system

Abstract

The system according to this embodiment aims to automate and efficiently perform optical communication route design. [Solution] The system according to the embodiment comprises a reception unit, a design unit, an evaluation unit, and a provision unit. The reception unit inputs information between connection points. The design unit designs the optimal route based on the information input by the reception unit. The evaluation unit evaluates the route designed by the design unit. The provision unit provides the route information evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the route design of optical communication, it depends on the experience of designers and data collection, and there is a problem of high human and time costs.

[0005] The system according to the embodiment aims to automate and efficiently perform the route design of optical communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a design unit, an evaluation unit, and a provision unit. The reception unit inputs information between connection points. The design unit designs the optimal route based on the information input by the reception unit. The evaluation unit evaluates the route designed by the design unit. The provision unit provides the route information evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently perform optical communication route design. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The automated route design system according to an embodiment of the present invention is a system that combines optical fiber route design with an in-house developed WebGIS to reduce the human and time costs in optical communication route design. This system is provided as SaaS (Software as a Service), reducing the burden on designers and enabling efficient route design. First, the user inputs information between connection points. Next, the system uses WebGIS to automatically design the optimal route based on the input information. WebGIS is a tool for analyzing geographic information and calculating the optimal route. This eliminates the need for designers to manually design routes, significantly reducing time and effort. Furthermore, this system also evaluates the designed route. The evaluation includes factors such as route length, cost, and reliability. The system comprehensively evaluates these factors and proposes the optimal route. This allows designers to obtain information for selecting the optimal route. This automated route design system reduces the human and time costs in optical communication route design and enables efficient design. Also, because it is provided as SaaS, users can access and use the system via the internet. This allows designers to use the system from anywhere, enabling flexible work styles. This enables automated route design systems to reduce the human and time costs involved in optical communication route design, resulting in more efficient design.

[0029] The automated route design system according to this embodiment comprises a reception unit, a design unit, an evaluation unit, and a provision unit. The reception unit inputs information between connection points. For example, a user can access the system via the internet and input information between connection points. The design unit designs the optimal route based on the information input by the reception unit. The design unit analyzes geographic information using, for example, WebGIS and calculates the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. The evaluation unit evaluates the route designed by the design unit. For example, the evaluation unit evaluates factors such as route length, cost, and reliability. The evaluation unit comprehensively evaluates these factors and proposes the optimal route. The provision unit provides information about the route evaluated by the evaluation unit. For example, the provision unit provides information about the evaluated route to the user. As a result, the automated route design system according to this embodiment can reduce the human and time costs in optical communication route design by inputting information between connection points and designing, evaluating, and providing the optimal route.

[0030] The reception unit inputs information between connection points. For example, users can access the system via the internet and input information between connection points. Specifically, users input location information and connection requirements for connection points through a dedicated web interface. The web interface is designed for intuitive operation, allowing users to input location information simply by clicking on connection points on a map. Technical conditions such as bandwidth, latency, and redundancy can also be input as connection requirements. This allows users to easily communicate detailed connection requirements to the system. Furthermore, the reception unit has a function to automatically verify the input information and detect input errors and inconsistencies. For example, if the location of a connection point contradicts existing infrastructure, or if the entered bandwidth is unrealistic, it displays a warning to the user and prompts correction. This ensures that the reception unit provides accurate and reliable information to the system. The reception unit can also save and reuse past input data. This allows users to design new routes based on data from past projects, enabling more efficient work.

[0031] The design department designs the optimal route based on the information entered by the reception department. For example, the design department uses WebGIS to analyze geographic information and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Specifically, the design department calculates the optimal route based on the input location information of connection points, taking into account geographical obstacles and existing infrastructure. For example, it prioritizes routes that avoid natural obstacles such as mountainous areas and rivers, and routes that follow existing roads and railways. The design department also generates multiple route candidates and evaluates the cost and reliability of each route. This allows for the selection of the route best suited to the user's requirements. Furthermore, the design department uses AI to improve the efficiency of route design. The AI ​​learns from past route design data and provides algorithms that quickly calculate the optimal route. For example, the AI ​​can learn optimal route design patterns under specific conditions based on past successes and failures, and apply them to new projects. This allows the design department to design the optimal route more quickly and accurately.

[0032] The evaluation unit evaluates the routes designed by the design unit. The evaluation unit assesses factors such as route length, cost, and reliability. Specifically, it calculates the total distance of the designed routes and identifies the shortest route. It also estimates the cost of constructing the routes and selects routes that are feasible within the budget. Furthermore, for reliability evaluation, it considers route redundancy and recovery time in the event of a failure. For example, if a route relies on a single failure point, it assesses the risk and improves reliability by adding redundant routes. The evaluation unit comprehensively evaluates these factors and proposes the optimal route. The evaluation unit can also customize evaluation criteria according to user requirements. For example, it prioritizes cost for cost-conscious users and redundancy for reliability-conscious users. This allows the evaluation unit to provide the route best suited to the user's needs. Additionally, the evaluation unit has a function to visually display evaluation results. For example, it can display evaluation results in graphs and charts to allow users to intuitively understand them. This enables the evaluation unit to provide users with clear and easy-to-understand evaluation results.

[0033] The service provider provides route information evaluated by the evaluation provider. For example, the service provider provides users with information on the evaluated routes. Specifically, the service provider presents users with detailed information on the optimal route based on the evaluation results. Users can check the evaluation results through a web interface and view the reasons for selecting the optimal route and detailed information on each route. The service provider makes the evaluation results downloadable in PDF or Excel format for later reference. The service provider also has a function to automatically generate the necessary documents and procedures for actual construction work based on the route selected by the user. For example, it automatically generates and provides users with detailed route maps, construction procedures, and lists of necessary materials. This allows users to quickly begin construction work based on the evaluated route. Furthermore, the service provider has a function to collect feedback from users and use it to improve the system. For example, it provides a feedback form where users can input comments and correction requests regarding the provided route information, and this feedback is reflected in system improvements. This allows the service provider to respond flexibly to user needs and improve the overall quality of the system.

[0034] The design department can analyze geographic information using WebGIS and calculate the optimal route. For example, the design department can analyze geographic information using WebGIS and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Therefore, using WebGIS improves the accuracy of analyzing geographic information and calculating the optimal route. Some or all of the above-described processes in the design department may be performed using, for example, a generative AI, or without a generative AI. For example, the design department can input geographic information into a generative AI and have the generative AI calculate the optimal route.

[0035] The evaluation unit can evaluate factors such as route length, cost, and reliability. For example, the evaluation unit can evaluate the route length. The evaluation unit can also evaluate the route cost. The evaluation unit can also evaluate the route reliability. By evaluating factors such as route length, cost, and reliability, the accuracy of selecting the optimal route is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input factors such as route length, cost, and reliability into the AI ​​and have the AI ​​perform the evaluation.

[0036] The service provider can provide the user with information on the evaluated routes. The service provider can, for example, provide the user with information on the evaluated routes. The service provider can also, for example, provide the information on the evaluated routes through a web application. The service provider can also, for example, provide the information on the evaluated routes through a mobile application. By providing the user with information on the evaluated routes, the user can obtain information to select the optimal route. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the information on the evaluated routes into an AI and have the AI ​​perform the information provision.

[0037] The reception unit allows users to access the system via the internet and input information between connection points. For example, the reception unit can accept users accessing the system using a web browser and inputting information between connection points. The reception unit can also accept users accessing the system using a mobile app and inputting information between connection points. The reception unit can also accept users accessing the system using an API and inputting information between connection points. This enables flexible work arrangements by allowing users to access the system via the internet and input information between connection points. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the information between connection points entered by the user into an AI and have the AI ​​process the information.

[0038] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information between connection points that the user has frequently entered in the past as a candidate. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest information between connection points to be used during a specific time period based on the user's past input history. By analyzing the user's past input history, the reception unit can provide the optimal input method and improve input efficiency. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into AI and have the AI ​​select the optimal input method.

[0039] The reception unit can filter connection point information input based on the user's current projects and areas of interest. For example, the reception unit can prioritize displaying connection point information related to the user's current project. The reception unit can also suggest highly relevant connection point information based on the user's areas of interest. The reception unit can also filter optimal connection point information by referring to the user's past project history. This provides highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's projects and areas of interest into AI and have the AI ​​perform the information filtering.

[0040] The reception unit can prioritize inputting highly relevant information when inputting information between connection points, taking into account the user's geographical location. For example, the reception unit can prioritize displaying information between the nearest connection points based on the user's current location. The reception unit can also suggest highly relevant information between connection points based on the user's geographical location. The reception unit can also filter information between optimal connection points by referring to the user's past travel history. This provides highly relevant information by taking the user's geographical location into consideration. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​perform information filtering.

[0041] The reception unit can analyze the user's social media activity and input relevant information when inputting information between connection points. For example, the reception unit can suggest information between connection points related to the user's current areas of interest based on the user's social media activity. The reception unit can also display optimal information between connection points based on the user's location information on social media. The reception unit can also filter information between connection points that is highly relevant by referring to the user's activity history on social media. In this way, it provides highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform information filtering.

[0042] The design department can adjust the level of detail in its route design based on the importance of the connection points. For example, the design department might design routes between important connection points in detail and use visually clear representations. For example, the design department might design routes between less important connection points concisely and use concise representations. The design department can also dynamically adjust the level of detail in its design based on the importance of the connection points. This allows for efficient route design by adjusting the level of detail in the design based on the importance of the connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input importance data between connection points into an AI and have the AI ​​perform the adjustment of the level of detail in the design.

[0043] The design department can apply different design algorithms to route design depending on the category of connection points. For example, the design department can apply a high-precision design algorithm to routes between major connection points. For example, the design department can apply a simpler design algorithm to routes between secondary connection points. The design department can also dynamically select the optimal design algorithm depending on the category of connection points. This enables optimal route design by applying different design algorithms depending on the category of connection points. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input connection point category data into AI and have the AI ​​select the design algorithm.

[0044] The design department can determine design priorities based on the submission timing of connection points during route design. For example, the design department may prioritize the design of routes between connection points that have been submitted early. The design department may also prioritize the design of routes between connection points that are close to being submitted. The design department may also dynamically adjust design priorities based on submission timing. This enables efficient route design by determining design priorities based on the submission timing of connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input connection point submission timing data into AI and have the AI ​​perform the determination of design priorities.

[0045] The design department can adjust the design order based on the relationships between connection points when designing routes. For example, the design department may prioritize designing routes between major connection points. The design department may also prioritize designing routes between highly related connection points. The design department may also dynamically adjust the design order based on the relationships between connection points. This allows for efficient route design by adjusting the design order based on the relationships between connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input connection point relationship data into AI and have the AI ​​perform the adjustment of the design order.

[0046] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships between routes during the evaluation process. For example, the evaluation unit can analyze the interrelationships between routes to improve the accuracy of the evaluation. The evaluation unit can also apply optimal evaluation criteria by considering the interrelationships between routes. The evaluation unit can also dynamically adjust the accuracy of the evaluation based on the interrelationships between routes. This improves the accuracy of the evaluation by considering the interrelationships between routes. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input route interrelationship data into AI and have AI perform the improvement of evaluation accuracy.

[0047] The evaluation unit can perform evaluations while considering the attribute information of the connection points. The evaluation unit can, for example, improve the accuracy of the evaluation based on the attribute information of the connection points. The evaluation unit can also, for example, apply the optimal evaluation criteria by considering the attribute information of the connection points. The evaluation unit can also, for example, dynamically adjust the accuracy of the evaluation based on the attribute information of the connection points. This improves the accuracy of the evaluation by considering the attribute information of the connection points. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the attribute information data of the connection points into AI and have the AI ​​perform the evaluation.

[0048] The evaluation unit can perform evaluations while considering the geographical distribution of routes. For example, the evaluation unit can analyze the geographical distribution of routes to improve the accuracy of the evaluation. For example, the evaluation unit can also apply optimal evaluation criteria while considering the geographical distribution of routes. For example, the evaluation unit can dynamically adjust the accuracy of the evaluation based on the geographical distribution of routes. This improves the accuracy of the evaluation by considering the geographical distribution of routes. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of routes into AI and have the AI ​​perform the evaluation.

[0049] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature along the route during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature along the route. The evaluation unit can also apply optimal evaluation criteria based on relevant literature along the route. For example, the evaluation unit can dynamically adjust the accuracy of its evaluation based on relevant literature along the route. This improves the accuracy of the evaluation by referring to relevant literature along the route. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data along the route into AI and have AI perform the evaluation.

[0050] The service provider can improve the accuracy of its service provision by considering the interrelationships between routes during the provision process. For example, the service provider can analyze the interrelationships between routes to improve the accuracy of the service provision. For example, the service provider can provide optimal information by considering the interrelationships between routes. For example, the service provider can dynamically adjust the accuracy of the service provision based on the interrelationships between routes. This improves the accuracy of the service provision by considering the interrelationships between routes. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input route interrelationship data into AI and have the AI ​​perform the service provision.

[0051] The service provider can provide information while considering the attribute information of the connection point. The service provider can improve the accuracy of the service based on the attribute information of the connection point. The service provider can also provide optimal information by considering the attribute information of the connection point. The service provider can also dynamically adjust the accuracy of the service based on the attribute information of the connection point. This improves the accuracy of the service by considering the attribute information of the connection point. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the attribute information data of the connection point into AI and have the AI ​​perform the service.

[0052] The service provider can provide information while considering the geographical distribution of routes. For example, the service provider can analyze the geographical distribution of routes to improve the accuracy of the service. For example, the service provider can provide optimal information by considering the geographical distribution of routes. For example, the service provider can dynamically adjust the accuracy of the service based on the geographical distribution of routes. This improves the accuracy of the service by considering the geographical distribution of routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical distribution data of routes into AI and have the AI ​​perform the service provision.

[0053] The service provider can improve the accuracy of its service by referring to related literature along the route during the service provision process. For example, the service provider can improve the accuracy of its service by referring to related literature along the route. The service provider can also provide optimal information based on related literature along the route. The service provider can also dynamically adjust the accuracy of its service based on related literature along the route. This improves the accuracy of the service by referring to related literature along the route. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input related literature data along the route into AI and have AI perform the service provision.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception unit can analyze user input in real time, automatically detect errors and inconsistencies, and suggest corrections. For example, if a user incorrectly enters the name of a connection point, the reception unit will suggest the correct name. Furthermore, if there are inconsistencies in the distance or location information between connection points, the reception unit can also suggest corrections. In addition, if the information entered by the user is incomplete, the reception unit can automatically generate and present questions to the user to supplement the missing information. This reduces user input errors and enables route design based on accurate information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user input data into AI and have the AI ​​perform error detection and suggest corrections.

[0056] The evaluation unit can assess the environmental impact of a route. For example, if a route passes through a nature reserve, it can assess the impact and propose an alternative route. The evaluation unit can also assess the impact of traffic congestion and noise if a route passes through an urban area. Furthermore, if a route passes through agricultural land, it can assess the impact on agricultural activities and propose an optimal route. By assessing the environmental impact of a route, sustainable route design becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input environmental impact data into AI and have the AI ​​perform the evaluation.

[0057] The reception desk can automatically refer to relevant laws, regulations, and guidelines based on user input and present applicable regulations. For example, if a user designs a route in a specific area, it can automatically refer to the area's construction regulations and environmental protection laws and present applicable regulations. Furthermore, the reception desk can check whether the information entered by the user complies with the regulations and suggest corrections as needed. It can also provide detailed explanations of new regulations when the user enters information to address them. This allows the user to design routes that comply with legal regulations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input legal data into AI and have the AI ​​perform the referencing and application of regulations.

[0058] The evaluation unit can assess the economic impact of a route. For example, it can evaluate the impact on the economic activity of the areas through which the route passes and propose the optimal route. If the route passes through a commercial area, the evaluation unit can also evaluate the impact on commercial activity in that area. If the route passes through a residential area, it can also evaluate the impact on the lives of residents and propose the optimal route. By evaluating the economic impact of a route, it becomes possible to design routes that take local communities into consideration. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input economic impact data into AI and have the AI ​​perform the evaluation.

[0059] The design department can design routes while considering the future expandability of connection points. For example, if there is a possibility of adding new connection points in the future, the route can be designed to accommodate such expansion. The design department can also design flexible routes, for example, by considering future technological advancements. Furthermore, the design department can design routes with a margin to accommodate future increases in demand. This enables route design that considers future expandability, resulting in efficient design from a long-term perspective. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input future expandability data into AI and have the AI ​​perform the design.

[0060] The evaluation unit can assess the safety of a route. For example, if a route passes through an area with a high risk of natural disasters, it can assess that risk and propose an alternative route. The evaluation unit can also assess the risk if a route passes through an area with a high crime rate. Furthermore, if a route passes through an area with a high number of traffic accidents, it can assess that risk and propose an optimal route. By assessing the safety of a route, it becomes possible to design a route that minimizes risk. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input safety data into AI and have the AI ​​perform the evaluation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk inputs information between connection points. For example, a user can access the system via the internet and input information between connection points. Step 2: The design department designs the optimal route based on the information entered by the reception department. For example, they might use WebGIS to analyze geographic information and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Step 3: The evaluation department evaluates the route designed by the design department. For example, it evaluates factors such as route length, cost, and reliability. The evaluation department comprehensively evaluates these factors and proposes the optimal route. Step 4: The provisioning unit provides route information evaluated by the evaluation unit. For example, it provides the user with information about the evaluated route.

[0063] (Example of form 2) The automated route design system according to an embodiment of the present invention is a system that combines optical fiber route design with an in-house developed WebGIS to reduce the human and time costs in optical communication route design. This system is provided as SaaS (Software as a Service), reducing the burden on designers and enabling efficient route design. First, the user inputs information between connection points. Next, the system uses WebGIS to automatically design the optimal route based on the input information. WebGIS is a tool for analyzing geographic information and calculating the optimal route. This eliminates the need for designers to manually design routes, significantly reducing time and effort. Furthermore, this system also evaluates the designed route. The evaluation includes factors such as route length, cost, and reliability. The system comprehensively evaluates these factors and proposes the optimal route. This allows designers to obtain information for selecting the optimal route. This automated route design system reduces the human and time costs in optical communication route design and enables efficient design. Also, because it is provided as SaaS, users can access and use the system via the internet. This allows designers to use the system from anywhere, enabling flexible work styles. This enables automated route design systems to reduce the human and time costs involved in optical communication route design, resulting in more efficient design.

[0064] The automated route design system according to this embodiment comprises a reception unit, a design unit, an evaluation unit, and a provision unit. The reception unit inputs information between connection points. For example, a user can access the system via the internet and input information between connection points. The design unit designs the optimal route based on the information input by the reception unit. The design unit analyzes geographic information using, for example, WebGIS and calculates the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. The evaluation unit evaluates the route designed by the design unit. For example, the evaluation unit evaluates factors such as route length, cost, and reliability. The evaluation unit comprehensively evaluates these factors and proposes the optimal route. The provision unit provides information about the route evaluated by the evaluation unit. For example, the provision unit provides information about the evaluated route to the user. As a result, the automated route design system according to this embodiment can reduce the human and time costs in optical communication route design by inputting information between connection points and designing, evaluating, and providing the optimal route.

[0065] The reception unit inputs information between connection points. For example, users can access the system via the internet and input information between connection points. Specifically, users input location information and connection requirements for connection points through a dedicated web interface. The web interface is designed for intuitive operation, allowing users to input location information simply by clicking on connection points on a map. Technical conditions such as bandwidth, latency, and redundancy can also be input as connection requirements. This allows users to easily communicate detailed connection requirements to the system. Furthermore, the reception unit has a function to automatically verify the input information and detect input errors and inconsistencies. For example, if the location of a connection point contradicts existing infrastructure, or if the entered bandwidth is unrealistic, it displays a warning to the user and prompts correction. This ensures that the reception unit provides accurate and reliable information to the system. The reception unit can also save and reuse past input data. This allows users to design new routes based on data from past projects, enabling more efficient work.

[0066] The design department designs the optimal route based on the information entered by the reception department. For example, the design department uses WebGIS to analyze geographic information and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Specifically, the design department calculates the optimal route based on the input location information of connection points, taking into account geographical obstacles and existing infrastructure. For example, it prioritizes routes that avoid natural obstacles such as mountainous areas and rivers, and routes that follow existing roads and railways. The design department also generates multiple route candidates and evaluates the cost and reliability of each route. This allows for the selection of the route best suited to the user's requirements. Furthermore, the design department uses AI to improve the efficiency of route design. The AI ​​learns from past route design data and provides algorithms that quickly calculate the optimal route. For example, the AI ​​can learn optimal route design patterns under specific conditions based on past successes and failures, and apply them to new projects. This allows the design department to design the optimal route more quickly and accurately.

[0067] The evaluation unit evaluates the routes designed by the design unit. The evaluation unit assesses factors such as route length, cost, and reliability. Specifically, it calculates the total distance of the designed routes and identifies the shortest route. It also estimates the cost of constructing the routes and selects routes that are feasible within the budget. Furthermore, for reliability evaluation, it considers route redundancy and recovery time in the event of a failure. For example, if a route relies on a single failure point, it assesses the risk and improves reliability by adding redundant routes. The evaluation unit comprehensively evaluates these factors and proposes the optimal route. The evaluation unit can also customize evaluation criteria according to user requirements. For example, it prioritizes cost for cost-conscious users and redundancy for reliability-conscious users. This allows the evaluation unit to provide the route best suited to the user's needs. Additionally, the evaluation unit has a function to visually display evaluation results. For example, it can display evaluation results in graphs and charts to allow users to intuitively understand them. This enables the evaluation unit to provide users with clear and easy-to-understand evaluation results.

[0068] The service provider provides route information evaluated by the evaluation provider. For example, the service provider provides users with information on the evaluated routes. Specifically, the service provider presents users with detailed information on the optimal route based on the evaluation results. Users can check the evaluation results through a web interface and view the reasons for selecting the optimal route and detailed information on each route. The service provider makes the evaluation results downloadable in PDF or Excel format for later reference. The service provider also has a function to automatically generate the necessary documents and procedures for actual construction work based on the route selected by the user. For example, it automatically generates and provides users with detailed route maps, construction procedures, and lists of necessary materials. This allows users to quickly begin construction work based on the evaluated route. Furthermore, the service provider has a function to collect feedback from users and use it to improve the system. For example, it provides a feedback form where users can input comments and correction requests regarding the provided route information, and this feedback is reflected in system improvements. This allows the service provider to respond flexibly to user needs and improve the overall quality of the system.

[0069] The design department can analyze geographic information using WebGIS and calculate the optimal route. For example, the design department can analyze geographic information using WebGIS and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Therefore, using WebGIS improves the accuracy of analyzing geographic information and calculating the optimal route. Some or all of the above-described processes in the design department may be performed using, for example, a generative AI, or without a generative AI. For example, the design department can input geographic information into a generative AI and have the generative AI calculate the optimal route.

[0070] The evaluation unit can evaluate factors such as route length, cost, and reliability. For example, the evaluation unit can evaluate the route length. The evaluation unit can also evaluate the route cost. The evaluation unit can also evaluate the route reliability. By evaluating factors such as route length, cost, and reliability, the accuracy of selecting the optimal route is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input factors such as route length, cost, and reliability into the AI ​​and have the AI ​​perform the evaluation.

[0071] The service provider can provide the user with information on the evaluated routes. The service provider can, for example, provide the user with information on the evaluated routes. The service provider can also, for example, provide the information on the evaluated routes through a web application. The service provider can also, for example, provide the information on the evaluated routes through a mobile application. By providing the user with information on the evaluated routes, the user can obtain information to select the optimal route. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the information on the evaluated routes into an AI and have the AI ​​perform the information provision.

[0072] The reception unit allows users to access the system via the internet and input information between connection points. For example, the reception unit can accept users accessing the system using a web browser and inputting information between connection points. The reception unit can also accept users accessing the system using a mobile app and inputting information between connection points. The reception unit can also accept users accessing the system using an API and inputting information between connection points. This enables flexible work arrangements by allowing users to access the system via the internet and input information between connection points. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the information between connection points entered by the user into an AI and have the AI ​​process the information.

[0073] The reception unit can estimate the user's emotions and adjust the timing of information input between connection points based on the estimated emotions. For example, if the user is stressed, the reception unit can simplify the input procedure and request only the minimum necessary information. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input to allow for rapid information input between connection points. This reduces the user's burden by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information between connection points that the user has frequently entered in the past as a candidate. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest information between connection points to be used during a specific time period based on the user's past input history. By analyzing the user's past input history, the reception unit can provide the optimal input method and improve input efficiency. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into AI and have the AI ​​select the optimal input method.

[0075] The reception unit can filter connection point information input based on the user's current projects and areas of interest. For example, the reception unit can prioritize displaying connection point information related to the user's current project. The reception unit can also suggest highly relevant connection point information based on the user's areas of interest. The reception unit can also filter optimal connection point information by referring to the user's past project history. This provides highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's projects and areas of interest into AI and have the AI ​​perform the information filtering.

[0076] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize important information and postpone detailed information. If the user is relaxed, the reception desk may prioritize detailed information and may also suggest a customizable input method. If the user is in a hurry, the reception desk may provide an interface to quickly enter the most important information. This reduces the user's burden by prioritizing information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize inputting highly relevant information when inputting information between connection points, taking into account the user's geographical location. For example, the reception unit can prioritize displaying information between the nearest connection points based on the user's current location. The reception unit can also suggest highly relevant information between connection points based on the user's geographical location. The reception unit can also filter information between optimal connection points by referring to the user's past travel history. This provides highly relevant information by taking the user's geographical location into consideration. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into AI and have the AI ​​perform information filtering.

[0078] The reception unit can analyze the user's social media activity and input relevant information when inputting information between connection points. For example, the reception unit can suggest information between connection points related to the user's current areas of interest based on the user's social media activity. The reception unit can also display optimal information between connection points based on the user's location information on social media. The reception unit can also filter information between connection points that is highly relevant by referring to the user's activity history on social media. In this way, it provides highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform information filtering.

[0079] The design department can estimate the user's emotions and adjust the representation of the route design based on the estimated emotions. For example, if the user is relaxed, the design department may provide a detailed route design using a visually easy-to-understand representation. If the user is in a hurry, the design department may also provide a concise and to-the-point route design. If the user is stressed, the design department may also provide a simple and intuitive route design. This helps the user understand the route design by adjusting its representation according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can input user emotion data into a generative AI and have the generative AI adjust the representation of the route design.

[0080] The design department can adjust the level of detail in its route design based on the importance of the connection points. For example, the design department might design routes between important connection points in detail and use visually clear representations. For example, the design department might design routes between less important connection points concisely and use concise representations. The design department can also dynamically adjust the level of detail in its design based on the importance of the connection points. This allows for efficient route design by adjusting the level of detail in the design based on the importance of the connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input importance data between connection points into an AI and have the AI ​​perform the adjustment of the level of detail in the design.

[0081] The design department can apply different design algorithms to route design depending on the category of connection points. For example, the design department can apply a high-precision design algorithm to routes between major connection points. For example, the design department can apply a simpler design algorithm to routes between secondary connection points. The design department can also dynamically select the optimal design algorithm depending on the category of connection points. This enables optimal route design by applying different design algorithms depending on the category of connection points. Some or all of the above processes in the design department may be performed using AI, for example, or without AI. For example, the design department can input connection point category data into AI and have the AI ​​select the design algorithm.

[0082] The design department can estimate the user's emotions and adjust the length of the design based on those emotions. For example, if the user is in a hurry, the design department can provide a short, concise design. If the user is relaxed, the design department can provide a longer design with detailed explanations. If the user is stressed, the design department can provide a simple and intuitive design. This reduces the user's burden by adjusting the design length according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can input user emotion data into a generative AI and have the generative AI adjust the design length.

[0083] The design department can determine design priorities based on the submission timing of connection points during route design. For example, the design department may prioritize the design of routes between connection points that have been submitted early. The design department may also prioritize the design of routes between connection points that are close to being submitted. The design department may also dynamically adjust design priorities based on submission timing. This enables efficient route design by determining design priorities based on the submission timing of connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input connection point submission timing data into AI and have the AI ​​perform the determination of design priorities.

[0084] The design department can adjust the design order based on the relationships between connection points when designing routes. For example, the design department may prioritize designing routes between major connection points. The design department may also prioritize designing routes between highly related connection points. The design department may also dynamically adjust the design order based on the relationships between connection points. This allows for efficient route design by adjusting the design order based on the relationships between connection points. Some or all of the above processes in the design department may be performed using AI, for example, or not using AI. For example, the design department can input connection point relationship data into AI and have the AI ​​perform the adjustment of the design order.

[0085] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may use detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit may use concise evaluation criteria. For example, if the user is stressed, the evaluation unit may use simple and intuitive evaluation criteria. This reduces the burden on the user by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0086] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships between routes during the evaluation process. For example, the evaluation unit can analyze the interrelationships between routes to improve the accuracy of the evaluation. The evaluation unit can also apply optimal evaluation criteria by considering the interrelationships between routes. The evaluation unit can also dynamically adjust the accuracy of the evaluation based on the interrelationships between routes. This improves the accuracy of the evaluation by considering the interrelationships between routes. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input route interrelationship data into AI and have AI perform the improvement of evaluation accuracy.

[0087] The evaluation unit can perform evaluations while considering the attribute information of the connection points. The evaluation unit can, for example, improve the accuracy of the evaluation based on the attribute information of the connection points. The evaluation unit can also, for example, apply the optimal evaluation criteria by considering the attribute information of the connection points. The evaluation unit can also, for example, dynamically adjust the accuracy of the evaluation based on the attribute information of the connection points. This improves the accuracy of the evaluation by considering the attribute information of the connection points. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the attribute information data of the connection points into AI and have the AI ​​perform the evaluation.

[0088] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize displaying detailed evaluation results. For example, if the user is in a hurry, the evaluation unit may prioritize displaying concise evaluation results. For example, if the user is stressed, the evaluation unit may prioritize displaying simple and intuitive evaluation results. This helps the user understand the evaluation results by adjusting the display order according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the evaluation results.

[0089] The evaluation unit can perform evaluations while considering the geographical distribution of routes. For example, the evaluation unit can analyze the geographical distribution of routes to improve the accuracy of the evaluation. For example, the evaluation unit can also apply optimal evaluation criteria while considering the geographical distribution of routes. For example, the evaluation unit can dynamically adjust the accuracy of the evaluation based on the geographical distribution of routes. This improves the accuracy of the evaluation by considering the geographical distribution of routes. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of routes into AI and have the AI ​​perform the evaluation.

[0090] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature along the route during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature along the route. The evaluation unit can also apply optimal evaluation criteria based on relevant literature along the route. For example, the evaluation unit can dynamically adjust the accuracy of its evaluation based on relevant literature along the route. This improves the accuracy of the evaluation by referring to relevant literature along the route. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data along the route into AI and have AI perform the evaluation.

[0091] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is relaxed, the information provider may prioritize detailed information. If the user is in a hurry, the information provider may prioritize concise information. If the user is stressed, the information provider may prioritize simple and intuitive information. This reduces the user's burden by prioritizing information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0092] The service provider can improve the accuracy of its service provision by considering the interrelationships between routes during the provision process. For example, the service provider can analyze the interrelationships between routes to improve the accuracy of the service provision. For example, the service provider can provide optimal information by considering the interrelationships between routes. For example, the service provider can dynamically adjust the accuracy of the service provision based on the interrelationships between routes. This improves the accuracy of the service provision by considering the interrelationships between routes. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input route interrelationship data into AI and have the AI ​​perform the service provision.

[0093] The service provider can provide information while considering the attribute information of the connection point. The service provider can improve the accuracy of the service based on the attribute information of the connection point. The service provider can also provide optimal information by considering the attribute information of the connection point. The service provider can also dynamically adjust the accuracy of the service based on the attribute information of the connection point. This improves the accuracy of the service by considering the attribute information of the connection point. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the attribute information data of the connection point into AI and have the AI ​​perform the service.

[0094] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed information in a visually easy-to-understand manner. If the user is in a hurry, the service provider can also display concise information that gets straight to the point. If the user is stressed, the service provider can also display simple and intuitive information. This helps the user understand the information by adjusting how it is displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the information is displayed.

[0095] The service provider can provide information while considering the geographical distribution of routes. For example, the service provider can analyze the geographical distribution of routes to improve the accuracy of the service. For example, the service provider can provide optimal information by considering the geographical distribution of routes. For example, the service provider can dynamically adjust the accuracy of the service based on the geographical distribution of routes. This improves the accuracy of the service by considering the geographical distribution of routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical distribution data of routes into AI and have the AI ​​perform the service provision.

[0096] The service provider can improve the accuracy of its service by referring to related literature along the route during the service provision process. For example, the service provider can improve the accuracy of its service by referring to related literature along the route. The service provider can also provide optimal information based on related literature along the route. The service provider can also dynamically adjust the accuracy of its service based on related literature along the route. This improves the accuracy of the service by referring to related literature along the route. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input related literature data along the route into AI and have AI perform the service provision.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze user input in real time, automatically detect errors and inconsistencies, and suggest corrections. For example, if a user incorrectly enters the name of a connection point, the reception unit will suggest the correct name. Furthermore, if there are inconsistencies in the distance or location information between connection points, the reception unit can also suggest corrections. In addition, if the information entered by the user is incomplete, the reception unit can automatically generate and present questions to the user to supplement the missing information. This reduces user input errors and enables route design based on accurate information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user input data into AI and have the AI ​​perform error detection and suggest corrections.

[0099] The design department can estimate the user's emotions and adjust the complexity of the route design based on those emotions. For example, if the user is stressed, the design department can provide a simple and intuitive route design. If the user is relaxed, it can provide a detailed route design and use visually easy-to-understand language. If the user is in a hurry, it can provide a concise and to-the-point route design. This helps the user understand the route design by adjusting its complexity according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can input user emotion data into a generative AI and have the generative AI adjust the complexity of the route design.

[0100] The evaluation unit can assess the environmental impact of a route. For example, if a route passes through a nature reserve, it can assess the impact and propose an alternative route. The evaluation unit can also assess the impact of traffic congestion and noise if a route passes through an urban area. Furthermore, if a route passes through agricultural land, it can assess the impact on agricultural activities and propose an optimal route. By assessing the environmental impact of a route, sustainable route design becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input environmental impact data into AI and have the AI ​​perform the evaluation.

[0101] The service provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is relaxed, the information can be provided in a detailed report format. If the user is in a hurry, the information can be provided in a concise bulleted list format. If the user is stressed, the information can be provided using visually easy-to-understand graphs and charts. This helps the user understand the information by adjusting the format according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the information format.

[0102] The reception desk can automatically refer to relevant laws, regulations, and guidelines based on user input and present applicable regulations. For example, if a user designs a route in a specific area, it can automatically refer to the area's construction regulations and environmental protection laws and present applicable regulations. Furthermore, the reception desk can check whether the information entered by the user complies with the regulations and suggest corrections as needed. It can also provide detailed explanations of new regulations when the user enters information to address them. This allows the user to design routes that comply with legal regulations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input legal data into AI and have the AI ​​perform the referencing and application of regulations.

[0103] The design department can estimate the user's emotions and adjust the interactivity of the route design based on those emotions. For example, if the user is relaxed, a detailed interactive map can be provided, allowing the user to freely customize the route. If the user is in a hurry, a concise interface can be provided, allowing them to quickly select a route. If the user is stressed, a simple and intuitive interface can be provided, reducing the user's burden. This improves user convenience by adjusting interactivity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using AI or not. For example, the design department can input user emotion data into a generative AI and have the generative AI perform the interactivity adjustment.

[0104] The evaluation unit can assess the economic impact of a route. For example, it can evaluate the impact on the economic activity of the areas through which the route passes and propose the optimal route. If the route passes through a commercial area, the evaluation unit can also evaluate the impact on commercial activity in that area. If the route passes through a residential area, it can also evaluate the impact on the lives of residents and propose the optimal route. By evaluating the economic impact of a route, it becomes possible to design routes that take local communities into consideration. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input economic impact data into AI and have the AI ​​perform the evaluation.

[0105] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is relaxed, detailed technical information can be provided. If the user is in a hurry, concise information can be provided. If the user is stressed, visually easy-to-understand information can be provided. This helps the user understand the information by adjusting the level of detail according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail of the information.

[0106] The design department can design routes while considering the future expandability of connection points. For example, if there is a possibility of adding new connection points in the future, the route can be designed to accommodate such expansion. The design department can also design flexible routes, for example, by considering future technological advancements. Furthermore, the design department can design routes with a margin to accommodate future increases in demand. This enables route design that considers future expandability, resulting in efficient design from a long-term perspective. Some or all of the above processes in the design department may be performed using AI, for example, or not. For example, the design department can input future expandability data into AI and have the AI ​​perform the design.

[0107] The evaluation unit can assess the safety of a route. For example, if a route passes through an area with a high risk of natural disasters, it can assess that risk and propose an alternative route. The evaluation unit can also assess the risk if a route passes through an area with a high crime rate. Furthermore, if a route passes through an area with a high number of traffic accidents, it can assess that risk and propose an optimal route. By assessing the safety of a route, it becomes possible to design a route that minimizes risk. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input safety data into AI and have the AI ​​perform the evaluation.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk inputs information between connection points. For example, a user can access the system via the internet and input information between connection points. Step 2: The design department designs the optimal route based on the information entered by the reception department. For example, they might use WebGIS to analyze geographic information and calculate the optimal route. WebGIS is a tool for analyzing geographic information and calculating the optimal route. Step 3: The evaluation department evaluates the route designed by the design department. For example, it evaluates factors such as route length, cost, and reliability. The evaluation department comprehensively evaluates these factors and proposes the optimal route. Step 4: The provisioning unit provides route information evaluated by the evaluation unit. For example, it provides the user with information about the evaluated route.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, design unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input information between connection points using the reception device 38 of the smart device 14. The design unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes geographic information using WebGIS and calculates the optimal route. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates elements such as route length, cost, and reliability. The provision unit provides the user with the evaluated route information using, for example, the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, design unit, evaluation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input information between connection points using the microphone 238 of the smart glasses 214. The design unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes geographic information using WebGIS and calculates the optimal route. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which evaluates elements such as route length, cost, and reliability. The provision unit provides the user with information on the evaluated route using, for example, the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, design unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input information between connection points using the microphone 238 of the headset terminal 314. The design unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes geographic information using WebGIS and calculates the optimal route. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates elements such as route length, cost, and reliability. The provision unit provides the user with information on the evaluated route using, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, design unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input information between connection points using the microphone 238 of the robot 414. The design unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes geographic information using WebGIS and calculates the optimal route. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates elements such as route length, cost, and reliability. The provision unit provides the user with information on the evaluated route using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception unit that inputs information between connection points, A design unit that designs the optimal route based on the information entered by the reception unit, An evaluation unit that evaluates the route designed by the design unit, The system comprises a providing unit that provides route information evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned design department, We analyze geographical information using WebGIS and calculate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit described above, Evaluate factors such as route length, cost, and reliability. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide users with information on the evaluated routes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Users access the system via the internet and input information between connection points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input between connection points based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting information between connection points, filtering is performed based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting information between connection points, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting information between connection points, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, It estimates the user's emotions and adjusts the way route design is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, When designing a route, adjust the level of detail based on the importance of the connection points. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, When designing a route, different design algorithms are applied depending on the category of the connection point. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, The system estimates the user's emotions and adjusts the design length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, When designing the route, prioritize the design based on when connection points are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned design department, When designing the route, adjust the design order based on the relationships between connection points. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit described above, During evaluation, consider the interrelationships between routes to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit described above, During evaluation, the attribute information of the connection points will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit described above, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit described above, During the evaluation, the geographical distribution of the routes will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit described above, During evaluation, refer to relevant literature related to the route to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing data, we improve the accuracy of the data delivery by considering the interrelationships between routes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the attribute information of the connection point will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the geographical distribution of the routes will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the information, we will refer to related literature along the route to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that inputs information between connection points, A design unit that designs the optimal route based on the information entered by the reception unit, An evaluation unit that evaluates the route designed by the design unit, The system comprises a providing unit that provides route information evaluated by the evaluation unit. A system characterized by the following features.

2. The aforementioned design department, We analyze geographical information using WebGIS and calculate the optimal route. The system according to feature 1.

3. The evaluation unit, Evaluate factors such as route length, cost, and reliability. The system according to feature 1.

4. The aforementioned supply unit is, Provide users with information on the evaluated routes. The system according to feature 1.

5. The aforementioned reception unit is Users access the system via the internet and input information between connection points. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input between connection points based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When inputting information between connection points, filtering is performed based on the user's current project and areas of interest. The system according to feature 1.

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