system

Abstract

The system according to this embodiment aims to analyze the behavioral patterns of visually impaired individuals and prioritize administrative support accordingly. [Solution] The system according to the embodiment comprises a measurement unit, a data collection unit, an analysis unit, a priority assignment unit, and a proposal unit. The measurement unit measures the behavioral patterns of visually impaired persons. The data collection unit collects the data measured by the measurement unit. The analysis unit analyzes the data collected by the data collection unit and identifies problems. The priority assignment unit assigns priorities for administrative support based on the problems identified by the analysis unit. The proposal unit proposes administrative support based on the priorities assigned by the priority assignment unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 prioritization of administrative support based on the behavior patterns of visually impaired persons has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the behavior patterns of visually impaired persons and assign priorities to administrative support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, a data collection unit, an analysis unit, a priority assignment unit, and a proposal unit. The measurement unit measures the behavioral patterns of visually impaired persons. The data collection unit collects the data measured by the measurement unit. The analysis unit analyzes the data collected by the data collection unit and identifies problems. The priority assignment unit assigns priorities for administrative support based on the problems identified by the analysis unit. The proposal unit proposes administrative support based on the priorities assigned by the priority assignment unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the behavioral patterns of visually impaired persons and prioritize administrative support. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system for creating an environment in which visually impaired people can go out with peace of mind. In this system, visually impaired people are fitted with a GPS device and a device that records their location, and data is collected on what kind of problems occurred, where, and at what time of day. The collected data is analyzed by a generating AI to identify the distribution and priority of problems. For example, problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the lack of toilets for the visually impaired may be identified. Based on these problems, administrative support is prioritized, and efforts are made to engage with local governments. SoftBank's leadership in this initiative ensures that more effective measures are implemented. First, a GPS device and a device that records their location are fitted to measure the behavioral patterns of visually impaired people. Next, the collected data is analyzed by a generating AI to identify the distribution and priority of problems. Finally, based on the identified problems, administrative support is prioritized, and efforts are made to engage with local governments. This makes it possible to create an environment in which visually impaired people can go out with peace of mind.

[0029] The system according to the embodiment comprises a measurement unit, a data collection unit, an analysis unit, a prioritization unit, and a proposal unit. The measurement unit measures the behavioral patterns of visually impaired persons. The measurement unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. The data collection unit collects the data measured by the measurement unit. The data collection unit collects, for example, location information from the GPS device and data from the device for recording the situation. The analysis unit analyzes the data collected by the data collection unit and identifies problems. The analysis unit analyzes the data using a generating AI and identifies the distribution and priority of the problems. For example, the analysis unit identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the lack of toilets for the visually impaired. The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. The prioritization unit prioritizes based on, for example, the urgency and scope of impact of the problems. The proposal unit proposes administrative support based on the prioritization assigned by the prioritization unit. The proposal department, for example, will lobby local governments based on the identified problems. This will help create an environment where visually impaired people can go out with peace of mind.

[0030] The measurement unit measures the behavioral patterns of visually impaired individuals. This unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. Specifically, the GPS device records the visually impaired person's movement route and location information in real time, while the situation recording device collects voice memos, ambient sounds, and sensor data such as vibration and temperature. This allows for a detailed understanding of the environment in which the visually impaired person behaves. Furthermore, the measurement unit can also record the movements of the white cane and guide dog used by the visually impaired person. For example, a sensor attached to the tip of the white cane detects the ground conditions and the presence of obstacles, and collects this data. A device for recording the guide dog's movements and reactions is also installed, allowing for an understanding of how the visually impaired person interacts with the guide dog. This enables the measurement unit to comprehensively measure the behavioral patterns of visually impaired individuals and provide detailed data.

[0031] The data collection unit collects data measured by the measurement unit. For example, the data collection unit collects location information from GPS devices and data from devices that record conditions. Specifically, the data collection unit transmits the movement routes and location information of visually impaired individuals to a central database in real time, and similarly collects voice memos, ambient sounds, vibrations, temperature, and other sensor data. This allows for a detailed understanding of the environment in which visually impaired individuals are behaving. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a visually impaired person stays in a particular area for a long time, the collection frequency can be increased to collect detailed data on that area. The data collection unit also centrally manages the collected data and can collaborate with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0032] The analysis unit analyzes the data collected by the data collection unit to identify problems. The analysis unit uses generative AI to analyze the data and identify the distribution and priority of problems. Specifically, the generative AI analyzes the movement routes and behavioral patterns of visually impaired people to identify what kind of problems are occurring at what locations. For example, it identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the absence of restrooms for the visually impaired. Based on this data, the generative AI evaluates the urgency and scope of impact of the problems and assigns them priority. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can analyze the frequency of problems occurring in specific areas or time periods to plan future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. For example, the prioritization unit prioritizes based on the urgency and scope of impact of the problems. Specifically, it evaluates the urgency of the problems and determines the priority based on the scope of impact, using data analyzed by the generating AI. For example, if there is a shortage of directional signs, the unit prioritizes the area considering the traffic volume and the frequency of use by visually impaired people. Similarly, if a bicycle path is dangerous, the unit prioritizes it based on the accident rate and the frequency of use by visually impaired people. This allows the prioritization unit to provide criteria for prompt and appropriate responses to problems faced by visually impaired people. Furthermore, the prioritization unit can continuously review and improve the prioritization process. For example, it can update the prioritization criteria based on new data and feedback to provide more accurate prioritization. This ensures that the prioritization unit always provides appropriate prioritization based on the latest information, enabling effective support for visually impaired people.

[0034] The Proposal Department proposes administrative support based on the priorities assigned by the Prioritization Department. For example, the Proposal Department engages with local governments based on identified problems. Specifically, it proposes concrete support measures to local governments based on the priorities determined by the Prioritization Department. For example, it proposes the installation of new directional signs in areas lacking them, and strengthens safety measures in areas where bicycle paths are dangerous. It also proposes changing the layout of tables in restaurants to make them more accessible to visually impaired people if they are unsure of their location, and proposes the installation of new restrooms if there are none for the visually impaired. In this way, the Proposal Department can provide concrete support measures to create an environment where visually impaired people can go out with peace of mind. Furthermore, the Proposal Department can monitor the implementation status of the proposals and provide additional suggestions or improvements as needed. For example, after the proposed support measures are implemented, it evaluates their effectiveness and proposes further improvements as needed. In this way, the Proposal Department can continuously support visually impaired people and maintain an environment where they can go out with peace of mind.

[0035] The measurement unit includes a GPS device attached to a visually impaired person and a device for recording the situation. For example, the measurement unit uses the GPS device attached to the visually impaired person to acquire the visually impaired person's location information in real time. The measurement unit can also record the situation around the visually impaired person using the situation recording device. For example, the measurement unit uses a camera to record video of the visually impaired person's surroundings. The measurement unit can also use an audio recording device to record audio around the visually impaired person. This allows for accurate measurement of the visually impaired person's behavioral patterns. Some or all of the above-described processes in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's location information and surrounding situation into the AI, which can then analyze the data to identify behavioral patterns.

[0036] The analysis unit uses generative AI to analyze data and identify the distribution and priority of problems. For example, the analysis unit inputs collected data into the generative AI, which analyzes the data to identify problems. For example, the analysis unit uses the generative AI to identify specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, unclear table locations in restaurants, and lack of restrooms for the visually impaired. The analysis unit can also use the generative AI to geographically visualize the distribution of problems. For example, the analysis unit uses the generative AI to plot the locations of problems on a map, visually displaying the distribution of problems. Furthermore, the analysis unit can also use the generative AI to identify the priority of problems. For example, the analysis unit uses the generative AI to identify priority based on the urgency and scope of impact of the problems. This allows for accurate identification of the distribution and priority of problems by using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which analyzes the data to identify problems.

[0037] The proposal department engages with local governments based on identified issues. For example, the proposal department submits proposals to local governments based on identified issues. The proposal department can also hold meetings with local governments based on identified issues. For example, the proposal department discusses the identified issues with local governments and proposes solutions. The proposal department can also collect feedback from local governments based on identified issues. For example, the proposal department revises the proposal based on feedback from local governments. In this way, effective measures can be implemented by engaging with local governments based on identified issues. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the identified issues into AI, and the AI ​​can generate proposals.

[0038] The proposal department is led by a specific company. For example, the proposal department can implement more effective measures by having SoftBank take the lead. The proposal department has SoftBank take the lead in the project and coordinate to implement the measures. For example, the proposal department has SoftBank strengthen cooperation with local governments and build a cooperative system for implementing the measures. The proposal department also has SoftBank provide funding and secure the resources necessary to implement the measures. For example, the proposal department has SoftBank provide funding for the implementation of the measures and support the progress of the project. The proposal department also has SoftBank provide technical support to implement the measures and provide technical assistance for the implementation of the measures. For example, the proposal department has SoftBank provide technical support for the implementation of the measures and support the success of the project. In this way, more effective measures can be implemented by having SoftBank take the lead. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the progress of a project led by SoftBank into the AI, and the AI ​​can manage the progress of the project.

[0039] The analysis unit identifies specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, difficulty in determining the location of tables in restaurants, and the lack of restrooms for the visually impaired. For example, the analysis unit inputs collected data into a generating AI, which analyzes the data to identify specific problems. For instance, the analysis unit can use the generating AI to identify insufficient directional signs on roads. It can also use the generating AI to identify dangerous bicycle lanes. Furthermore, the analysis unit can use the generating AI to identify difficulty in determining the location of tables in restaurants. It can also use the generating AI to identify the lack of restrooms for the visually impaired. By identifying specific problems, an environment can be created where visually impaired people can go out with peace of mind. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into an AI, which analyzes the data to identify specific problems.

[0040] The measurement unit selects the optimal measurement method by referring to the visually impaired person's past behavioral patterns during measurement. For example, the measurement unit prioritizes measurements at places the visually impaired person has frequently visited in the past. The measurement unit can also concentrate measurements at specific time periods based on the visually impaired person's past behavioral patterns. For example, the measurement unit prioritizes measurements at places the visually impaired person has visited at specific time periods in the past. Furthermore, the measurement unit can analyze the visually impaired person's past behavioral patterns and select the most efficient measurement method. For example, the measurement unit selects the optimal measurement method based on the visually impaired person's past behavioral patterns. This allows the optimal measurement method to be selected by referring to past behavioral patterns. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's past behavioral patterns into AI, and the AI ​​can select the optimal measurement method.

[0041] The measurement unit filters the measurement data based on the visually impaired person's current activity during measurement. For example, if the visually impaired person is walking, the measurement unit prioritizes measuring data related to walking. If the visually impaired person is resting, the measurement unit can also filter and measure data related to resting. For example, the measurement unit filters the data collected while the visually impaired person is resting. Furthermore, if the visually impaired person is using public transportation, the measurement unit can also filter and measure data related to their usage. For example, the measurement unit filters the data when the visually impaired person is using public transportation. This allows for the collection of more relevant data by filtering the data based on the current activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's current activity into the AI, and the AI ​​can filter the data.

[0042] The measurement unit prioritizes measuring highly relevant data, taking into account the geographical location information of the visually impaired person during measurement. For example, if the visually impaired person is at an intersection, the measurement unit prioritizes measuring data related to the safety of the intersection. If the visually impaired person is at a public transport station, the measurement unit can also prioritize measuring data related to the usage of the station. For example, the measurement unit prioritizes measuring data when the visually impaired person is at the station. Furthermore, if the visually impaired person is in a park, the measurement unit can prioritize measuring data related to the park's facilities. For example, the measurement unit prioritizes measuring data when the visually impaired person is in the park. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, or it may be performed without AI. For example, the measurement unit can input the geographical location information of the visually impaired person into the AI, which can then prioritize measuring highly relevant data.

[0043] The measurement unit analyzes the social media activity of visually impaired individuals and measures relevant data during measurement. For example, if a visually impaired person mentions a specific location on social media, the measurement unit prioritizes measuring data related to that location. The measurement unit can also prioritize measuring data related to a specific issue if a visually impaired person mentions a specific issue on social media. For example, the measurement unit prioritizes measuring data related to an issue mentioned by a visually impaired person on social media. Furthermore, if a visually impaired person mentions a specific event on social media, the measurement unit prioritizes measuring data related to that event. For example, the measurement unit prioritizes measuring data related to an event mentioned by a visually impaired person on social media. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the social media activity of a visually impaired person into AI, which can then measure the relevant data.

[0044] The data collection unit selects the optimal collection method by referring to the visually impaired person's past data collection history during collection. For example, the data collection unit prioritizes collecting data that the visually impaired person has frequently collected in the past. The data collection unit can also concentrate collection during specific time periods based on the visually impaired person's past data collection history. For example, the data collection unit prioritizes collecting data that the visually impaired person has collected during specific time periods in the past. The data collection unit can also analyze the visually impaired person's past data collection history and select the most efficient collection method. For example, the data collection unit selects the optimal collection method based on the visually impaired person's past data collection history. This allows the optimal collection method to be selected by referring to past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the visually impaired person's past data collection history into AI, and the AI ​​can select the optimal collection method.

[0045] The data collection unit filters the collected data based on the visually impaired person's current activities. For example, if the visually impaired person is walking, the data collection unit prioritizes collecting data related to walking. If the visually impaired person is resting, the data collection unit can also filter and collect data related to resting. For example, the data collection unit filters the data collected when the visually impaired person is resting. Furthermore, if the visually impaired person is using public transportation, the data collection unit can also filter and collect data related to their usage. For example, the data collection unit filters the data when the visually impaired person is using public transportation. This allows for the collection of more relevant data by filtering the data based on the current activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the visually impaired person's current activities into the AI, which can then filter the data.

[0046] The data collection unit prioritizes collecting highly relevant data, taking into account the geographical location information of visually impaired individuals. For example, if a visually impaired person is at an intersection, the data collection unit prioritizes collecting data related to the safety of the intersection. If a visually impaired person is at a public transport station, the data collection unit can also prioritize collecting data related to the station's usage. For example, the data collection unit prioritizes collecting data while a visually impaired person is at the station. Furthermore, if a visually impaired person is in a park, the data collection unit can prioritize collecting data related to the park's facilities. For example, the data collection unit prioritizes collecting data while a visually impaired person is in the park. This allows for the priority 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, or not. For example, the data collection unit can input the geographical location information of a visually impaired person into the AI, which can then prioritize collecting highly relevant data.

[0047] The data collection unit analyzes the social media activity of visually impaired individuals and collects relevant data during the collection process. For example, if a visually impaired person mentions a specific location on social media, the data collection unit prioritizes collecting data related to that location. The data collection unit can also prioritize collecting data related to a specific issue if a visually impaired person mentions that issue on social media. For example, the data collection unit prioritizes collecting data related to an issue mentioned by a visually impaired person on social media. Furthermore, if a visually impaired person mentions a specific event on social media, the data collection unit prioritizes collecting data related to that event. For example, the data collection unit prioritizes collecting data related to an event mentioned by a visually impaired person on social media. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media activity of visually impaired individuals into an AI, which can then collect the relevant data.

[0048] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0049] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a traffic analysis algorithm to traffic data. The analysis unit can also apply a facility analysis algorithm to public facility data. For example, the analysis unit applies a facility analysis algorithm to public facility data. Furthermore, the analysis unit can apply a social media analysis algorithm to social media data. For example, the analysis unit applies a social media analysis algorithm to social media data. This improves the accuracy of the analysis by applying the most appropriate analysis algorithm for each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the data category into the AI, which can then apply the most appropriate analysis algorithm.

[0050] The analysis unit determines the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may also determine the priority of analysis based on the data collection period. In this way, by determining the priority of analysis according to the data collection period, the most recent data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0051] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0052] The priority assignment unit adjusts the level of detail of the priority based on the importance of the data when assigning priorities. For example, the priority assignment unit assigns detailed priorities to data with high importance. The priority assignment unit can also assign simplified priorities to data with low importance. For example, the priority assignment unit assigns simplified priorities to data with low importance. The priority assignment unit can also determine the priority order according to the importance of the data. For example, the priority assignment unit determines the priority order based on the importance of the data. This allows for the implementation of efficient measures by adjusting the level of detail of the priority according to the importance of the data. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the priority.

[0053] The priority assignment unit applies different priority assignment algorithms depending on the data category when assigning priorities. For example, the priority assignment unit applies a transportation priority assignment algorithm to transportation data. The priority assignment unit can also apply a facility priority assignment algorithm to public facility data. For example, the priority assignment unit applies a facility priority assignment algorithm to public facility data. Furthermore, the priority assignment unit can apply a social media priority assignment algorithm to social media data. For example, the priority assignment unit applies a social media priority assignment algorithm to social media data. This improves the accuracy of policies by applying the most appropriate priority assignment algorithm according to the data category. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the data category into AI, and the AI ​​can apply the most appropriate priority assignment algorithm.

[0054] The priority assignment unit adjusts the order of priorities based on the data collection period when assigning priorities. For example, the priority assignment unit assigns priority to the most recent data first. The priority assignment unit can also assign priority to older data later. For example, the priority assignment unit assigns priority to older data later. The priority assignment unit can also determine the order of priorities according to the data collection period. For example, the priority assignment unit determines the order of priorities based on the data collection period. By adjusting the order of priorities according to the data collection period, the most recent data can be reflected in policies first. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the data collection period into the AI, and the AI ​​can adjust the order of priorities.

[0055] The priority assignment unit adjusts the order of priorities based on the relevance of the data when assigning priorities. For example, the priority assignment unit assigns priority preferentially to highly relevant data. The priority assignment unit can also assign priority to less relevant data, delaying its processing. For example, the priority assignment unit assigns priority to less relevant data, delaying its processing. The priority assignment unit can also determine the order of priorities based on the relevance of the data. For example, the priority assignment unit determines the order of priorities based on the relevance of the data. This allows for the implementation of efficient measures by adjusting the order of priorities according to the relevance of the data. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the relevance of the data into the AI, which can then adjust the order of priorities.

[0056] The proposal department adjusts the level of detail in its proposals based on the importance of the issues. For example, it provides detailed proposals for high-priority issues and simplified proposals for low-priority issues. The proposal department can also prioritize proposals based on the importance of the issues. This allows for efficient proposals by adjusting the level of detail according to the importance of the issues. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the importance of the issues into the AI, which can then adjust the level of detail in the proposals.

[0057] The proposal unit applies different proposal algorithms depending on the category of the problem when making a proposal. For example, for a transportation problem, the proposal unit applies a transportation proposal algorithm. For a public facilities problem, the proposal unit can also apply a facilities proposal algorithm. For example, the proposal unit applies a facilities proposal algorithm to a public facilities problem. Furthermore, the proposal unit can also apply a social media proposal algorithm to a social media problem. For example, the proposal unit applies a social media proposal algorithm to a social media problem. This improves the accuracy of the proposal by applying the most suitable proposal algorithm according to the category of the problem. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category of the problem into the AI, and the AI ​​can apply the most suitable proposal algorithm.

[0058] The proposal department determines the priority of proposals based on when the problems occurred. For example, the proposal department will prioritize proposals for the most recent problems. The proposal department may also postpone proposals for older problems. For example, the proposal department may postpone proposals for older problems. The proposal department may also determine the priority of proposals based on when the problems occurred. For example, the proposal department will determine the priority of proposals based on when the problems occurred. This allows for prioritizing proposals for the most recent problems by determining the priority of proposals based on when the problems occurred. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of the problems into the AI, and the AI ​​can determine the priority of proposals.

[0059] The proposal department adjusts the order of proposals based on the relevance of the issues when making proposals. For example, the proposal department prioritizes proposals for highly relevant issues. The proposal department may also postpone proposals for less relevant issues. For example, the proposal department may postpone proposals for less relevant issues. The proposal department may also determine the order of proposals based on the relevance of the issues. For example, the proposal department may determine the order of proposals based on the relevance of the issues. This allows for efficient proposals by adjusting the order of proposals according to the relevance of the issues. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the relevance of the issues into the AI, and the AI ​​can adjust the order of proposals.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The system can be further equipped with vibration sensors to measure the behavioral patterns of visually impaired individuals. These vibration sensors detect vibrations felt by visually impaired individuals while walking, measuring their walking rhythm and speed. For example, a vibration sensor can detect ground vibrations felt by a visually impaired person while walking, identifying their walking rhythm. It can also measure the intensity of vibrations felt by the visually impaired person while walking, determining their walking speed. Furthermore, the vibration sensor can analyze the vibration patterns felt by the visually impaired person while walking, evaluating their walking stability. This allows for a more detailed measurement of the walking patterns of visually impaired individuals using vibration sensors.

[0062] The measurement unit can be further equipped with a temperature sensor to measure the behavioral patterns of visually impaired individuals. The temperature sensor measures the temperature around the visually impaired person and detects changes in the environment. For example, the temperature sensor can measure the air temperature when the visually impaired person is outdoors and identify changes in the temperature. It can also measure the room temperature when the visually impaired person is indoors and identify changes in the room temperature. Furthermore, the temperature sensor can analyze the temperature changes that the visually impaired person feels while moving and evaluate the environment of their movement path. This allows for a more detailed measurement of the behavioral patterns of visually impaired individuals by using a temperature sensor.

[0063] The proposal department can make further individualized support suggestions based on the behavioral patterns of visually impaired individuals. For example, if a visually impaired person experiences difficulties in a particular location, the proposal department can propose specific support measures for that location. Furthermore, if a visually impaired person experiences difficulties during a specific time of day, the proposal department can propose specific support measures for that time of day. In addition, if a visually impaired person experiences difficulties during a specific activity, the proposal department can propose specific support measures for that activity. This allows for the provision of effective support tailored to the needs of visually impaired individuals by making individualized support suggestions.

[0064] The measurement unit can be further equipped with pressure sensors to measure the behavioral patterns of visually impaired individuals. The pressure sensors measure the pressure felt by the visually impaired person and identify details of their behavior. For example, the pressure sensors can measure the pressure felt on the soles of the feet while a visually impaired person is walking, identifying their walking rhythm and speed. The pressure sensors can also measure the pressure on the seat when a visually impaired person is sitting, identifying the duration of sitting and their posture. Furthermore, the pressure sensors can measure the weight of objects held by a visually impaired person, identifying details of those objects. In this way, the behavioral patterns of visually impaired individuals can be measured in more detail by using pressure sensors.

[0065] The analysis unit can further utilize image recognition technology to analyze the behavioral patterns of visually impaired individuals. Image recognition technology analyzes the surrounding video of the visually impaired person and identifies details of their behavior. For example, image recognition technology can analyze the scenery a visually impaired person sees while walking and identify their walking path. It can also analyze the layout of a room when a visually impaired person is indoors and identify details of their movement. Furthermore, image recognition technology can analyze the types of objects a visually impaired person is carrying and identify details of their belongings. In this way, by using image recognition technology, the behavioral patterns of visually impaired individuals can be analyzed in more detail.

[0066] Based on the behavioral patterns of visually impaired individuals, the proposal department can further propose collaboration with local communities. For example, if a visually impaired person experiences difficulties in a particular area, the proposal department can implement support measures in collaboration with that local community. Furthermore, if a visually impaired person experiences difficulties during a specific time period, the proposal department can propose that the local community provide support during that time. Additionally, if a visually impaired person experiences difficulties during a specific activity, the proposal department can propose that the local community participate in that activity to provide support. By proposing collaboration with local communities, it becomes possible to provide effective support tailored to the needs of visually impaired individuals.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The measurement unit measures the behavioral patterns of the visually impaired person. The measurement unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. Step 2: The collection unit collects the data measured by the measurement unit. The collection unit collects, for example, location information from a GPS device and data from a device that records conditions. Step 3: The analysis unit analyzes the data collected by the collection unit and identifies problems. The analysis unit uses generative AI to analyze the data and identify the distribution and priority of problems. For example, the analysis unit identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the absence of restrooms for the visually impaired. Step 4: The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. The prioritization unit prioritizes based, for example, on the urgency and scope of impact of the problems. Step 5: The proposal team proposes administrative support based on the priorities assigned by the prioritization team. The proposal team then engages with local governments, for example, based on the identified issues.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system for creating an environment in which visually impaired people can go out with peace of mind. In this system, visually impaired people are fitted with a GPS device and a device that records their location, and data is collected on what kind of problems occurred, where, and at what time of day. The collected data is analyzed by a generating AI to identify the distribution and priority of problems. For example, problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the lack of toilets for the visually impaired may be identified. Based on these problems, administrative support is prioritized, and efforts are made to engage with local governments. SoftBank's leadership in this initiative ensures that more effective measures are implemented. First, a GPS device and a device that records their location are fitted to measure the behavioral patterns of visually impaired people. Next, the collected data is analyzed by a generating AI to identify the distribution and priority of problems. Finally, based on the identified problems, administrative support is prioritized, and efforts are made to engage with local governments. This makes it possible to create an environment in which visually impaired people can go out with peace of mind.

[0070] The system according to the embodiment comprises a measurement unit, a data collection unit, an analysis unit, a prioritization unit, and a proposal unit. The measurement unit measures the behavioral patterns of visually impaired persons. The measurement unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. The data collection unit collects the data measured by the measurement unit. The data collection unit collects, for example, location information from the GPS device and data from the device for recording the situation. The analysis unit analyzes the data collected by the data collection unit and identifies problems. The analysis unit analyzes the data using a generating AI and identifies the distribution and priority of the problems. For example, the analysis unit identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the lack of toilets for the visually impaired. The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. The prioritization unit prioritizes based on, for example, the urgency and scope of impact of the problems. The proposal unit proposes administrative support based on the prioritization assigned by the prioritization unit. The proposal department, for example, will lobby local governments based on the identified problems. This will help create an environment where visually impaired people can go out with peace of mind.

[0071] The measurement unit measures the behavioral patterns of visually impaired individuals. This unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. Specifically, the GPS device records the visually impaired person's movement route and location information in real time, while the situation recording device collects voice memos, ambient sounds, and sensor data such as vibration and temperature. This allows for a detailed understanding of the environment in which the visually impaired person behaves. Furthermore, the measurement unit can also record the movements of the white cane and guide dog used by the visually impaired person. For example, a sensor attached to the tip of the white cane detects the ground conditions and the presence of obstacles, and collects this data. A device for recording the guide dog's movements and reactions is also installed, allowing for an understanding of how the visually impaired person interacts with the guide dog. This enables the measurement unit to comprehensively measure the behavioral patterns of visually impaired individuals and provide detailed data.

[0072] The data collection unit collects data measured by the measurement unit. For example, the data collection unit collects location information from GPS devices and data from devices that record conditions. Specifically, the data collection unit transmits the movement routes and location information of visually impaired individuals to a central database in real time, and similarly collects voice memos, ambient sounds, vibrations, temperature, and other sensor data. This allows for a detailed understanding of the environment in which visually impaired individuals are behaving. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a visually impaired person stays in a particular area for a long time, the collection frequency can be increased to collect detailed data on that area. The data collection unit also centrally manages the collected data and can collaborate with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0073] The analysis unit analyzes the data collected by the data collection unit to identify problems. The analysis unit uses generative AI to analyze the data and identify the distribution and priority of problems. Specifically, the generative AI analyzes the movement routes and behavioral patterns of visually impaired people to identify what kind of problems are occurring at what locations. For example, it identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the absence of restrooms for the visually impaired. Based on this data, the generative AI evaluates the urgency and scope of impact of the problems and assigns them priority. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can analyze the frequency of problems occurring in specific areas or time periods to plan future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0074] The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. For example, the prioritization unit prioritizes based on the urgency and scope of impact of the problems. Specifically, it evaluates the urgency of the problems and determines the priority based on the scope of impact, using data analyzed by the generating AI. For example, if there is a shortage of directional signs, the unit prioritizes the area considering the traffic volume and the frequency of use by visually impaired people. Similarly, if a bicycle path is dangerous, the unit prioritizes it based on the accident rate and the frequency of use by visually impaired people. This allows the prioritization unit to provide criteria for prompt and appropriate responses to problems faced by visually impaired people. Furthermore, the prioritization unit can continuously review and improve the prioritization process. For example, it can update the prioritization criteria based on new data and feedback to provide more accurate prioritization. This ensures that the prioritization unit always provides appropriate prioritization based on the latest information, enabling effective support for visually impaired people.

[0075] The Proposal Department proposes administrative support based on the priorities assigned by the Prioritization Department. For example, the Proposal Department engages with local governments based on identified problems. Specifically, it proposes concrete support measures to local governments based on the priorities determined by the Prioritization Department. For example, it proposes the installation of new directional signs in areas lacking them, and strengthens safety measures in areas where bicycle paths are dangerous. It also proposes changing the layout of tables in restaurants to make them more accessible to visually impaired people if they are unsure of their location, and proposes the installation of new restrooms if there are none for the visually impaired. In this way, the Proposal Department can provide concrete support measures to create an environment where visually impaired people can go out with peace of mind. Furthermore, the Proposal Department can monitor the implementation status of the proposals and provide additional suggestions or improvements as needed. For example, after the proposed support measures are implemented, it evaluates their effectiveness and proposes further improvements as needed. In this way, the Proposal Department can continuously support visually impaired people and maintain an environment where they can go out with peace of mind.

[0076] The measurement unit includes a GPS device attached to a visually impaired person and a device for recording the situation. For example, the measurement unit uses the GPS device attached to the visually impaired person to acquire the visually impaired person's location information in real time. The measurement unit can also record the situation around the visually impaired person using the situation recording device. For example, the measurement unit uses a camera to record video of the visually impaired person's surroundings. The measurement unit can also use an audio recording device to record audio around the visually impaired person. This allows for accurate measurement of the visually impaired person's behavioral patterns. Some or all of the above-described processes in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's location information and surrounding situation into the AI, which can then analyze the data to identify behavioral patterns.

[0077] The analysis unit uses generative AI to analyze data and identify the distribution and priority of problems. For example, the analysis unit inputs collected data into the generative AI, which analyzes the data to identify problems. For example, the analysis unit uses the generative AI to identify specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, unclear table locations in restaurants, and lack of restrooms for the visually impaired. The analysis unit can also use the generative AI to geographically visualize the distribution of problems. For example, the analysis unit uses the generative AI to plot the locations of problems on a map, visually displaying the distribution of problems. Furthermore, the analysis unit can also use the generative AI to identify the priority of problems. For example, the analysis unit uses the generative AI to identify priority based on the urgency and scope of impact of the problems. This allows for accurate identification of the distribution and priority of problems by using the generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which analyzes the data to identify problems.

[0078] The proposal department engages with local governments based on identified issues. For example, the proposal department submits proposals to local governments based on identified issues. The proposal department can also hold meetings with local governments based on identified issues. For example, the proposal department discusses the identified issues with local governments and proposes solutions. The proposal department can also collect feedback from local governments based on identified issues. For example, the proposal department revises the proposal based on feedback from local governments. In this way, effective measures can be implemented by engaging with local governments based on identified issues. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the identified issues into AI, and the AI ​​can generate proposals.

[0079] The proposal department is led by a specific company. For example, the proposal department can implement more effective measures by having SoftBank take the lead. The proposal department has SoftBank take the lead in the project and coordinate to implement the measures. For example, the proposal department has SoftBank strengthen cooperation with local governments and build a cooperative system for implementing the measures. The proposal department also has SoftBank provide funding and secure the resources necessary to implement the measures. For example, the proposal department has SoftBank provide funding for the implementation of the measures and support the progress of the project. The proposal department also has SoftBank provide technical support to implement the measures and provide technical assistance for the implementation of the measures. For example, the proposal department has SoftBank provide technical support for the implementation of the measures and support the success of the project. In this way, more effective measures can be implemented by having SoftBank take the lead. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the progress of a project led by SoftBank into the AI, and the AI ​​can manage the progress of the project.

[0080] The analysis unit identifies specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, difficulty in determining the location of tables in restaurants, and the lack of restrooms for the visually impaired. For example, the analysis unit inputs collected data into a generating AI, which analyzes the data to identify specific problems. For instance, the analysis unit can use the generating AI to identify insufficient directional signs on roads. It can also use the generating AI to identify dangerous bicycle lanes. Furthermore, the analysis unit can use the generating AI to identify difficulty in determining the location of tables in restaurants. It can also use the generating AI to identify the lack of restrooms for the visually impaired. By identifying specific problems, an environment can be created where visually impaired people can go out with peace of mind. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into an AI, which analyzes the data to identify specific problems.

[0081] The measurement unit estimates the emotions of visually impaired individuals and adjusts the timing of measurements based on the estimated emotions. For example, the measurement unit can capture the facial expressions of visually impaired individuals with a camera and estimate their emotions using an emotion estimation algorithm. For example, the measurement unit can calculate an emotion score based on changes in facial expressions. The measurement unit can also record the voice of visually impaired individuals and estimate their emotions using voice analysis technology. For example, the measurement unit can analyze the tone and speed of their voice and calculate an emotion score. The measurement unit can also collect biometric data (heart rate and skin electrical activity) from visually impaired individuals using sensors and estimate their emotions using an emotion estimation algorithm. For example, the measurement unit can calculate an emotion score based on fluctuations in heart rate. This allows for the collection of more appropriate data by adjusting the timing of measurements according to the emotions of visually impaired individuals. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using AI or not. For example, the measurement unit inputs image data of a visually impaired person captured by a camera into a generating AI, which can then estimate their emotions.

[0082] The measurement unit selects the optimal measurement method by referring to the visually impaired person's past behavioral patterns during measurement. For example, the measurement unit prioritizes measurements at places the visually impaired person has frequently visited in the past. The measurement unit can also concentrate measurements at specific time periods based on the visually impaired person's past behavioral patterns. For example, the measurement unit prioritizes measurements at places the visually impaired person has visited at specific time periods in the past. Furthermore, the measurement unit can analyze the visually impaired person's past behavioral patterns and select the most efficient measurement method. For example, the measurement unit selects the optimal measurement method based on the visually impaired person's past behavioral patterns. This allows the optimal measurement method to be selected by referring to past behavioral patterns. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's past behavioral patterns into AI, and the AI ​​can select the optimal measurement method.

[0083] The measurement unit filters the measurement data based on the visually impaired person's current activity during measurement. For example, if the visually impaired person is walking, the measurement unit prioritizes measuring data related to walking. If the visually impaired person is resting, the measurement unit can also filter and measure data related to resting. For example, the measurement unit filters the data collected while the visually impaired person is resting. Furthermore, if the visually impaired person is using public transportation, the measurement unit can also filter and measure data related to their usage. For example, the measurement unit filters the data when the visually impaired person is using public transportation. This allows for the collection of more relevant data by filtering the data based on the current activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the visually impaired person's current activity into the AI, and the AI ​​can filter the data.

[0084] The measurement unit estimates the emotions of visually impaired individuals and determines the priority of data to measure based on the estimated emotions. For example, if a visually impaired person is feeling anxious, the measurement unit prioritizes measuring data to alleviate anxiety. If a visually impaired person is relaxed, the measurement unit can also prioritize measuring data to maintain that relaxed state. For example, the measurement unit prioritizes measuring data when a visually impaired person is relaxed. Furthermore, if a visually impaired person is agitated, the measurement unit can prioritize measuring data to manage that agitated state. For example, the measurement unit prioritizes measuring data when a visually impaired person is agitated. This allows for the priority collection of more important data by determining the data priority according to the emotions of the visually impaired person. 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 measurement unit may be performed using AI or not. For example, the measurement unit can input the emotional data of a visually impaired person into an AI, which can then determine the data priority.

[0085] The measurement unit prioritizes measuring highly relevant data, taking into account the geographical location information of the visually impaired person during measurement. For example, if the visually impaired person is at an intersection, the measurement unit prioritizes measuring data related to the safety of the intersection. If the visually impaired person is at a public transport station, the measurement unit can also prioritize measuring data related to the usage of the station. For example, the measurement unit prioritizes measuring data when the visually impaired person is at the station. Furthermore, if the visually impaired person is in a park, the measurement unit can prioritize measuring data related to the park's facilities. For example, the measurement unit prioritizes measuring data when the visually impaired person is in the park. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, or it may be performed without AI. For example, the measurement unit can input the geographical location information of the visually impaired person into the AI, which can then prioritize measuring highly relevant data.

[0086] The measurement unit analyzes the social media activity of visually impaired individuals and measures relevant data during measurement. For example, if a visually impaired person mentions a specific location on social media, the measurement unit prioritizes measuring data related to that location. The measurement unit can also prioritize measuring data related to a specific issue if a visually impaired person mentions a specific issue on social media. For example, the measurement unit prioritizes measuring data related to an issue mentioned by a visually impaired person on social media. Furthermore, if a visually impaired person mentions a specific event on social media, the measurement unit prioritizes measuring data related to that event. For example, the measurement unit prioritizes measuring data related to an event mentioned by a visually impaired person on social media. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the social media activity of a visually impaired person into AI, which can then measure the relevant data.

[0087] The data collection unit estimates the emotions of visually impaired individuals and adjusts the timing of data collection based on the estimated emotions. For example, if a visually impaired person is stressed, the data collection unit reduces the frequency of collection, and if they are relaxed, it increases the frequency. The data collection unit can also temporarily stop collection if a visually impaired person is tired and resume it after rest. For example, the data collection unit temporarily stops collection when a visually impaired person is tired. The data collection unit can also shorten the collection interval to collect more detailed data if a visually impaired person is agitated. For example, the data collection unit shortens the collection interval when a visually impaired person is agitated. By adjusting the collection timing according to the emotions of visually impaired individuals, more appropriate data can be collected. 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 or not. For example, the data collection unit can input the emotional data of visually impaired individuals into an AI, which can then adjust the collection timing.

[0088] The data collection unit selects the optimal collection method by referring to the visually impaired person's past data collection history during collection. For example, the data collection unit prioritizes collecting data that the visually impaired person has frequently collected in the past. The data collection unit can also concentrate collection during specific time periods based on the visually impaired person's past data collection history. For example, the data collection unit prioritizes collecting data that the visually impaired person has collected during specific time periods in the past. The data collection unit can also analyze the visually impaired person's past data collection history and select the most efficient collection method. For example, the data collection unit selects the optimal collection method based on the visually impaired person's past data collection history. This allows the optimal collection method to be selected by referring to past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the visually impaired person's past data collection history into AI, and the AI ​​can select the optimal collection method.

[0089] The data collection unit filters the collected data based on the visually impaired person's current activities. For example, if the visually impaired person is walking, the data collection unit prioritizes collecting data related to walking. If the visually impaired person is resting, the data collection unit can also filter and collect data related to resting. For example, the data collection unit filters the data collected when the visually impaired person is resting. Furthermore, if the visually impaired person is using public transportation, the data collection unit can also filter and collect data related to their usage. For example, the data collection unit filters the data when the visually impaired person is using public transportation. This allows for the collection of more relevant data by filtering the data based on the current activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the visually impaired person's current activities into the AI, which can then filter the data.

[0090] The data collection unit estimates the emotions of visually impaired individuals and determines the priority of data to collect based on the estimated emotions. For example, if a visually impaired person is feeling anxious, the data collection unit prioritizes collecting data to alleviate that anxiety. If a visually impaired person is relaxed, the data collection unit may also prioritize collecting data to maintain that relaxed state. For example, the data collection unit may prioritize collecting data when a visually impaired person is relaxed. Furthermore, if a visually impaired person is agitated, the data collection unit may prioritize collecting data to manage that agitated state. For example, the data collection unit may prioritize collecting data when a visually impaired person is agitated. This allows for the priority collection of more important data by determining the data priority according to the emotions of the visually impaired person. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the emotional data of visually impaired individuals into an AI, which can then determine the data priority.

[0091] The data collection unit prioritizes collecting highly relevant data, taking into account the geographical location information of visually impaired individuals. For example, if a visually impaired person is at an intersection, the data collection unit prioritizes collecting data related to the safety of the intersection. If a visually impaired person is at a public transport station, the data collection unit can also prioritize collecting data related to the station's usage. For example, the data collection unit prioritizes collecting data while a visually impaired person is at the station. Furthermore, if a visually impaired person is in a park, the data collection unit can prioritize collecting data related to the park's facilities. For example, the data collection unit prioritizes collecting data while a visually impaired person is in the park. This allows for the priority 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, or not. For example, the data collection unit can input the geographical location information of a visually impaired person into the AI, which can then prioritize collecting highly relevant data.

[0092] The data collection unit analyzes the social media activity of visually impaired individuals and collects relevant data during the collection process. For example, if a visually impaired person mentions a specific location on social media, the data collection unit prioritizes collecting data related to that location. The data collection unit can also prioritize collecting data related to a specific issue if a visually impaired person mentions that issue on social media. For example, the data collection unit prioritizes collecting data related to an issue mentioned by a visually impaired person on social media. Furthermore, if a visually impaired person mentions a specific event on social media, the data collection unit prioritizes collecting data related to that event. For example, the data collection unit prioritizes collecting data related to an event mentioned by a visually impaired person on social media. This allows for the priority collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media activity of visually impaired individuals into an AI, which can then collect the relevant data.

[0093] The analysis unit estimates the emotions of visually impaired individuals and adjusts the presentation of the analysis based on the estimated emotions. For example, if a visually impaired person is relaxed, the generation AI generates an analysis result that proceeds at a relaxed pace. If a visually impaired person is in a hurry, the generation AI can also generate an analysis result that emphasizes the shortest route. For example, the analysis unit generates an analysis result when a visually impaired person is in a hurry. Furthermore, if a visually impaired person is excited, the generation AI can also generate an analysis result with visually stimulating effects. For example, the analysis unit generates an analysis result when a visually impaired person is excited. By adjusting the presentation of the analysis according to the emotions of the visually impaired person, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or 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 analysis unit may be performed using AI or not. For example, the analysis unit can input emotional data from visually impaired individuals into the AI, which can then adjust how the analysis is presented.

[0094] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0095] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a traffic analysis algorithm to traffic data. The analysis unit can also apply a facility analysis algorithm to public facility data. For example, the analysis unit applies a facility analysis algorithm to public facility data. Furthermore, the analysis unit can apply a social media analysis algorithm to social media data. For example, the analysis unit applies a social media analysis algorithm to social media data. This improves the accuracy of the analysis by applying the most appropriate analysis algorithm for each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the data category into the AI, which can then apply the most appropriate analysis algorithm.

[0096] The analysis unit estimates the emotions of the visually impaired person and adjusts the length of the analysis based on the estimated emotions. For example, if the visually impaired person is in a hurry, the generation AI will generate a short, concise analysis result. If the visually impaired person is relaxed, the generation AI can also generate a longer analysis result that includes detailed explanations. For example, the analysis unit will generate an analysis result when the visually impaired person is relaxed. Furthermore, if the visually impaired person is excited, the generation AI can generate an analysis result with visually stimulating effects added. For example, the analysis unit will generate an analysis result when the visually impaired person is excited. By adjusting the length of the analysis according to the emotions of the visually impaired person, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using AI or not. For example, the analysis unit can input emotional data from visually impaired individuals into the AI, which can then adjust the length of the analysis.

[0097] The analysis unit determines the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may also determine the priority of analysis based on the data collection period. In this way, by determining the priority of analysis according to the data collection period, the most recent data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0098] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit can determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0099] The priority assignment unit estimates the emotions of visually impaired individuals and adjusts the priority assignment method based on the estimated emotions. For example, if a visually impaired person is feeling anxious, the priority assignment unit will assign a high priority to measures that alleviate anxiety. If a visually impaired person is relaxed, the priority assignment unit can also assign a high priority to measures that maintain that relaxed state. For example, the priority assignment unit will assign a high priority to measures that are appropriate when a visually impaired person is relaxed. Furthermore, if a visually impaired person is agitated, the priority assignment unit can also assign a high priority to measures that manage that agitated state. For example, the priority assignment unit will assign a high priority to measures that are appropriate when a visually impaired person is agitated. By adjusting the priority assignment method according to the emotions of visually impaired individuals, more appropriate measures can be implemented. 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 processing described above in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input emotional data of visually impaired individuals into the AI, which can then adjust the priority assignment method.

[0100] The priority assignment unit adjusts the level of detail of the priority based on the importance of the data when assigning priorities. For example, the priority assignment unit assigns detailed priorities to data with high importance. The priority assignment unit can also assign simplified priorities to data with low importance. For example, the priority assignment unit assigns simplified priorities to data with low importance. The priority assignment unit can also determine the priority order according to the importance of the data. For example, the priority assignment unit determines the priority order based on the importance of the data. This allows for the implementation of efficient measures by adjusting the level of detail of the priority according to the importance of the data. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the priority.

[0101] The priority assignment unit applies different priority assignment algorithms depending on the data category when assigning priorities. For example, the priority assignment unit applies a transportation priority assignment algorithm to transportation data. The priority assignment unit can also apply a facility priority assignment algorithm to public facility data. For example, the priority assignment unit applies a facility priority assignment algorithm to public facility data. Furthermore, the priority assignment unit can apply a social media priority assignment algorithm to social media data. For example, the priority assignment unit applies a social media priority assignment algorithm to social media data. This improves the accuracy of policies by applying the most appropriate priority assignment algorithm according to the data category. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the data category into AI, and the AI ​​can apply the most appropriate priority assignment algorithm.

[0102] The priority assignment unit estimates the emotions of visually impaired individuals and determines the priority order based on the estimated emotions. For example, if a visually impaired person is feeling anxious, the priority assignment unit will assign a high priority to measures that alleviate anxiety. If a visually impaired person is relaxed, the priority assignment unit can also assign a high priority to measures that maintain that relaxed state. For example, the priority assignment unit will assign a high priority to measures that are appropriate when a visually impaired person is relaxed. Furthermore, if a visually impaired person is agitated, the priority assignment unit can also assign a high priority to measures that manage that agitated state. For example, the priority assignment unit will assign a high priority to measures that are appropriate when a visually impaired person is agitated. By determining the priority order according to the emotions of visually impaired individuals, more appropriate measures can be implemented. 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 processing described above in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input emotional data of visually impaired individuals into the AI, which can then determine the priority order.

[0103] The priority assignment unit adjusts the order of priorities based on the data collection period when assigning priorities. For example, the priority assignment unit assigns priority to the most recent data first. The priority assignment unit can also assign priority to older data later. For example, the priority assignment unit assigns priority to older data later. The priority assignment unit can also determine the order of priorities according to the data collection period. For example, the priority assignment unit determines the order of priorities based on the data collection period. By adjusting the order of priorities according to the data collection period, the most recent data can be reflected in policies first. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the data collection period into the AI, and the AI ​​can adjust the order of priorities.

[0104] The priority assignment unit adjusts the order of priorities based on the relevance of the data when assigning priorities. For example, the priority assignment unit assigns priority preferentially to highly relevant data. The priority assignment unit can also assign priority to less relevant data, delaying its processing. For example, the priority assignment unit assigns priority to less relevant data, delaying its processing. The priority assignment unit can also determine the order of priorities based on the relevance of the data. For example, the priority assignment unit determines the order of priorities based on the relevance of the data. This allows for the implementation of efficient measures by adjusting the order of priorities according to the relevance of the data. Some or all of the above processing in the priority assignment unit may be performed using AI or not. For example, the priority assignment unit can input the relevance of the data into the AI, which can then adjust the order of priorities.

[0105] The suggestion unit estimates the emotions of visually impaired individuals and adjusts the presentation of suggestions based on the estimated emotions. For example, if a visually impaired person is relaxed, the suggestion unit will make suggestions that proceed at a relaxed pace. If a visually impaired person is in a hurry, the suggestion unit may also make suggestions that emphasize the shortest route. For example, the suggestion unit may make suggestions for when a visually impaired person is in a hurry. Furthermore, if a visually impaired person is excited, the suggestion unit may add visually stimulating effects to the suggestions. For example, the suggestion unit may make suggestions for when a visually impaired person is excited. In this way, by adjusting the presentation of suggestions according to the emotions of visually impaired individuals, more appropriate suggestions can be made. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the emotional data of visually impaired individuals into an AI, and the AI ​​can adjust the presentation of suggestions.

[0106] The proposal department adjusts the level of detail in its proposals based on the importance of the issues. For example, it provides detailed proposals for high-priority issues and simplified proposals for low-priority issues. The proposal department can also prioritize proposals based on the importance of the issues. This allows for efficient proposals by adjusting the level of detail according to the importance of the issues. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the importance of the issues into the AI, which can then adjust the level of detail in the proposals.

[0107] The proposal unit applies different proposal algorithms depending on the category of the problem when making a proposal. For example, for a transportation problem, the proposal unit applies a transportation proposal algorithm. For a public facilities problem, the proposal unit can also apply a facilities proposal algorithm. For example, the proposal unit applies a facilities proposal algorithm to a public facilities problem. Furthermore, the proposal unit can also apply a social media proposal algorithm to a social media problem. For example, the proposal unit applies a social media proposal algorithm to a social media problem. This improves the accuracy of the proposal by applying the most suitable proposal algorithm according to the category of the problem. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category of the problem into the AI, and the AI ​​can apply the most suitable proposal algorithm.

[0108] The suggestion unit estimates the emotions of a visually impaired person and adjusts the length of the suggestion based on the estimated emotions. For example, if the visually impaired person is in a hurry, the suggestion unit will provide a short, concise suggestion. If the visually impaired person is relaxed, the suggestion unit may provide a longer suggestion that includes detailed explanations. For example, the suggestion unit will provide a suggestion for when the visually impaired person is relaxed. Furthermore, if the visually impaired person is excited, the suggestion unit may provide a suggestion that includes visually stimulating effects. For example, the suggestion unit will provide a suggestion for when the visually impaired person is excited. By adjusting the length of the suggestion according to the emotions of the visually impaired person, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the emotional data of the visually impaired person into an AI, which can then adjust the length of the suggestion.

[0109] The proposal department determines the priority of proposals based on when the problems occurred. For example, the proposal department will prioritize proposals for the most recent problems. The proposal department may also postpone proposals for older problems. For example, the proposal department may postpone proposals for older problems. The proposal department may also determine the priority of proposals based on when the problems occurred. For example, the proposal department will determine the priority of proposals based on when the problems occurred. This allows for prioritizing proposals for the most recent problems by determining the priority of proposals based on when the problems occurred. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of the problems into the AI, and the AI ​​can determine the priority of proposals.

[0110] The proposal department adjusts the order of proposals based on the relevance of the issues when making proposals. For example, the proposal department prioritizes proposals for highly relevant issues. The proposal department may also postpone proposals for less relevant issues. For example, the proposal department may postpone proposals for less relevant issues. The proposal department may also determine the order of proposals based on the relevance of the issues. For example, the proposal department may determine the order of proposals based on the relevance of the issues. This allows for efficient proposals by adjusting the order of proposals according to the relevance of the issues. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the relevance of the issues into the AI, and the AI ​​can adjust the order of proposals.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The system can be further equipped with vibration sensors to measure the behavioral patterns of visually impaired individuals. These vibration sensors detect vibrations felt by visually impaired individuals while walking, measuring their walking rhythm and speed. For example, a vibration sensor can detect ground vibrations felt by a visually impaired person while walking, identifying their walking rhythm. It can also measure the intensity of vibrations felt by the visually impaired person while walking, determining their walking speed. Furthermore, the vibration sensor can analyze the vibration patterns felt by the visually impaired person while walking, evaluating their walking stability. This allows for a more detailed measurement of the walking patterns of visually impaired individuals using vibration sensors.

[0113] The measurement unit can be further equipped with a temperature sensor to measure the behavioral patterns of visually impaired individuals. The temperature sensor measures the temperature around the visually impaired person and detects changes in the environment. For example, the temperature sensor can measure the air temperature when the visually impaired person is outdoors and identify changes in the temperature. It can also measure the room temperature when the visually impaired person is indoors and identify changes in the room temperature. Furthermore, the temperature sensor can analyze the temperature changes that the visually impaired person feels while moving and evaluate the environment of their movement path. This allows for a more detailed measurement of the behavioral patterns of visually impaired individuals by using a temperature sensor.

[0114] The analysis unit can further utilize speech recognition technology to analyze the behavioral patterns of visually impaired individuals. Speech recognition technology analyzes the voices emitted by visually impaired individuals to identify their intended actions. For example, speech recognition technology can analyze voice commands uttered by visually impaired individuals to identify their destination. Furthermore, speech recognition technology can analyze the tone and speed of the voices emitted by visually impaired individuals to estimate their emotional state. Additionally, speech recognition technology can analyze the content of the voices emitted by visually impaired individuals to identify the details of their actions. This allows for a more detailed analysis of the behavioral patterns of visually impaired individuals by utilizing speech recognition technology.

[0115] The proposal department can make further individualized support suggestions based on the behavioral patterns of visually impaired individuals. For example, if a visually impaired person experiences difficulties in a particular location, the proposal department can propose specific support measures for that location. Furthermore, if a visually impaired person experiences difficulties during a specific time of day, the proposal department can propose specific support measures for that time of day. In addition, if a visually impaired person experiences difficulties during a specific activity, the proposal department can propose specific support measures for that activity. This allows for the provision of effective support tailored to the needs of visually impaired individuals by making individualized support suggestions.

[0116] The suggestion function can estimate the emotions of a visually impaired person and adjust the content of its suggestions based on those estimated emotions. For example, if a visually impaired person is feeling anxious, the suggestion function can offer suggestions to alleviate that anxiety. It can also offer suggestions to maintain a relaxed state if the visually impaired person is relaxed. Furthermore, if the visually impaired person is agitated, the suggestion function can offer suggestions to manage that agitation. This allows for more appropriate support to be provided by adjusting the content of suggestions according to the emotions of the visually impaired person.

[0117] The measurement unit can be further equipped with pressure sensors to measure the behavioral patterns of visually impaired individuals. The pressure sensors measure the pressure felt by the visually impaired person and identify details of their behavior. For example, the pressure sensors can measure the pressure felt on the soles of the feet while a visually impaired person is walking, identifying their walking rhythm and speed. The pressure sensors can also measure the pressure on the seat when a visually impaired person is sitting, identifying the duration of sitting and their posture. Furthermore, the pressure sensors can measure the weight of objects held by a visually impaired person, identifying details of those objects. In this way, the behavioral patterns of visually impaired individuals can be measured in more detail by using pressure sensors.

[0118] The measurement unit can estimate the emotions of visually impaired individuals and adjust the measurement accuracy based on the estimated emotions. For example, if a visually impaired person is feeling anxious, the measurement unit can increase the measurement accuracy to collect more detailed data. Conversely, if a visually impaired person is relaxed, the measurement unit can reduce the burden of data collection by lowering the measurement accuracy. Furthermore, if a visually impaired person is agitated, the measurement unit can adjust the measurement accuracy to ensure the reliability of the data. In this way, by adjusting the measurement accuracy according to the emotions of visually impaired individuals, more appropriate data can be collected.

[0119] The analysis unit can further utilize image recognition technology to analyze the behavioral patterns of visually impaired individuals. Image recognition technology analyzes the surrounding video of the visually impaired person and identifies details of their behavior. For example, image recognition technology can analyze the scenery a visually impaired person sees while walking and identify their walking path. It can also analyze the layout of a room when a visually impaired person is indoors and identify details of their movement. Furthermore, image recognition technology can analyze the types of objects a visually impaired person is carrying and identify details of their belongings. In this way, by using image recognition technology, the behavioral patterns of visually impaired individuals can be analyzed in more detail.

[0120] The analysis unit can estimate the emotions of visually impaired individuals and determine the priority of analysis based on those estimated emotions. For example, if a visually impaired person is feeling anxious, the analysis unit will prioritize analyzing data to alleviate that anxiety. Similarly, if a visually impaired person is relaxed, the analysis unit can prioritize analyzing data to maintain that relaxed state. Furthermore, if a visually impaired person is agitated, the analysis unit can prioritize analyzing data to manage that agitated state. This allows for prioritizing analysis based on the emotions of the visually impaired person, thereby enabling the analysis of more important data.

[0121] Based on the behavioral patterns of visually impaired individuals, the proposal department can further propose collaboration with local communities. For example, if a visually impaired person experiences difficulties in a particular area, the proposal department can implement support measures in collaboration with that local community. Furthermore, if a visually impaired person experiences difficulties during a specific time period, the proposal department can propose that the local community provide support during that time. Additionally, if a visually impaired person experiences difficulties during a specific activity, the proposal department can propose that the local community participate in that activity to provide support. By proposing collaboration with local communities, it becomes possible to provide effective support tailored to the needs of visually impaired individuals.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The measurement unit measures the behavioral patterns of the visually impaired person. The measurement unit includes, for example, a GPS device attached to the visually impaired person and a device for recording the situation. Step 2: The collection unit collects the data measured by the measurement unit. The collection unit collects, for example, location information from a GPS device and data from a device that records conditions. Step 3: The analysis unit analyzes the data collected by the collection unit and identifies problems. The analysis unit uses generative AI to analyze the data and identify the distribution and priority of problems. For example, the analysis unit identifies specific problems such as a lack of directional signs on roads, dangerous bicycle lanes, difficulty in finding tables in restaurants, and the absence of restrooms for the visually impaired. Step 4: The prioritization unit prioritizes administrative support based on the problems identified by the analysis unit. The prioritization unit prioritizes based, for example, on the urgency and scope of impact of the problems. Step 5: The proposal team proposes administrative support based on the priorities assigned by the prioritization team. The proposal team then engages with local governments, for example, based on the identified issues.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] For example, the measurement unit is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the GPS device and the status recording device are controlled by the computer 36 of the smart device 14 and the processor 28 of the data processing unit 12. The data collection unit collects data via the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a generating AI. The priority assignment unit and the proposal unit are implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] For example, the measurement unit is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the GPS device and the status recording device are controlled by the computer 36 of the smart glasses 214 and the processor 28 of the data processing unit 12. The data collection unit collects data via the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using generated AI. The priority assignment unit and the proposal unit are implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] For example, the measurement unit is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the GPS device and the status recording device are controlled by the computer 36 of the headset terminal 314 and the processor 28 of the data processing unit 12. The data collection unit collects data via the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a generation AI. The priority assignment unit and the proposal unit are implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] For example, the measurement unit is implemented in at least one of the robot 414 and the data processing unit 12. For example, the GPS device and the status recording device are controlled by the computer 36 of the robot 414 and the processor 28 of the data processing unit 12. The data acquisition unit collects data via the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using a generating AI. The priority assignment unit and the proposal unit are implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A measurement unit that measures the behavioral patterns of visually impaired people, A collection unit that collects data measured by the measurement unit, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies problems, Based on the problems identified by the aforementioned analysis unit, a prioritization unit is used to assign priorities for administrative support. The system comprises: a proposal unit that proposes administrative support based on the priority assigned by the priority assignment unit; A system characterized by the following features. (Note 2) The aforementioned measuring unit is Includes a GPS device and a device for recording the situation, which are attached to visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We use generative AI to analyze data and identify the distribution and priority of problems. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the identified issues, we will engage with local governments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, A specific company takes the lead The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Identify specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, difficulty finding tables in restaurants, and lack of restrooms for the visually impaired. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned measuring unit is The system estimates the emotions of visually impaired individuals and adjusts the timing of measurements based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned measuring unit is During measurement, the optimal measurement method is selected by referring to the past behavioral patterns of visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned measuring unit is During measurement, the measurement data is filtered based on the current activities of the visually impaired person. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned measuring unit is To estimate the emotions of visually impaired individuals and to prioritize the data to be measured based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned measuring unit is During measurement, the geographical location information of visually impaired individuals is taken into consideration, and highly relevant data is measured preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned measuring unit is During measurement, the social media activity of visually impaired individuals is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the emotions of visually impaired individuals and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the optimal collection method is selected by referring to the past data collection history of visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the collected data is filtered based on the current activities of visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is This system estimates the emotions of visually impaired individuals and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the collection of highly relevant data will be prioritized, taking into account the geographical location information of visually impaired individuals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, we analyze the social media activity of visually impaired individuals and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate the emotions of visually impaired individuals and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, The system estimates the emotions of visually impaired individuals and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The priority assignment unit, The system estimates the emotions of visually impaired individuals and adjusts the priority assignment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The priority assignment unit, When assigning priorities, adjust the level of detail of the priority based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The priority assignment unit, When assigning priorities, different priority assignment algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The priority assignment unit, The system estimates the emotions of visually impaired individuals and determines priority levels based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The priority assignment unit, When assigning priorities, adjust the order of priorities based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The priority assignment unit, When assigning priorities, adjust the order of priorities based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, The system estimates the emotions of visually impaired individuals and adjusts the way proposals are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, The system estimates the emotions of visually impaired individuals and adjusts the length of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making proposals, adjust the order of the proposals based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 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 measurement unit that measures the behavioral patterns of visually impaired people, A collection unit that collects data measured by the measurement unit, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies problems, Based on the problems identified by the aforementioned analysis unit, a prioritization unit is used to assign priorities for administrative support. The system comprises: a proposal unit that proposes administrative support based on the priority assigned by the priority assignment unit; A system characterized by the following features.

2. The aforementioned measuring unit is Includes a GPS device and a device for recording the situation, which are attached to visually impaired individuals. The system according to feature 1.

3. The aforementioned analysis unit, We use generative AI to analyze data and identify the distribution and priority of problems. The system according to feature 1.

4. The aforementioned proposal section is, Based on the identified issues, we will engage with local governments. The system according to feature 1.

5. The aforementioned proposal section is, A specific company takes the lead The system according to feature 1.

6. The aforementioned analysis unit, Identify specific problems such as insufficient directional signs on roads, dangerous bicycle lanes, difficulty finding tables in restaurants, and lack of restrooms for the visually impaired. The system according to feature 1.

7. The aforementioned measuring unit is The system estimates the emotions of visually impaired individuals and adjusts the timing of measurements based on these estimated emotions. The system according to feature 1.

8. The aforementioned measuring unit is During measurement, the optimal measurement method is selected by referring to the past behavioral patterns of visually impaired individuals. The system according to feature 1.

9. The aforementioned measuring unit is During measurement, the measurement data is filtered based on the current activities of the visually impaired person. The system according to feature 1.

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