System

Abstract

To provide a system for the proposal of optimal routes to individual users in the use of public transportation and the improvement of overall efficiency.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit receives information related to movement from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a route based on the analysis result obtained by the analysis unit. The management unit manages interactions with individual users and improves overall efficiency.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it has not done enough to suggest optimal routes to individual users when using public transportation and to improve overall efficiency.

[0005] The system according to the embodiment aims to propose optimal routes to individual users when using public transportation, thereby improving overall efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit accepts information related to travel from a user. The analysis unit analyzes the information accepted by the reception unit. The proposal unit proposes a route based on the analysis result obtained by the analysis unit. The management unit manages interactions with each individual to improve overall efficiency. Effect of the Invention

[0007] The system according to the embodiment can suggest optimal routes to individual users when using public transportation, thereby improving overall efficiency. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The people flow optimization system according to the embodiment of the present invention is a system that optimizes people flow centered on public transportation. In this system, users communicate about their movements through chat, and information about the user's movements is collected, and an optimized route is proposed to each individual. The proposal is made by a generation AI. Furthermore, a mother AI manages the interactions with each individual, and reflects the movement proposals that aim for overall optimization in the proposals to the service users. For example, when multiple users move in the same direction at the same time, the mother AI detects this and proposes the optimal means of transportation. In addition to chat interactions, word-of-mouth information is collected from posts on SNS to achieve more accurate movement optimization. For example, congestion information and delay information on transportation on SNS are collected, and the movement route is adjusted based on that information. This mechanism allows users to move efficiently, and optimizes the use of public transportation. For example, a route that departs a little earlier can be proposed to avoid congestion during rush hour. In addition, the optimal route to avoid congestion can be proposed even when moving to a tourist spot. As a result, the people flow optimization system can efficiently optimize the movement of users and optimize the use of public transportation.

[0029] The people flow optimization system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit accepts information on movement from a user. The information on movement from a user includes, for example, a means of transportation, a starting point and a destination, and a travel time, but is not limited to such examples. The reception unit accepts the movement information from the user through, for example, a chat interface. The reception unit can also accept the user's movement information in real time. The analysis unit analyzes the information accepted by the reception unit. The analysis is performed using, for example, a data analysis method or an algorithm, but is not limited to such examples. For example, the analysis unit analyzes the user's movement pattern using a machine learning algorithm. The analysis unit can also analyze the information accepted in real time and adjust the movement route. The proposal unit proposes a route based on the analysis result obtained by the analysis unit. The route proposal is performed based on criteria such as, for example, the shortest distance, the shortest time, and consideration of traffic conditions, but is not limited to such examples. For example, the proposal unit proposes an optimal route using a generation AI. The proposal unit can also update the information on the user's movement in real time and make a proposal based on the latest information. The management unit manages interactions with each individual and achieves overall optimization. The management unit, for example, manages movement information of multiple users and makes suggestions to improve overall efficiency. For example, when multiple users move in the same direction at the same time, the management unit suggests the optimal means of transportation. The management unit can also manage user movement information in real time and achieve overall optimization. As a result, the people flow optimization system according to the embodiment can efficiently accept and analyze user movement information, propose optimal routes, and achieve overall optimization.

[0030] The reception unit receives information on travel from the user. The information on travel from the user includes, but is not limited to, for example, a means of transportation, a starting point and a destination, and travel time. The reception unit receives the travel information from the user through, for example, a chat interface. Specifically, the chat interface uses natural language processing technology to understand the user's input and asks appropriate questions to efficiently collect necessary information. For example, when a user inputs "I want to go from point A to point B at 8 o'clock tomorrow morning," the reception unit asks additional questions such as "What means of transportation will you use?" and "Do you want a specific route?" to collect detailed travel information. In addition, the reception unit can learn the user's past travel history and preferences and ask more personalized questions. Furthermore, the reception unit can also receive the user's travel information in real time. For example, even if the user changes his or her plans during travel, the new information can be immediately received through the chat interface and reflected in the entire system. This allows the reception unit to quickly and accurately collect information on the user's travel and smoothly provide the data to the next analysis unit.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using a data analysis method or algorithm, but is not limited to such examples. Specifically, the analysis unit analyzes the user's movement pattern using a machine learning algorithm. For example, based on past movement data, it predicts which route the user prefers at which time of day and proposes an optimal movement route. The analysis unit can also analyze the information received in real time and adjust the movement route. For example, it obtains traffic congestion and public transportation delay information in real time and proposes an optimal alternative route to the user. Furthermore, the analysis unit can detect unusual movement patterns and abnormal data using an anomaly detection algorithm and issue an early warning. As a result, the analysis unit can quickly and accurately analyze the user's movement information and build a foundation for providing an optimal movement route.

[0032] The suggestion unit proposes a route based on the analysis results obtained by the analysis unit. The route proposal is made based on criteria such as the shortest distance, the shortest time, and consideration of traffic conditions, but is not limited to such examples. Specifically, the suggestion unit proposes an optimal route using a generation AI. The generation AI simulates multiple routes based on the user's movement information and real-time traffic conditions, and selects the most efficient route. For example, the generation AI proposes a route that can be reached in the shortest time if the user is in a hurry, and proposes a scenic route if the user wants to enjoy the scenery. The suggestion unit can also update information about the user's movement in real time and make proposals based on the latest information. For example, the proposed route is instantly revised in response to traffic congestion or changes in weather, and notified to the user. As a result, the suggestion unit can provide an optimal movement route according to the user's needs, improving the efficiency and comfort of movement.

[0033] The management unit manages interactions with each individual and optimizes the system as a whole. For example, the management unit manages the travel information of multiple users and makes suggestions to improve overall efficiency. Specifically, when multiple users travel in the same direction at the same time, the management unit proposes the optimal means of transportation. For example, the management unit proposes carpooling or the use of a shuttle bus to multiple users heading to the same destination, thereby reducing traffic volume and improving travel efficiency. The management unit can also manage the user's travel information in real time and optimize the system as a whole. For example, when traffic volume is concentrated in a specific time period, the management unit proposes a change in travel time to the user to alleviate congestion during peak hours. Furthermore, the management unit collects user feedback and uses it to improve the system as a whole. For example, the user evaluates the proposed route and means of transportation, and improves the proposed algorithm based on the evaluation. This allows the management unit to efficiently manage the user's travel information and make optimal suggestions to improve overall travel efficiency.

[0034] The people flow optimization system includes a collection unit that collects information from SNS. The collection unit collects information from SNS. Information from SNS includes, for example, posts on X (formerly Twitter (registered trademark)) and check-in information on Facebook (registered trademark), but is not limited to such examples. The collection unit collects, for example, congestion information and transportation delay information on SNS. The collection unit can also collect word-of-mouth information on SNS and use it to adjust travel routes. For example, the collection unit proposes a route to avoid congestion based on congestion information on SNS. The collection unit can also propose a route to avoid delays based on transportation delay information on SNS. As a result, the collection unit can collect information from SNS to enable more accurate travel optimization.

[0035] The collection unit can collect congestion status or delay status of transportation on the SNS. The collection unit, for example, collects congestion information on the SNS. The congestion information includes, for example, the number of posts and the frequency of appearance of a specific keyword, but is not limited to such examples. The collection unit, for example, collects delay information of transportation on the SNS. The delay information includes, for example, delay time and cause of delay, but is not limited to such examples. The collection unit can adjust the travel route based on the congestion information on the SNS and the delay information of the transportation. For example, the collection unit proposes a route to avoid congestion based on the congestion information. The collection unit can also propose a route to avoid delay based on the delay information. This makes it possible for the collection unit to adjust the travel route by collecting congestion information on the SNS and delay information of the transportation.

[0036] The analysis unit can analyze the collected information and adjust the travel route. The analysis unit, for example, analyzes the collected information. The analysis is performed, for example, using a data analysis method or an algorithm, but is not limited to such examples. The analysis unit, for example, analyzes the collected congestion information and proposes a route to avoid congestion. The analysis unit can also analyze the collected delay information and propose a route to avoid delays. For example, the analysis unit analyzes the collected information using a machine learning algorithm and proposes an optimal travel route. This makes it possible for the analysis unit to adjust the travel route by analyzing the collected information.

[0037] The suggestion unit can update information related to the user's movement in real time. The suggestion unit, for example, updates information related to the user's movement in real time. Real-time updates include, for example, update frequency and technology used, but are not limited to such examples. The suggestion unit, for example, accepts the user's movement information in real time and makes a proposal based on the latest information. The suggestion unit can also adjust the movement route based on the information accepted in real time. For example, the suggestion unit proposes a route to avoid congestion based on congestion information updated in real time. The suggestion unit can also propose a route to avoid delays based on delay information updated in real time. As a result, the suggestion unit can make a proposal based on the latest information by updating information related to the user's movement in real time.

[0038] The management unit can manage the movement information of multiple users and improve overall efficiency. The management unit, for example, manages the movement information of multiple users. The management includes, for example, improving processing speed and optimizing resources, but is not limited to such examples. For example, when multiple users move in the same direction in the same time period, the management unit proposes an optimal means of transportation. In addition, the management unit can select a management method for avoiding congestion based on the movement information of multiple users. For example, the management unit manages the movement information of multiple users in real time and makes a proposal to improve overall efficiency. As a result, the management unit manages the movement information of multiple users, making it possible to achieve overall optimization.

[0039] The reception unit can analyze the user's past movement history and select the optimal reception method. The reception unit, for example, analyzes the user's past movement history. The past movement history includes, for example, past movement routes and means of transportation used, but is not limited to such examples. The reception unit, for example, preferentially receives means of transportation that the user has frequently used in the past. The reception unit can also analyze the user's past movement patterns and propose the optimal reception time. For example, the reception unit selects the optimal reception method for a specific day of the week or time period from the user's past movement history. As a result, the reception unit can select the optimal reception method by analyzing the user's past movement history.

[0040] The reception unit can perform filtering based on the user's current living situation and field of interest when receiving the travel information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when receiving the travel information. The living situation includes, for example, occupation, home environment, daily schedule, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, etc., but is not limited to such examples. For example, when the user is at work, the reception unit preferentially receives travel information related to work. In addition, when the user is on vacation, the reception unit can also preferentially receive travel information related to tourist spots and leisure facilities. For example, the reception unit filters and receives related travel information based on the user's field of interest. As a result, the reception unit can receive more relevant information by filtering the travel information based on the user's living situation and field of interest.

[0041] The reception unit can preferentially receive information with high relevance in consideration of the geographical location information of the user when receiving the movement information. For example, the reception unit preferentially receives information with high relevance in consideration of the geographical location information of the user when receiving the movement information. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, the reception unit preferentially receives movement information of a place close to the user's current location. The reception unit can also preferentially receive movement information of a place close to the user's destination. For example, the reception unit preferentially receives movement information of a place between the user's current location and the destination. Thereby, the reception unit can preferentially receive information with high relevance by considering the geographical location information of the user.

[0042] The reception unit can analyze the social media activity of the user and receive related information when receiving the movement information. For example, the reception unit analyzes the social media activity of the user when receiving the movement information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit preferentially receives movement information of a place mentioned by the user on SNS. In addition, the reception unit can also receive movement information based on the content of posts of accounts that the user follows on SNS. For example, the reception unit analyzes the activity history of the user on SNS and receives related movement information. As a result, the reception unit can preferentially receive related information by analyzing the social media activity of the user.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the movement information during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the movement information. The importance includes, for example, urgency and the scope of influence, but is not limited to such examples. For example, the analysis unit performs a detailed analysis of movement information with high importance. In addition, the analysis unit can also perform a simplified analysis of movement information with low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance. As a result, the analysis unit can perform a detailed analysis of more important information by adjusting the level of detail of the analysis based on the importance of the movement information.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the movement information during analysis. The analysis unit applies different analysis algorithms depending on the category of the movement information, for example. The categories include, but are not limited to, for example, by transportation means or by destination. For example, the analysis unit applies an analysis algorithm that takes into account traffic conditions to the movement information of public transportation. The analysis unit can also apply an analysis algorithm that takes into account road conditions to the movement information of private cars. For example, the analysis unit applies an analysis algorithm that takes into account pedestrian movement lines to the movement information of walking. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the movement information.

[0045] The analysis unit can determine the priority of the analysis based on the submission time of the movement information during the analysis. The analysis unit determines the priority of the analysis based on, for example, the submission time of the movement information. The submission time includes, for example, the submission date and time and the submission frequency, but is not limited to such examples. The analysis unit, for example, preferentially analyzes the latest movement information. In addition, the analysis unit can postpone the submission of movement information that has been submitted earlier. For example, the analysis unit adjusts the priority of the analysis in stages according to the submission time. As a result, the analysis unit can preferentially analyze the latest information by determining the priority of the analysis based on the submission time of the movement information.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the movement information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the movement information. The relevance includes, for example, the degree of coincidence of information and the frequency of occurrence of related keywords, but is not limited to such examples. For example, the analysis unit preferentially analyzes highly relevant movement information. In addition, the analysis unit can postpone the analysis of less relevant movement information. For example, the analysis unit adjusts the order of analysis step by step according to the relevance. Thereby, the analysis unit can preferentially analyze more relevant information by adjusting the order of analysis based on the relevance of the movement information.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel route at the time of proposal. The suggestion unit adjusts the level of detail of the proposal based on, for example, the importance of the travel route. The importance includes, for example, urgency and an impact range, but is not limited to such examples. For example, the suggestion unit makes a detailed proposal for a travel route with a high importance. In addition, the suggestion unit can also make a simplified proposal for a travel route with a low importance. For example, the suggestion unit adjusts the level of detail of the proposal in stages according to the importance. Thereby, the suggestion unit can make a detailed proposal for a more important route by adjusting the level of detail of the proposal based on the importance of the travel route.

[0048] The suggestion unit can apply different proposal algorithms depending on the category of the travel route at the time of proposal. The suggestion unit applies different proposal algorithms depending on the category of the travel route, for example. The category includes, for example, by transportation means or by destination, but is not limited to such examples. For example, the suggestion unit applies a proposal algorithm that takes into account traffic conditions to a travel route of a public transportation system. In addition, the suggestion unit can also apply a proposal algorithm that takes into account road conditions to a travel route of a private car. For example, the suggestion unit applies a proposal algorithm that takes into account pedestrian traffic lines to a travel route of a walking route. In this way, the suggestion unit can provide a more appropriate proposal by applying different proposal algorithms depending on the category of the travel route.

[0049] The suggestion unit can determine the priority of the proposal based on the submission time of the travel route at the time of proposal. The suggestion unit determines the priority of the proposal based on, for example, the submission time of the travel route. The submission time includes, for example, the submission date and time and the submission frequency, but is not limited to such examples. The suggestion unit, for example, preferentially proposes the latest travel route. In addition, the suggestion unit can postpone the travel route that was submitted earlier. For example, the suggestion unit gradually adjusts the priority of the proposal according to the submission time. Thereby, the suggestion unit can preferentially propose the latest information by determining the priority of the proposal based on the submission time of the travel route.

[0050] The suggestion unit can adjust the order of proposal based on the relevance of the travel route at the time of proposal. The suggestion unit adjusts the order of proposal based on, for example, the relevance of the travel route. The relevance includes, for example, the degree of coincidence of information and the frequency of appearance of related keywords, but is not limited to such examples. For example, the suggestion unit preferentially proposes a highly relevant travel route. In addition, the suggestion unit can postpone a less relevant travel route. For example, the suggestion unit adjusts the order of proposal in stages according to the relevance. Thereby, the suggestion unit can preferentially propose more relevant information by adjusting the order of proposal based on the relevance of the travel route.

[0051] The management unit can select an optimal management method by analyzing the movement information of multiple users during management. The management unit, for example, analyzes the movement information of multiple users. The analysis includes, for example, user behavior patterns and past data analysis, but is not limited to such examples. For example, when multiple users move in the same direction during the same time period, the management unit proposes an optimal means of transportation. In addition, the management unit can also select a management method to avoid congestion based on the movement information of multiple users. For example, the management unit manages the movement information of multiple users in real time and selects an efficient management method. As a result, the management unit can select an optimal management method by analyzing the movement information of multiple users.

[0052] The management unit can customize the management means based on the user's current living situation during management. The management unit customizes the management means based on the user's current living situation, for example. Living situations include, but are not limited to, for example, occupation, home environment, and daily schedule. For example, when the user is at work, the management unit preferentially manages travel information related to work. In addition, when the user is on vacation, the management unit can also preferentially manage travel information related to tourist spots and leisure facilities. For example, the management unit customizes and manages related travel information based on the user's living situation. As a result, the management unit can provide more appropriate management by customizing the management means based on the user's living situation.

[0053] The management unit can select an optimal management method in consideration of the geographical location information of the user during management. The management unit selects an optimal management method in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, the management unit preferentially manages movement information of a location close to the user's current location. The management unit can also preferentially manage movement information of a location close to the user's destination. For example, the management unit preferentially manages movement information of a location between the user's current location and the destination. This allows the management unit to select an optimal management method by considering the geographical location information of the user.

[0054] During management, the management unit can analyze the user's social media activity and suggest management measures. The management unit, for example, analyzes the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. For example, the management unit preferentially manages movement information of places mentioned by the user on SNS. The management unit can also manage movement information based on the content of posts of accounts the user follows on SNS. For example, the management unit analyzes the user's activity history on SNS and manages related movement information. As a result, the management unit can preferentially manage related information by analyzing the user's social media activity.

[0055] The collection unit can analyze the contents of posts on the SNS at the time of collection to collect optimal information. The collection unit, for example, analyzes the contents of posts on the SNS. The posted contents include, for example, congestion information and transportation delay information, but are not limited to such examples. The collection unit, for example, collects congestion information on the SNS and uses it to adjust the travel route. The collection unit can also collect transportation delay information on the SNS and uses it to adjust the travel route. For example, the collection unit collects word-of-mouth information on the SNS and uses it to adjust the travel route. In this way, the collection unit can collect optimal information by analyzing the contents of posts on the SNS.

[0056] The collection unit can improve the accuracy of collection by referring to the user's past posting history at the time of collection. The collection unit, for example, refers to the user's past posting history. The past posting history includes, for example, travel information posted in the past and information on a specific location, but is not limited to such examples. The collection unit, for example, collects related information based on travel information posted in the past by the user. The collection unit can also collect information on a specific location from the user's past posting history. For example, the collection unit analyzes the user's past posting history and collects optimal information. As a result, the collection unit can improve the accuracy of collection by referring to the user's past posting history.

[0057] The collection unit can collect information taking into consideration the geographical distribution on the SNS when collecting the information. The collection unit, for example, collects information taking into consideration the geographical distribution on the SNS. The geographical distribution includes, for example, the geographical distribution of posts and information on a specific region, but is not limited to such examples. The collection unit, for example, analyzes the geographical distribution of posts on the SNS and collects information on a specific region. The collection unit can also collect congestion information based on the geographical distribution on the SNS. For example, the collection unit collects transportation delay information taking into consideration the geographical distribution on the SNS. In this way, the collection unit can collect information on a specific region by taking into consideration the geographical distribution on the SNS.

[0058] The collection unit can improve the accuracy of collection by referring to related literature on the SNS during collection. The collection unit, for example, refers to related literature on the SNS. The related literature includes, for example, congestion information and transportation delay information, but is not limited to such examples. For example, the collection unit refers to related literature on the SNS to help adjust a travel route. The collection unit can also collect congestion information based on the related literature on the SNS. For example, the collection unit analyzes related literature on the SNS and collects transportation delay information. As a result, the collection unit can improve the accuracy of collection by referring to related literature on the SNS.

[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 analysis unit can take into account the user's past movement history when analyzing the user's movement pattern. For example, it can preferentially suggest transportation means and routes that have been frequently used in the past. It can also analyze movement patterns on specific days of the week or time periods from the past movement history and suggest optimal movement routes. Furthermore, it can learn the user's preferred transportation means and routes based on the past movement history and make individually customized suggestions. This allows the analysis unit to suggest more accurate movement routes by taking into account the user's past movement history.

[0061] When collecting information from the SNS, the collection unit can analyze the user's social media activities. For example, related movement information can be collected based on the accounts the user follows and the contents of posts that the user has liked. In addition, information about places and events mentioned by the user on the SNS can be preferentially collected. Furthermore, the user's activity history on the SNS can be analyzed to collect information based on specific interests and concerns. This allows the collection unit to collect more relevant information by analyzing the user's social media activities.

[0062] The collection unit can take geographical distribution into consideration when collecting information about congestion on SNS and delays in public transport. For example, the frequency and content of posts in a specific area can be analyzed to grasp the congestion situation in that area. In addition, when collecting delay information for a specific transport, posts from the area through which that transport passes can be collected preferentially. Furthermore, travel information about a specific area can be collected based on the geographical distribution, and the information can be used to adjust travel routes. This allows the collection unit to collect information with higher accuracy by taking geographical distribution into consideration.

[0063] When analyzing collected information, the analysis unit can apply different analysis algorithms depending on the category of movement information. For example, an analysis algorithm that takes into account traffic conditions can be applied to movement information of public transportation. Also, an analysis algorithm that takes into account road conditions can be applied to movement information of private vehicles. Furthermore, an analysis algorithm that takes into account pedestrian movement lines can be applied to walking movement information. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of movement information.

[0064] The suggestion unit can filter the information on the user's movement based on the user's current living situation and areas of interest when updating the information on the user's movement in real time. For example, when the user is at work, work-related movement information can be preferentially suggested. Also, when the user is on vacation, movement information related to tourist spots and leisure facilities can be preferentially suggested. Furthermore, related movement information can be filtered and suggested based on the user's areas of interest. As a result, the suggestion unit can suggest more relevant information by filtering the movement information based on the user's living situation and areas of interest.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives information about travel from the user. The information about travel from the user includes, for example, a means of transportation, a starting point and a destination, and travel time. The reception unit can receive travel information from the user through a chat interface, and can also receive information in real time. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using a data analysis method or algorithm, for example, a machine learning algorithm is used to analyze the user's movement pattern. The analysis unit can also analyze the information received in real time and adjust the movement route. Step 3: The suggestion unit proposes a route based on the analysis results obtained by the analysis unit. Route suggestions are based on criteria such as the shortest distance, shortest time, and consideration of traffic conditions. For example, a generation AI can be used to suggest optimal routes, updating information about the user's movements in real time and making suggestions based on the latest information. Step 4: The management unit manages interactions with each individual and aims for overall optimization. The management unit manages the movement information of multiple users and makes suggestions to improve overall efficiency. For example, if multiple users are traveling in the same direction at the same time, it will suggest the optimal means of transportation. It is also possible to manage user movement information in real time and aim for overall optimization.

[0067] (Example 2) The people flow optimization system according to the embodiment of the present invention is a system that optimizes people flow centered on public transportation. In this system, users communicate about their movements through chat, and information about the user's movements is collected, and an optimized route is proposed to each individual. The proposal is made by a generation AI. Furthermore, a mother AI manages the interactions with each individual, and reflects the movement proposals that aim for overall optimization in the proposals to the service users. For example, when multiple users move in the same direction at the same time, the mother AI detects this and proposes the optimal means of transportation. In addition to chat interactions, word-of-mouth information is collected from posts on SNS to achieve more accurate movement optimization. For example, congestion information and delay information on transportation on SNS are collected, and the movement route is adjusted based on that information. This mechanism allows users to move efficiently, and optimizes the use of public transportation. For example, a route that departs a little earlier can be proposed to avoid congestion during rush hour. In addition, the optimal route to avoid congestion can be proposed even when moving to a tourist spot. As a result, the people flow optimization system can efficiently optimize the movement of users and optimize the use of public transportation.

[0068] The people flow optimization system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit accepts information on movement from a user. The information on movement from a user includes, for example, a means of transportation, a starting point and a destination, and a travel time, but is not limited to such examples. The reception unit accepts the movement information from the user through, for example, a chat interface. The reception unit can also accept the user's movement information in real time. The analysis unit analyzes the information accepted by the reception unit. The analysis is performed using, for example, a data analysis method or an algorithm, but is not limited to such examples. For example, the analysis unit analyzes the user's movement pattern using a machine learning algorithm. The analysis unit can also analyze the information accepted in real time and adjust the movement route. The proposal unit proposes a route based on the analysis result obtained by the analysis unit. The route proposal is performed based on criteria such as, for example, the shortest distance, the shortest time, and consideration of traffic conditions, but is not limited to such examples. For example, the proposal unit proposes an optimal route using a generation AI. The proposal unit can also update the information on the user's movement in real time and make a proposal based on the latest information. The management unit manages interactions with each individual and achieves overall optimization. The management unit, for example, manages movement information of multiple users and makes suggestions to improve overall efficiency. For example, when multiple users move in the same direction at the same time, the management unit suggests the optimal means of transportation. The management unit can also manage user movement information in real time and achieve overall optimization. As a result, the people flow optimization system according to the embodiment can efficiently accept and analyze user movement information, propose optimal routes, and achieve overall optimization.

[0069] The reception unit receives information on travel from the user. The information on travel from the user includes, but is not limited to, for example, a means of transportation, a starting point and a destination, and travel time. The reception unit receives the travel information from the user through, for example, a chat interface. Specifically, the chat interface uses natural language processing technology to understand the user's input and asks appropriate questions to efficiently collect necessary information. For example, when a user inputs "I want to go from point A to point B at 8 o'clock tomorrow morning," the reception unit asks additional questions such as "What means of transportation will you use?" and "Do you want a specific route?" to collect detailed travel information. In addition, the reception unit can learn the user's past travel history and preferences and ask more personalized questions. Furthermore, the reception unit can also receive the user's travel information in real time. For example, even if the user changes his or her plans during travel, the new information can be immediately received through the chat interface and reflected in the entire system. This allows the reception unit to quickly and accurately collect information on the user's travel and smoothly provide the data to the next analysis unit.

[0070] The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using a data analysis method or algorithm, but is not limited to such examples. Specifically, the analysis unit analyzes the user's movement pattern using a machine learning algorithm. For example, based on past movement data, it predicts which route the user prefers at which time of day and proposes an optimal movement route. The analysis unit can also analyze the information received in real time and adjust the movement route. For example, it obtains traffic congestion and public transportation delay information in real time and proposes an optimal alternative route to the user. Furthermore, the analysis unit can detect unusual movement patterns and abnormal data using an anomaly detection algorithm and issue an early warning. As a result, the analysis unit can quickly and accurately analyze the user's movement information and build a foundation for providing an optimal movement route.

[0071] The suggestion unit proposes a route based on the analysis results obtained by the analysis unit. The route proposal is made based on criteria such as the shortest distance, the shortest time, and consideration of traffic conditions, but is not limited to such examples. Specifically, the suggestion unit proposes an optimal route using a generation AI. The generation AI simulates multiple routes based on the user's movement information and real-time traffic conditions, and selects the most efficient route. For example, the generation AI proposes a route that can be reached in the shortest time if the user is in a hurry, and proposes a scenic route if the user wants to enjoy the scenery. The suggestion unit can also update information about the user's movement in real time and make proposals based on the latest information. For example, the proposed route is instantly revised in response to traffic congestion or changes in weather, and notified to the user. As a result, the suggestion unit can provide an optimal movement route according to the user's needs, improving the efficiency and comfort of movement.

[0072] The management unit manages interactions with each individual and optimizes the system as a whole. For example, the management unit manages the travel information of multiple users and makes suggestions to improve overall efficiency. Specifically, when multiple users travel in the same direction at the same time, the management unit proposes the optimal means of transportation. For example, the management unit proposes carpooling or the use of a shuttle bus to multiple users heading to the same destination, thereby reducing traffic volume and improving travel efficiency. The management unit can also manage the user's travel information in real time and optimize the system as a whole. For example, when traffic volume is concentrated in a specific time period, the management unit proposes a change in travel time to the user to alleviate congestion during peak hours. Furthermore, the management unit collects user feedback and uses it to improve the system as a whole. For example, the user evaluates the proposed route and means of transportation, and improves the proposed algorithm based on the evaluation. This allows the management unit to efficiently manage the user's travel information and make optimal suggestions to improve overall travel efficiency.

[0073] The people flow optimization system includes a collection unit that collects information from SNS. The collection unit collects information from SNS. Information from SNS includes, for example, posts on X (formerly Twitter) and check-in information on Facebook, but is not limited to such examples. The collection unit collects, for example, congestion information and transportation delay information on SNS. The collection unit can also collect word-of-mouth information on SNS and use it to adjust travel routes. For example, the collection unit suggests a route to avoid congestion based on congestion information on SNS. The collection unit can also suggest a route to avoid delays based on transportation delay information on SNS. As a result, the collection unit can collect information from SNS to enable more accurate travel optimization.

[0074] The collection unit can collect congestion status or delay status of transportation on the SNS. The collection unit, for example, collects congestion information on the SNS. The congestion information includes, for example, the number of posts and the frequency of appearance of a specific keyword, but is not limited to such examples. The collection unit, for example, collects delay information of transportation on the SNS. The delay information includes, for example, delay time and cause of delay, but is not limited to such examples. The collection unit can adjust the travel route based on the congestion information on the SNS and the delay information of the transportation. For example, the collection unit proposes a route to avoid congestion based on the congestion information. The collection unit can also propose a route to avoid delay based on the delay information. This makes it possible for the collection unit to adjust the travel route by collecting congestion information on the SNS and delay information of the transportation.

[0075] The analysis unit can analyze the collected information and adjust the travel route. The analysis unit, for example, analyzes the collected information. The analysis is performed, for example, using a data analysis method or an algorithm, but is not limited to such examples. The analysis unit, for example, analyzes the collected congestion information and proposes a route to avoid congestion. The analysis unit can also analyze the collected delay information and propose a route to avoid delays. For example, the analysis unit analyzes the collected information using a machine learning algorithm and proposes an optimal travel route. This makes it possible for the analysis unit to adjust the travel route by analyzing the collected information.

[0076] The suggestion unit can update information related to the user's movement in real time. The suggestion unit, for example, updates information related to the user's movement in real time. Real-time updates include, for example, update frequency and technology used, but are not limited to such examples. The suggestion unit, for example, accepts the user's movement information in real time and makes a proposal based on the latest information. The suggestion unit can also adjust the movement route based on the information accepted in real time. For example, the suggestion unit proposes a route to avoid congestion based on congestion information updated in real time. The suggestion unit can also propose a route to avoid delays based on delay information updated in real time. As a result, the suggestion unit can make a proposal based on the latest information by updating information related to the user's movement in real time.

[0077] The management unit can manage the movement information of multiple users and improve overall efficiency. The management unit, for example, manages the movement information of multiple users. The management includes, for example, improving processing speed and optimizing resources, but is not limited to such examples. For example, when multiple users move in the same direction in the same time period, the management unit proposes an optimal means of transportation. In addition, the management unit can select a management method for avoiding congestion based on the movement information of multiple users. For example, the management unit manages the movement information of multiple users in real time and makes a proposal to improve overall efficiency. As a result, the management unit manages the movement information of multiple users, making it possible to achieve overall optimization.

[0078] The reception unit can estimate the user's emotion and adjust the timing of receiving the movement information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is stressed, the reception unit delays the reception timing and receives information in a relaxed state. In addition, when the user is in a hurry, the reception unit can also immediately receive the movement information. For example, when the user is relaxed, the reception unit receives the movement information at an appropriate timing. Thereby, the reception unit can receive information at a more appropriate timing by adjusting the reception timing of the movement information based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The reception unit can analyze the user's past movement history and select the optimal reception method. The reception unit, for example, analyzes the user's past movement history. The past movement history includes, for example, past movement routes and means of transportation used, but is not limited to such examples. The reception unit, for example, preferentially receives means of transportation that the user has frequently used in the past. The reception unit can also analyze the user's past movement patterns and propose the optimal reception time. For example, the reception unit selects the optimal reception method for a specific day of the week or time period from the user's past movement history. As a result, the reception unit can select the optimal reception method by analyzing the user's past movement history.

[0080] The reception unit can perform filtering based on the user's current living situation and field of interest when receiving the travel information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when receiving the travel information. The living situation includes, for example, occupation, home environment, daily schedule, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, topics of interest, etc., but is not limited to such examples. For example, when the user is at work, the reception unit preferentially receives travel information related to work. In addition, when the user is on vacation, the reception unit can also preferentially receive travel information related to tourist spots and leisure facilities. For example, the reception unit filters and receives related travel information based on the user's field of interest. As a result, the reception unit can receive more relevant information by filtering the travel information based on the user's living situation and field of interest.

[0081] The reception unit can estimate the user's emotion and determine the priority of the movement information to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. The emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is stressed, the reception unit postpones information of low importance. In addition, when the user is relaxed, the reception unit can also preferentially receive information of high importance. For example, when the user is in a hurry, the reception unit preferentially receives information of high urgency. In this way, the reception unit can preferentially receive more important information by determining the priority of the movement information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The reception unit can preferentially receive information with high relevance in consideration of the geographical location information of the user when receiving the movement information. For example, the reception unit preferentially receives information with high relevance in consideration of the geographical location information of the user when receiving the movement information. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, the reception unit preferentially receives movement information of a place close to the user's current location. The reception unit can also preferentially receive movement information of a place close to the user's destination. For example, the reception unit preferentially receives movement information of a place between the user's current location and the destination. Thereby, the reception unit can preferentially receive information with high relevance by considering the geographical location information of the user.

[0083] The reception unit can analyze the social media activity of the user and receive related information when receiving the movement information. For example, the reception unit analyzes the social media activity of the user when receiving the movement information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit preferentially receives movement information of a place mentioned by the user on SNS. In addition, the reception unit can also receive movement information based on the content of posts of accounts that the user follows on SNS. For example, the reception unit analyzes the activity history of the user on SNS and receives related movement information. As a result, the reception unit can preferentially receive related information by analyzing the social media activity of the user.

[0084] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to such examples. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, when the user is in a hurry, the analysis unit provides an analysis result that covers the main points. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the movement information during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the movement information. The importance includes, for example, urgency and the scope of influence, but is not limited to such examples. For example, the analysis unit performs a detailed analysis of movement information with high importance. In addition, the analysis unit can also perform a simplified analysis of movement information with low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance. As a result, the analysis unit can perform a detailed analysis of more important information by adjusting the level of detail of the analysis based on the importance of the movement information.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the movement information during analysis. The analysis unit applies different analysis algorithms depending on the category of the movement information, for example. The categories include, but are not limited to, for example, by transportation means or by destination. For example, the analysis unit applies an analysis algorithm that takes into account traffic conditions to the movement information of public transportation. The analysis unit can also apply an analysis algorithm that takes into account road conditions to the movement information of private cars. For example, the analysis unit applies an analysis algorithm that takes into account pedestrian movement lines to the movement information of walking. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of the movement information.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to such examples. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, when the user is excited, the analysis unit provides a visually stimulating analysis result. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The analysis unit can determine the priority of the analysis based on the submission time of the movement information during the analysis. The analysis unit determines the priority of the analysis based on, for example, the submission time of the movement information. The submission time includes, for example, the submission date and time and the submission frequency, but is not limited to such examples. The analysis unit, for example, preferentially analyzes the latest movement information. In addition, the analysis unit can postpone the submission of movement information that has been submitted earlier. For example, the analysis unit adjusts the priority of the analysis in stages according to the submission time. As a result, the analysis unit can preferentially analyze the latest information by determining the priority of the analysis based on the submission time of the movement information.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the movement information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the movement information. The relevance includes, for example, the degree of coincidence of information and the frequency of occurrence of related keywords, but is not limited to such examples. For example, the analysis unit preferentially analyzes highly relevant movement information. In addition, the analysis unit can postpone the analysis of less relevant movement information. For example, the analysis unit adjusts the order of analysis step by step according to the relevance. Thereby, the analysis unit can preferentially analyze more relevant information by adjusting the order of analysis based on the relevance of the movement information.

[0090] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. The emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is nervous, the suggestion unit provides a simple and highly visible proposal. In addition, the suggestion unit can also provide a detailed proposal when the user is relaxed. For example, when the user is in a hurry, the suggestion unit provides a proposal that covers the main points. Thereby, the suggestion unit can provide a more appropriate proposal by adjusting the way of expressing the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The suggestion unit can adjust the level of detail of the proposal based on the importance of the travel route at the time of proposal. The suggestion unit adjusts the level of detail of the proposal based on, for example, the importance of the travel route. The importance includes, for example, urgency and an impact range, but is not limited to such examples. For example, the suggestion unit makes a detailed proposal for a travel route with a high importance. In addition, the suggestion unit can also make a simplified proposal for a travel route with a low importance. For example, the suggestion unit adjusts the level of detail of the proposal in stages according to the importance. Thereby, the suggestion unit can make a detailed proposal for a more important route by adjusting the level of detail of the proposal based on the importance of the travel route.

[0092] The suggestion unit can apply different proposal algorithms depending on the category of the travel route at the time of proposal. The suggestion unit applies different proposal algorithms depending on the category of the travel route, for example. The category includes, for example, by transportation means or by destination, but is not limited to such examples. For example, the suggestion unit applies a proposal algorithm that takes into account traffic conditions to a travel route of a public transportation system. In addition, the suggestion unit can also apply a proposal algorithm that takes into account road conditions to a travel route of a private car. For example, the suggestion unit applies a proposal algorithm that takes into account pedestrian traffic lines to a travel route of a walking route. In this way, the suggestion unit can provide a more appropriate proposal by applying different proposal algorithms depending on the category of the travel route.

[0093] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. The emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, the suggestion unit provides a short and to-the-point proposal when the user is in a hurry. The suggestion unit can also provide a detailed proposal when the user is relaxed. For example, the suggestion unit provides a visually stimulating proposal when the user is excited. Thereby, the suggestion unit can provide a more appropriate proposal by adjusting the length of the proposal based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The suggestion unit can determine the priority of the proposal based on the submission time of the travel route at the time of proposal. The suggestion unit determines the priority of the proposal based on, for example, the submission time of the travel route. The submission time includes, for example, the submission date and time and the submission frequency, but is not limited to such examples. The suggestion unit, for example, preferentially proposes the latest travel route. In addition, the suggestion unit can postpone the travel route that was submitted earlier. For example, the suggestion unit gradually adjusts the priority of the proposal according to the submission time. Thereby, the suggestion unit can preferentially propose the latest information by determining the priority of the proposal based on the submission time of the travel route.

[0095] The suggestion unit can adjust the order of proposal based on the relevance of the travel route at the time of proposal. The suggestion unit adjusts the order of proposal based on, for example, the relevance of the travel route. The relevance includes, for example, the degree of coincidence of information and the frequency of appearance of related keywords, but is not limited to such examples. For example, the suggestion unit preferentially proposes a highly relevant travel route. In addition, the suggestion unit can postpone a less relevant travel route. For example, the suggestion unit adjusts the order of proposal in stages according to the relevance. Thereby, the suggestion unit can preferentially propose more relevant information by adjusting the order of proposal based on the relevance of the travel route.

[0096] The management unit can estimate the user's emotion and adjust the management method based on the estimated user's emotion. The management unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to such examples. For example, when the user is nervous, the management unit provides a simple and highly visible management method. In addition, the management unit can also provide a detailed management method when the user is relaxed. For example, when the user is in a hurry, the management unit provides a management method that focuses on the main points. In this way, the management unit can provide more appropriate management by adjusting the management method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The management unit can select an optimal management method by analyzing the movement information of multiple users during management. The management unit, for example, analyzes the movement information of multiple users. The analysis includes, for example, user behavior patterns and past data analysis, but is not limited to such examples. For example, when multiple users move in the same direction during the same time period, the management unit proposes an optimal means of transportation. In addition, the management unit can also select a management method to avoid congestion based on the movement information of multiple users. For example, the management unit manages the movement information of multiple users in real time and selects an efficient management method. As a result, the management unit can select an optimal management method by analyzing the movement information of multiple users.

[0098] The management unit can customize the management means based on the user's current living situation during management. The management unit customizes the management means based on the user's current living situation, for example. Living situations include, but are not limited to, for example, occupation, home environment, and daily schedule. For example, when the user is at work, the management unit preferentially manages travel information related to work. In addition, when the user is on vacation, the management unit can also preferentially manage travel information related to tourist spots and leisure facilities. For example, the management unit customizes and manages related travel information based on the user's living situation. As a result, the management unit can provide more appropriate management by customizing the management means based on the user's living situation.

[0099] The management unit can estimate the user's emotion and determine the priority of management based on the estimated user's emotion. The management unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is stressed, the management unit postpones information of low importance. In addition, when the user is relaxed, the management unit can also manage information of high importance with priority. For example, when the user is in a hurry, the management unit manages information of high urgency with priority. In this way, the management unit can manage more important information with priority by determining the priority of management based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The management unit can select an optimal management method in consideration of the geographical location information of the user during management. The management unit selects an optimal management method in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, the management unit preferentially manages movement information of a location close to the user's current location. The management unit can also preferentially manage movement information of a location close to the user's destination. For example, the management unit preferentially manages movement information of a location between the user's current location and the destination. This allows the management unit to select an optimal management method by considering the geographical location information of the user.

[0101] During management, the management unit can analyze the user's social media activity and suggest management measures. The management unit, for example, analyzes the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. For example, the management unit preferentially manages movement information of places mentioned by the user on SNS. The management unit can also manage movement information based on the content of posts of accounts the user follows on SNS. For example, the management unit analyzes the user's activity history on SNS and manages related movement information. As a result, the management unit can preferentially manage related information by analyzing the user's social media activity.

[0102] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is stressed, the collection unit postpones information of low importance. In addition, when the user is relaxed, the collection unit can also collect information of high importance with priority. For example, when the user is in a hurry, the collection unit collects information of high urgency with priority. Thereby, the collection unit can collect more important information with priority by determining the priority of information to be collected based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The collection unit can analyze the contents of posts on the SNS at the time of collection to collect optimal information. The collection unit, for example, analyzes the contents of posts on the SNS. The posted contents include, for example, congestion information and transportation delay information, but are not limited to such examples. The collection unit, for example, collects congestion information on the SNS and uses it to adjust the travel route. The collection unit can also collect transportation delay information on the SNS and uses it to adjust the travel route. For example, the collection unit collects word-of-mouth information on the SNS and uses it to adjust the travel route. In this way, the collection unit can collect optimal information by analyzing the contents of posts on the SNS.

[0104] The collection unit can improve the accuracy of collection by referring to the user's past posting history at the time of collection. The collection unit, for example, refers to the user's past posting history. The past posting history includes, for example, travel information posted in the past and information on a specific location, but is not limited to such examples. The collection unit, for example, collects related information based on travel information posted in the past by the user. The collection unit can also collect information on a specific location from the user's past posting history. For example, the collection unit analyzes the user's past posting history and collects optimal information. As a result, the collection unit can improve the accuracy of collection by referring to the user's past posting history.

[0105] The collection unit can estimate the user's emotion and adjust the display method of the information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to such examples. For example, when the user is nervous, the collection unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the collection unit can also provide a display method that includes detailed information. For example, when the user is in a hurry, the collection unit provides a display method that focuses on the main points. This allows the collection unit to provide more appropriate information by adjusting the display method of information based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The collection unit can collect information taking into consideration the geographical distribution on the SNS when collecting the information. The collection unit, for example, collects information taking into consideration the geographical distribution on the SNS. The geographical distribution includes, for example, the geographical distribution of posts and information on a specific region, but is not limited to such examples. The collection unit, for example, analyzes the geographical distribution of posts on the SNS and collects information on a specific region. The collection unit can also collect congestion information based on the geographical distribution on the SNS. For example, the collection unit collects transportation delay information taking into consideration the geographical distribution on the SNS. In this way, the collection unit can collect information on a specific region by taking into consideration the geographical distribution on the SNS.

[0107] The collection unit can improve the accuracy of collection by referring to related literature on the SNS during collection. The collection unit, for example, refers to related literature on the SNS. The related literature includes, for example, congestion information and transportation delay information, but is not limited to such examples. For example, the collection unit refers to related literature on the SNS to help adjust a travel route. The collection unit can also collect congestion information based on the related literature on the SNS. For example, the collection unit analyzes related literature on the SNS and collects transportation delay information. As a result, the collection unit can improve the accuracy of collection by referring to related literature on the SNS.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The analysis unit can take into account the user's past movement history when analyzing the user's movement pattern. For example, it can preferentially suggest transportation means and routes that have been frequently used in the past. It can also analyze movement patterns on specific days of the week or time periods from the past movement history and suggest optimal movement routes. Furthermore, it can learn the user's preferred transportation means and routes based on the past movement history and make individually customized suggestions. This allows the analysis unit to suggest more accurate movement routes by taking into account the user's past movement history.

[0110] When collecting information from the SNS, the collection unit can analyze the user's social media activities. For example, related movement information can be collected based on the accounts the user follows and the contents of posts that the user has liked. In addition, information about places and events mentioned by the user on the SNS can be preferentially collected. Furthermore, the user's activity history on the SNS can be analyzed to collect information based on specific interests and concerns. This allows the collection unit to collect more relevant information by analyzing the user's social media activities.

[0111] The collection unit can take geographical distribution into consideration when collecting information about congestion on SNS and delays in public transport. For example, the frequency and content of posts in a specific area can be analyzed to grasp the congestion situation in that area. In addition, when collecting delay information for a specific transport, posts from the area through which that transport passes can be collected preferentially. Furthermore, travel information about a specific area can be collected based on the geographical distribution, and the information can be used to adjust travel routes. This allows the collection unit to collect information with higher accuracy by taking geographical distribution into consideration.

[0112] When analyzing collected information, the analysis unit can apply different analysis algorithms depending on the category of movement information. For example, an analysis algorithm that takes into account traffic conditions can be applied to movement information of public transportation. Also, an analysis algorithm that takes into account road conditions can be applied to movement information of private vehicles. Furthermore, an analysis algorithm that takes into account pedestrian movement lines can be applied to walking movement information. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of movement information.

[0113] The suggestion unit can filter the information on the user's movement based on the user's current living situation and areas of interest when updating the information on the user's movement in real time. For example, when the user is at work, work-related movement information can be preferentially suggested. Also, when the user is on vacation, movement information related to tourist spots and leisure facilities can be preferentially suggested. Furthermore, related movement information can be filtered and suggested based on the user's areas of interest. As a result, the suggestion unit can suggest more relevant information by filtering the movement information based on the user's living situation and areas of interest.

[0114] When managing the movement information of multiple users, the management unit can estimate the emotions of the users and adjust the management method based on the estimated emotions of the users. For example, if the user is nervous, a simple and highly visible management method can be provided. Also, if the user is relaxed, a detailed management method can be provided. Furthermore, if the user is in a hurry, a management method that focuses on the main points can be provided. This allows the management unit to provide more appropriate management by adjusting the management method based on the emotions of the users.

[0115] The reception unit can estimate the user's emotions and adjust the timing of receiving the travel information based on the estimated user's emotions. For example, if the user is stressed, the reception timing can be delayed to receive information in a relaxed state. Also, if the user is in a hurry, the travel information can be received immediately. Furthermore, if the user is relaxed, the travel information can be received at an appropriate timing. As a result, the reception unit can receive information at a more appropriate timing by adjusting the reception timing of the travel information based on the user's emotions.

[0116] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions.

[0117] The suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is expressed based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be provided. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way in which the suggestion is expressed based on the user's emotion.

[0118] The collection unit can estimate the user's emotions and determine the priority order of information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, information of low importance can be postponed. Also, when the user is relaxed, information of high importance can be collected with priority. Furthermore, when the user is in a hurry, information of high urgency can be collected with priority. In this way, the collection unit can prioritize collecting more important information by determining the priority order of information to be collected based on the user's emotions.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit receives information about travel from the user. The information about travel from the user includes, for example, a means of transportation, a starting point and a destination, and travel time. The reception unit can receive travel information from the user through a chat interface, and can also receive information in real time. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using a data analysis method or algorithm, for example, a machine learning algorithm is used to analyze the user's movement pattern. The analysis unit can also analyze the information received in real time and adjust the movement route. Step 3: The suggestion unit proposes a route based on the analysis results obtained by the analysis unit. Route suggestions are based on criteria such as the shortest distance, shortest time, and consideration of traffic conditions. For example, a generation AI can be used to suggest optimal routes, updating information about the user's movements in real time and making suggestions based on the latest information. Step 4: The management unit manages interactions with each individual and aims for overall optimization. The management unit manages the movement information of multiple users and makes suggestions to improve overall efficiency. For example, if multiple users are traveling in the same direction at the same time, it will suggest the optimal means of transportation. It is also possible to manage user movement information in real time and aim for overall optimization.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, management unit, and collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts travel information from a user through a chat interface of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal route based on the analysis result. The management unit is realized by the specific processing unit 290 of the data processing device 12 and aims for overall optimization. The collection unit collects information from the SNS through the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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, the RAM 30, and the 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, management unit, and collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts travel information from a user through a chat interface of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal route based on the analysis result. The management unit is realized by the specific processing unit 290 of the data processing device 12 and aims for overall optimization. The collection unit collects information from the SNS through the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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, the RAM 30, and the 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, management unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit accepts travel information from a user through a chat interface of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal route based on the analysis result. The management unit is realized by the specific processing unit 290 of the data processing device 12 and aims for overall optimization. The collection unit collects information from the SNS through the communication I / F 44 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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, the RAM 30, and the 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0173] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, management unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts movement information from a user through a chat interface of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal route based on the analysis result. The management unit is realized by the specific processing unit 290 of the data processing device 12 and aims for overall optimization. The collection unit collects information from the SNS through the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception unit that receives information regarding movement from a user; an analysis unit that analyzes the information received by the reception unit; a suggestion unit that proposes a route based on the analysis result obtained by the analysis unit; A management department that manages interactions with each individual and improves overall efficiency. A system characterized by: (Appendix 2) Equipped with a collection section that collects information from SNS 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect information on congestion and delays in public transport on social media 3. The system of claim 2, (Appendix 4) The analysis unit is Analyze the collected information and adjust the travel route 2. The system of claim 1, (Appendix 5) The suggestion unit, Real-time updates on user movements 2. The system of claim 1, (Appendix 6) The management unit Manage the movement information of multiple users and improve overall efficiency 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust the timing of receiving travel information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past movement history and select the optimal reception method 2. The system of claim 1, (Appendix 9) The reception unit is When receiving travel information, filtering is performed based on the user's current living situation and areas of interest. 2. The system of claim 1, (Appendix 10) The reception unit is The user's emotions are estimated, and the priority of the travel information to be received is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting travel information, the system takes into consideration the user's geographic location information and prioritizes accepting information that is highly relevant. 2. The system of claim 1, (Appendix 12) The reception unit is When receiving travel information, analyze the user's social media activity and receive related information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of movement information 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of movement information. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when movement information is submitted 2. The system of claim 1, (Appendix 18) The analysis unit is Adjust the order of analysis during analysis based on the relevance of travel information 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the level of detail in the suggestions based on the importance of the travel route 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the travel route. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, At the time of proposal, the proposals will be prioritized based on when the travel route was submitted. 2. The system of claim 1, (Appendix 24) The suggestion unit, During suggestions, adjust the order of suggestions based on the relevance of the travel route 2. The system of claim 1, (Appendix 25) The management unit To estimate a user's emotion and adjust a management method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The management unit During management, the movement information of multiple users is analyzed to select the optimal management method. 2. The system of claim 1, (Appendix 27) The management unit During management, customize the management experience based on the user's current life situation 2. The system of claim 1, (Appendix 28) The management unit To estimate a user's emotion and determine a management priority based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The management unit During management, the optimal management method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The management unit During management, analyze users' social media activity and suggest management measures 2. The system of claim 1, (Appendix 31) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 3. The system of claim 2, (Appendix 32) The collecting unit includes: When collecting data, we analyze the content posted on social media to collect the most appropriate information. 3. The system of claim 2, (Appendix 33) The collecting unit includes: When collecting data, the accuracy of the data collection is improved by referring to the user's past posting history. 3. The system of claim 2, (Appendix 34) The collecting unit includes: Inferring user emotions and adjusting the way information is displayed based on the inferred user emotions 3. The system of claim 2, (Appendix 35) The collecting unit includes: When collecting information, take into account the geographical distribution on SNS. 3. The system of claim 2, (Appendix 36) The collecting unit includes: When collecting data, refer to related literature on SNS to improve the accuracy of the data collection. 3. The system of claim 2, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives information regarding movement from a user; an analysis unit that analyzes the information received by the reception unit; a suggestion unit that proposes a route based on the analysis result obtained by the analysis unit; A management department that manages interactions with each individual and improves overall efficiency. A system characterized by:

2. Equipped with a collection section that collects information from SNS 2. The system of claim 1 .

3. The collecting unit includes: Collect information on congestion and delays in public transport on social media 3. The system of claim 2.

4. The suggestion unit, Real-time updates on user movements 2. The system of claim 1 .

5. The management unit Manage the movement information of multiple users and improve overall efficiency 2. The system of claim 1 .

6. The reception unit is To estimate a user's emotion and adjust the timing of receiving travel information based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past movement history and select the optimal reception method 2. The system of claim 1 .

8. The reception unit is When receiving travel information, filtering is performed based on the user's current living situation and areas of interest.

2. The system of claim 1 .

9. The reception unit is The user's emotions are estimated, and the priority of the travel information to be received is determined based on the estimated user's emotions.

2. The system of claim 1 .

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