system

Abstract

The system according to this embodiment aims to improve the efficiency of providing services to businesses in congested areas. [Solution] The system according to the embodiment comprises a collection unit, an identification unit, and a generation unit. The collection unit collects past congestion area data. The identification unit analyzes the data collected by the collection unit and identifies congestion areas. The collection unit collects company data. The identification unit analyzes the company data collected by the collection unit and identifies companies. The collection unit collects DX service data. The identification unit analyzes the DX service data collected by the collection unit and identifies companies using DX services. The generation unit generates service proposals for the companies identified by the identification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 as a 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, the provision of enterprise-oriented services in the convergence area is not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of providing enterprise-oriented services in the convergence area.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an identification unit, and a generation unit. The collection unit collects historical congestion area data. The identification unit analyzes the data collected by the collection unit and identifies congestion areas. The collection unit collects company data. The identification unit analyzes the company data collected by the collection unit and identifies companies. The collection unit collects DX service data. The identification unit analyzes the DX service data collected by the collection unit and identifies companies using DX services. The generation unit generates service proposals for the companies identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the provision of enterprise services in congested areas. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 linked 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] <T 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 system according to an embodiment of the present invention is a system that improves profitability by providing congestion avoidance services to companies using generative AI. This system collects and analyzes historical congestion area data, company data, and DX service data to generate target customers and service proposals. For example, the system analyzes historical congestion area data to identify areas where congestion is likely to occur. Next, the system analyzes company data to identify which companies operate in those areas. Furthermore, the system analyzes DX service data to identify which companies are using DX services. By combining this data, the system generates target customers and service proposals. For example, historical congestion area data reveals that a particular stadium is in a congestion-prone area, and the company operating that stadium is identified from the company data. Furthermore, if it is found that that company is using DX services from the DX service data, a proposal to provide congestion avoidance services to that company can be made. This mechanism enables effective sales promotion for companies, and an improvement in profitability can be expected. In addition, by utilizing generative AI, target customers can be efficiently identified and optimal service proposals can be generated. As a result, the system can improve profitability by providing congestion avoidance services to companies.

[0029] The system according to the embodiment comprises a collection unit, an identification unit, and a generation unit. The collection unit collects historical congestion area data. The collection unit can collect historical congestion area data such as traffic data and communication data. The collection unit can also collect data using AI. The identification unit analyzes the data collected by the collection unit and identifies congestion areas. The identification unit can identify congestion areas by analyzing data using AI, for example. Based on historical data, the identification unit identifies which areas are prone to congestion. The collection unit collects corporate data. The collection unit can collect corporate data such as sales data and the number of employees. The collection unit can also collect data using AI. The identification unit analyzes the corporate data collected by the collection unit and identifies companies. The identification unit can identify companies by analyzing data using AI, for example. Based on corporate data, the identification unit identifies which companies are operating in specific areas. The collection unit collects DX service data. The collection unit can collect DX service data such as the usage status of cloud services and the adoption status of digital tools. The collection unit can also collect data using AI. The identification unit analyzes the DX service data collected by the collection unit and identifies companies that are using DX services. The identification unit can, for example, use AI to analyze the data and identify companies that are using DX services. Based on the DX service data, the identification unit identifies which companies are using DX services. The generation unit generates service proposals for the companies identified by the identification unit. The generation unit can, for example, generate service proposals using generation AI. The generation unit proposes congestion avoidance services to the identified companies. As a result, the system according to this embodiment can improve profitability by analyzing past congestion area data, company data, and DX service data, and generating target customers and service proposals.

[0030] The data collection unit collects historical congestion area data. For example, the data collection unit can collect historical congestion area data such as traffic data and communication data. Specifically, traffic data includes road congestion, traffic volume, and accident information. This data is obtained from traffic management systems, cameras, sensors, etc. Communication data includes mobile phone location information, data volume, and internet traffic data. This data is collected from telecommunications carrier networks. The data collection unit can also collect data using AI. AI can automate the data collection process and efficiently collect large amounts of data. For example, AI can acquire data in real time from traffic management systems and telecommunications carrier networks and store it in a database. AI can also evaluate data quality and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality data and improve the overall system performance.

[0031] The identification unit analyzes the data collected by the collection unit to identify congested areas. For example, the identification unit can use AI to analyze data and identify congested areas. Specifically, the AI ​​analyzes traffic and communication data to identify areas and time periods where congestion has occurred in the past. Using machine learning algorithms, the AI ​​learns data patterns and trends to predict areas prone to congestion. For example, based on past traffic data, the AI ​​can predict congestion at specific roads and intersections, and based on communication data, it can predict increases in communication traffic in specific areas. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the identification unit to quickly and accurately analyze collected data and identify congested areas in real time. In addition, the identification unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past congestion data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The data collection unit collects corporate data. For example, the data collection unit can collect corporate data such as sales data and the number of employees. Specifically, corporate sales data includes monthly and annual sales figures, sales breakdowns, and sales trends. This data is obtained from corporate financial reports and sales reports. The number of employees includes the total number of employees, the number of employees by department, and employee attributes (age, gender, job type, etc.). This data is collected from corporate human resources databases and labor management systems. The data collection unit can also collect data using AI. The AI ​​automatically retrieves data from publicly available corporate information and internal databases and stores it in the database. In addition, the AI ​​can evaluate the quality of the data and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality corporate data and improve the overall performance of the system.

[0033] The Identification Department analyzes the corporate data collected by the Collection Department to identify companies. For example, the Identification Department can use AI to analyze data and identify companies. Specifically, the AI ​​analyzes data such as sales figures and employee numbers to identify companies operating in specific areas. Using machine learning algorithms, the AI ​​learns data patterns and trends, and can predict the activity status of companies in specific areas. For example, based on sales data, the AI ​​can predict increases or decreases in sales in a specific area, and based on employee numbers, it can predict the staffing of companies in a specific area. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the Identification Department to quickly and accurately analyze collected corporate data and identify companies operating in specific areas in real time. In addition, the Identification Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical corporate data, it can predict fluctuations in corporate activity in specific regions and time periods, and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0034] The data collection unit collects DX service data. For example, the data collection unit can collect DX service data such as cloud service usage and digital tool implementation status. Specifically, cloud service usage data includes frequency of use, usage time, and types of services used. This data is obtained from cloud service provider log data and usage reports. Digital tool implementation status data includes the types of digital tools implemented by companies, their usage status, and the effects of implementation. This data is collected from internal company databases and tool provider reports. The data collection unit can also collect data using AI. The AI ​​automatically retrieves data from cloud service provider and tool provider databases and stores it in the database. Furthermore, the AI ​​can evaluate data quality and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality DX service data and improve the overall system performance.

[0035] The Identification Department analyzes the DX service data collected by the Collection Department to identify companies using DX services. For example, the Identification Department can use AI to analyze data and identify companies using DX services. Specifically, the AI ​​analyzes data such as cloud service usage and digital tool adoption to identify companies using DX services in specific areas. Using machine learning algorithms, the AI ​​learns data patterns and trends to predict DX service usage in specific areas. For example, the AI ​​can predict demand for cloud services in specific areas based on cloud service usage frequency, and predict the penetration of digital tools in specific areas based on digital tool adoption status. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the Identification Department to quickly and accurately analyze collected DX service data and identify companies using DX services in specific areas in real time. In addition, the Identification Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical DX service data, it can predict fluctuations in DX service usage in specific regions and time periods, and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0036] The generation unit generates proposals for companies identified by the identification unit. The generation unit can generate proposals using, for example, a generation AI. Specifically, the generation AI proposes congestion avoidance services based on data from the identified companies. Using natural language processing technology, the generation AI understands the needs and challenges of companies and generates optimal proposals accordingly. For example, the generation AI analyzes company sales data, employee numbers, and DX service usage to generate a proposal that specifically outlines the effects and benefits of introducing congestion avoidance services. Furthermore, the generation AI can learn from past proposals and success stories, enabling it to make effective proposals based on this knowledge. This allows the generation unit to quickly generate specific and effective proposals for identified companies, improving profitability. Moreover, the generation unit can continuously improve the generated proposals. For example, by collecting feedback on proposals and adjusting the generation AI's algorithm based on that feedback, it can provide more accurate proposals. The generation unit can also simulate multiple proposals and select the most effective one. This allows the generation unit to always provide highly accurate proposals based on the latest information, supporting quick and appropriate responses.

[0037] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and prioritize its use. For example, the data collection unit can analyze past data collection history to identify areas for improvement in collection methods and optimize them. For example, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal collection method. This allows for the selection of the optimal collection method and improvement of data collection efficiency by analyzing past data collection history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of data related to a particular event during the period in which that event is held. For example, the data collection unit can filter the data to be collected by considering seasonal trends. For example, the data collection unit can adjust the priority of the data to be collected based on specific events or seasons. This allows for the efficient collection of highly relevant data by filtering the data to be collected based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a specific event or season into a generating AI and have the generating AI perform the filtering of the data to be collected.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region. For example, the data collection unit can adjust the priority of the data to be collected by considering geographical location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and prioritize the collection of relevant data. For example, the data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit can analyze keywords on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The specific unit can improve specific accuracy by considering the interrelationships of data during data analysis. For example, the specific unit can analyze the interrelationships of data and improve specific accuracy. For example, the specific unit can optimize specific algorithms by considering the interrelationships of data. For example, the specific unit can improve specific accuracy based on the interrelationships of data. In this way, specific accuracy can be improved by considering the interrelationships of data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input the interrelationships of data into a generating AI and have the generating AI perform the improvement of specific accuracy.

[0042] The identification unit can perform identification by considering the attribute information of a specific target during data analysis. For example, the identification unit can analyze the attribute information of a specific target and improve the accuracy of the identification. For example, the identification unit can optimize a specific algorithm by considering the attribute information of a specific target. For example, the identification unit can improve the accuracy of the identification based on the attribute information of a specific target. In this way, the accuracy of the identification can be improved by considering the attribute information of a specific target. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the attribute information of a specific target into a generating AI and cause the generating AI to perform the improvement of the accuracy of the identification.

[0043] The identification unit can perform identification while considering geographical distribution during data analysis. For example, the identification unit can analyze geographical distribution to improve the accuracy of identification. For example, the identification unit can optimize a specific algorithm by considering geographical distribution. For example, the identification unit can improve the accuracy of identification based on geographical distribution. In this way, the accuracy of identification can be improved by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input geographical distribution into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0044] The specific unit can improve specific accuracy by referring to relevant literature during data analysis. For example, the specific unit can improve specific accuracy by referring to relevant literature. For example, the specific unit can optimize specific algorithms based on relevant literature. For example, the specific unit can improve specific accuracy by considering relevant literature. Thus, specific accuracy can be improved by referring to relevant literature. Some or all of the above processing in the specific unit may be performed using AI, for example, or without using AI. For example, the specific unit can input relevant literature into a generating AI and have the generating AI perform the improvement of specific accuracy.

[0045] The generation unit can improve the accuracy of the generation by considering the interrelationships of the data when generating proposals. For example, the generation unit can analyze the interrelationships of the data to improve the accuracy of the generation. For example, the generation unit can optimize the generation algorithm by considering the interrelationships of the data. For example, the generation unit can improve the accuracy of the generation based on the interrelationships of the data. In this way, the accuracy of proposal generation can be improved by considering the interrelationships of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the improvement of the generation accuracy.

[0046] The generation unit can generate proposals while considering the attribute information of the target company. The generation unit can, for example, analyze the attribute information of the target company to improve the accuracy of the generation. The generation unit can, for example, optimize the generation algorithm by considering the attribute information of the target company. The generation unit can, for example, improve the accuracy of the generation based on the attribute information of the target company. In this way, the accuracy of proposal generation can be improved by considering the attribute information of the target company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the attribute information of the target company into the generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0047] The generation unit can perform generation while considering geographical distribution when generating proposals. The generation unit can, for example, analyze geographical distribution to improve generation accuracy. The generation unit can, for example, optimize the generation algorithm by considering geographical distribution. The generation unit can, for example, improve generation accuracy based on geographical distribution. In this way, the accuracy of proposal generation can be improved by considering geographical distribution. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0048] The generation unit can improve the accuracy of the generation by referring to relevant literature when generating proposals. The generation unit can, for example, refer to relevant literature to improve the accuracy of the generation. The generation unit can, for example, optimize the generation algorithm based on relevant literature. The generation unit can, for example, improve the accuracy of the generation by considering relevant literature. In this way, the accuracy of proposal generation can be improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input relevant literature into the generation AI and have the generation AI perform the improvement of the generation accuracy.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze a user's behavioral history and select data to collect based on the user's interests. For example, if a user has previously participated in a specific event, the unit will prioritize collecting data related to that event. The data collection unit can also analyze a user's web browsing history and collect data related to topics of interest. Furthermore, the data collection unit can analyze a user's purchase history and collect data related to purchased products. By selecting data to collect based on the user's behavioral history, more relevant data can be collected efficiently.

[0051] The generation unit can improve the accuracy of proposal generation by considering past user feedback. For example, it can analyze what kind of feedback users have given to proposals in the past and optimize the proposals based on that feedback. The generation unit can adjust the content of the proposals by considering past user feedback. Furthermore, the generation unit can determine the priority of proposals based on user feedback. In this way, the accuracy of proposal generation can be improved by considering past user feedback.

[0052] The data collection unit can adjust the timing of data collection, taking into account the user's health condition. For example, if the user is unwell, the frequency of data collection can be reduced to lessen the user's burden. If the user is healthy, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's health condition. This allows the timing of data collection to be adjusted to take the user's health condition into consideration, thereby reducing the user's burden.

[0053] The data collection unit can adjust the collection frequency while considering the battery level of the user's device. For example, if the user's device battery level is low, the data collection frequency is reduced to conserve battery power. If the user's device battery level is sufficient, the data collection unit can increase the data collection frequency to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the battery level of the user's device. This reduces the burden on the user by adjusting the data collection frequency while considering the battery level of the user's device.

[0054] The generation unit can improve the accuracy of its proposal generation by considering the user's past behavior patterns. For example, it can analyze what kinds of proposals the user has accepted in the past and optimize the proposals based on those behavior patterns. The generation unit can adjust the content of the proposals by considering the user's past behavior patterns. Furthermore, the generation unit can determine the priority of the proposals based on the user's behavior patterns. In this way, the accuracy of proposal generation can be improved by considering the user's past behavior patterns.

[0055] The data collection unit can adjust the timing of data collection, taking into account the user's network connection status. For example, if the user's network connection is unstable, it can reduce the frequency of data collection and wait until the connection stabilizes. If the user's network connection is stable, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's network connection status. By adjusting the timing of data collection to take the user's network connection status into consideration, the burden on the user can be reduced.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects historical congestion area data. For example, it can collect historical congestion area data such as traffic data and communication data. The data collection unit can also collect data using AI. Step 2: The identification unit analyzes the data collected by the collection unit to identify congested areas. For example, it uses AI to analyze the data and identify areas where congestion is likely to occur based on past data. Step 3: The data collection unit collects company data. For example, it can collect company data such as sales data and the number of employees. The data collection unit can also use AI to collect data. Step 4: The Identification Unit analyzes the company data collected by the Collection Unit to identify companies. For example, it might use AI to analyze the data and identify which companies are operating in a specific area based on the company data. Step 5: The data collection unit collects DX service data. For example, it can collect DX service data such as the usage status of cloud services and the adoption status of digital tools. The data collection unit can also collect data using AI. Step 6: The Identification Unit analyzes the DX service data collected by the Collection Unit and identifies companies that are using DX services. For example, it may use AI to analyze the data and identify which companies are using DX services based on the DX service data. Step 7: The generation unit generates service proposals for the companies identified by the identification unit. For example, it uses a generation AI to generate service proposals and proposes congestion avoidance services to the identified companies.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that improves profitability by providing congestion avoidance services to companies using generative AI. This system collects and analyzes historical congestion area data, company data, and DX service data to generate target customers and service proposals. For example, the system analyzes historical congestion area data to identify areas where congestion is likely to occur. Next, the system analyzes company data to identify which companies operate in those areas. Furthermore, the system analyzes DX service data to identify which companies are using DX services. By combining this data, the system generates target customers and service proposals. For example, historical congestion area data reveals that a particular stadium is in a congestion-prone area, and the company operating that stadium is identified from the company data. Furthermore, if it is found that that company is using DX services from the DX service data, a proposal to provide congestion avoidance services to that company can be made. This mechanism enables effective sales promotion for companies, and an improvement in profitability can be expected. In addition, by utilizing generative AI, target customers can be efficiently identified and optimal service proposals can be generated. As a result, the system can improve profitability by providing congestion avoidance services to companies.

[0059] The system according to the embodiment comprises a collection unit, an identification unit, and a generation unit. The collection unit collects historical congestion area data. The collection unit can collect historical congestion area data such as traffic data and communication data. The collection unit can also collect data using AI. The identification unit analyzes the data collected by the collection unit and identifies congestion areas. The identification unit can identify congestion areas by analyzing data using AI, for example. Based on historical data, the identification unit identifies which areas are prone to congestion. The collection unit collects corporate data. The collection unit can collect corporate data such as sales data and the number of employees. The collection unit can also collect data using AI. The identification unit analyzes the corporate data collected by the collection unit and identifies companies. The identification unit can identify companies by analyzing data using AI, for example. Based on corporate data, the identification unit identifies which companies are operating in specific areas. The collection unit collects DX service data. The collection unit can collect DX service data such as the usage status of cloud services and the adoption status of digital tools. The collection unit can also collect data using AI. The identification unit analyzes the DX service data collected by the collection unit and identifies companies that are using DX services. The identification unit can, for example, use AI to analyze the data and identify companies that are using DX services. Based on the DX service data, the identification unit identifies which companies are using DX services. The generation unit generates service proposals for the companies identified by the identification unit. The generation unit can, for example, generate service proposals using generation AI. The generation unit proposes congestion avoidance services to the identified companies. As a result, the system according to this embodiment can improve profitability by analyzing past congestion area data, company data, and DX service data, and generating target customers and service proposals.

[0060] The data collection unit collects historical congestion area data. For example, the data collection unit can collect historical congestion area data such as traffic data and communication data. Specifically, traffic data includes road congestion, traffic volume, and accident information. This data is obtained from traffic management systems, cameras, sensors, etc. Communication data includes mobile phone location information, data volume, and internet traffic data. This data is collected from telecommunications carrier networks. The data collection unit can also collect data using AI. AI can automate the data collection process and efficiently collect large amounts of data. For example, AI can acquire data in real time from traffic management systems and telecommunications carrier networks and store it in a database. AI can also evaluate data quality and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality data and improve the overall system performance.

[0061] The identification unit analyzes the data collected by the collection unit to identify congested areas. For example, the identification unit can use AI to analyze data and identify congested areas. Specifically, the AI ​​analyzes traffic and communication data to identify areas and time periods where congestion has occurred in the past. Using machine learning algorithms, the AI ​​learns data patterns and trends to predict areas prone to congestion. For example, based on past traffic data, the AI ​​can predict congestion at specific roads and intersections, and based on communication data, it can predict increases in communication traffic in specific areas. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the identification unit to quickly and accurately analyze collected data and identify congested areas in real time. In addition, the identification unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past congestion data, it can predict fluctuations in risk in specific regions and time periods and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The data collection unit collects corporate data. For example, the data collection unit can collect corporate data such as sales data and the number of employees. Specifically, corporate sales data includes monthly and annual sales figures, sales breakdowns, and sales trends. This data is obtained from corporate financial reports and sales reports. The number of employees includes the total number of employees, the number of employees by department, and employee attributes (age, gender, job type, etc.). This data is collected from corporate human resources databases and labor management systems. The data collection unit can also collect data using AI. The AI ​​automatically retrieves data from publicly available corporate information and internal databases and stores it in the database. In addition, the AI ​​can evaluate the quality of the data and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality corporate data and improve the overall performance of the system.

[0063] The Identification Department analyzes the corporate data collected by the Collection Department to identify companies. For example, the Identification Department can use AI to analyze data and identify companies. Specifically, the AI ​​analyzes data such as sales figures and employee numbers to identify companies operating in specific areas. Using machine learning algorithms, the AI ​​learns data patterns and trends, and can predict the activity status of companies in specific areas. For example, based on sales data, the AI ​​can predict increases or decreases in sales in a specific area, and based on employee numbers, it can predict the staffing of companies in a specific area. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the Identification Department to quickly and accurately analyze collected corporate data and identify companies operating in specific areas in real time. In addition, the Identification Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical corporate data, it can predict fluctuations in corporate activity in specific regions and time periods, and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0064] The data collection unit collects DX service data. For example, the data collection unit can collect DX service data such as cloud service usage and digital tool implementation status. Specifically, cloud service usage data includes frequency of use, usage time, and types of services used. This data is obtained from cloud service provider log data and usage reports. Digital tool implementation status data includes the types of digital tools implemented by companies, their usage status, and the effects of implementation. This data is collected from internal company databases and tool provider reports. The data collection unit can also collect data using AI. The AI ​​automatically retrieves data from cloud service provider and tool provider databases and stores it in the database. Furthermore, the AI ​​can evaluate data quality and remove inaccurate or missing data. This allows the data collection unit to efficiently collect high-quality DX service data and improve the overall system performance.

[0065] The Identification Department analyzes the DX service data collected by the Collection Department to identify companies using DX services. For example, the Identification Department can use AI to analyze data and identify companies using DX services. Specifically, the AI ​​analyzes data such as cloud service usage and digital tool adoption to identify companies using DX services in specific areas. Using machine learning algorithms, the AI ​​learns data patterns and trends to predict DX service usage in specific areas. For example, the AI ​​can predict demand for cloud services in specific areas based on cloud service usage frequency, and predict the penetration of digital tools in specific areas based on digital tool adoption status. Furthermore, the AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the Identification Department to quickly and accurately analyze collected DX service data and identify companies using DX services in specific areas in real time. In addition, the Identification Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical DX service data, it can predict fluctuations in DX service usage in specific regions and time periods, and formulate future countermeasures. This allows the specific unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The generation unit generates proposals for companies identified by the identification unit. The generation unit can generate proposals using, for example, a generation AI. Specifically, the generation AI proposes congestion avoidance services based on data from the identified companies. Using natural language processing technology, the generation AI understands the needs and challenges of companies and generates optimal proposals accordingly. For example, the generation AI analyzes company sales data, employee numbers, and DX service usage to generate a proposal that specifically outlines the effects and benefits of introducing congestion avoidance services. Furthermore, the generation AI can learn from past proposals and success stories, enabling it to make effective proposals based on this knowledge. This allows the generation unit to quickly generate specific and effective proposals for identified companies, improving profitability. Moreover, the generation unit can continuously improve the generated proposals. For example, by collecting feedback on proposals and adjusting the generation AI's algorithm based on that feedback, it can provide more accurate proposals. The generation unit can also simulate multiple proposals and select the most effective one. This allows the generation unit to always provide highly accurate proposals based on the latest information, supporting quick and appropriate responses.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0068] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and prioritize its use. For example, the data collection unit can analyze past data collection history to identify areas for improvement in collection methods and optimize them. For example, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal collection method. This allows for the selection of the optimal collection method and improvement of data collection efficiency by analyzing past data collection history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of data related to a particular event during the period in which that event is held. For example, the data collection unit can filter the data to be collected by considering seasonal trends. For example, the data collection unit can adjust the priority of the data to be collected based on specific events or seasons. This allows for the efficient collection of highly relevant data by filtering the data to be collected based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to a specific event or season into a generating AI and have the generating AI perform the filtering of the data to be collected.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, by determining the priority of data to collect according to the user's emotions, important data can be collected preferentially. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region. For example, the data collection unit can adjust the priority of the data to be collected by considering geographical location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and prioritize the collection of relevant data. For example, the data collection unit can analyze users' social media activity and collect relevant data. For example, the data collection unit can analyze keywords on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0073] The identification unit can estimate the user's emotions and adjust specific criteria based on the estimated user emotions. For example, if the user is stressed, the identification unit can relax the specific criteria to reduce the user's burden. For example, if the user is relaxed, the identification unit can tighten the specific criteria to perform a more detailed identification. For example, if the user is in a hurry, the identification unit can quickly adjust the specific criteria to perform a rapid identification. This reduces the user's burden by adjusting the specific criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The specific unit can improve specific accuracy by considering the interrelationships of data during data analysis. For example, the specific unit can analyze the interrelationships of data and improve specific accuracy. For example, the specific unit can optimize specific algorithms by considering the interrelationships of data. For example, the specific unit can improve specific accuracy based on the interrelationships of data. In this way, specific accuracy can be improved by considering the interrelationships of data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input the interrelationships of data into a generating AI and have the generating AI perform the improvement of specific accuracy.

[0075] The identification unit can perform identification by considering the attribute information of a specific target during data analysis. For example, the identification unit can analyze the attribute information of a specific target and improve the accuracy of the identification. For example, the identification unit can optimize a specific algorithm by considering the attribute information of a specific target. For example, the identification unit can improve the accuracy of the identification based on the attribute information of a specific target. In this way, the accuracy of the identification can be improved by considering the attribute information of a specific target. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the attribute information of a specific target into a generating AI and cause the generating AI to perform the improvement of the accuracy of the identification.

[0076] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is stressed, the identification unit can prioritize displaying important results. For example, if the user is relaxed, the identification unit can prioritize displaying detailed results. For example, if the user is in a hurry, the identification unit can adjust the order in which results are displayed quickly. This reduces the user's burden by adjusting the order in which specific results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The identification unit can perform identification while considering geographical distribution during data analysis. For example, the identification unit can analyze geographical distribution to improve the accuracy of identification. For example, the identification unit can optimize a specific algorithm by considering geographical distribution. For example, the identification unit can improve the accuracy of identification based on geographical distribution. In this way, the accuracy of identification can be improved by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input geographical distribution into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0078] The specific unit can improve specific accuracy by referring to relevant literature during data analysis. For example, the specific unit can improve specific accuracy by referring to relevant literature. For example, the specific unit can optimize specific algorithms based on relevant literature. For example, the specific unit can improve specific accuracy by considering relevant literature. Thus, specific accuracy can be improved by referring to relevant literature. Some or all of the above processing in the specific unit may be performed using AI, for example, or without using AI. For example, the specific unit can input relevant literature into a generating AI and have the generating AI perform the improvement of specific accuracy.

[0079] The generation unit can estimate the user's emotions and determine the priority of the offers to be generated based on the estimated user emotions. For example, if the user is stressed, the generation unit can prioritize generating important offers. For example, if the user is relaxed, the generation unit can prioritize generating detailed offers. For example, if the user is in a hurry, the generation unit can quickly generate offers. In this way, by determining the priority of offers according to the user's emotions, important offers can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0080] The generation unit can improve the accuracy of the generation by considering the interrelationships of the data when generating proposals. For example, the generation unit can analyze the interrelationships of the data to improve the accuracy of the generation. For example, the generation unit can optimize the generation algorithm by considering the interrelationships of the data. For example, the generation unit can improve the accuracy of the generation based on the interrelationships of the data. In this way, the accuracy of proposal generation can be improved by considering the interrelationships of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the interrelationships of the data into the generation AI and have the generation AI perform the improvement of the generation accuracy.

[0081] The generation unit can generate proposals while considering the attribute information of the target company. The generation unit can, for example, analyze the attribute information of the target company to improve the accuracy of the generation. The generation unit can, for example, optimize the generation algorithm by considering the attribute information of the target company. The generation unit can, for example, improve the accuracy of the generation based on the attribute information of the target company. In this way, the accuracy of proposal generation can be improved by considering the attribute information of the target company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the attribute information of the target company into the generation AI and have the generation AI perform the task of improving the accuracy of the generation.

[0082] The generation unit can estimate the user's emotions and adjust the display method of the generated suggestions based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide a simple display method. For example, if the user is relaxed, the generation unit can provide a detailed display method. For example, if the user is in a hurry, the generation unit can provide a method that allows for quick display. This reduces the user's burden by adjusting the display method of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The generation unit can perform generation while considering geographical distribution when generating proposals. The generation unit can, for example, analyze geographical distribution to improve generation accuracy. The generation unit can, for example, optimize the generation algorithm by considering geographical distribution. The generation unit can, for example, improve generation accuracy based on geographical distribution. In this way, the accuracy of proposal generation can be improved by considering geographical distribution. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution into a generation AI and have the generation AI perform the improvement of generation accuracy.

[0084] The generation unit can improve the accuracy of the generation by referring to relevant literature when generating proposals. The generation unit can, for example, refer to relevant literature to improve the accuracy of the generation. The generation unit can, for example, optimize the generation algorithm based on relevant literature. The generation unit can, for example, improve the accuracy of the generation by considering relevant literature. In this way, the accuracy of proposal generation can be improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input relevant literature into the generation AI and have the generation AI perform the improvement of the generation accuracy.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can analyze a user's behavioral history and select data to collect based on the user's interests. For example, if a user has previously participated in a specific event, the unit will prioritize collecting data related to that event. The data collection unit can also analyze a user's web browsing history and collect data related to topics of interest. Furthermore, the data collection unit can analyze a user's purchase history and collect data related to purchased products. By selecting data to collect based on the user's behavioral history, more relevant data can be collected efficiently.

[0087] The identification unit can estimate the user's emotions and adjust the parameters of a specific algorithm based on the estimated emotions. For example, if the user is stressed, the algorithm parameters can be relaxed to reduce the user's burden. If the user is relaxed, the identification unit can tighten the algorithm parameters to perform more detailed identification. Furthermore, if the user is in a hurry, the identification unit can quickly adjust the algorithm parameters to perform rapid identification. In this way, by adjusting the algorithm parameters according to the user's emotions, the user's burden can be reduced.

[0088] The generation unit can improve the accuracy of proposal generation by considering past user feedback. For example, it can analyze what kind of feedback users have given to proposals in the past and optimize the proposals based on that feedback. The generation unit can adjust the content of the proposals by considering past user feedback. Furthermore, the generation unit can determine the priority of proposals based on user feedback. In this way, the accuracy of proposal generation can be improved by considering past user feedback.

[0089] The data collection unit can adjust the timing of data collection, taking into account the user's health condition. For example, if the user is unwell, the frequency of data collection can be reduced to lessen the user's burden. If the user is healthy, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's health condition. This allows the timing of data collection to be adjusted to take the user's health condition into consideration, thereby reducing the user's burden.

[0090] The generation unit can estimate the user's emotions when generating service proposals and adjust the content of the proposals based on those emotions. For example, if the user is stressed, it can generate a simple and easy-to-understand proposal. If the user is relaxed, the generation unit can generate a detailed and complex proposal. Furthermore, if the user is in a hurry, the generation unit can generate a proposal that can be quickly understood. By adjusting the content of the proposals according to the user's emotions, the burden on the user can be reduced.

[0091] The data collection unit can adjust the collection frequency while considering the battery level of the user's device. For example, if the user's device battery level is low, the data collection frequency is reduced to conserve battery power. If the user's device battery level is sufficient, the data collection unit can increase the data collection frequency to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the battery level of the user's device. This reduces the burden on the user by adjusting the data collection frequency while considering the battery level of the user's device.

[0092] The system can estimate the user's emotions and adjust the display format of specific results based on those emotions. For example, if the user is stressed, the results can be displayed in a concise and visually easy-to-understand format. If the user is relaxed, the system can display the results in a format that includes detailed information. Furthermore, if the user is in a hurry, the system can display the results in a format that can be quickly understood. By adjusting the display format of specific results according to the user's emotions, the system can reduce the user's burden.

[0093] The generation unit can improve the accuracy of its proposal generation by considering the user's past behavior patterns. For example, it can analyze what kinds of proposals the user has accepted in the past and optimize the proposals based on those behavior patterns. The generation unit can adjust the content of the proposals by considering the user's past behavior patterns. Furthermore, the generation unit can determine the priority of the proposals based on the user's behavior patterns. In this way, the accuracy of proposal generation can be improved by considering the user's past behavior patterns.

[0094] The data collection unit can adjust the timing of data collection, taking into account the user's network connection status. For example, if the user's network connection is unstable, it can reduce the frequency of data collection and wait until the connection stabilizes. If the user's network connection is stable, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's network connection status. By adjusting the timing of data collection to take the user's network connection status into consideration, the burden on the user can be reduced.

[0095] The generation unit can estimate the user's emotions when generating service proposals and adjust the timing of proposal presentation based on the estimated emotions. For example, if the user is stressed, the presentation of proposals can be delayed, waiting until the user is relaxed. If the user is relaxed, the generation unit can present proposals immediately. Furthermore, if the user is in a hurry, the generation unit can present proposals quickly. In this way, by adjusting the timing of proposal presentation according to the user's emotions, the burden on the user can be reduced.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects historical congestion area data. For example, it can collect historical congestion area data such as traffic data and communication data. The data collection unit can also collect data using AI. Step 2: The identification unit analyzes the data collected by the collection unit to identify congested areas. For example, it uses AI to analyze the data and identify areas where congestion is likely to occur based on past data. Step 3: The data collection unit collects company data. For example, it can collect company data such as sales data and the number of employees. The data collection unit can also use AI to collect data. Step 4: The Identification Unit analyzes the company data collected by the Collection Unit to identify companies. For example, it might use AI to analyze the data and identify which companies are operating in a specific area based on the company data. Step 5: The data collection unit collects DX service data. For example, it can collect DX service data such as the usage status of cloud services and the adoption status of digital tools. The data collection unit can also collect data using AI. Step 6: The Identification Unit analyzes the DX service data collected by the Collection Unit and identifies companies that are using DX services. For example, it may use AI to analyze the data and identify which companies are using DX services based on the DX service data. Step 7: The generation unit generates service proposals for the companies identified by the identification unit. For example, it uses a generation AI to generate service proposals and proposes congestion avoidance services to the identified companies.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, identification unit, generation unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and analyzes the data with the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify congested areas and companies. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which generates service proposals using a generation AI. The emotion estimation function is implemented, for example, by the control unit 46A of the smart device 14, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, identification unit, generation unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to identify congested areas and companies. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, which generates service proposals using a generation AI. The emotion estimation function is implemented, for example, by the control unit 46A of the smart glasses 214, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, identification unit, generation unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the data using the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify congestion areas and companies. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates service proposals using a generation AI. The emotion estimation function is implemented, for example, by the control unit 46A of the headset terminal 314, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, identification unit, generation unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and analyzes the data with the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify congested areas and companies. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates service proposals using a generation AI. The emotion estimation function is implemented, for example, by the control unit 46A of the robot 414, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects past congestion area data, A unit that analyzes the data collected by the aforementioned collection unit and identifies congestion areas, The data collection department collects corporate data, The company data collected by the aforementioned collection unit is analyzed and identified by the identification unit, The data collection unit collects DX service data, The collection unit analyzes the DX service data collected by the collection unit and identifies companies that are using DX services, The system comprises a generation unit that generates a proposal for a company identified by the aforementioned identification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, filter the data to be collected based on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, It estimates the user's emotions and adjusts certain criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The specified part is, When analyzing data, consider the interrelationships between data to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, During data analysis, identify specific targets by considering their attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The specified part is, It estimates the user's emotions and adjusts the order in which specific results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The specified part is, When analyzing data, identify the data while considering its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, When analyzing data, refer to relevant literature to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of the offerings generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating proposals, we improve the accuracy of the generation by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating proposals, the system takes into account the attribute information of the target company. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We estimate the user's emotions and adjust how the suggested offerings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating proposals, the geographical distribution is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating proposals, we refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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 data collection unit that collects past congestion area data, A unit that analyzes the data collected by the aforementioned collection unit and identifies congestion areas, The data collection department collects corporate data, The company data collected by the aforementioned collection unit is analyzed and identified by the identification unit, The data collection unit collects DX service data, The collection unit analyzes the DX service data collected by the collection unit and identifies companies that are using DX services, The system comprises a generation unit that generates a proposal for a company identified by the aforementioned identification unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting data, filter the data to be collected based on specific events or seasons. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

7. The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system according to feature 1.

8. The specified part is, It estimates the user's emotions and adjusts certain criteria based on those estimated emotions. The system according to feature 1.

9. The specified part is, When analyzing data, consider the interrelationships between data to improve specific accuracy. The system according to feature 1.

10. The specified part is, During data analysis, identify specific targets by considering their attribute information. The system according to feature 1.

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