System

Abstract

An object of the system according to the embodiment is to enable even a non-engineer to easily develop a unique business application.SOLUTION: A system includes an interface providing unit, an analysis unit, a proposal unit, and an integration unit. The interface-providing unit provides an intuitive interface. The analysis unit analyzes the provided information of the company through the interface provided by the interface-providing unit. The proposal unit proposes an application function and a design according to a need specific to a business type on the basis of the information analyzed by the analysis unit. The integration unit integrates the functions proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, it was difficult for non-technical people to develop unique business applications, making digitalization a high hurdle.

[0005] The system according to the embodiment aims to enable even non-technical people to easily develop their own business applications. [Means for solving the problem]

[0006] The system according to the embodiment includes an interface providing unit, an analysis unit, a proposal unit, and an integration unit. The interface providing unit provides an intuitive interface. The analysis unit analyzes information provided by companies through the interface provided by the interface providing unit. The proposal unit proposes app functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit. The integration unit integrates the functions proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable even non-technical people to easily develop their own business applications. [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 business application development platform according to an embodiment of the present invention is a system that allows even non-technical people to easily develop their own business applications. This system analyzes information provided by companies, proposes optimal application functions and designs tailored to the needs specific to each industry, and integrates payment methods and customer engagement functions. This allows the business application development platform to easily develop and manage business applications, even for non-technical people.

[0029] A business application development platform according to an embodiment includes an interface providing unit, an analysis unit, a proposal unit, and an integration unit. The interface providing unit provides an intuitive interface. For example, a drag-and-drop function allows a user to easily arrange the layout and functions of an application. The interface providing unit can also learn a user's operation history and predict and suggest the next required operation. The analysis unit analyzes information provided by companies. For example, the analysis unit uses data mining technology to analyze product information and service information provided by companies. The analysis unit can also analyze past data from companies to predict future needs. The proposal unit proposes application functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit. For example, in the restaurant industry, the proposal unit proposes menu display, reservation functions, and takeout ordering functions. The proposal unit can also use an emotion estimation function to propose personalized application functions based on the user's emotions. The integration unit integrates the functions proposed by the proposal unit. For example, the integration unit integrates payment methods such as credit card payment, electronic money, and QR code payment into the application. The integration unit can also implement customer engagement functions, such as push notifications, coupon distribution, and customer review functions, into the app. This allows the business app development platform according to the embodiment to easily develop and manage business apps, even for non-technical individuals. For example, restaurants and food service companies can use this platform to easily create apps with menu display and reservation functions, thereby increasing customer engagement. Furthermore, the integration of payment methods allows customers to make payments smoothly. Furthermore, the generation AI analyzes app usage and suggests necessary updates, ensuring that the app is always up-to-date.

[0030] The interface providing unit uses a drag-and-drop function to allow users to easily arrange the layout and functions of an app. For example, the interface providing unit uses a generation AI to analyze the user's past operation history and predict and suggest the next required operation. For example, it may prioritize displaying functions that the user uses frequently. The interface providing unit also learns the user's operation patterns and suggests the next operation to be performed in real time. For example, it may highlight the next field to be filled in while filling out a form. The interface providing unit also uses a generation AI to suggest the optimal operation procedure based on the user's operation history. For example, it may present shortcuts to simplify complex operations. This allows users to intuitively arrange the app's layout and functions.

[0031] In the case of the restaurant business, the suggestion unit can suggest menu display, reservation functions, and takeout ordering functions. For example, the suggestion unit uses a generation AI to analyze the user's voice instructions and change the app layout through voice control. For example, if the user says, "Move the button to the top right," the button will move automatically. Also, when the suggestion unit gives a voice instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, if the user says, "Add a new form," a form will be added. Also, the suggestion unit analyzes voice instructions and reflects the design changes requested by the user in real time. For example, if the user says, "Change the background color to blue," the background color will change instantly. This makes it possible to suggest app functions specialized for the restaurant business.

[0032] The integration unit can integrate credit card, electronic money, and QR code payment methods into the app. For example, the integration unit uses an emotion estimation function to analyze in real time the stress and satisfaction felt by the user during operation, and simplifies the interface if stress increases. The integration unit also analyzes the user's satisfaction using the emotion estimation function and makes suggestions to improve the interface if satisfaction is low. For example, it simplifies parts of the interface that are complicated to operate. The integration unit also uses the emotion estimation function to dynamically change the color and layout of the interface according to the user's emotions. For example, it changes to a relaxing color if the user is feeling high stress. This allows a variety of payment methods to be integrated into the app.

[0033] The integration unit can implement customer engagement functions such as push notifications, coupon distribution, and customer review functions into the app. In the integration unit, for example, the generation AI analyzes customer behavior data and proposes the optimal engagement strategy. For example, it may propose personalized promotions based on the customer's purchase history. The integration unit also customizes the engagement strategy based on the customer behavior data. For example, it may propose push notifications that are effective at specific times of the day. The integration unit also analyzes customer behavior data in real time and proposes an engagement strategy instantly. For example, it may suggest related products when a customer views a specific product. This allows customer engagement functions to be implemented in the app.

[0034] The analysis unit analyzes app usage and user feedback and can suggest updates and improvements. For example, the analysis unit uses a generation AI to monitor app usage in real time and automatically suggest necessary updates. For example, if a specific function is frequently used, that function will be enhanced. The analysis unit also uses a generation AI to suggest optimal update content based on app usage. For example, it will improve areas that users are dissatisfied with. The analysis unit also uses a generation AI to analyze app usage in real time and immediately suggest update content. For example, it will suggest adding new functions. This makes it possible to suggest updates and improvements based on app usage and user feedback.

[0035] The interface providing unit can learn the user's operation history and predict and suggest the next required operation. For example, the generation AI in the interface providing unit analyzes the user's past operation history and predicts and suggests the next required operation. For example, it may prioritize displaying functions that the user uses frequently. The interface providing unit also learns the user's operation patterns and suggests the next operation to be performed in real time. For example, it may highlight the next field to be entered while filling out a form. The generation AI in the interface providing unit also suggests the optimal operation procedure based on the user's operation history. For example, it may present shortcuts to simplify complex operations. This makes it possible to predict and suggest the next required operation based on the user's operation history.

[0036] The interface providing unit can analyze the user's voice instructions and design the app through voice operation. For example, the generation AI in the interface providing unit analyzes the user's voice instructions and changes the layout of the app through voice operation. For example, if the user instructs the unit to "move the button to the top right," the button will move automatically. Also, when the user gives a voice instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, if the user says "add a new form," a form will be added. Also, the interface providing unit analyzes the voice instructions and reflects the design changes requested by the user in real time. For example, if the user says "change the background color to blue," the background color will change instantly. This makes it possible to design the app based on the user's voice instructions.

[0037] The interface providing unit can analyze the user's handwritten input and automatically generate an app layout from a sketch. In the interface providing unit, for example, a generation AI analyzes the user's handwritten input and automatically generates an app layout based on the sketch. For example, it recognizes and arranges handwritten buttons and text fields. The interface providing unit also analyzes the handwritten input and reflects the shapes and characters drawn by the user in the app design. For example, it automatically generates handwritten menu icons. The interface providing unit also analyzes a wireframe drawn by the user, and the generation AI creates a prototype of the app based on the wireframe. This makes it possible to automatically generate an app layout based on the user's handwritten input.

[0038] The interface providing unit can recognize user gestures and design the app through gesture operations. In the interface providing unit, for example, the generation AI recognizes user gestures and changes the layout of the app through gesture operations. For example, a swipe operation moves elements on the screen. In addition, when the user issues an instruction to add a function to the app through a gesture, the generation AI adds the function according to the instruction. For example, a pinch operation adds a new widget. In addition, the interface providing unit uses gesture recognition to reflect design changes requested by the user in real time. For example, a double tap enlarges the display of a specific element. This makes it possible to design the app based on the user's gesture operations.

[0039] The analysis unit can analyze a company's past data and predict and propose future needs. For example, the analysis unit uses generative AI to analyze a company's past sales data and customer feedback to predict and propose future needs. For example, it can propose new promotions based on seasonal sales trends. The analysis unit can also analyze a company's growth patterns based on past data and predict future needs. For example, it can propose entry into new markets. The analysis unit can also analyze a company's past data and predict future needs to propose new features and services. For example, it can make personalized proposals based on customer purchasing history. This makes it possible to predict and propose future needs based on a company's past data.

[0040] The analysis unit can analyze the apps of a company's competitors and suggest points of differentiation. For example, the analysis unit uses generative AI to analyze a competitor's app and suggest points of differentiation. For example, it can suggest unique features that competitors do not offer. The analysis unit can also analyze the design and functions of a competitor's app to identify points where the company can differentiate itself. For example, it can suggest new designs to improve the user experience. The analysis unit can also analyze reviews and ratings of a competitor's app to suggest points where the company should improve. For example, it can suggest features that complement the competitor's weaknesses. This makes it possible to analyze a company's competitor's app and suggest points of differentiation.

[0041] The analysis unit can analyze success stories from different industries and propose best practices for those industries. For example, the generative AI can analyze success stories from different industries and propose best practices for those industries. For example, it can apply success stories from the food and beverage industry to the retail industry. The analysis unit can also propose best practices that companies should adopt based on success stories from different industries. For example, it can apply customer engagement techniques from the service industry to the manufacturing industry. The generative AI can also analyze success stories from different industries and propose new ideas to help companies increase their competitiveness. For example, it can introduce innovations from the technology industry to traditional industries. This makes it possible to propose best practices for other industries based on success stories from different industries.

[0042] The analysis unit can analyze region-specific needs and propose the optimal app features for each region. For example, the generation AI in the analysis unit analyzes region-specific needs and proposes the optimal app features for each region. For example, it proposes features that suit the local culture and customs. The analysis unit also analyzes local market data and proposes app features specialized for that region. For example, it adds a function that provides local event information. The analysis unit also analyzes region-specific user feedback using the generation AI and proposes the optimal design and features for each region. For example, it provides an interface that supports the local language and dialect. This makes it possible to propose the optimal app features for each region based on the region's specific needs.

[0043] The integration department can analyze a company's transaction data and propose the optimal payment method. In the integration department, for example, the generation AI analyzes a company's transaction data and proposes the optimal payment method. For example, it selects the optimal payment method based on transaction volume and frequency. The integration department also uses the generation AI to propose cost-effective payment methods based on the company's transaction data. For example, it prioritizes proposals for payment methods with low fees. The integration department also uses the generation AI to analyze a company's transaction data and propose the optimal payment method based on the customer's payment habits. For example, it proposes payment methods that the customer frequently uses. This makes it possible to propose the optimal payment method based on a company's transaction data.

[0044] The integration department can analyze the security risks of payment methods and propose risk mitigation measures. For example, the generation AI in the integration department analyzes the security risks of each payment method and proposes risk mitigation measures. For example, it proposes measures to reduce the risk of fraudulent use of credit card payments. The integration department also analyzes the security risks of payment methods and provides specific risk mitigation measures to companies. For example, it proposes the introduction of two-step authentication. The integration department also monitors the security risks of payment methods in real time using the generation AI, and immediately proposes measures if the risk increases. For example, it issues an alert if it detects signs of unauthorized access. This makes it possible to analyze the security risks of payment methods and propose risk mitigation measures.

[0045] The integration department can analyze payment methods in different countries and regions and propose globally compatible payment methods. For example, the generation AI in the integration department analyzes payment methods in different countries and regions and proposes globally compatible payment methods. For example, it automatically selects the major payment methods in each country. The integration department also analyzes payment methods in different regions and proposes the optimal payment method for companies expanding globally. For example, it makes proposals based on the popularity rate of payment methods in each region. The generation AI in the integration department also analyzes the regulations and laws regarding payment methods in each country and proposes points that companies must comply with. For example, it presents the legal requirements for payment methods in a specific country. This makes it possible to propose globally compatible payment methods based on the payment methods in different countries and regions.

[0046] The integration department can propose customizable payment methods that suit a company's business model. For example, the generation AI in the integration department analyzes a company's business model and proposes customizable payment methods accordingly. For example, it proposes the payment method that is best suited to a subscription model. The generation AI in the integration department also customizes the optimal payment method based on the business model. For example, it proposes payment methods that are suitable for B2B transactions. The generation AI in the integration department also proposes customizable payment methods based on the company's business model and supports implementation. For example, it provides an installment payment option. This makes it possible to propose customizable payment methods that suit a company's business model.

[0047] The integration unit can analyze customer behavioral data and propose the optimal engagement strategy. In the integration unit, for example, the generation AI analyzes customer behavioral data and proposes the optimal engagement strategy. For example, it may propose personalized promotions based on the customer's purchasing history. In addition, the generation AI in the integration unit customizes the engagement strategy based on the customer behavioral data. For example, it may propose push notifications that are effective at specific times of the day. In addition, the generation AI in the integration unit analyzes customer behavioral data in real time and proposes an engagement strategy instantly. For example, it may suggest related products when a customer views a specific product. This makes it possible to propose the optimal engagement strategy based on customer behavioral data.

[0048] The integration unit can analyze a customer's purchase history and propose personalized promotions. In the integration unit, for example, the generation AI analyzes a customer's purchase history and proposes personalized promotions. For example, it may offer discounts on specific products based on past purchase history. In addition, the integration unit uses the generation AI to propose individually customized promotions based on the customer's purchase history. For example, it may provide special offers for products that the customer frequently purchases. In addition, the integration unit uses the generation AI to analyze a customer's purchase history in real time and propose personalized promotions instantly. For example, it may provide discounts on products that the customer has added to their cart. This makes it possible to propose personalized promotions based on the customer's purchase history.

[0049] The integration department can analyze engagement methods from different industries and propose best practices from those industries. For example, the generative AI in the integration department analyzes engagement methods from different industries and proposes best practices from those industries. For example, methods from the entertainment industry can be applied to the retail industry. The integration department can also propose best practices that companies should adopt based on engagement methods from different industries. For example, customer loyalty programs from the financial industry can be applied to the service industry. The generative AI in the integration department can also analyze engagement methods from different industries and propose new ideas to help companies increase their competitiveness. For example, innovations from the technology industry can be introduced into traditional industries. This makes it possible to propose best practices from other industries based on engagement methods from different industries.

[0050] The integration unit can analyze customers' social media data and propose engagement strategies that utilize social media. In the integration unit, for example, the generation AI analyzes customers' social media data and proposes engagement strategies that utilize social media. For example, it provides personalized advertisements based on the content of customers' posts. In addition, the integration unit has the generation AI customize the engagement strategy based on the social media data. For example, it provides content related to topics that interest the customer. In addition, the integration unit has the generation AI analyze customers' social media data in real time and propose instant engagement strategies. For example, it provides related promotions when a customer participates in a specific event. This makes it possible to propose engagement strategies that utilize social media based on customers' social media data.

[0051] The analysis unit monitors app usage in real time and can automatically suggest necessary updates. For example, the generation AI in the analysis unit monitors app usage in real time and automatically suggests necessary updates. For example, if a specific function is used frequently, that function will be enhanced. The analysis unit also allows the generation AI to suggest optimal update content based on app usage. For example, it will improve areas that cause user dissatisfaction. The analysis unit also allows the generation AI to analyze app usage in real time and immediately suggest update content. For example, it will suggest adding new functions. This makes it possible to monitor app usage in real time and automatically suggest necessary updates.

[0052] The analysis unit can analyze user feedback and automatically extract and propose improvements. In the analysis unit, for example, the generation AI analyzes user feedback and automatically extracts and proposes improvements. For example, it identifies areas where users are dissatisfied and proposes improvements. The analysis unit also allows the generation AI to propose optimal improvements based on user feedback. For example, it makes suggestions to add new functions that users request. The analysis unit also allows the generation AI to analyze user feedback in real time and immediately propose improvements. For example, it improves the interface based on user opinions. This makes it possible to automatically extract and propose improvements based on user feedback.

[0053] The analysis unit analyzes the characteristics of different platforms (iOS, Android, etc.) and can propose the optimal update content for each platform. For example, the generation AI in the analysis unit analyzes the characteristics of different platforms such as iOS and Android and proposes the optimal update content for each. For example, it proposes UI improvements specialized for iOS. The analysis unit also analyzes user behavior data for each platform and the generation AI proposes the optimal update content for each platform. For example, it proposes adding features specialized for Android users. The analysis unit also analyzes the technical constraints of different platforms and proposes the optimal update content based on that. For example, it proposes function improvements that are compatible with the latest version of iOS. This makes it possible to propose the optimal update content based on the characteristics of different platforms.

[0054] The analysis unit can analyze the app's performance data and suggest updates to improve performance. In the analysis unit, for example, the generation AI analyzes the app's performance data and suggests updates to improve performance. For example, it suggests improvements to shorten the app's startup time. In addition, the analysis unit allows the generation AI to suggest optimal performance improvement measures based on the app's performance data. For example, it suggests code optimization to reduce memory usage. In addition, the analysis unit allows the generation AI to analyze the app's performance data in real time and immediately suggest updates to improve performance. For example, it suggests bug fixes based on crash reports. This makes it possible to suggest updates to improve performance based on the app's performance data.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The business app development platform can also be equipped with an interface provider that recognizes user gestures and allows app design based on gesture operations. For example, a swipe moves elements on the screen. When a user issues a gesture instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, a pinch adds a new widget. Gesture recognition can also be used to reflect design changes requested by the user in real time. For example, a double tap enlarges a specific element. This allows app design based on user gesture operations.

[0057] The interface providing unit can analyze the user's handwritten input and automatically generate the app layout from the sketch. For example, the generation AI analyzes the user's handwritten input and automatically generates the app layout based on the sketch. It recognizes and arranges handwritten buttons and text fields. It also analyzes the handwritten input and reflects the shapes and characters drawn by the user in the app design. For example, it automatically generates handwritten menu icons. It also analyzes the user's handwritten wireframe and creates an app prototype based on the wireframe. This makes it possible to automatically generate the app layout based on the user's handwritten input.

[0058] The analysis unit can analyze the apps of a company's competitors and suggest points of differentiation. For example, generative AI can analyze a competitor's app and suggest points of differentiation. It can suggest unique features that competitors do not offer. It can also analyze the design and functions of competitors' apps to identify points where the company can differentiate itself. For example, it can suggest new designs to improve the user experience. It can also analyze reviews and ratings of competitors' apps to suggest areas where the company should improve. For example, it can suggest features that complement competitors' weaknesses. This makes it possible to analyze a company's competitors' apps and suggest points of differentiation.

[0059] The analysis unit can analyze success stories from different industries and propose best practices for those industries. For example, the generative AI can analyze success stories from different industries and propose best practices for those industries. Success stories from the food and beverage industry can be applied to the retail industry. Furthermore, best practices that companies should adopt can be proposed based on success stories from different industries. For example, customer engagement techniques from the service industry can be applied to the manufacturing industry. Furthermore, the generative AI can analyze success stories from different industries and propose new ideas to help companies increase their competitiveness. For example, innovations from the technology industry can be introduced into traditional industries. This makes it possible to propose best practices for other industries based on success stories from different industries.

[0060] The analysis unit can analyze region-specific needs and propose the optimal app features for each region. For example, the generation AI analyzes region-specific needs and proposes the optimal app features for each region. It proposes features that are suited to the region's culture and customs. It also analyzes region's market data and proposes app features specialized for that region. For example, it could add a function that provides information about local events. The generation AI also analyzes region's user feedback and proposes the optimal design and features for each region. For example, it could provide an interface that supports the region's language and dialect. This makes it possible to propose the optimal app features for each region based on the region's specific needs.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The interface provider provides an intuitive interface. For example, by using a drag-and-drop function, the user can easily arrange the layout and functions of the app. The interface provider can also learn the user's operation history and predict and suggest the next required operation. Step 2: The analysis unit analyzes the information provided by the company. For example, it uses data mining technology to analyze product and service information provided by the company. The analysis unit can also analyze the company's past data and predict future needs. Step 3: The proposal unit proposes app functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit. For example, in the case of the restaurant industry, it proposes menu display, reservation functions, and takeout ordering functions. The proposal unit can also use an emotion estimation function to propose personalized app functions based on the user's emotions. Step 4: The integration unit integrates the features proposed by the proposal unit. For example, it integrates payment methods such as credit card payments, electronic money, and QR code payments into the app. The integration unit can also implement customer engagement features such as push notifications, coupon distribution, and customer review functions into the app.

[0063] (Example 2) The business application development platform according to an embodiment of the present invention is a system that allows even non-technical people to easily develop their own business applications. This system analyzes information provided by companies, proposes optimal application functions and designs tailored to the needs specific to each industry, and integrates payment methods and customer engagement functions. This allows the business application development platform to easily develop and manage business applications, even for non-technical people.

[0064] A business application development platform according to an embodiment includes an interface providing unit, an analysis unit, a proposal unit, and an integration unit. The interface providing unit provides an intuitive interface. For example, a drag-and-drop function allows a user to easily arrange the layout and functions of an application. The interface providing unit can also learn a user's operation history and predict and suggest the next required operation. The analysis unit analyzes information provided by companies. For example, the analysis unit uses data mining technology to analyze product information and service information provided by companies. The analysis unit can also analyze past data from companies to predict future needs. The proposal unit proposes application functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit. For example, in the restaurant industry, the proposal unit proposes menu display, reservation functions, and takeout ordering functions. The proposal unit can also use an emotion estimation function to propose personalized application functions based on the user's emotions. The integration unit integrates the functions proposed by the proposal unit. For example, the integration unit integrates payment methods such as credit card payment, electronic money, and QR code payment into the application. The integration unit can also implement customer engagement functions, such as push notifications, coupon distribution, and customer review functions, into the app. This allows the business app development platform according to the embodiment to easily develop and manage business apps, even for non-technical individuals. For example, restaurants and food service companies can use this platform to easily create apps with menu display and reservation functions, thereby increasing customer engagement. Furthermore, the integration of payment methods allows customers to make payments smoothly. Furthermore, the generation AI analyzes app usage and suggests necessary updates, ensuring that the app is always up-to-date.

[0065] The interface providing unit uses a drag-and-drop function to allow users to easily arrange the layout and functions of an app. For example, the interface providing unit uses a generation AI to analyze the user's past operation history and predict and suggest the next required operation. For example, it may prioritize displaying functions that the user uses frequently. The interface providing unit also learns the user's operation patterns and suggests the next operation to be performed in real time. For example, it may highlight the next field to be filled in while filling out a form. The interface providing unit also uses a generation AI to suggest the optimal operation procedure based on the user's operation history. For example, it may present shortcuts to simplify complex operations. This allows users to intuitively arrange the app's layout and functions.

[0066] In the case of the restaurant business, the suggestion unit can suggest menu display, reservation functions, and takeout ordering functions. For example, the suggestion unit uses a generation AI to analyze the user's voice instructions and change the app layout through voice control. For example, if the user says, "Move the button to the top right," the button will move automatically. Also, when the suggestion unit gives a voice instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, if the user says, "Add a new form," a form will be added. Also, the suggestion unit analyzes voice instructions and reflects the design changes requested by the user in real time. For example, if the user says, "Change the background color to blue," the background color will change instantly. This makes it possible to suggest app functions specialized for the restaurant business.

[0067] The integration unit can integrate credit card, electronic money, and QR code payment methods into the app. For example, the integration unit uses an emotion estimation function to analyze in real time the stress and satisfaction felt by the user during operation, and simplifies the interface if stress increases. The integration unit also analyzes the user's satisfaction using the emotion estimation function and makes suggestions to improve the interface if satisfaction is low. For example, it simplifies parts of the interface that are complicated to operate. The integration unit also uses the emotion estimation function to dynamically change the color and layout of the interface according to the user's emotions. For example, it changes to a relaxing color if the user is feeling high stress. This allows a variety of payment methods to be integrated into the app.

[0068] The integration unit can implement customer engagement functions such as push notifications, coupon distribution, and customer review functions into the app. In the integration unit, for example, the generation AI analyzes customer behavior data and proposes the optimal engagement strategy. For example, it may propose personalized promotions based on the customer's purchase history. The integration unit also customizes the engagement strategy based on the customer behavior data. For example, it may propose push notifications that are effective at specific times of the day. The integration unit also analyzes customer behavior data in real time and proposes an engagement strategy instantly. For example, it may suggest related products when a customer views a specific product. This allows customer engagement functions to be implemented in the app.

[0069] The analysis unit analyzes app usage and user feedback and can suggest updates and improvements. For example, the analysis unit uses a generation AI to monitor app usage in real time and automatically suggest necessary updates. For example, if a specific function is frequently used, that function will be enhanced. The analysis unit also uses a generation AI to suggest optimal update content based on app usage. For example, it will improve areas that users are dissatisfied with. The analysis unit also uses a generation AI to analyze app usage in real time and immediately suggest update content. For example, it will suggest adding new functions. This makes it possible to suggest updates and improvements based on app usage and user feedback.

[0070] The interface providing unit can learn the user's operation history and predict and suggest the next required operation. For example, the generation AI in the interface providing unit analyzes the user's past operation history and predicts and suggests the next required operation. For example, it may prioritize displaying functions that the user uses frequently. The interface providing unit also learns the user's operation patterns and suggests the next operation to be performed in real time. For example, it may highlight the next field to be entered while filling out a form. The generation AI in the interface providing unit also suggests the optimal operation procedure based on the user's operation history. For example, it may present shortcuts to simplify complex operations. This makes it possible to predict and suggest the next required operation based on the user's operation history.

[0071] The interface providing unit can analyze the user's voice instructions and design the app through voice operation. For example, the generation AI in the interface providing unit analyzes the user's voice instructions and changes the layout of the app through voice operation. For example, if the user instructs the unit to "move the button to the top right," the button will move automatically. Also, when the user gives a voice instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, if the user says "add a new form," a form will be added. Also, the interface providing unit analyzes the voice instructions and reflects the design changes requested by the user in real time. For example, if the user says "change the background color to blue," the background color will change instantly. This makes it possible to design the app based on the user's voice instructions.

[0072] The interface providing unit can use the emotion estimation function to analyze in real time the stress and satisfaction felt by the user during operation and dynamically adjust the interface. For example, the interface providing unit uses the emotion estimation function to analyze in real time the stress felt by the user during operation and simplify the interface if stress increases. The interface providing unit also analyzes the user's satisfaction using the emotion estimation function and makes suggestions to improve the interface if satisfaction is low. For example, it simplifies parts of the operation that are complicated. The interface providing unit also uses the emotion estimation function to dynamically change the color and layout of the interface according to the user's emotions. For example, if stress is high, it changes to a color that is relaxing. This makes it possible to dynamically adjust the interface based on the user's emotions.

[0073] The interface providing unit can analyze the user's handwritten input and automatically generate an app layout from a sketch. In the interface providing unit, for example, a generation AI analyzes the user's handwritten input and automatically generates an app layout based on the sketch. For example, it recognizes and arranges handwritten buttons and text fields. The interface providing unit also analyzes the handwritten input and reflects the shapes and characters drawn by the user in the app design. For example, it automatically generates handwritten menu icons. The interface providing unit also analyzes a wireframe drawn by the user, and the generation AI creates a prototype of the app based on the wireframe. This makes it possible to automatically generate an app layout based on the user's handwritten input.

[0074] The interface providing unit can recognize user gestures and design the app through gesture operations. In the interface providing unit, for example, the generation AI recognizes user gestures and changes the layout of the app through gesture operations. For example, a swipe operation moves elements on the screen. In addition, when the user issues an instruction to add a function to the app through a gesture, the generation AI adds the function according to the instruction. For example, a pinch operation adds a new widget. In addition, the interface providing unit uses gesture recognition to reflect design changes requested by the user in real time. For example, a double tap enlarges the display of a specific element. This makes it possible to design the app based on the user's gesture operations.

[0075] The interface providing unit can use the emotion estimation function to propose an interface design that reinforces the positive emotions felt by the user during operation. For example, the interface providing unit uses the emotion estimation function to analyze the positive emotions felt by the user during operation and proposes an interface design that reinforces those emotions. For example, the interface providing unit preferentially displays designs that make the user feel joy. The interface providing unit also analyzes the user's positive emotions using the emotion estimation function and proposes a color scheme or layout that reinforces those emotions. For example, it uses colors that the user prefers. The interface providing unit also uses the emotion estimation function to propose animations or effects that reinforce the positive emotions felt by the user during operation. For example, it displays a congratulatory animation upon success. This makes it possible to propose an interface design that reinforces the user's positive emotions.

[0076] The analysis unit can analyze a company's past data and predict and propose future needs. For example, the analysis unit uses generative AI to analyze a company's past sales data and customer feedback to predict and propose future needs. For example, it can propose new promotions based on seasonal sales trends. The analysis unit can also analyze a company's growth patterns based on past data and predict future needs. For example, it can propose entry into new markets. The analysis unit can also analyze a company's past data and predict future needs to propose new features and services. For example, it can make personalized proposals based on customer purchasing history. This makes it possible to predict and propose future needs based on a company's past data.

[0077] The analysis unit can analyze the apps of a company's competitors and suggest points of differentiation. For example, the analysis unit uses generative AI to analyze a competitor's app and suggest points of differentiation. For example, it can suggest unique features that competitors do not offer. The analysis unit can also analyze the design and functions of a competitor's app to identify points where the company can differentiate itself. For example, it can suggest new designs to improve the user experience. The analysis unit can also analyze reviews and ratings of a competitor's app to suggest points where the company should improve. For example, it can suggest features that complement the competitor's weaknesses. This makes it possible to analyze a company's competitor's app and suggest points of differentiation.

[0078] The analysis unit can use the emotion estimation function to suggest personalized app functions based on the user's emotions. For example, the analysis unit uses the emotion estimation function to suggest personalized app functions based on the user's emotions. For example, if the user is feeling stressed, the analysis unit suggests a function that helps the user relax. The analysis unit also analyzes the user's emotion data and suggests personalized designs and functions based on the emotions. For example, designs that make the user feel happy are preferentially displayed. The analysis unit also uses the emotion estimation function to suggest personalized notifications and messages based on the user's emotions. For example, if the user is excited, the analysis unit sends a special offer. This makes it possible to suggest personalized app functions based on the user's emotions.

[0079] The analysis unit can analyze success stories from different industries and propose best practices for those industries. For example, the generative AI can analyze success stories from different industries and propose best practices for those industries. For example, it can apply success stories from the food and beverage industry to the retail industry. The analysis unit can also propose best practices that companies should adopt based on success stories from different industries. For example, it can apply customer engagement techniques from the service industry to the manufacturing industry. The generative AI can also analyze success stories from different industries and propose new ideas to help companies increase their competitiveness. For example, it can introduce innovations from the technology industry to traditional industries. This makes it possible to propose best practices for other industries based on success stories from different industries.

[0080] The analysis unit can analyze region-specific needs and propose the optimal app features for each region. For example, the generation AI in the analysis unit analyzes region-specific needs and proposes the optimal app features for each region. For example, it proposes features that suit the local culture and customs. The analysis unit also analyzes local market data and proposes app features specialized for that region. For example, it adds a function that provides local event information. The analysis unit also analyzes region-specific user feedback using the generation AI and proposes the optimal design and features for each region. For example, it provides an interface that supports the local language and dialect. This makes it possible to propose the optimal app features for each region based on the region's specific needs.

[0081] The analysis unit can use the emotion estimation function to suggest a design theme based on the user's emotions. For example, the analysis unit uses the emotion estimation function to suggest a design theme based on the user's emotions. For example, it suggests colors and layouts that will relax the user. The analysis unit also analyzes the user's emotion data and suggests a design theme based on the emotion. For example, it preferentially displays designs that excite the user. The analysis unit also uses the emotion estimation function to suggest a customized design theme based on the user's emotions. For example, it provides a design that will make the user feel happy. This makes it possible to suggest a design theme based on the user's emotions.

[0082] The integration department can analyze a company's transaction data and propose the optimal payment method. In the integration department, for example, the generation AI analyzes a company's transaction data and proposes the optimal payment method. For example, it selects the optimal payment method based on transaction volume and frequency. The integration department also uses the generation AI to propose cost-effective payment methods based on the company's transaction data. For example, it prioritizes proposals for payment methods with low fees. The integration department also uses the generation AI to analyze a company's transaction data and propose the optimal payment method based on the customer's payment habits. For example, it proposes payment methods that the customer frequently uses. This makes it possible to propose the optimal payment method based on a company's transaction data.

[0083] The integration department can analyze the security risks of payment methods and propose risk mitigation measures. For example, the generation AI in the integration department analyzes the security risks of each payment method and proposes risk mitigation measures. For example, it proposes measures to reduce the risk of fraudulent use of credit card payments. The integration department also analyzes the security risks of payment methods and provides specific risk mitigation measures to companies. For example, it proposes the introduction of two-step authentication. The integration department also monitors the security risks of payment methods in real time using the generation AI, and immediately proposes measures if the risk increases. For example, it issues an alert if it detects signs of unauthorized access. This makes it possible to analyze the security risks of payment methods and propose risk mitigation measures.

[0084] The integration unit can use the emotion estimation function to propose a payment interface based on emotions so that the user can make a payment with confidence. The integration unit, for example, uses the emotion estimation function to propose an interface based on emotions so that the user can make a payment with confidence. For example, it uses colors and designs that make the user feel relaxed. The integration unit also analyzes the user's emotion data and proposes an interface design to increase the sense of security. For example, it displays a message that gives the user a sense of security if the user feels anxious. The integration unit also uses the emotion estimation function to propose a customized interface based on emotions so that the user can make a payment with confidence. For example, it provides a design and layout that the user prefers. In this way, it is possible to propose a payment interface based on emotions so that the user can make a payment with confidence.

[0085] The integration department can analyze payment methods in different countries and regions and propose globally compatible payment methods. For example, the generation AI in the integration department analyzes payment methods in different countries and regions and proposes globally compatible payment methods. For example, it automatically selects the major payment methods in each country. The integration department also analyzes payment methods in different regions and proposes the optimal payment method for companies expanding globally. For example, it makes proposals based on the popularity rate of payment methods in each region. The generation AI in the integration department also analyzes the regulations and laws regarding payment methods in each country and proposes points that companies must comply with. For example, it presents the legal requirements for payment methods in a specific country. This makes it possible to propose globally compatible payment methods based on the payment methods in different countries and regions.

[0086] The integration department can propose customizable payment methods that suit a company's business model. For example, the generation AI in the integration department analyzes a company's business model and proposes customizable payment methods accordingly. For example, it proposes the payment method that is best suited to a subscription model. The generation AI in the integration department also customizes the optimal payment method based on the business model. For example, it proposes payment methods that are suitable for B2B transactions. The generation AI in the integration department also proposes customizable payment methods based on the company's business model and supports implementation. For example, it provides an installment payment option. This makes it possible to propose customizable payment methods that suit a company's business model.

[0087] The integration unit can use the emotion estimation function to suggest the payment method that the user feels most comfortable with. For example, the integration unit uses the emotion estimation function to suggest the payment method that the user feels most comfortable with. For example, it prioritizes suggesting payment methods that the user feels safe with. The integration unit also analyzes the user's emotion data to suggest payment methods that increase comfort. For example, it selects payment methods that the user does not feel stressed with. The integration unit also uses the emotion estimation function to customize and suggest the payment method that the user feels most comfortable with. For example, it prioritizes displaying payment methods that the user prefers. This makes it possible to suggest the payment method that the user feels most comfortable with.

[0088] The integration unit can analyze customer behavioral data and propose the optimal engagement strategy. In the integration unit, for example, the generation AI analyzes customer behavioral data and proposes the optimal engagement strategy. For example, it may propose personalized promotions based on the customer's purchasing history. In addition, the generation AI in the integration unit customizes the engagement strategy based on the customer behavioral data. For example, it may propose push notifications that are effective at specific times of the day. In addition, the generation AI in the integration unit analyzes customer behavioral data in real time and proposes an engagement strategy instantly. For example, it may suggest related products when a customer views a specific product. This makes it possible to propose the optimal engagement strategy based on customer behavioral data.

[0089] The integration unit can analyze a customer's purchase history and propose personalized promotions. In the integration unit, for example, the generation AI analyzes a customer's purchase history and proposes personalized promotions. For example, it may offer discounts on specific products based on past purchase history. In addition, the integration unit uses the generation AI to propose individually customized promotions based on the customer's purchase history. For example, it may provide special offers for products that the customer frequently purchases. In addition, the integration unit uses the generation AI to analyze a customer's purchase history in real time and propose personalized promotions instantly. For example, it may provide discounts on products that the customer has added to their cart. This makes it possible to propose personalized promotions based on the customer's purchase history.

[0090] The integration unit can use the emotion estimation function to suggest an engagement function based on the customer's emotions. For example, the integration unit uses the emotion estimation function to suggest an engagement function based on the customer's emotions. For example, it provides a promotion that makes the customer happy. The integration unit also analyzes the customer's emotion data and suggests an engagement function based on the emotions. For example, it provides content that helps the customer relax when the customer is feeling stressed. The integration unit also uses the emotion estimation function to suggest a personalized engagement function based on the customer's emotions. For example, it provides an event or campaign that excites the customer. In this way, it is possible to suggest an engagement function based on the customer's emotions.

[0091] The integration department can analyze engagement methods from different industries and propose best practices from those industries. For example, the generative AI in the integration department analyzes engagement methods from different industries and proposes best practices from those industries. For example, methods from the entertainment industry can be applied to the retail industry. The integration department can also propose best practices that companies should adopt based on engagement methods from different industries. For example, customer loyalty programs from the financial industry can be applied to the service industry. The generative AI in the integration department can also analyze engagement methods from different industries and propose new ideas to help companies increase their competitiveness. For example, innovations from the technology industry can be introduced into traditional industries. This makes it possible to propose best practices from other industries based on engagement methods from different industries.

[0092] The integration unit can analyze customers' social media data and propose engagement strategies that utilize social media. In the integration unit, for example, the generation AI analyzes customers' social media data and proposes engagement strategies that utilize social media. For example, it provides personalized advertisements based on the content of customers' posts. In addition, the integration unit has the generation AI customize the engagement strategy based on the social media data. For example, it provides content related to topics that interest the customer. In addition, the integration unit has the generation AI analyze customers' social media data in real time and propose instant engagement strategies. For example, it provides related promotions when a customer participates in a specific event. This makes it possible to propose engagement strategies that utilize social media based on customers' social media data.

[0093] The integration unit can use the emotion estimation function to collect feedback based on customer emotions and improve the engagement function. For example, the integration unit uses the emotion estimation function to collect feedback based on customer emotions and improve the engagement function. For example, when a customer feels dissatisfied, the integration unit identifies areas for improvement. The integration unit also analyzes customer emotion data and collects feedback based on that emotion. For example, the integration unit enhances functions that the customer felt happy with. The integration unit also uses the emotion estimation function to collect personalized feedback based on customer emotions and improve the engagement function. For example, the integration unit provides events or campaigns that excite customers. In this way, the integration unit can collect feedback based on customer emotions and improve the engagement function.

[0094] The analysis unit monitors app usage in real time and can automatically suggest necessary updates. For example, the generation AI in the analysis unit monitors app usage in real time and automatically suggests necessary updates. For example, if a specific function is used frequently, that function will be enhanced. The analysis unit also allows the generation AI to suggest optimal update content based on app usage. For example, it will improve areas that cause user dissatisfaction. The analysis unit also allows the generation AI to analyze app usage in real time and immediately suggest update content. For example, it will suggest adding new functions. This makes it possible to monitor app usage in real time and automatically suggest necessary updates.

[0095] The analysis unit can analyze user feedback and automatically extract and propose improvements. In the analysis unit, for example, the generation AI analyzes user feedback and automatically extracts and proposes improvements. For example, it identifies areas where users are dissatisfied and proposes improvements. The analysis unit also allows the generation AI to propose optimal improvements based on user feedback. For example, it makes suggestions to add new functions that users request. The analysis unit also allows the generation AI to analyze user feedback in real time and immediately propose improvements. For example, it improves the interface based on user opinions. This makes it possible to automatically extract and propose improvements based on user feedback.

[0096] The analysis unit can use the emotion estimation function to suggest update content based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to suggest update content based on the user's emotions. For example, it identifies areas that the user is dissatisfied with and improves those areas. The analysis unit also analyzes the user's emotion data and suggests update content based on those emotions. For example, it enhances functions that bring joy to the user. The analysis unit also uses the emotion estimation function to suggest personalized update content based on the user's emotions. For example, it adds a new function that excites the user. In this way, it is possible to suggest update content based on the user's emotions.

[0097] The analysis unit analyzes the characteristics of different platforms (iOS, Android, etc.) and can propose the optimal update content for each platform. For example, the generation AI in the analysis unit analyzes the characteristics of different platforms such as iOS and Android and proposes the optimal update content for each. For example, it proposes UI improvements specialized for iOS. The analysis unit also analyzes user behavior data for each platform and the generation AI proposes the optimal update content for each platform. For example, it proposes adding features specialized for Android users. The analysis unit also analyzes the technical constraints of different platforms and proposes the optimal update content based on that. For example, it proposes function improvements that are compatible with the latest version of iOS. This makes it possible to propose the optimal update content based on the characteristics of different platforms.

[0098] The analysis unit can analyze the app's performance data and suggest updates to improve performance. In the analysis unit, for example, the generation AI analyzes the app's performance data and suggests updates to improve performance. For example, it suggests improvements to shorten the app's startup time. In addition, the analysis unit allows the generation AI to suggest optimal performance improvement measures based on the app's performance data. For example, it suggests code optimization to reduce memory usage. In addition, the analysis unit allows the generation AI to analyze the app's performance data in real time and immediately suggest updates to improve performance. For example, it suggests bug fixes based on crash reports. This makes it possible to suggest updates to improve performance based on the app's performance data.

[0099] The analysis unit can use the emotion estimation function to suggest adding new functions based on the user's emotions. The analysis unit, for example, uses the emotion estimation function to suggest adding new functions based on the user's emotions. For example, a new game function that excites the user is added. The analysis unit also analyzes the user's emotion data and suggests adding new functions based on the emotions. For example, a new music function that helps the user relax. The analysis unit also uses the emotion estimation function to suggest adding new functions that are personalized based on the user's emotions. For example, a new social function that brings joy to the user is added. This makes it possible to suggest adding new functions based on the user's emotions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The business app development platform can also be equipped with an interface provider that recognizes user gestures and allows app design based on gesture operations. For example, a swipe moves elements on the screen. When a user issues a gesture instruction to add a function to the app, the generation AI adds the function according to the instruction. For example, a pinch adds a new widget. Gesture recognition can also be used to reflect design changes requested by the user in real time. For example, a double tap enlarges a specific element. This allows app design based on user gesture operations.

[0102] The interface providing unit can analyze the user's handwritten input and automatically generate the app layout from the sketch. For example, the generation AI analyzes the user's handwritten input and automatically generates the app layout based on the sketch. It recognizes and arranges handwritten buttons and text fields. It also analyzes the handwritten input and reflects the shapes and characters drawn by the user in the app design. For example, it automatically generates handwritten menu icons. It also analyzes the user's handwritten wireframe and creates an app prototype based on the wireframe. This makes it possible to automatically generate the app layout based on the user's handwritten input.

[0103] The analysis unit can analyze the apps of a company's competitors and suggest points of differentiation. For example, generative AI can analyze a competitor's app and suggest points of differentiation. It can suggest unique features that competitors do not offer. It can also analyze the design and functions of competitors' apps to identify points where the company can differentiate itself. For example, it can suggest new designs to improve the user experience. It can also analyze reviews and ratings of competitors' apps to suggest areas where the company should improve. For example, it can suggest features that complement competitors' weaknesses. This makes it possible to analyze a company's competitors' apps and suggest points of differentiation.

[0104] The analysis unit can analyze success stories from different industries and propose best practices for those industries. For example, the generative AI can analyze success stories from different industries and propose best practices for those industries. Success stories from the food and beverage industry can be applied to the retail industry. Furthermore, best practices that companies should adopt can be proposed based on success stories from different industries. For example, customer engagement techniques from the service industry can be applied to the manufacturing industry. Furthermore, the generative AI can analyze success stories from different industries and propose new ideas to help companies increase their competitiveness. For example, innovations from the technology industry can be introduced into traditional industries. This makes it possible to propose best practices for other industries based on success stories from different industries.

[0105] The analysis unit can analyze region-specific needs and propose the optimal app features for each region. For example, the generation AI analyzes region-specific needs and proposes the optimal app features for each region. It proposes features that are suited to the region's culture and customs. It also analyzes region's market data and proposes app features specialized for that region. For example, it could add a function that provides information about local events. The generation AI also analyzes region's user feedback and proposes the optimal design and features for each region. For example, it could provide an interface that supports the region's language and dialect. This makes it possible to propose the optimal app features for each region based on the region's specific needs.

[0106] The analysis unit can use the emotion estimation function to suggest design themes based on the user's emotions. For example, the emotion estimation function can be used to analyze the positive emotions felt by the user during operation and suggest an interface design to reinforce those emotions. Designs that make the user feel joyful can be displayed preferentially. The emotion estimation function can also be used to analyze the user's positive emotions and suggest color schemes and layouts to reinforce those emotions. For example, the emotion estimation function can be used to suggest animations and effects to reinforce the positive emotions felt by the user during operation. For example, a congratulatory animation can be displayed upon success. This makes it possible to suggest interface designs to reinforce the user's positive emotions.

[0107] The integration unit can use the emotion estimation function to propose an emotion-based payment interface so that the user can make a payment with peace of mind. For example, the emotion estimation function can be used to propose an emotion-based interface so that the user can make a payment with peace of mind. Colors and designs that relax the user can be used. The integration unit can also analyze the user's emotion data and propose an interface design to increase the sense of security. For example, a message that gives a sense of security can be displayed if the user feels anxious. The integration unit can also use the emotion estimation function to propose a customized emotion-based interface so that the user can make a payment with peace of mind. For example, a design or layout that the user prefers can be provided. This makes it possible to propose an emotion-based payment interface so that the user can make a payment with peace of mind.

[0108] The integration unit can use the emotion estimation function to suggest the payment method that the user feels most comfortable with. For example, the emotion estimation function is used to suggest the payment method that the user feels most comfortable with. Payment methods that the user feels safe with are prioritized. The integration unit also analyzes the user's emotion data and suggests payment methods that enhance comfort. For example, a payment method that the user does not feel stressed with is selected. The integration unit also uses the emotion estimation function to customize and suggest the payment method that the user feels most comfortable with. For example, the payment method that the user prefers is displayed with priority. This makes it possible to suggest the payment method that the user feels most comfortable with.

[0109] The integration unit can use the emotion estimation function to suggest engagement functions based on the customer's emotions. For example, the emotion estimation function is used to suggest engagement functions based on the customer's emotions, and a promotion that makes the customer happy is provided. The integration unit also analyzes the customer's emotion data and suggests engagement functions based on the emotions, and for example, if the customer is feeling stressed, content that helps the customer relax is provided. The integration unit also uses the emotion estimation function to suggest personalized engagement functions based on the customer's emotions, and for example, events or campaigns that excite the customer are provided. This makes it possible to suggest engagement functions based on the customer's emotions.

[0110] The analysis unit can use the emotion estimation function to suggest adding new functions based on the user's emotions. For example, the emotion estimation function can be used to suggest adding new functions based on the user's emotions, such as adding a new game function that will excite the user. The analysis unit can also analyze the user's emotion data and suggest adding new functions based on the emotions, such as adding a new music function that will help the user relax. The emotion estimation function can also be used to suggest adding new functions that are personalized based on the user's emotions, such as adding a new social function that will bring joy to the user. This makes it possible to suggest adding new functions based on the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The interface provider provides an intuitive interface. For example, by using a drag-and-drop function, the user can easily arrange the layout and functions of the app. The interface provider can also learn the user's operation history and predict and suggest the next required operation. Step 2: The analysis unit analyzes the information provided by the company. For example, it uses data mining technology to analyze product and service information provided by the company. The analysis unit can also analyze the company's past data and predict future needs. Step 3: The proposal unit proposes app functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit. For example, in the case of the restaurant industry, it proposes menu display, reservation functions, and takeout ordering functions. The proposal unit can also use an emotion estimation function to propose personalized app functions based on the user's emotions. Step 4: The integration unit integrates the features proposed by the proposal unit. For example, it integrates payment methods such as credit card payments, electronic money, and QR code payments into the app. The integration unit can also implement customer engagement features such as push notifications, coupon distribution, and customer review functions into the app.

[0113] 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.

[0114] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

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

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

[0148] The data processing device 12 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0160] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, to avoid confusion and 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.

[0179] 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. [Explanation of symbols]

[0180] 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. an interface providing unit that provides an intuitive interface; an analysis unit that analyzes the information provided by the company through the interface provided by the interface providing unit; a proposal unit that proposes application functions and designs tailored to industry-specific needs based on the information analyzed by the analysis unit; an integration unit that integrates the functions proposed by the proposal unit. A system characterized by:

2. The interface providing unit Drag-and-drop functionality allows users to easily arrange app layouts and features.

2. The system of claim 1.

3. The proposal unit For restaurants, we offer menu display, reservation functionality, and takeout ordering.

2. The system of claim 1.

4. The integration unit Integrate credit card payments, electronic money, and the aforementioned QR code payments into the app.

2. The system of claim 1.

5. The integration unit Implement customer engagement features in the app, such as push notifications, coupon delivery, and customer review functionality.

2. The system of claim 1.

6. The analysis unit Analyzing app usage and user feedback to suggest updates and improvements 2. The system of claim 1.

7. The interface providing unit Learns the user's operation history and predicts and suggests the next necessary operation 2. The system of claim 1.

8. The interface providing unit Analyze user voice commands and design apps with voice control 2. The system of claim 1.

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