system of claim 1.

5. The information providing unit Proposes photo spots in places visited by the user and encourages sharing on the SNS 2. The system of claim 1.

Images