system

Abstract

The system according to the embodiment aims to accurately grasp the needs of people with disabilities and develop and provide products and services based on those needs. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a development unit, a provision unit, an application unit, and a public relations unit. The collection unit collects survey responses. The analysis unit analyzes the data collected by the collection unit. The development unit develops products or services based on the needs identified by the analysis unit. The provision unit provides the products or services developed by the development unit. The application unit submits applications for assistance. The public relations unit publicizes these efforts.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately identify the needs of people with disabilities and develop and provide products and services based on those needs, so there is room for improvement.

[0005] The system according to the embodiment aims to accurately grasp the needs of people with disabilities and develop and provide products and services based on those needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a development unit, a provision unit, an application unit, and a public relations unit. The collection unit collects questionnaire responses. The analysis unit analyzes the data collected by the collection unit. The development unit develops products or services based on the needs identified by the analysis unit. The provision unit provides the products or services developed by the development unit. The application unit submits applications for assistance. The public relations unit publicizes these efforts. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the needs of people with disabilities and develop and provide products and services based on those needs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The disability support system according to an embodiment of the present invention identifies the needs of individuals with disabilities and provides optimal products and services. This disability support system asks all individuals with disabilities to fill out a questionnaire, and those who find it difficult to complete it are assigned a supporter to complete it on their behalf. Each response is paid 1,000 yen, meaning that if half of the individuals complete the questionnaire, the cost will be 5 billion yen. Based on the survey results, an additional 5 billion yen is invested to develop the necessary products and services optimally suited to individuals with disabilities. This allows for efficient provision of necessary products and services, and may even qualify for government assistance if applied for. This initiative also contributes to society and raises awareness. For example, the questionnaire includes questions designed to identify the specific needs and difficulties individuals with disabilities face in their daily lives. These questions may include issues with daily life, support needs, and evaluations of welfare services currently used. Next, a system is developed to collect the questionnaire responses. For those who find it difficult to complete the questionnaire online, responses can be made by mail or telephone. Supporters can also complete the questionnaire on behalf of individuals who find it difficult to complete the questionnaire. An analysis unit is established to analyze the survey results and identify the needs of individuals with disabilities based on the collected data. A development unit is then established to develop products and services based on the identified needs. A provision unit is then established to provide the developed products and services directly to individuals with disabilities. In addition, an application department will be established to receive government assistance and handle the necessary procedures. Finally, a public relations department will be established to widely publicize these efforts and raise awareness of them as a contribution to society. These departments will work together to build a system that provides products and services that meet the needs of people with disabilities and contributes to improving the welfare of society as a whole. This will enable the disability support system to understand the needs of people with disabilities and provide the most appropriate products and services.

[0029] The disability support system according to the embodiment includes a collection unit, an analysis unit, a development unit, a provision unit, an application unit, and a public relations unit. The collection unit collects questionnaire responses. For example, the collection unit collects questionnaires using an online form. The collection unit can also collect questionnaires by mail. The collection unit can also collect questionnaires through telephone interviews. For example, the collection unit uses an online form to allow persons with disabilities to respond to questionnaires via the Internet. The collection unit collects questionnaires by mail and provides return envelopes so that respondents can easily return the questionnaires. The collection unit also collects questionnaire responses directly from persons with disabilities and their supporters through telephone interviews. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis. The analysis unit can also analyze free-form responses using text mining technology. The analysis unit can also identify the needs of persons with disabilities from the data using machine learning algorithms. For example, the analysis unit aggregates questionnaire response data using statistical analysis to identify trends in the needs of persons with disabilities. The analysis unit also uses text mining technology to extract important keywords from the free-form responses and identify the specific needs of persons with disabilities. The analysis unit also uses machine learning algorithms to automatically identify the needs of persons with disabilities from the data. The development unit develops products or services based on the needs identified by the analysis unit. The development unit, for example, develops software. The development unit may also develop hardware. The development unit may also provide consulting services. For example, the development unit develops software that meets the needs of persons with disabilities so that they can use it in their daily lives. The development unit may also develop hardware that meets the needs of persons with disabilities so that they can provide products that are easy for persons with disabilities to use. The development unit also provides consulting services that meet the needs of persons with disabilities so that they can receive the support they need. The provision unit provides the products or services developed by the development unit to persons with disabilities. The provision unit provides the products or services, for example, through online distribution.The provision department can also provide products through physical delivery. Furthermore, the provision department can also provide products and services through on-site services. For example, the provision department can make products and services available to people with disabilities via the internet through online distribution. Furthermore, the provision department can deliver products to people with disabilities' homes through physical delivery. Furthermore, the provision department can make products and services available directly to people with disabilities through on-site services. The application department handles procedures for receiving government assistance. For example, the application department can prepare application forms. Furthermore, the application department can prepare necessary documents. Furthermore, the application department can submit application forms. For example, the application department can prepare an application form for receiving government assistance and fill in the necessary information. Furthermore, the application department can prepare the necessary documents and submit them along with the application. Furthermore, the application department can submit the application form to the relevant government agency and handle the procedures for receiving assistance. The public relations department publicizes these efforts and increases recognition as a social contribution. For example, the public relations department can disseminate information through press releases. Furthermore, the public relations department can disseminate information through social media campaigns. Furthermore, the public relations department may disseminate information through advertising. For example, the public relations department may widely publicize the efforts of the disability assistance system through press releases. The public relations department may also communicate the efforts of the disability assistance system to many people through social media campaigns. The public relations department may also widely publicize the efforts of the disability assistance system through advertising. In this way, the disability assistance system according to the embodiment can understand the needs of persons with disabilities and provide optimal products and services.

[0030] The collection unit can accept responses online, by mail, or by telephone. The collection unit, for example, collects questionnaires using an online form. For example, the collection unit can set up a questionnaire form on a website so that people with disabilities can respond via the Internet. The collection unit can also collect questionnaires by mail. For example, the collection unit can mail out questionnaires so that respondents can fill them out and return them in a return envelope. The collection unit can also collect questionnaires through telephone interviews. For example, the collection unit can set up a call center and have operators conduct the questionnaire over the phone. This makes it possible to collect questionnaire responses in a variety of ways. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically compile data entered into the online form using AI and prepare it for analysis.

[0031] The analysis unit can identify the needs of persons with disabilities based on the collected data. The analysis unit can analyze the data using, for example, statistical analysis. For example, the analysis unit can aggregate questionnaire response data and understand trends in the needs of persons with disabilities. The analysis unit can also analyze free-form responses using text mining technology. For example, the analysis unit can extract important keywords from the free-form responses and identify the specific needs of persons with disabilities. The analysis unit can also automatically identify the needs of persons with disabilities from the data using a machine learning algorithm. For example, the analysis unit can analyze questionnaire data using a machine learning algorithm to identify the needs of persons with disabilities. This makes it possible to identify the specific needs of persons with disabilities from the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the collected data into AI, which analyzes the data and identifies the needs of persons with disabilities.

[0032] The development department can develop products or services based on needs. The development department, for example, develops software. For example, the development department develops software that meets the needs of people with disabilities so that they can use it in their daily lives. The development department can also develop hardware. For example, the development department develops hardware that meets the needs of people with disabilities so that they can provide products that are easy for people with disabilities to use. The development department can also provide consulting services. For example, the development department provides consulting services that meet the needs of people with disabilities so that they can receive the support they need. This makes it possible to develop products and services that meet the needs of people with disabilities. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department can use AI to design products and services based on the needs of people with disabilities.

[0033] The provision unit can provide the developed product or service to persons with disabilities. For example, the provision unit provides the product or service through online distribution. For example, the provision unit allows persons with disabilities to use the product or service via the Internet. The provision unit can also provide the product through physical delivery. For example, the provision unit delivers the product to the home of the person with disabilities. Furthermore, the provision unit can provide the product or service through local services. For example, the provision unit allows persons with disabilities to use the product or service directly. This allows the developed product or service to be provided directly to persons with disabilities. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit uses AI to select the optimal delivery method for persons with disabilities and provide the product or service.

[0034] The application department can carry out procedures to receive assistance. The application department, for example, prepares an application form. For example, the application department prepares an application form to receive assistance from the government and fills in the necessary information. The application department can also prepare necessary documents. For example, the application department prepares necessary documents to be submitted along with the application form. Furthermore, the application department can also submit the application form. For example, the application department submits the application form to a relevant government agency and carries out procedures to receive assistance. This allows the procedure to receive assistance from the government to be carried out. Some or all of the above-mentioned processing in the application department may be performed using, for example, AI, or may be performed without using AI. For example, the application department can use AI to automate the creation of application forms and the preparation of necessary documents.

[0035] The public relations department can publicize these efforts and increase awareness of them as a social contribution. The public relations department, for example, disseminates information through press releases. For example, the public relations department widely publicizes the disability support system's efforts through press releases. The public relations department can also disseminate information through social media campaigns. For example, the public relations department can inform many people about the disability support system's efforts through social media campaigns. The public relations department can also disseminate information through advertising. For example, the public relations department widely publicizes the disability support system's efforts through advertising. This can increase awareness of them as a social contribution. Some or all of the above-mentioned processing in the public relations department may be performed using, for example, AI, or may be performed without using AI. For example, the public relations department can use AI to automatically generate the content of press releases and social media campaigns to effectively disseminate information.

[0036] The collection unit can analyze the user's past response history when answering a questionnaire and automatically generate optimal questions. The collection unit, for example, automatically generates related questions based on the user's past responses. For example, the collection unit stores the user's past response data in a database and generates relevant questions using AI. The collection unit can also analyze the user's past response patterns and preferentially present questions related to the user's areas of interest. For example, the collection unit analyzes the user's past response patterns and generates questions related to the user's areas of interest. Furthermore, the collection unit can automatically generate important unanswered questions from the user's past response history. For example, the collection unit analyzes the user's past response history and identifies and generates important unanswered questions. This makes it possible to automatically generate optimal questions based on the user's past response history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit inputs past response data into AI, which then automatically generates optimal questions.

[0037] The collection unit can customize questions based on the user's living situation and areas of interest when answering a questionnaire. The collection unit, for example, provides relevant questions based on the user's living situation (e.g., home environment, occupation). For example, the collection unit collects data on the user's living situation and generates relevant questions using AI. The collection unit can also customize questions based on the user's areas of interest (e.g., health, education). For example, the collection unit collects data on the user's areas of interest and generates customized questions using AI. Furthermore, the collection unit can provide questions that include specific examples based on the user's living situation and areas of interest. For example, the collection unit generates questions that include specific examples based on the user's living situation and areas of interest. This allows the questions to be customized based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit inputs data on the user's living situation and areas of interest into AI, which then generates customized questions.

[0038] When answering a questionnaire, the collection unit can prioritize presenting highly relevant questions by taking into account the user's geographical location information. The collection unit, for example, provides questions related to issues specific to the region based on the user's place of residence. For example, the collection unit acquires the user's geographical location information from GPS data or an IP address and generates questions related to issues specific to the region. The collection unit can also prioritize presenting questions related to nearby welfare services based on the user's geographical location information. For example, the collection unit analyzes the user's geographical location information and generates questions related to nearby welfare services. The collection unit can also provide questions related to local traffic conditions by taking into account the user's geographical location information. For example, the collection unit generates questions related to local traffic conditions based on the user's geographical location information. This makes it possible to present highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit inputs geographical location information to AI, which then generates highly relevant questions.

[0039] The collection unit can analyze the user's social media activity and present relevant questions when the user answers the survey. For example, the collection unit analyzes the user's social media posts and provides questions related to topics of interest. For example, the collection unit analyzes the user's social media accounts, identifies topics of interest from the posts, and generates questions. The collection unit can also adjust the appropriate amount of questions based on the frequency of the user's social media activity. For example, the collection unit analyzes the frequency of the user's social media activity and adjusts the amount of questions. Furthermore, the collection unit can customize questions based on the interests of the user's followers and friends on social media. For example, the collection unit analyzes the interests of the user's followers and friends and generates questions. This makes it possible to present relevant questions based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs social media data into AI, which then generates relevant questions.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit prioritizes detailed analysis of data with high importance. For example, the analysis unit evaluates the importance of the collected data and performs a detailed analysis of the data with high importance. The analysis unit can also use a simple analysis method for data with low importance. For example, the analysis unit selects a simple analysis method for data with low importance to quickly obtain results. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of the data. For example, the analysis unit allocates more resources to data with high importance and performs a detailed analysis. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the importance of the data, and the AI ​​adjusts the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm for the medical field to health-related data. For example, the analysis unit collects health-related data and analyzes it using an analysis algorithm for the medical field. The analysis unit can also apply an analysis algorithm for the education field to education-related data. For example, the analysis unit collects education-related data and analyzes it using an analysis algorithm for the education field. The analysis unit can also apply an analysis algorithm for the environment field to data related to living environments. For example, the analysis unit collects data related to living environments and analyzes it using an analysis algorithm for the environment field. This makes it possible to apply an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI classify the data categories, and the AI ​​applies an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit postpones analysis of older data. Furthermore, the analysis unit can appropriately allocate analysis resources depending on the time when the data was collected. For example, the analysis unit allocates more resources to the most recent data and performs a more detailed analysis. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the time when the data was collected, and the AI ​​determines the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of the data. For example, the analysis unit allocates more resources to highly relevant data and performs detailed analysis. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the relevance of the data, and the AI ​​adjusts the order of analysis.

[0044] During development, the development department can adjust the level of detail of development based on the importance of the identified needs. For example, the development department performs detailed development for needs of high importance. For example, the development department evaluates the importance of identified needs and performs detailed design and development for the needs of high importance. The development department can also perform simple development for needs of low importance. For example, the development department performs simple design and development for needs of low importance. Furthermore, the development department can appropriately allocate development resources according to the importance of the needs. For example, the development department allocates more resources to needs of high importance and performs detailed development. This makes it possible to adjust the level of detail of development according to the importance of the needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate the importance of needs, and the AI ​​adjusts the level of detail of development.

[0045] During development, the development department can apply different development methods depending on the category of needs. For example, the development department applies development methods from the medical field to health-related needs. For example, the development department collects health-related needs and develops products and services using development methods from the medical field. The development department can also apply development methods from the education field to education-related needs. For example, the development department collects education-related needs and develops products and services using development methods from the education field. The development department can also apply development methods from the environment field to needs related to living environments. For example, the development department collects living environment-related needs and develops products and services using development methods from the environment field. This makes it possible to apply an appropriate development method depending on the category of needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI classify need categories, and the AI ​​applies an appropriate development method.

[0046] During development, the development department can determine development priorities based on when the needs were submitted. For example, the development department prioritizes development of the latest needs. For example, the development department evaluates when the needs were submitted and prioritizes development of the latest needs. The development department can also postpone development of older needs. For example, the development department postpones development of older needs. Furthermore, the development department can appropriately allocate development resources depending on when the needs were submitted. For example, the development department allocates more resources to the latest needs and performs detailed development. This makes it possible to determine development priorities based on when the needs were submitted. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate when the needs were submitted, and the AI ​​determines development priorities.

[0047] During development, the development department can adjust the order of development based on the relevance of needs. For example, the development department prioritizes development of highly relevant needs. For example, the development department evaluates the relevance of needs and prioritizes development of highly relevant needs. The development department can also postpone development of less relevant needs. For example, the development department postpones development of less relevant needs. Furthermore, the development department can appropriately allocate development resources according to the relevance of needs. For example, the development department allocates more resources to highly relevant needs and performs detailed development. This makes it possible to adjust the order of development based on the relevance of needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate the relevance of needs, and the AI ​​adjusts the order of development.

[0048] The provision unit can select the optimal provision method by analyzing the user's past usage history when providing the service. The provision unit selects the optimal provision method, for example, based on services the user has used in the past. For example, the provision unit stores the user's past usage history in a database and selects the optimal provision method using AI. The provision unit can also prioritize providing frequently used services based on the user's past usage history. For example, the provision unit analyzes the user's past usage history, identifies frequently used services, and prioritizes providing them. The provision unit can also analyze the user's past usage history and select the most efficient provision method. For example, the provision unit analyzes the user's past usage history and identifies and selects the most efficient provision method. This allows the optimal provision method to be selected based on the user's past usage history. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit inputs the past usage history into AI, which selects the optimal provision method.

[0049] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. The providing unit selects the optimal means of provision based on, for example, the user's living situation (e.g., family environment, occupation). For example, the providing unit collects data regarding the user's living situation and selects the optimal means of provision using AI. The providing unit can also customize the means of provision according to the user's living situation. For example, the providing unit customizes the means of provision based on the user's living situation. Furthermore, the providing unit can select a means of provision that includes specific examples, taking the user's living situation into consideration. For example, the providing unit selects a means of provision that includes specific examples based on the user's living situation. This makes it possible to customize the means of provision according to the user's living situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs data regarding the living situation into AI, and the AI ​​selects the optimal means of provision.

[0050] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the service. The providing unit, for example, provides a region-specific service based on the user's place of residence. For example, the providing unit acquires the user's geographical location information from GPS data or an IP address and selects a region-specific service. The providing unit can also prioritize providing nearby welfare services based on the user's geographical location information. For example, the providing unit analyzes the user's geographical location information, identifies nearby welfare services, and provides them preferentially. Furthermore, the providing unit can select a providing method according to local traffic conditions by taking into account the user's geographical location information. For example, the providing unit selects a providing method according to local traffic conditions based on the user's geographical location information. This makes it possible to select the optimal providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information into AI, which selects the optimal providing method.

[0051] The providing unit can analyze the user's social media activity and suggest a means of delivery at the time of provision. For example, the providing unit analyzes the user's social media posts and provides a service based on the user's topics of interest. For example, the providing unit analyzes the user's social media accounts, identifies the user's topics of interest from the posts, and selects a service. The providing unit can also suggest an appropriate means of delivery based on the frequency of the user's social media activity. For example, the providing unit analyzes the frequency of the user's social media activity and selects an appropriate means of delivery. Furthermore, the providing unit can customize the means of delivery based on the areas of interest of the user's followers and friends on social media. For example, the providing unit analyzes the areas of interest of the user's followers and friends and selects a means of delivery. This makes it possible to suggest the optimal means of delivery based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit inputs social media data into AI, which then suggests the optimal means of delivery.

[0052] When submitting an application, the application unit can optimize the application algorithm by referring to past application data. The application unit, for example, proposes an optimal application method based on the past application data. For example, the application unit stores past application data in a database and proposes an optimal application method using AI. The application unit can also provide an application document format with a high success rate based on the past application data. For example, the application unit analyzes past application data to identify and provide an application document format with a high success rate. The application unit can also analyze past application data and optimize an algorithm for improving the success rate of the application. For example, the application unit analyzes past application data and optimizes an algorithm for improving the success rate of the application. This allows the application algorithm to be optimized based on the past application data. Some or all of the above-mentioned processing in the application unit may be performed using AI, for example, or may be performed without using AI. For example, the application unit inputs past application data into AI, which proposes an optimal application method and optimizes the algorithm.

[0053] At the time of application, the application unit can weight the application data based on the submission time of the application documents. The application unit, for example, prioritizes processing the most recent application documents. For example, the application unit evaluates the submission time of the application documents and prioritizes processing the most recent application documents. The application unit can also postpone processing older application documents. For example, the application unit postpones processing of older application documents. Furthermore, the application unit can weight the application data according to the submission time of the application documents. For example, the application unit evaluates the submission time of the application documents and weights the application data according to the submission time. This allows the application data to be weighted based on the submission time of the application documents. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit has AI evaluate the submission time of the application documents, and the AI ​​weights the application data.

[0054] When displaying public information, the public relations department can select the optimal display method by referring to the user's past reaction history. The public relations department selects the optimal display method, for example, based on the user's past reaction history. For example, the public relations department stores the user's past reaction history in a database and selects the optimal display method using AI. The public relations department can also prioritize displaying public information related to topics of interest to the user based on the user's past reaction history. For example, the public relations department analyzes the user's past reaction history, identifies public information related to topics of interest, and displays it preferentially. The public relations department can also analyze the user's past reaction history and select the most effective display method. For example, the public relations department analyzes the user's past reaction history and identifies and selects the most effective display method. This allows the optimal display method to be selected based on the user's past reaction history. Some or all of the above-mentioned processing in the public relations department may be performed using AI, for example, or may be performed without using AI. For example, the public relations department inputs the user's past reaction history into AI, which selects the optimal display method.

[0055] When displaying a promotional message, the public relations department can select an optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the public relations department provides a display method that matches the screen size. For example, the public relations department acquires the user's device information and selects a display method optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the public relations department can also provide a display method optimized for a larger screen. For example, the public relations department acquires the user's device information and selects a display method optimized for the tablet's screen size. Furthermore, if the user is using a smartwatch, the public relations department can also provide a simple and highly visible display method. For example, the public relations department acquires the user's device information and selects a display method optimized for the smartwatch's screen size. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the public relations department may be performed using, for example, AI, or may be performed without using AI. For example, the public relations department inputs device information into AI, which selects the optimal display method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can identify the needs of persons with disabilities based on the collected data. For example, it can analyze the data using statistical analysis, compile questionnaire response data, and understand trends in the needs of persons with disabilities. It can also use text mining technology to extract important keywords from free-form responses and identify the specific needs of persons with disabilities. It can also use machine learning algorithms to automatically identify the needs of persons with disabilities from the data. The analysis unit inputs the collected data into AI, which then analyzes the data and identifies the needs of persons with disabilities.

[0058] The development department can develop products or services based on needs. For example, they can develop software to make it accessible to people with disabilities in their daily lives. They can also develop hardware to provide products that are easy for people with disabilities to use. They can also provide consulting services to ensure people with disabilities receive the support they need. The development department can use AI to design products and services based on the needs of people with disabilities.

[0059] The provision unit can provide the developed products or services to people with disabilities. For example, it can provide products or services through online distribution so that people with disabilities can use them over the internet, or through physical delivery to deliver products to the homes of people with disabilities, or through on-site services so that people with disabilities can use them directly. The provision unit uses AI to select the optimal delivery method for people with disabilities and provide products or services.

[0060] The application department can carry out procedures to receive assistance from the government. For example, it prepares application forms and fills in the necessary information. It also prepares the necessary documents and submits them along with the application. It then submits the application to the relevant government agency and carries out the procedures to receive assistance. The application department can use AI to automate the creation of application forms and the preparation of necessary documents.

[0061] The public relations department can publicize these efforts and raise awareness of them as a social contribution. For example, they can disseminate information through press releases to widely publicize the disability support system's efforts. They can also disseminate information through social media campaigns to reach many people. They can also disseminate information through advertising to widely publicize the disability support system's efforts. The public relations department can use AI to automatically generate the content of press releases and social media campaigns to effectively disseminate information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection department collects survey responses. The collection department collects survey responses, for example, through an online form, by mail, or through telephone interviews. By using an online form, people with disabilities can respond to the survey via the internet, and by mail, a return envelope can be provided to make it easy for respondents to return the survey. Through telephone interviews, survey responses are collected directly from people with disabilities and their supporters. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis, text mining technology, and machine learning algorithms to identify the needs of persons with disabilities. Statistical analysis is used to aggregate the survey response data, text mining technology is used to extract important keywords from the free-form responses, and machine learning algorithms are used to automatically identify the needs of persons with disabilities from the data. Step 3: The development department develops products or services based on the needs identified by the analysis department. The development department develops software, hardware, and consulting services to make them usable in daily life for people with disabilities. For example, the development department develops software and hardware that meets the needs of people with disabilities, providing products that are easy for people with disabilities to use. The development department also provides consulting services to ensure that people with disabilities receive the support they need. Step 4: The supply department provides the products or services developed by the development department to people with disabilities. The supply department provides products and services through online distribution, physical delivery, and on-site services. For example, online distribution allows people with disabilities to use products and services over the internet, physical delivery allows products to be delivered to people with disabilities' homes, and on-site services allow people with disabilities to use products and services directly. Step 5: The Application Department completes the procedures to receive assistance from the government. The Application Department creates an application form, prepares the necessary documents, and submits the application. For example, the Application Department creates an application form to receive assistance from the government and fills in the necessary information. They prepare the necessary documents and submit them along with the application. They submit the application to the relevant government agency and complete the procedures to receive assistance. Step 6: The Public Relations Department will publicize these efforts and raise awareness of them as a social contribution. The Public Relations Department will disseminate information through press releases, social media campaigns, and advertising. For example, the Public Relations Department will widely publicize the Disability Support System's efforts through press releases and reach many people through social media campaigns. The Public Relations Department will also widely publicize the Disability Support System's efforts through advertising.

[0064] (Example 2) The disability support system according to an embodiment of the present invention identifies the needs of individuals with disabilities and provides optimal products and services. This disability support system asks all individuals with disabilities to fill out a questionnaire, and those who find it difficult to complete it are assigned a supporter to complete it on their behalf. Each response is paid 1,000 yen, meaning that if half of the individuals complete the questionnaire, the cost will be 5 billion yen. Based on the survey results, an additional 5 billion yen is invested to develop the necessary products and services optimally suited to individuals with disabilities. This allows for efficient provision of necessary products and services, and may even qualify for government assistance if applied for. This initiative also contributes to society and raises awareness. For example, the questionnaire includes questions designed to identify the specific needs and difficulties individuals with disabilities face in their daily lives. These questions may include issues with daily life, support needs, and evaluations of welfare services currently used. Next, a system is developed to collect the questionnaire responses. For those who find it difficult to complete the questionnaire online, responses can be made by mail or telephone. Supporters can also complete the questionnaire on behalf of individuals who find it difficult to complete the questionnaire. An analysis unit is established to analyze the survey results and identify the needs of individuals with disabilities based on the collected data. A development unit is then established to develop products and services based on the identified needs. A provision unit is then established to provide the developed products and services directly to individuals with disabilities. In addition, an application department will be established to receive government assistance and handle the necessary procedures. Finally, a public relations department will be established to widely publicize these efforts and raise awareness of them as a contribution to society. These departments will work together to build a system that provides products and services that meet the needs of people with disabilities and contributes to improving the welfare of society as a whole. This will enable the disability support system to understand the needs of people with disabilities and provide the most appropriate products and services.

[0065] The disability support system according to the embodiment includes a collection unit, an analysis unit, a development unit, a provision unit, an application unit, and a public relations unit. The collection unit collects questionnaire responses. For example, the collection unit collects questionnaires using an online form. The collection unit can also collect questionnaires by mail. The collection unit can also collect questionnaires through telephone interviews. For example, the collection unit uses an online form to allow persons with disabilities to respond to questionnaires via the Internet. The collection unit collects questionnaires by mail and provides return envelopes so that respondents can easily return the questionnaires. The collection unit also collects questionnaire responses directly from persons with disabilities and their supporters through telephone interviews. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis. The analysis unit can also analyze free-form responses using text mining technology. The analysis unit can also identify the needs of persons with disabilities from the data using machine learning algorithms. For example, the analysis unit aggregates questionnaire response data using statistical analysis to identify trends in the needs of persons with disabilities. The analysis unit also uses text mining technology to extract important keywords from the free-form responses and identify the specific needs of persons with disabilities. The analysis unit also uses machine learning algorithms to automatically identify the needs of persons with disabilities from the data. The development unit develops products or services based on the needs identified by the analysis unit. The development unit, for example, develops software. The development unit may also develop hardware. The development unit may also provide consulting services. For example, the development unit develops software that meets the needs of persons with disabilities so that they can use it in their daily lives. The development unit may also develop hardware that meets the needs of persons with disabilities so that they can provide products that are easy for persons with disabilities to use. The development unit also provides consulting services that meet the needs of persons with disabilities so that they can receive the support they need. The provision unit provides the products or services developed by the development unit to persons with disabilities. The provision unit provides the products or services, for example, through online distribution.The provision department can also provide products through physical delivery. Furthermore, the provision department can also provide products and services through on-site services. For example, the provision department can make products and services available to people with disabilities via the internet through online distribution. Furthermore, the provision department can deliver products to people with disabilities' homes through physical delivery. Furthermore, the provision department can make products and services available directly to people with disabilities through on-site services. The application department handles procedures for receiving government assistance. For example, the application department can prepare application forms. Furthermore, the application department can prepare necessary documents. Furthermore, the application department can submit application forms. For example, the application department can prepare an application form for receiving government assistance and fill in the necessary information. Furthermore, the application department can prepare the necessary documents and submit them along with the application. Furthermore, the application department can submit the application form to the relevant government agency and handle the procedures for receiving assistance. The public relations department publicizes these efforts and increases recognition as a social contribution. For example, the public relations department can disseminate information through press releases. Furthermore, the public relations department can disseminate information through social media campaigns. Furthermore, the public relations department may disseminate information through advertising. For example, the public relations department may widely publicize the efforts of the disability assistance system through press releases. The public relations department may also communicate the efforts of the disability assistance system to many people through social media campaigns. The public relations department may also widely publicize the efforts of the disability assistance system through advertising. In this way, the disability assistance system according to the embodiment can understand the needs of persons with disabilities and provide optimal products and services.

[0066] The collection unit can accept responses online, by mail, or by telephone. The collection unit, for example, collects questionnaires using an online form. For example, the collection unit can set up a questionnaire form on a website so that people with disabilities can respond via the Internet. The collection unit can also collect questionnaires by mail. For example, the collection unit can mail out questionnaires so that respondents can fill them out and return them in a return envelope. The collection unit can also collect questionnaires through telephone interviews. For example, the collection unit can set up a call center and have operators conduct the questionnaire over the phone. This makes it possible to collect questionnaire responses in a variety of ways. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically compile data entered into the online form using AI and prepare it for analysis.

[0067] The analysis unit can identify the needs of persons with disabilities based on the collected data. The analysis unit can analyze the data using, for example, statistical analysis. For example, the analysis unit can aggregate questionnaire response data and understand trends in the needs of persons with disabilities. The analysis unit can also analyze free-form responses using text mining technology. For example, the analysis unit can extract important keywords from the free-form responses and identify the specific needs of persons with disabilities. The analysis unit can also automatically identify the needs of persons with disabilities from the data using a machine learning algorithm. For example, the analysis unit can analyze questionnaire data using a machine learning algorithm to identify the needs of persons with disabilities. This makes it possible to identify the specific needs of persons with disabilities from the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the collected data into AI, which analyzes the data and identifies the needs of persons with disabilities.

[0068] The development department can develop products or services based on needs. The development department, for example, develops software. For example, the development department develops software that meets the needs of people with disabilities so that they can use it in their daily lives. The development department can also develop hardware. For example, the development department develops hardware that meets the needs of people with disabilities so that they can provide products that are easy for people with disabilities to use. The development department can also provide consulting services. For example, the development department provides consulting services that meet the needs of people with disabilities so that they can receive the support they need. This makes it possible to develop products and services that meet the needs of people with disabilities. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department can use AI to design products and services based on the needs of people with disabilities.

[0069] The provision unit can provide the developed product or service to persons with disabilities. For example, the provision unit provides the product or service through online distribution. For example, the provision unit allows persons with disabilities to use the product or service via the Internet. The provision unit can also provide the product through physical delivery. For example, the provision unit delivers the product to the home of the person with disabilities. Furthermore, the provision unit can provide the product or service through local services. For example, the provision unit allows persons with disabilities to use the product or service directly. This allows the developed product or service to be provided directly to persons with disabilities. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit uses AI to select the optimal delivery method for persons with disabilities and provide the product or service.

[0070] The application department can carry out procedures to receive assistance. The application department, for example, prepares an application form. For example, the application department prepares an application form to receive assistance from the government and fills in the necessary information. The application department can also prepare necessary documents. For example, the application department prepares necessary documents to be submitted along with the application form. Furthermore, the application department can also submit the application form. For example, the application department submits the application form to a relevant government agency and carries out procedures to receive assistance. This allows the procedure to receive assistance from the government to be carried out. Some or all of the above-mentioned processing in the application department may be performed using, for example, AI, or may be performed without using AI. For example, the application department can use AI to automate the creation of application forms and the preparation of necessary documents.

[0071] The public relations department can publicize these efforts and increase awareness of them as a social contribution. The public relations department, for example, disseminates information through press releases. For example, the public relations department widely publicizes the disability support system's efforts through press releases. The public relations department can also disseminate information through social media campaigns. For example, the public relations department can inform many people about the disability support system's efforts through social media campaigns. The public relations department can also disseminate information through advertising. For example, the public relations department widely publicizes the disability support system's efforts through advertising. This can increase awareness of them as a social contribution. Some or all of the above-mentioned processing in the public relations department may be performed using, for example, AI, or may be performed without using AI. For example, the public relations department can use AI to automatically generate the content of press releases and social media campaigns to effectively disseminate information.

[0072] The collection unit can estimate the user's emotions and adjust the survey questions based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit provides simple and short questions to reduce the burden of answering. For example, the collection unit can capture the user's facial expressions with a camera and determine whether the user is feeling stressed using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can provide detailed questions to gain deeper insights. For example, the collection unit can record the user's voice and determine whether the user is relaxed using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize important questions and provide quick answers. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and determine whether the user is in a hurry using an emotion estimation algorithm. This allows the survey questions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs facial expression data of the user captured by a camera into the generation AI, which then estimates the user's emotions and adjusts the content of the questions.

[0073] The collection unit can analyze the user's past response history when answering a questionnaire and automatically generate optimal questions. The collection unit, for example, automatically generates related questions based on the user's past responses. For example, the collection unit stores the user's past response data in a database and generates relevant questions using AI. The collection unit can also analyze the user's past response patterns and preferentially present questions related to the user's areas of interest. For example, the collection unit analyzes the user's past response patterns and generates questions related to the user's areas of interest. Furthermore, the collection unit can automatically generate important unanswered questions from the user's past response history. For example, the collection unit analyzes the user's past response history and identifies and generates important unanswered questions. This makes it possible to automatically generate optimal questions based on the user's past response history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit inputs past response data into AI, which then automatically generates optimal questions.

[0074] The collection unit can customize questions based on the user's living situation and areas of interest when answering a questionnaire. The collection unit, for example, provides relevant questions based on the user's living situation (e.g., home environment, occupation). For example, the collection unit collects data on the user's living situation and generates relevant questions using AI. The collection unit can also customize questions based on the user's areas of interest (e.g., health, education). For example, the collection unit collects data on the user's areas of interest and generates customized questions using AI. Furthermore, the collection unit can provide questions that include specific examples based on the user's living situation and areas of interest. For example, the collection unit generates questions that include specific examples based on the user's living situation and areas of interest. This allows the questions to be customized based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit inputs data on the user's living situation and areas of interest into AI, which then generates customized questions.

[0075] The collection unit can estimate the user's emotions and select a questionnaire response method based on the estimated user emotions. For example, if the user is nervous, the collection unit provides a simple multiple-choice response method. For example, the collection unit captures the user's facial expressions with a camera and determines whether the user is nervous using an emotion estimation algorithm. The collection unit can also provide a free-form response method if the user is relaxed. For example, the collection unit records the user's voice and determines whether the user is relaxed using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize voice input to enable a quick response. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and determines whether the user is in a hurry using an emotion estimation algorithm. This allows the selection of a questionnaire response method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs facial expression data of the user captured by a camera into the generation AI, which then infers the emotion and selects a response method.

[0076] When answering a questionnaire, the collection unit can prioritize presenting highly relevant questions by taking into account the user's geographical location information. The collection unit, for example, provides questions related to issues specific to the region based on the user's place of residence. For example, the collection unit acquires the user's geographical location information from GPS data or an IP address and generates questions related to issues specific to the region. The collection unit can also prioritize presenting questions related to nearby welfare services based on the user's geographical location information. For example, the collection unit analyzes the user's geographical location information and generates questions related to nearby welfare services. The collection unit can also provide questions related to local traffic conditions by taking into account the user's geographical location information. For example, the collection unit generates questions related to local traffic conditions based on the user's geographical location information. This makes it possible to present highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit inputs geographical location information to AI, which then generates highly relevant questions.

[0077] The collection unit can analyze the user's social media activity and present relevant questions when the user answers the survey. For example, the collection unit analyzes the user's social media posts and provides questions related to topics of interest. For example, the collection unit analyzes the user's social media accounts, identifies topics of interest from the posts, and generates questions. The collection unit can also adjust the appropriate amount of questions based on the frequency of the user's social media activity. For example, the collection unit analyzes the frequency of the user's social media activity and adjusts the amount of questions. Furthermore, the collection unit can customize questions based on the interests of the user's followers and friends on social media. For example, the collection unit analyzes the interests of the user's followers and friends and generates questions. This makes it possible to present relevant questions based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs social media data into AI, which then generates relevant questions.

[0078] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can quickly obtain results using a simple analysis method. For example, the analysis unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and select a simple analysis method. Furthermore, if the user is relaxed, the analysis unit can obtain deeper insights using a detailed analysis method. For example, the analysis unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and select a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of important data. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and prioritize analysis of important data. This allows the data analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs facial expression data of the user captured by a camera into the generation AI, which then estimates the emotion and adjusts the analysis method.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit prioritizes detailed analysis of data with high importance. For example, the analysis unit evaluates the importance of the collected data and performs a detailed analysis of the data with high importance. The analysis unit can also use a simple analysis method for data with low importance. For example, the analysis unit selects a simple analysis method for data with low importance to quickly obtain results. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of the data. For example, the analysis unit allocates more resources to data with high importance and performs a detailed analysis. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the importance of the data, and the AI ​​adjusts the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm for the medical field to health-related data. For example, the analysis unit collects health-related data and analyzes it using an analysis algorithm for the medical field. The analysis unit can also apply an analysis algorithm for the education field to education-related data. For example, the analysis unit collects education-related data and analyzes it using an analysis algorithm for the education field. The analysis unit can also apply an analysis algorithm for the environment field to data related to living environments. For example, the analysis unit collects data related to living environments and analyzes it using an analysis algorithm for the environment field. This makes it possible to apply an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI classify the data categories, and the AI ​​applies an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the user's facial expression with a camera, determines whether the user is nervous using an emotion estimation algorithm, and selects a simple display method. The analysis unit can also provide a display method that includes more detailed information if the user is relaxed. For example, the analysis unit records the user's voice, determines whether the user is relaxed using voice analysis technology, and selects a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor, determines whether the user is in a hurry using an emotion estimation algorithm, and selects a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit inputs facial expression data of a user captured by a camera into the generation AI, and the generation AI estimates the emotion and adjusts the display method.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit postpones analysis of older data. Furthermore, the analysis unit can appropriately allocate analysis resources depending on the time when the data was collected. For example, the analysis unit allocates more resources to the most recent data and performs a more detailed analysis. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the time when the data was collected, and the AI ​​determines the priority of analysis.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of the data. For example, the analysis unit allocates more resources to highly relevant data and performs detailed analysis. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit has AI evaluate the relevance of the data, and the AI ​​adjusts the order of analysis.

[0084] The development department can estimate a user's emotions and adjust the development method for products and services based on the estimated user emotions. For example, if a user is feeling stressed, the development department can develop a simple and easy-to-use product. For example, the development department can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine whether the user is feeling stressed, and design a simple product. Alternatively, the development department can develop a product with more detailed functions if the user is relaxed. For example, the development department can record the user's voice, use voice analysis technology to determine whether the user is relaxed, and design a product with more detailed functions. Furthermore, the development department can develop a service that can be used quickly if the user is in a hurry. For example, the development department can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, use an emotion estimation algorithm to determine whether the user is in a hurry, and design a service that can be used quickly. This allows the development method for products and services to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processes in the development department may be performed using AI, for example, or may be performed without using AI. For example, the development department inputs facial expression data of the user captured by a camera into the generation AI, which then infers the emotion and adjusts the development method.

[0085] During development, the development department can adjust the level of detail of development based on the importance of the identified needs. For example, the development department performs detailed development for needs of high importance. For example, the development department evaluates the importance of identified needs and performs detailed design and development for the needs of high importance. The development department can also perform simple development for needs of low importance. For example, the development department performs simple design and development for needs of low importance. Furthermore, the development department can appropriately allocate development resources according to the importance of the needs. For example, the development department allocates more resources to needs of high importance and performs detailed development. This makes it possible to adjust the level of detail of development according to the importance of the needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate the importance of needs, and the AI ​​adjusts the level of detail of development.

[0086] During development, the development department can apply different development methods depending on the category of needs. For example, the development department applies development methods from the medical field to health-related needs. For example, the development department collects health-related needs and develops products and services using development methods from the medical field. The development department can also apply development methods from the education field to education-related needs. For example, the development department collects education-related needs and develops products and services using development methods from the education field. The development department can also apply development methods from the environment field to needs related to living environments. For example, the development department collects living environment-related needs and develops products and services using development methods from the environment field. This makes it possible to apply an appropriate development method depending on the category of needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI classify need categories, and the AI ​​applies an appropriate development method.

[0087] The development department can estimate a user's emotions and prioritize the development of products and services based on the estimated user emotions. For example, if a user is feeling stressed, the development department can prioritize the development of products that help reduce stress. For example, the development department can capture the user's facial expressions with a camera, use an emotion estimation algorithm to determine whether the user is feeling stressed, and design a product that helps reduce stress. Alternatively, if a user is relaxed, the development department can prioritize the development of products that promote relaxation. For example, the development department can record the user's voice, use voice analysis technology to determine whether the user is relaxed, and design a product that promotes relaxation. Furthermore, if a user is in a hurry, the development department can prioritize the development of services that can be used quickly. For example, the development department can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, use an emotion estimation algorithm to determine whether the user is in a hurry, and design a service that can be used quickly. This allows the development department to prioritize the development of products and services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the development department may be performed using AI, for example, or may be performed without using AI. For example, the development department inputs facial expression data of a user captured by a camera into the generation AI, and the generation AI infers emotions and determines priorities.

[0088] During development, the development department can determine development priorities based on when the needs were submitted. For example, the development department prioritizes development of the latest needs. For example, the development department evaluates when the needs were submitted and prioritizes development of the latest needs. The development department can also postpone development of older needs. For example, the development department postpones development of older needs. Furthermore, the development department can appropriately allocate development resources depending on when the needs were submitted. For example, the development department allocates more resources to the latest needs and performs detailed development. This makes it possible to determine development priorities based on when the needs were submitted. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate when the needs were submitted, and the AI ​​determines development priorities.

[0089] During development, the development department can adjust the order of development based on the relevance of needs. For example, the development department prioritizes development of highly relevant needs. For example, the development department evaluates the relevance of needs and prioritizes development of highly relevant needs. The development department can also postpone development of less relevant needs. For example, the development department postpones development of less relevant needs. Furthermore, the development department can appropriately allocate development resources according to the relevance of needs. For example, the development department allocates more resources to highly relevant needs and performs detailed development. This makes it possible to adjust the order of development based on the relevance of needs. Some or all of the above-mentioned processing in the development department may be performed using, for example, AI, or may be performed without using AI. For example, the development department has AI evaluate the relevance of needs, and the AI ​​adjusts the order of development.

[0090] The provision unit can estimate a user's emotions and adjust the method of providing products or services based on the estimated user emotions. For example, if the user is feeling stressed, the provision unit selects a simple and quick method of providing products or services. For example, the provision unit captures the user's facial expression with a camera, determines whether the user is feeling stressed using an emotion estimation algorithm, and selects a simple method of providing products or services. Furthermore, if the user is relaxed, the provision unit can select a method of providing products or services that includes detailed explanations. For example, the provision unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and select a method of providing products or services that includes detailed explanations. Furthermore, if the user is in a hurry, the provision unit can select a quick method of providing products or services. For example, the provision unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and select a quick method of providing products or services. This allows the method of providing products or services to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs facial expression data of the user captured by a camera into the generation AI, and the generation AI estimates the emotion and adjusts the presentation method.

[0091] The provision unit can select the optimal provision method by analyzing the user's past usage history when providing the service. The provision unit selects the optimal provision method, for example, based on services the user has used in the past. For example, the provision unit stores the user's past usage history in a database and selects the optimal provision method using AI. The provision unit can also prioritize providing frequently used services based on the user's past usage history. For example, the provision unit analyzes the user's past usage history, identifies frequently used services, and prioritizes providing them. The provision unit can also analyze the user's past usage history and select the most efficient provision method. For example, the provision unit analyzes the user's past usage history and identifies and selects the most efficient provision method. This allows the optimal provision method to be selected based on the user's past usage history. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit inputs the past usage history into AI, which selects the optimal provision method.

[0092] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. The providing unit selects the optimal means of provision based on, for example, the user's living situation (e.g., family environment, occupation). For example, the providing unit collects data regarding the user's living situation and selects the optimal means of provision using AI. The providing unit can also customize the means of provision according to the user's living situation. For example, the providing unit customizes the means of provision based on the user's living situation. Furthermore, the providing unit can select a means of provision that includes specific examples, taking the user's living situation into consideration. For example, the providing unit selects a means of provision that includes specific examples based on the user's living situation. This makes it possible to customize the means of provision according to the user's living situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs data regarding the living situation into AI, and the AI ​​selects the optimal means of provision.

[0093] The providing unit can estimate the user's emotions and prioritize products and services to provide based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize products that help reduce stress. For example, the providing unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and select products that help reduce stress. Furthermore, if the user is relaxed, the providing unit can prioritize products that promote relaxation. For example, the providing unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and select products that promote relaxation. Furthermore, if the user is in a hurry, the providing unit can prioritize services that can be used quickly. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and select services that can be used quickly. This allows the prioritization of products and services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs facial expression data of a user captured by a camera to the generating AI, and the generating AI estimates emotions and determines priorities.

[0094] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing the service. The providing unit, for example, provides a region-specific service based on the user's place of residence. For example, the providing unit acquires the user's geographical location information from GPS data or an IP address and selects a region-specific service. The providing unit can also prioritize providing nearby welfare services based on the user's geographical location information. For example, the providing unit analyzes the user's geographical location information, identifies nearby welfare services, and provides them preferentially. Furthermore, the providing unit can select a providing method according to local traffic conditions by taking into account the user's geographical location information. For example, the providing unit selects a providing method according to local traffic conditions based on the user's geographical location information. This makes it possible to select the optimal providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information into AI, which selects the optimal providing method.

[0095] The providing unit can analyze the user's social media activity and suggest a means of delivery at the time of provision. For example, the providing unit analyzes the user's social media posts and provides a service based on the user's topics of interest. For example, the providing unit analyzes the user's social media accounts, identifies the user's topics of interest from the posts, and selects a service. The providing unit can also suggest an appropriate means of delivery based on the frequency of the user's social media activity. For example, the providing unit analyzes the frequency of the user's social media activity and selects an appropriate means of delivery. Furthermore, the providing unit can customize the means of delivery based on the areas of interest of the user's followers and friends on social media. For example, the providing unit analyzes the areas of interest of the user's followers and friends and selects a means of delivery. This makes it possible to suggest the optimal means of delivery based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit inputs social media data into AI, which then suggests the optimal means of delivery.

[0096] The application unit can estimate the user's emotions and adjust the method of creating application documents based on the estimated user emotions. For example, if the user is feeling stressed, the application unit creates a simple and easy-to-understand application document. For example, the application unit captures the user's facial expression with a camera, determines whether the user is feeling stressed using an emotion estimation algorithm, and creates a simple application document. Furthermore, if the user is relaxed, the application unit can create an application document with more detailed information. For example, the application unit records the user's voice, determines whether the user is relaxed using voice analysis technology, and creates a detailed application document. Furthermore, if the user is in a hurry, the application unit can provide an application document that can be created quickly. For example, the application unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor, determines whether the user is in a hurry using an emotion estimation algorithm, and provides an application document that can be created quickly. This allows the method of creating application documents to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit inputs facial expression data of the user captured by a camera into the generation AI, which then estimates the emotion and adjusts the creation method.

[0097] When submitting an application, the application unit can optimize the application algorithm by referring to past application data. The application unit, for example, proposes an optimal application method based on the past application data. For example, the application unit stores past application data in a database and proposes an optimal application method using AI. The application unit can also provide an application document format with a high success rate based on the past application data. For example, the application unit analyzes past application data to identify and provide an application document format with a high success rate. The application unit can also analyze past application data and optimize an algorithm for improving the success rate of the application. For example, the application unit analyzes past application data and optimizes an algorithm for improving the success rate of the application. This allows the application algorithm to be optimized based on the past application data. Some or all of the above-mentioned processing in the application unit may be performed using AI, for example, or may be performed without using AI. For example, the application unit inputs past application data into AI, which proposes an optimal application method and optimizes the algorithm.

[0098] The request unit can estimate the user's emotions and prioritize requests based on the estimated user emotions. For example, if the user is feeling stressed, the request unit prioritizes requests that help reduce stress. For example, the request unit captures the user's facial expression with a camera, determines whether the user is feeling stressed using an emotion estimation algorithm, and prioritizes requests that help reduce stress. Furthermore, if the user is relaxed, the request unit can prioritize requests that promote relaxation. For example, the request unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize requests that promote relaxation. Furthermore, if the user is in a hurry, the request unit can prioritize requests that can be processed quickly. For example, the request unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and prioritize requests that can be processed quickly. This allows the priority of requests to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the application unit may be performed using AI, or may be performed without using AI. For example, the application unit inputs facial expression data of a user captured by a camera into the generation AI, and the generation AI estimates emotions and determines priorities.

[0099] At the time of application, the application unit can weight the application data based on the submission time of the application documents. The application unit, for example, prioritizes processing the most recent application documents. For example, the application unit evaluates the submission time of the application documents and prioritizes processing the most recent application documents. The application unit can also postpone processing older application documents. For example, the application unit postpones processing of older application documents. Furthermore, the application unit can weight the application data according to the submission time of the application documents. For example, the application unit evaluates the submission time of the application documents and weights the application data according to the submission time. This allows the application data to be weighted based on the submission time of the application documents. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit has AI evaluate the submission time of the application documents, and the AI ​​weights the application data.

[0100] The PR unit can estimate the user's emotions and adjust the display method of the PR based on the estimated user emotions. For example, if the user is nervous, the PR unit provides PR in subdued colors. For example, the PR unit captures the user's facial expression with a camera, determines whether the user is nervous using an emotion estimation algorithm, and selects PR in subdued colors. The PR unit can also provide PR in bright colors if the user is having fun. For example, the PR unit can record the user's voice, determine whether the user is having fun using voice analysis technology, and select PR in bright colors. Furthermore, if the user is tired, the PR unit can provide simple, highly visible PR. For example, the PR unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is tired using an emotion estimation algorithm, and select simple, highly visible PR. This allows the display method of the PR to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the public relations department may be performed using AI, or may be performed without using AI. For example, the public relations department inputs facial expression data of the user captured by a camera into the generation AI, which then estimates the emotion and adjusts the display method.

[0101] When displaying public information, the public relations department can select the optimal display method by referring to the user's past reaction history. The public relations department selects the optimal display method, for example, based on the user's past reaction history. For example, the public relations department stores the user's past reaction history in a database and selects the optimal display method using AI. The public relations department can also prioritize displaying public information related to topics of interest to the user based on the user's past reaction history. For example, the public relations department analyzes the user's past reaction history, identifies public information related to topics of interest, and displays it preferentially. The public relations department can also analyze the user's past reaction history and select the most effective display method. For example, the public relations department analyzes the user's past reaction history and identifies and selects the most effective display method. This allows the optimal display method to be selected based on the user's past reaction history. Some or all of the above-mentioned processing in the public relations department may be performed using AI, for example, or may be performed without using AI. For example, the public relations department inputs the user's past reaction history into AI, which selects the optimal display method.

[0102] The PR unit can estimate the user's emotions and prioritize PR based on the estimated user emotions. For example, if the user is feeling stressed, the PR unit prioritizes displaying PR that helps reduce stress. For example, the PR unit captures the user's facial expression with a camera, determines whether the user is feeling stressed using an emotion estimation algorithm, and selects PR that helps reduce stress. Furthermore, if the user is relaxed, the PR unit can prioritize displaying PR that promotes relaxation. For example, the PR unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and select PR that promotes relaxation. Furthermore, if the user is in a hurry, the PR unit can prioritize displaying PR that can be used quickly. For example, the PR unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and select PR that can be used quickly. This allows the priority of PR to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the public relations department may be performed using AI, or may be performed without using AI. For example, the public relations department inputs facial expression data of a user captured by a camera into the generation AI, and the generation AI estimates the emotion and determines the priority.

[0103] When displaying a promotional message, the public relations department can select an optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the public relations department provides a display method that matches the screen size. For example, the public relations department acquires the user's device information and selects a display method optimized for the smartphone's screen size. Furthermore, if the user is using a tablet, the public relations department can also provide a display method optimized for a larger screen. For example, the public relations department acquires the user's device information and selects a display method optimized for the tablet's screen size. Furthermore, if the user is using a smartwatch, the public relations department can also provide a simple and highly visible display method. For example, the public relations department acquires the user's device information and selects a display method optimized for the smartwatch's screen size. This allows the optimal display method to be selected based on the user's device information. Some or all of the above-described processing in the public relations department may be performed using, for example, AI, or may be performed without using AI. For example, the public relations department inputs device information into AI, which selects the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, development unit, provision unit, application unit, and public relations unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14 and adjusts the content of questions. The analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12. The development unit develops products and services using the specific processing unit 290 of the data processing device 12. The provision unit provides products and services using the control unit 46A of the smart device 14. The application unit performs application procedures using the specific processing unit 290 of the data processing device 12. The public relations unit disseminates information using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, development unit, provision unit, application unit, and public relations unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the content of questions. The analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12. The development unit develops products and services using the specific processing unit 290 of the data processing device 12. The provision unit provides products and services using the control unit 46A of the smart glasses 214. The application unit performs application procedures using the specific processing unit 290 of the data processing device 12. The public relations unit disseminates information using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, development unit, provision unit, application unit, and public relations unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit estimates the user's emotions using the camera 42 and microphone 238 of the headset type terminal 314 and adjusts the content of the questions. The analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12. The development unit develops products and services using the specific processing unit 290 of the data processing device 12. The provision unit provides products and services using the control unit 46A of the headset type terminal 314. The application unit performs application procedures using the specific processing unit 290 of the data processing device 12. The public relations unit disseminates information using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, development unit, provision unit, application unit, and public relations unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414 and adjusts the content of the questions. The analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12. The development unit develops products and services using the specific processing unit 290 of the data processing device 12. The provision unit provides products and services using the control unit 46A of the robot 414. The application unit performs application procedures using the specific processing unit 290 of the data processing device 12. The public relations unit disseminates information using the control unit 46A of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can estimate the user's emotions and adjust the content of survey questions based on the estimated user emotions. For example, if the user is feeling stressed, simple and short questions are provided to reduce the burden of answering. The collection unit captures the user's facial expressions with a camera and determines whether the user is feeling stressed using an emotion estimation algorithm. If the user is relaxed, detailed questions are provided to gain deeper insights. The collection unit records the user's voice and determines whether the user is relaxed using voice analysis technology. Furthermore, if the user is in a hurry, important questions are presented preferentially to enable quick answers. The collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and determines whether the user is in a hurry using an emotion estimation algorithm. This allows the content of survey questions to be adjusted according to the user's emotions.

[0106] The analysis unit can identify the needs of persons with disabilities based on the collected data. For example, it can analyze the data using statistical analysis, compile questionnaire response data, and understand trends in the needs of persons with disabilities. It can also use text mining technology to extract important keywords from free-form responses and identify the specific needs of persons with disabilities. It can also use machine learning algorithms to automatically identify the needs of persons with disabilities from the data. The analysis unit inputs the collected data into AI, which then analyzes the data and identifies the needs of persons with disabilities.

[0107] The development department can develop products or services based on needs. For example, they can develop software to make it accessible to people with disabilities in their daily lives. They can also develop hardware to provide products that are easy for people with disabilities to use. They can also provide consulting services to ensure people with disabilities receive the support they need. The development department can use AI to design products and services based on the needs of people with disabilities.

[0108] The provision unit can provide the developed products or services to people with disabilities. For example, it can provide products or services through online distribution so that people with disabilities can use them over the internet, or through physical delivery to deliver products to the homes of people with disabilities, or through on-site services so that people with disabilities can use them directly. The provision unit uses AI to select the optimal delivery method for people with disabilities and provide products or services.

[0109] The application department can carry out procedures to receive assistance from the government. For example, it prepares application forms and fills in the necessary information. It also prepares the necessary documents and submits them along with the application. It then submits the application to the relevant government agency and carries out the procedures to receive assistance. The application department can use AI to automate the creation of application forms and the preparation of necessary documents.

[0110] The public relations department can publicize these efforts and raise awareness of them as a social contribution. For example, they can disseminate information through press releases to widely publicize the disability support system's efforts. They can also disseminate information through social media campaigns to reach many people. They can also disseminate information through advertising to widely publicize the disability support system's efforts. The public relations department can use AI to automatically generate the content of press releases and social media campaigns to effectively disseminate information.

[0111] The collection unit can estimate the user's emotions and select a questionnaire response method based on the estimated user emotions. For example, if the user is nervous, a simple multiple-choice response method is provided. The collection unit captures the user's facial expression with a camera and determines whether the user is nervous using an emotion estimation algorithm. Furthermore, if the user is relaxed, a free-form response method can also be provided. The collection unit records the user's voice and determines whether the user is relaxed using voice analysis technology. Furthermore, if the user is in a hurry, voice input can be prioritized to enable a quick response. The collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and determines whether the user is in a hurry using an emotion estimation algorithm. This allows the questionnaire response method to be selected according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is feeling stressed, a simple analysis method can be used to quickly obtain results. The analysis unit captures the user's facial expression with a camera, uses an emotion estimation algorithm to determine whether the user is feeling stressed, and selects a simple analysis method. Furthermore, if the user is relaxed, a detailed analysis method can be used to obtain deeper insights. The analysis unit records the user's voice, uses voice analysis technology to determine whether the user is relaxed, and selects a detailed analysis method. Furthermore, if the user is in a hurry, important data can be prioritized for analysis. The analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, uses an emotion estimation algorithm to determine whether the user is in a hurry, and prioritizes the analysis of important data. This allows the data analysis method to be adjusted according to the user's emotions.

[0113] The development department can estimate a user's emotions and adjust the development methods for products and services based on the estimated user emotions. For example, if a user is feeling stressed, the development department can develop a simple and easy-to-use product. The development department can capture the user's facial expression with a camera, use an emotion estimation algorithm to determine whether the user is feeling stressed, and then design a simple product. Alternatively, if a user is relaxed, the development department can develop a product with more detailed functions. The development department can record the user's voice, use voice analysis technology to determine whether the user is relaxed, and then design a product with more detailed functions. Furthermore, if a user is in a hurry, the development department can develop a service that can be used quickly. The development department can collect the user's biometric data (heart rate and electrodermal activity) with sensors, use an emotion estimation algorithm to determine whether the user is in a hurry, and design a service that can be used quickly. This allows the development methods for products and services to be adjusted according to the user's emotions.

[0114] The provision unit can estimate the user's emotions and adjust the method of providing products and services based on the estimated user emotions. For example, if the user is feeling stressed, a simple and quick method of providing products and services is selected. The provision unit captures the user's facial expression with a camera, determines whether the user is feeling stressed using an emotion estimation algorithm, and selects a simple method of providing products and services. If the user is relaxed, a method of providing products and services that includes detailed explanations can be selected. The provision unit records the user's voice, determines whether the user is relaxed using voice analysis technology, and selects a method of providing products and services that includes detailed explanations. If the user is in a hurry, a quick method of providing products and services can be selected. The provision unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor, determines whether the user is in a hurry using an emotion estimation algorithm, and selects a quick method of providing products and services. This allows the method of providing products and services to be adjusted according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection department collects survey responses. The collection department collects survey responses, for example, through an online form, by mail, or through telephone interviews. By using an online form, people with disabilities can respond to the survey via the internet, and by mail, a return envelope can be provided to make it easy for respondents to return the survey. Through telephone interviews, survey responses are collected directly from people with disabilities and their supporters. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis, text mining technology, and machine learning algorithms to identify the needs of persons with disabilities. Statistical analysis is used to aggregate the survey response data, text mining technology is used to extract important keywords from the free-form responses, and machine learning algorithms are used to automatically identify the needs of persons with disabilities from the data. Step 3: The development department develops products or services based on the needs identified by the analysis department. The development department develops software, hardware, and consulting services to make them usable in daily life for people with disabilities. For example, the development department develops software and hardware that meets the needs of people with disabilities, providing products that are easy for people with disabilities to use. The development department also provides consulting services to ensure that people with disabilities receive the support they need. Step 4: The supply department provides the products or services developed by the development department to people with disabilities. The supply department provides products and services through online distribution, physical delivery, and on-site services. For example, online distribution allows people with disabilities to use products and services over the internet, physical delivery allows products to be delivered to people with disabilities' homes, and on-site services allow people with disabilities to use products and services directly. Step 5: The Application Department completes the procedures to receive assistance from the government. The Application Department creates an application form, prepares the necessary documents, and submits the application. For example, the Application Department creates an application form to receive assistance from the government and fills in the necessary information. They prepare the necessary documents and submit them along with the application. They submit the application to the relevant government agency and complete the procedures to receive assistance. Step 6: The Public Relations Department will publicize these efforts and raise awareness of them as a social contribution. The Public Relations Department will disseminate information through press releases, social media campaigns, and advertising. For example, the Public Relations Department will widely publicize the Disability Support System's efforts through press releases and reach many people through social media campaigns. The Public Relations Department will also widely publicize the Disability Support System's efforts through advertising.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects responses to the questionnaire; an analysis unit that analyzes the data collected by the collection unit; a development department that develops products or services based on the needs identified by the analysis department; a provision department that provides the products or services developed by the development department; an application section for receiving assistance; A public relations department will be established to publicize these initiatives. A system characterized by:

2. The collecting unit Accept responses online, by mail, or by phone 2. The system of claim 1.

3. The analysis unit Identifying the needs of people with disabilities based on collected data 2. The system of claim 1.

4. The development department: Develop a product or service based on a need 2. The system of claim 1.

5. The providing unit Providing developed products or services to people with disabilities 2. The system of claim 1.

6. The application department Applying for assistance 2. The system of claim 1.

7. The Public Relations Department: Publicize these efforts and increase recognition as a contribution to society 2. The system of claim 1.

8. The collecting unit Estimate user emotions and adjust survey questions based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit When answering a survey, the system analyzes the user's past response history and automatically generates the most appropriate questions.

2. The system of claim 1.

10. The collecting unit When completing a survey, customize the questions based on your life situation and interests 2. The system of claim 1.

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