System

Abstract

To provide an automated system for efficiently and quickly providing IT solution at a low cost.SOLUTION: The system includes means for a user to input information, means for the server to receive the information and request the generation AI to analyze the information, means for the generation AI to propose an optimal solution based on the analysis result, means for the server to display the proposed content to the user, means for the user to select the proposed solution, means for the server to generate an introduction plan based on the selection result and execute an introduction procedure, means for the server to monitor the system after the introduction and provide continuous support, means for the generation AI to generate an answer to a query from the user, means for the server to notify the user of the answer, and means for the server to periodically analyze the usage status of the system and notify the user of additional proposals and improvement measures.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] In recent years, many small companies and startups have found it difficult to make optimal choices when introducing IT solutions due to budget and resource constraints. Furthermore, the process from proposing an IT solution to implementing it and providing customer success requires a lot of human resources, making the procedures complicated and time-consuming. To solve this problem, there is a demand for an automated system that can provide IT solutions efficiently and quickly at low cost. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input information and for a terminal to send the information to a server, a means for the server to receive the information and request analysis from a generation AI, and a means for the generation AI to propose an optimal solution based on the analysis results. Furthermore, the server displays the proposals to the user and includes a means for the user to select a proposed solution. The server generates an implementation plan based on the selection results and executes the implementation procedure. After implementation, the server monitors the system and provides ongoing support. The generation AI also includes a means for generating answers to user inquiries, and the server notifies the user of the answers. The server periodically analyzes system usage and notifies additional suggestions and improvements, thereby providing efficient and comprehensive IT solutions.

[0006] "Users" refers to people from small businesses and startups who use the system.

[0007] "Terminal" refers to a device used by a user to input information and send it to a server.

[0008] "Server" refers to a central management system that receives information sent by users, requests analysis from the generation AI, displays the proposed content to the user, and generates and executes an implementation plan based on the selection results.

[0009] "Generative AI" refers to artificial intelligence technology that analyzes information provided by a server, proposes optimal IT solutions, and generates appropriate answers to user inquiries.

[0010] "Solutions" refer to IT-related products and services proposed by generative AI in response to users' challenges and requests.

[0011] An "implementation plan" refers to the specific procedures and schedule for incorporating the solution selected by the user into the system.

[0012] "Customer success" refers to ongoing support and services to help users achieve satisfaction and success after the solution is implemented.

[0013] "Monitoring" refers to the process of monitoring the performance and usage of systems in which servers are installed in real time, and automatically detecting problems and areas for improvement.

[0014] "Inquiry" refers to a question or problem report that a user makes to the system.

[0015] "Regular Analysis" refers to the process by which the server periodically reviews system usage and provides additional recommendations and improvements. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that efficiently provides IT solutions to small businesses and startups. Specifically, it collects information from users through a website that utilizes generative AI, and then automates the entire process of proposing and implementing optimal IT solutions based on that information, all the way through to customer success.

[0038] System program and processing flow

[0039] 1. Collecting User Information

[0040] Users access the website and enter company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0041] The terminal transmits the input information to the server.

[0042] 2. Solution proposal

[0043] The server receives the information sent from the device and requests the generating AI to analyze it.

[0044] The generative AI analyzes the information input by the user and proposes the optimal IT solution (e.g., a cloud-based remote desktop solution).

[0045] The server displays this proposal to the user, who then confirms the details.

[0046] 3. Solution Selection and Implementation

[0047] The user selects the best solution from the proposed solutions.

[0048] The terminal transmits the selection result to the server.

[0049] The server generates an implementation plan based on the selection results and automatically executes specific configuration and installation procedures.

[0050] 4. Supporting Customer Success

[0051] The server monitors the system in real time after installation, monitoring performance and usage.

[0052] If users have any questions or problems, they can contact us through the website.

[0053] The server sends the query to the generation AI and requests analysis.

[0054] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[0055] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0056] Specific examples

[0057] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[0058] 1. Collecting User Information

[0059] An IT professional at TechStartup visits the website and enters "Optimizing the remote work environment" as an assignment.

[0060] The terminal transmits the information to the server.

[0061] 2. Solution proposal

[0062] The server receives the information and asks the generating AI to analyze it.

[0063] The generative AI generates a proposal called a "cloud-based remote desktop solution" and sends it back to the server.

[0064] The server displays this suggestion to the IT staff.

[0065] 3. Solution Selection and Implementation

[0066] The IT staff reviews the proposed solutions and selects one.

[0067] The terminal transmits the selection result to the server.

[0068] The server generates a deployment plan and automatically configures and installs the software.

[0069] 4. Supporting Customer Success

[0070] The server monitors the system in real time after installation.

[0071] If IT staff notice a problem, they can contact the website.

[0072] The server sends the query to the generating AI, requesting analysis and an answer.

[0073] The generation AI generates an answer, and the server notifies the person in charge.

[0074] The server periodically analyzes system usage and suggests improvements.

[0075] In this way, the system of the present invention provides an environment in which users can easily introduce IT solutions and also automates post-operation support, thereby helping small businesses efficiently develop and operate their IT infrastructure.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The user accesses the website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0079] Step 2:

[0080] The terminal transmits the input information to the server.

[0081] Step 3:

[0082] The server receives the information sent from the terminal and stores it in a database.

[0083] Step 4:

[0084] The server converts the received data into a format that is easy for the generation AI to analyze, and requests the generation AI to analyze it.

[0085] Step 5:

[0086] Generative AI analyzes company information and issues, and generates multiple optimal IT solutions.

[0087] Step 6:

[0088] The generation AI sends the generated proposals to the server.

[0089] Step 7:

[0090] The server converts the proposed content into a format that is easy for the user to view, and transmits it to the terminal for display.

[0091] Step 8:

[0092] The user selects the best solution from the proposed solutions.

[0093] Step 9:

[0094] The terminal transmits the user's selection result to the server.

[0095] Step 10:

[0096] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0097] Step 11:

[0098] The server will automatically perform the configuration and installation to complete the solution deployment.

[0099] Step 12:

[0100] The server monitors the system in real time after installation, monitoring performance and usage.

[0101] Step 13:

[0102] If a user has any questions or problems, they can contact us through the website.

[0103] Step 14:

[0104] The server receives an inquiry from the user and requests the generation AI to analyze it.

[0105] Step 15:

[0106] The generation AI analyzes the inquiry and generates an appropriate answer.

[0107] Step 16:

[0108] The server sends the answer from the generated AI to the user and displays it.

[0109] Step 17:

[0110] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0111] In this way, servers, terminals, and users work together at each step to efficiently realize the entire process from proposing IT solutions to implementation and customer success.

[0112] Example 1

[0113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0114] For today's small businesses and startups, the efficient implementation and operation of information technology solutions is crucial. However, these businesses often have limited technical resources and expertise, and therefore spend a great deal of time and money selecting, implementing, and supporting the appropriate solutions. Therefore, there is a need for a system that can simplify and automate these processes.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0116] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generative artificial intelligence model; a means for the generative AI model to propose an optimal information technology solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the terminal to send the selection results to the server; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system in real time after implementation and provide ongoing support; a means for the generative artificial intelligence model to generate answers to inquiries from the user; a means for the server to notify the user of the answers; and a means for the server to periodically analyze system usage and notify the user of additional suggestions and improvements, thereby enabling users to easily select, implement, and operate appropriate information technology solutions.

[0117] "User" refers to a company or individual who uses the system.

[0118] A "terminal" refers to a computer device operated by a user, and is a means for inputting information and transmitting it to a server.

[0119] "Server" refers to a central computer system that processes information sent from users and devices and works with generative AI models to propose and implement solutions.

[0120] "Generative artificial intelligence model" refers to an AI system that analyzes information provided by users and generates optimal information technology solutions.

[0121] "Information" refers to company information and current issues that users input into the system.

[0122] "Analysis" refers to the process by which a generative artificial intelligence model derives the optimal solution based on information provided by the user.

[0123] "Proposal" refers to the optimal information technology solution generated by the generative artificial intelligence model based on the analysis results.

[0124] "Implementation Plan" means a document describing the configuration and installation steps required to implement the proposed Solution.

[0125] "Monitoring" refers to the process of monitoring a system's performance and usage in real time after implementation.

[0126] "Inquiry" refers to a question or problem report that a user makes to the system.

[0127] "Answer" refers to the appropriate solution or information generated by a generative artificial intelligence model in response to a query.

[0128] "Usage status" refers to the performance and operational status of the system after its implementation, and refers to information that is periodically analyzed by the server.

[0129] This invention is a system for efficiently providing information technology solutions to small businesses and startups. Specifically, it collects information from users through a website using a generative artificial intelligence model, and then automates the entire process of proposing and implementing optimal information technology solutions based on that information, leading to customer success.

[0130] First, the user accesses a dedicated website via a browser. The user enters company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). To do this, the user uses a device such as a PC, tablet, or smartphone. The device then sends the entered information to the server in JSON format.

[0131] The server receives the information sent from the device and requests a generative artificial intelligence model, such as OpenAI's GPT-3, to analyze the data. At this time, the server converts the collected information into a format that is easy for the generative artificial intelligence model to analyze. The generative artificial intelligence model generates an appropriate information technology solution based on the user's input information. For example, a "cloud-based remote desktop solution" may be proposed. The server then converts the generated analysis results into HTML format and displays them on a website in a user-friendly format.

[0132] Next, the user reviews the displayed proposals and selects the optimal solution from multiple options. The selection results are then sent back to the server from the device. The server then automatically generates a deployment plan based on the selection results, and performs instance configuration and service provisioning using, for example, AWS cloud services or Microsoft Azure. Specific configuration and installation procedures are performed by automated scripts.

[0133] After implementation, the server uses monitoring tools such as Datadog and New Relic to monitor system performance and usage in real time. When users have questions or problems, they can make inquiries via the website. The server sends the inquiry to the generative AI model for analysis and response. The generative AI model generates an appropriate response, and the server notifies the user of the response. In addition, the server regularly analyzes system usage and uses the generative AI model to generate additional suggestions and improvements, which are then notified to the user.

[0134] As a concrete example, consider the case where the company TechStartup wants to implement a new remote work environment. The IT staff at TechStartup accesses the website and inputs the challenge of "optimizing the remote work environment." The generative AI then suggests a "cloud-based remote desktop solution," and the IT staff selects this solution. The server configures the AWS cloud-based remote desktop and automatically installs it. After implementation, the system is monitored using Datadog, and if the IT staff notices a problem, they can inquire on the website and the generative AI will provide an appropriate response.

[0135] An example of a prompt to be input to the generative AI model might be something like, "The user wants to 'optimize their remote work environment.' The company information is as follows: Company name: TechStartup, Number of employees: 50, Industry: Technology, Current challenge: To establish secure access from outside the company. Please propose the optimal information technology solution."

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] The user opens a browser and accesses a dedicated website. The user enters company information (company name, number of employees, industry) and current issues (optimizing the remote work environment). The input data here might be, for example, "TechStartup, 50, technology, optimizing the remote work environment." The entered information is sent from the device to the server in JSON format. At this point, the input is the user's company information and issues, and the output is the information sent to the server.

[0139] Step 2:

[0140] The server converts the received information into a format that is easy to parse. Specifically, the server converts JSON-formatted data into a parseable format and passes it to a generative AI model such as OpenAI's GPT-3. For example, converting JSON data into text format. The input of this step is the information sent from the device, and the output is an analysis request to the generative AI model.

[0141] Step 3:

[0142] The generative AI model analyzes the received information and generates the optimal information technology solution. The data calculations performed here are a process of evaluating various solutions based on the user's company information and challenges, and deriving the optimal solution. Specifically, it generates a proposal such as a "cloud-based remote desktop solution." The input to this step is the analysis request passed to the generative AI model, and the output is a proposed solution as the analysis result.

[0143] Step 4:

[0144] The server converts the suggestions received from the generative AI model into HTML format for display to the user. The output is a web page that is displayed in the user's browser. The input to this step is the suggestions from the generative AI model, and the output is an HTML page that is displayed to the user.

[0145] Step 5:

[0146] The user checks the displayed proposals and selects the optimal solution from the multiple options. The solution selected by the user is sent back to the server from the device. The input at this point is the user's selection result, and the output is the selection data sent to the server.

[0147] Step 6:

[0148] The server receives the selection results and automatically generates a deployment plan based on that information. Specifically, it configures instances and provisions services using AWS cloud services or Microsoft Azure. The server runs automated scripts to perform the necessary configuration and installation steps. The input for this step is the user's selection results, and the output is the specific steps in the deployment plan.

[0149] Step 7:

[0150] To monitor the system after the server is deployed, monitoring tools such as Datadog and New Relic are used to monitor the system's performance and usage in real time. The input of this step is data from the monitoring tool, and the output is system status information.

[0151] Step 8:

[0152] When a user has a question or problem with the system, they make an inquiry through the website. The inquiry is sent from the terminal to the server. The input of this step is the inquiry information from the user, and the output is the inquiry data sent to the server.

[0153] Step 9:

[0154] The server sends the query to the generative AI model, requesting analysis and an answer. The generative AI model generates an appropriate answer, and the server notifies the user of the answer. The input to this step is the query information from the user and an analysis request to the generative AI model, and the output is the answer data.

[0155] Step 10:

[0156] The server periodically analyzes system usage and generates additional suggestions and improvements using a generative AI model. It notifies the user of these suggestions and improvements. The input for this step is system usage data, and the output is notifications of suggestions and improvements.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] Operators of small brick-and-mortar stores face challenges such as increasing sales and streamlining inventory management. However, it is difficult for each store to independently find and implement the optimal IT solution, requiring a great deal of time and resources. Furthermore, selecting the right marketing strategy and inventory management system requires specialized knowledge, and many store operators lack the means to solve these challenges. Against this backdrop, there is a need for a system that proposes efficient and optimal IT solutions for brick-and-mortar stores and automates everything from implementation to operational support.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from a generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to inquiries from users; a means for the server to notify the users of the responses; a means for the server to periodically analyze system usage and notify additional suggestions and improvements; a means for collecting information about the operation of a physical store via an application installed on a smartphone and proposing a marketing strategy and an inventory management system based on the analysis results; and a means for sending information input by the user via the application to the generation AI and presenting the analysis results to the store operator. This enables physical store operators to easily introduce optimal IT solutions, improve sales, and streamline inventory management.

[0162] The "means for users to input information" refers to an interface that allows operators of physical stores to input basic information about their stores and the challenges they are currently facing.

[0163] "Means by which the device transmits input information to the server" refers to the communications protocols and infrastructure that allow smartphones and other devices to transmit user-entered information to a central server.

[0164] "Means by which the server receives information and requests analysis from the generation AI" refers to a series of processes in which the server sends the received user information to the generation AI system and requests analysis.

[0165] "Means for the generative AI to propose optimal solutions based on the analysis results" refers to the function in which the generative AI analyzes information entered by the user and generates appropriate IT solutions and operational improvement measures.

[0166] The "means by which the server displays the proposed content to the user" is a mechanism for displaying the solution generated as the analysis result on the user's terminal via the server.

[0167] The "means for the user to select a proposed solution" is an interface that allows the user to select the most suitable solution from the multiple solutions displayed.

[0168] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to a system that formulates an implementation plan based on the solution selected by the user and automatically performs the necessary settings and installation according to that plan.

[0169] "Means for the server to monitor the system after installation and provide ongoing support" is a mechanism that monitors installed systems in real time, analyzes their operational status, and automatically provides the necessary support.

[0170] "Means by which the generation AI generates answers to inquiries from users" refers to a function that analyzes the content of inquiries received from users and automatically generates appropriate answers.

[0171] The "means by which the server notifies the user of the answer" is a mechanism for promptly notifying the user of the generated answer.

[0172] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to a mechanism that periodically analyzes system usage data, automatically generates new suggestions and improvements, and notifies users.

[0173] "A means of collecting information about the operation of physical stores via an application installed on a smartphone and proposing marketing strategies and inventory management systems based on the analysis results" refers to a function that allows physical store operators to input information via a smartphone app and then proposes appropriate marketing strategies and inventory management systems based on that information.

[0174] "A means of sending information entered by the user through an application to a generation AI and presenting the analysis results to the store operator" refers to a system in which information entered via a smartphone app is sent to a generation AI and the analysis results are displayed to the store operator.

[0175] The present invention relates to a system for efficiently providing IT solutions to operators of small brick-and-mortar stores through a smartphone application that utilizes generative AI. The present invention is embodied in the following specific forms.

[0176] System Program

[0177] This system involves a series of processes in which users input information and the generative AI proposes optimal solutions based on that information. Specifically, users use a smartphone application to input the operational status and issues of their physical store, and send that data to a server. The server then requests the generative AI to analyze the received data and proposes appropriate marketing strategies and inventory management systems based on the analysis results.

[0178] Processing Description

[0179] Collecting user information

[0180] The user (physical store operator) first uses a smartphone application to enter information about their store, such as the store name, number of employees, type of business, and current operational challenges (e.g., improving inventory management efficiency, increasing sales, etc.). The hardware used is a smartphone, and the software is a dedicated application (e.g., an application developed with React Native).

[0181] Sending information from the device to the server

[0182] The device (smartphone) sends the entered information to the server. The communication technology and protocol used is HTTP or HTTPS, and communication is carried out with security in mind.

[0183] Data processing on the server

[0184] The server converts the information received from the user into a format that is easy to analyze. This includes processing such as data shaping and filtering. The software used is AWS Lambda and Node.js.

[0185] Analysis and solution proposals using generative AI

[0186] The server sends the formatted data to a generation AI (e.g., GPT-3) for analysis. The generation AI generates the optimal solution based on the user's input data and returns it to the server.

[0187] Display and selection of proposals

[0188] The server converts the analysis results received from the generation AI into a format that is easy for the user to view, and presents them to the user via a smartphone application. The user can then select the optimal solution from the presented solutions.

[0189] Generate and execute an implementation plan

[0190] Based on the selected solution, the server generates a deployment plan and automatically performs the necessary configuration and installation, including cloud-based resource configuration and automatic software installation.

[0191] Customer Success Support

[0192] The server monitors the system in real time after installation, notifying users if any problems occur and providing assistance. It also periodically analyzes system usage and notifies users of additional suggestions and improvements.

[0193] Examples of specific examples and prompts

[0194] For example, let's say a brick-and-mortar store called TechStore wants to streamline inventory management. The operator uses a smartphone application to enter the following information:

[0195] Store name: TechStore

[0196] Number of employees: 15

[0197] Industry: Home appliance sales

[0198] Challenge: Streamlining inventory management

[0199] This prompt is sent to the AI ​​generator, which then proposes the introduction of a barcode inventory management system as the analysis result. The server processes the analysis result and displays it to the user, who can then confirm and select the system, which will then be automatically introduced.

[0200] According to the present invention, operators of physical stores can easily introduce optimal IT solutions and improve operational efficiency, even without specialized knowledge.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] The user launches the smartphone application and enters store information (e.g., store name, number of employees, industry, issues, etc.).

[0204] Input: Store information (store name, number of employees, industry, issues)

[0205] Output: Store information data is saved in the device.

[0206] Step 2:

[0207] The terminal transmits the input store information to the server.

[0208] Input: Store information data stored in the device

[0209] Output: Store information data sent to the server

[0210] Step 3:

[0211] The server converts the received store information data into a format that is easy to analyze.

[0212] Input: Store information data sent to the server

[0213] Output: Store information data converted into a parsable format

[0214] Step 4:

[0215] The server sends analyzable store information data to the generation AI and requests it to analyze it.

[0216] Input: Store information data converted into a parsable format

[0217] Output: Analysis request data sent to the generation AI

[0218] Step 5:

[0219] Generative AI analyzes input data and generates optimal solutions (e.g., marketing strategies, inventory management systems, etc.).

[0220] Input: Analysis request data sent to the generation AI

[0221] Output: Generated optimal solution data

[0222] Step 6:

[0223] The server converts the solution data received from the generated AI into a format that is easy for the user to understand and displays it to the user through a smartphone application.

[0224] Input: Solution data received from the generation AI

[0225] Output: The solution data displayed to the user in a user-friendly format

[0226] Step 7:

[0227] The user selects the best solution from the presented solutions.

[0228] Input: User-friendly solution data displayed

[0229] Output: Selected solution data

[0230] Step 8:

[0231] The server generates a deployment plan based on the selected solution and automatically configures and installs according to that plan.

[0232] Input: Selected solution data

[0233] Output: Automatic configuration and installation steps

[0234] Step 9:

[0235] The server monitors the system in real time after installation, monitoring performance and usage.

[0236] Input: Settings and post-installation operational data

[0237] Output: Monitoring data collected in real time

[0238] Step 10:

[0239] If a user has any questions or problems, they can contact the company through the smartphone application.

[0240] Input: User inquiry

[0241] Output: Query data sent to the server

[0242] Step 11:

[0243] The server sends the query data to the generation AI, requesting analysis and an answer.

[0244] Input: Query data sent to the server

[0245] Output: Analysis request data sent to the generation AI

[0246] Step 12:

[0247] The generation AI analyzes the inquiry and generates an appropriate answer.

[0248] Input: Analysis request data sent to the generation AI

[0249] Output: Generated response data

[0250] Step 13:

[0251] The server notifies the user of the answer data received from the generating AI.

[0252] Input: Answer data received from the generation AI

[0253] Output: Answer data notified to the user

[0254] Step 14:

[0255] The server periodically analyzes system usage and notifies users of additional suggestions and improvements.

[0256] Input: Monitoring and usage data

[0257] Output: Additional suggestions and improvement notification data

[0258] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0259] This invention relates to a system for efficiently providing IT solutions to small businesses and startups. In particular, it is a system that combines generative AI and an emotion engine to recognize user emotions and propose more appropriate solutions. This system collects information from users via a website, and automates the entire process from proposing and implementing optimal IT solutions based on that information and emotions, to ensuring customer success.

[0260] System program and processing flow

[0261] 1. Collecting User Information

[0262] Users access the website and enter their company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, an emotion engine that recognizes the user's emotions is also running, and analyzes the user's emotions from the input information.

[0263] 2. Emotion Recognition by Emotion Engine

[0264] The emotion engine analyzes the user's input information and past usage history to recognize their emotions, which are then taken into consideration when proposing solutions.

[0265] 3. Solution proposal

[0266] The terminal transmits the input information and emotion information to the server.

[0267] The server receives the information and emotion data and requests the generative AI to analyze it. The generative AI generates optimal IT solutions based on the company information, issues, and recognized emotions.

[0268] The suggestions are adjusted based on the analysis results of the emotion engine and are displayed at the most appropriate time and in the most appropriate way for the user.

[0269] 4. Solution Selection and Implementation

[0270] The user selects the best solution from the proposed solutions.

[0271] The terminal transmits the selection result to the server.

[0272] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0273] The server will automatically perform the configuration and installation to complete the solution deployment.

[0274] 5. Supporting Customer Success

[0275] The server monitors the system in real time after installation, monitoring performance and usage.

[0276] If a user has any questions or problems, they can contact us through the website.

[0277] The server sends the query to the generating AI, requesting analysis and an answer.

[0278] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[0279] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0280] Specific examples

[0281] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[0282] 1. Collecting User Information

[0283] An IT professional accesses the website and enters "optimizing the remote work environment" as a task. At this time, the emotion engine recognizes emotions such as stress and anticipation from the IT professional's input.

[0284] 2. Emotion Recognition by Emotion Engine

[0285] The emotion engine analyzes input data and past usage history to recognize "high expectations" and "slight stress."

[0286] 3. Solution proposal

[0287] The device transmits the information and emotion data to the server.

[0288] The server requests the AI ​​to analyze the data, and the AI ​​generates a proposal called a "cloud-based remote desktop solution." The AI ​​uses the results of the emotion engine as a reference and displays the proposal to the user in a way that reduces stress.

[0289] 4. Solution Selection and Implementation

[0290] The IT staff member reviews the proposed solutions and selects one. At this time, the emotion engine also analyzes the staff member's reactions and uses this information in the next proposal.

[0291] The terminal transmits the selection result to the server.

[0292] The server generates an implementation plan based on the selection results and automatically configures and installs the system.

[0293] 5. Supporting Customer Success

[0294] The server monitors the system after installation.

[0295] If IT staff notice a problem, they can contact the website.

[0296] The server requests a query from the generation AI and notifies the person in charge of the appropriate answer.

[0297] The server periodically analyzes the system and proposes improvements, while an emotion engine analyzes the responses of the agents to improve the quality of support.

[0298] In this way, the system of the present invention incorporates the user's emotions to provide optimal IT solutions, and is capable of efficiently carrying out everything from implementation to support, providing an environment in which small businesses can make the most of their resources.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] A user accesses a website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, the input form includes questions about emotions (e.g., feelings about the current issues), and the user also enters answers to those questions.

[0302] Step 2:

[0303] The terminal transmits the input company information, issue, and emotion data to the server.

[0304] Step 3:

[0305] The server receives the information sent from the terminal, stores it in a database, and has the emotion engine analyze the input data.

[0306] Step 4:

[0307] The emotion engine analyzes the user's input information and emotional data to recognize specific emotions such as "stress" or "expectation." The analysis results are stored in an emotion information database.

[0308] Step 5:

[0309] The server provides the received company information and emotional information to the generation AI and requests it to analyze it.

[0310] Step 6:

[0311] Generative AI generates optimal IT solutions based on company information, challenges, and perceived sentiment, for example, proposing multiple "cloud-based remote desktop solutions" or "team collaboration tools."

[0312] Step 7:

[0313] The server adjusts the suggestions received from the generation AI based on the results of the emotion engine and displays them in the most appropriate format and at the most appropriate time for the user. For example, a user feeling stressed will be shown a concise and reassuring explanation.

[0314] Step 8:

[0315] The user selects the best solution from the displayed suggestions.

[0316] Step 9:

[0317] The terminal transmits the user's selection result to the server.

[0318] Step 10:

[0319] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0320] Step 11:

[0321] The server will automatically perform the configuration and installation to complete the solution deployment.

[0322] Step 12:

[0323] The server monitors the system in real time after installation, monitoring performance and usage.

[0324] Step 13:

[0325] If a user has a problem with the system or has a question, they can contact the system via the website.

[0326] Step 14:

[0327] The server receives an inquiry from the user and requests the generation AI to analyze it.

[0328] Step 15:

[0329] The generation AI analyzes the inquiry and generates an appropriate answer.

[0330] Step 16:

[0331] The server sends the answer from the generated AI to the user and displays it.

[0332] Step 17:

[0333] The server periodically analyzes system usage and, if necessary, provides additional suggestions or improvement measures. At this time, the emotion engine analyzes the user's reactions and uses them to improve the next suggestions and responses.

[0334] In this way, at each step, the server, terminal, emotion engine, generative AI, and user work together to realize a system that efficiently introduces and supports IT solutions while reflecting the user's emotions.

[0335] Example 2

[0336] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] Conventional IT solution proposal systems rarely provide appropriate proposals that take user emotions into consideration, making it difficult to select and implement efficient solutions. Furthermore, there was also the issue of insufficient ongoing support after implementation, making it difficult to improve user satisfaction.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means including an emotion engine that analyzes user input information and generates emotion data, means for reflecting the emotion data generated by the emotion engine in a solution proposal, and means for requesting analysis from a generative AI model. This makes it possible to propose an optimal solution that takes the user's emotions into consideration, thereby realizing efficient implementation and continuous support.

[0339] "User" refers to an end user who uses the system to input information and propose or select solutions.

[0340] A "terminal" is a computing device that a user uses to enter information and send information to a server.

[0341] A "server" is a computing device that receives information sent by users, analyzes it, proposes solutions, and monitors the system.

[0342] A "generative AI model" is an artificial intelligence algorithm that analyzes input information from users and generates optimal solutions.

[0343] The "emotion engine" is a system component that analyzes user input information and generates user emotion data.

[0344] "Emotion data" is data that indicates the user's emotional state, generated by the emotion engine.

[0345] A "solution" is a technical or operational proposal that is suitable for solving a user's problem.

[0346] An "implementation plan" is a document that outlines the specific steps for implementing the selected solution.

[0347] "Continuous support" is a service that provides regular monitoring and suggests improvements to ensure that the implemented solution functions properly.

[0348] An "inquiry" is an action in which a user reports a question or problem to the system and requests a solution.

[0349] A "prompt sentence" is a sentence used as input to a generative AI model, and includes analysis instructions and questions.

[0350] This invention is a system that efficiently provides IT solutions that take user emotions into consideration for small businesses and startups. The system begins when a user accesses the system using a web browser and enters information. The server, terminal, and generative AI model work together to analyze the user's input, propose appropriate solutions, and consistently automate the process from implementation to support.

[0351] When entering information, users use a web browser (e.g., Google Chrome or Firefox) to access a website built with HTML, CSS, and JavaScript. Users enter their company information (company name, number of employees, industry) and current challenges (e.g., optimizing the remote work environment) into a form. The input data is sent in real time to an emotion engine (e.g., Azure Emotion API), which analyzes the user's emotions.

[0352] The emotion engine uses NLP technology to analyze the input text data and generate emotion data (e.g., "expectation" or "stress"). The generated emotion data is sent from the device to a server. The server receives the company information, issues, and emotion data sent by the user, and sends the data to a generative AI model (e.g., OpenAI's GPT-3) for analysis.

[0353] The generative AI model analyzes the received prompt (e.g., "A software development company with 50 employees wants to optimize its remote work environment. The user's emotions are 'high expectations' and 'slight stress'. Please propose the optimal IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution"). At this time, the model also takes into account the results of the emotion engine and adjusts the proposal content to use the most appropriate wording for the user.

[0354] The server displays the generated solution proposals to the user, who then selects the optimal solution. The selection results are sent from the terminal to the server, which then generates a specific deployment plan. The plan includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts). The server then executes the automated scripts to complete the deployment of the solution.

[0355] After deployment, the server uses agent software (e.g., Prometheus) to monitor the system in real time. When a user inquires about a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer. The generative AI model generates an appropriate answer, which the server notifies the user. The server also periodically analyzes system usage, evaluates user reactions using an emotion engine, and appropriately notifies users of additional suggestions and improvements.

[0356] In this way, by efficiently and automatically providing optimal IT solutions that take user emotions into consideration and providing consistent support from implementation to support, we provide an environment where small companies and startups can make the most of their resources.

[0357] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0358] Step 1: Gather user information

[0359] The user accesses the system's website using a web browser (e.g., Google Chrome or Firefox) and enters data into a form to enter company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0360] Input data is sent to the emotion engine in real time. The input is text data entered by the user, and the output is emotion data.

[0361] Step 2: Emotion recognition by the emotion engine

[0362] The emotion engine analyzes the received text data using natural language processing (NLP) technology to extract the user's emotions.

[0363] The extracted emotional data (e.g., "expectation" or "stress") is returned to the device and then transmitted from the device to the server.

[0364] The input is text data entered by the user, and the output is generated emotion data.

[0365] Step 3: Propose a solution

[0366] The server receives the company information, the task, and the emotion data transmitted from the terminal.

[0367] The server sends the received data to the generative AI model and requests it to analyze it.

[0368] The generative AI model analyzes prompt statements (e.g., "A software development company with 50 employees wants to optimize its remote work environment. User emotions are 'high expectations' and 'slight stress'. Please suggest the best IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution").

[0369] The inputs are company information, challenges, and sentiment data, and the output is the generated solution proposal.

[0370] Step 4: Select a solution

[0371] Users can review the proposed solutions through a web interface and select the most suitable one.

[0372] The selection result is sent from the terminal to the server.

[0373] The input is the generated solution proposal and the output is the user-selected solution.

[0374] Step 5: Generate and execute a deployment plan

[0375] The server generates a specific deployment plan based on the selection results, which includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts).

[0376] The server runs automated scripts to automate configuration and installation tasks and complete the solution deployment.

[0377] The input is the user-selected solution, and the output is the completed configuration and installation using an automated script.

[0378] Step 6: Post-implementation system monitoring and support

[0379] The server uses agent software (e.g., Prometheus) to monitor the deployed system in real time, collecting and analyzing performance data and usage.

[0380] When a user submits a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer.

[0381] The generative AI model generates an appropriate answer, which the server notifies the user.

[0382] It regularly analyzes system usage, evaluates user reactions using an emotion engine, and provides additional suggestions and improvements as needed.

[0383] The inputs are system performance data and user queries, and the outputs are analysis results and answers from the generative AI model, as well as additional suggestions and improvements.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] It is difficult for small companies and startups to efficiently implement IT solutions while maximizing their resources, and there is no system in place to accurately recognize the emotions of passengers in autonomous vehicles and respond appropriately to ensure a comfortable experience. This leads to problems such as reduced passenger comfort and safety, and makes it difficult for companies to carry out their business efficiently.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0388] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to user inquiries; a means for the server to notify the user of the responses; a means for the server to periodically analyze system usage and notify the user of additional suggestions or improvements; a means for an emotion engine to analyze user emotions; a means for collecting passenger emotions using a camera and a microphone inside the autonomous vehicle; a means for the server to send passenger emotion data to the generation AI and generate a response; and a means for executing the generated response and customizing the in-vehicle environment and information provision. This enables autonomous vehicles to recognize passenger emotions in real time and provide a comfortable and safe environment.

[0389] "Means for users to input information" refers to the interface or device that users use to input their own information or tasks into the terminal.

[0390] The "means for transmitting information input by the terminal to the server" refers to a system or device for transmitting information input by the user to the server via a network.

[0391] "Means by which the server receives information and requests the generation AI to analyze it" refers to the process by which the server sends the information it receives to the generation AI and requests it to analyze it.

[0392] "Means by which generative AI proposes optimal solutions based on analysis results" refers to the process by which generative AI proposes IT solutions suitable for users based on the data analyzed.

[0393] "Means by which the server displays the proposal content to the user" refers to the means by which the server presents the proposal content obtained from the generation AI to the user in an easy-to-understand manner.

[0394] "Means for users to select proposed solutions" refers to the interface or process by which users select the most appropriate solution from the presented solutions.

[0395] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to the process by which the server creates a specific implementation plan based on the user's selection and configures and implements the system accordingly.

[0396] "Means for the server to monitor the system after implementation and provide ongoing support" refers to the process of monitoring whether the implemented system is operating properly and providing support or improvements as necessary.

[0397] "Means by which the generative AI generates answers to user inquiries" refers to the process by which the AI ​​receives and analyzes the content of the user's inquiry and generates an appropriate answer.

[0398] "Means for the server to notify the user of the answer" refers to the means by which the generation AI communicates the answer provided to the user.

[0399] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to the process of periodically analyzing system usage data and notifying users of new suggestions and improvements.

[0400] The "means by which the emotion engine analyzes the user's emotions" is an engine for analyzing the user's emotions from input information and past usage history.

[0401] "Means for collecting passenger emotions using cameras and microphones inside an autonomous vehicle" is a system that collects passengers' facial expressions and voices using cameras and microphones installed inside the vehicle.

[0402] "Means for the server to send passenger emotional data to the generation AI and generate a response" refers to the process of sending collected emotional data to the generation AI and generating an appropriate response.

[0403] "Means for executing the generated response and customizing the in-car environment and information provision" refers to means for adjusting the in-car environment and information provision based on the response content created by the generation AI.

[0404] This invention is a system that combines an emotion engine and generative AI to recognize user emotions and provide optimal solutions based on them. This system is primarily designed for small businesses and startups, but as an application example, we will introduce a passenger emotion response system in an autonomous vehicle.

[0405] In this embodiment, the system operates in the following manner.

[0406] 1. Hardware and Software

[0407] Hardware

[0408] Camera (to recognize passengers' facial expressions)

[0409] Microphone (to collect passenger voices)

[0410] Computer inside the vehicle (for data processing)

[0411] software

[0412] Emotion Engine

[0413] Generative AI (e.g., GPT-4)

[0414] Data analysis tools (e.g., TensorFlow)

[0415] Voice recognition software (e.g., Google Speech-to-Text)

[0416] 2. Data Collection

[0417] Using cameras and microphones inside the vehicle, facial expressions and voices of passengers are collected in real time, and the collected data is important for identifying passenger emotions.

[0418] 3. Emotion recognition

[0419] The collected data is sent to an Emotion Engine to analyze the passenger's emotions (e.g., stress, anxiety, joy, etc.), which allows the passenger's current emotional state to be identified.

[0420] 4. Interpretation and response generation by generative AI

[0421] The recognized emotion data is fed into generative AI, which then generates appropriate responses and suggestions accordingly – for example, if a passenger is feeling stressed, it will suggest playing relaxing music.

[0422] 5. Response Execution

[0423] Based on the generated responses, the in-car environment and information provision can be customized, for example by adjusting the lighting and temperature or playing relaxing music.

[0424] 6. Feedback and Improvement

[0425] The passenger's reaction to the generated response is monitored again using a camera and microphone, and the results are fed back to the generating AI to improve the quality of the next response.

[0426] Specific examples

[0427] For example, consider a case where a passenger feels stressed in a self-driving vehicle. Cameras and microphones capture the passenger's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine detects "stress" and sends the data to the generative AI. The generative AI then generates suggestions to reduce the "sense of stress" and instructs the car to play relaxing music. This entire process is automated.

[0428] Prompt Sentence Examples

[0429] For example, the prompt text to be input to the generative AI model is:

[0430] Based on the passenger's emotion analysis, generate an appropriate response to reduce the passenger's stress. For example, suggest playing relaxing music. Also consider the passenger's other emotions (happiness, anxiety, etc.) to generate the optimal response.

[0431] In this way, the system can improve passenger comfort and safety by combining emotion recognition and generative AI. The same method can also be applied to providing IT solutions to businesses, making optimal suggestions based on user emotions.

[0432] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0433] Step 1:

[0434] Data collection

[0435] The server uses cameras and microphones inside the autonomous vehicle to collect passenger facial expressions and voices in real time. The camera captures the passenger's facial features, and the microphone records the passenger's voice, forming a dataset that reflects the passenger's current emotions.

[0436] Input: Camera video data, audio data

[0437] Output: Collected facial expression and voice data

[0438] Step 2:

[0439] emotion recognition

[0440] The server sends the collected facial and voice data to the emotion engine for analysis. The emotion engine uses machine learning algorithms to identify passenger emotions (stress, anxiety, joy, etc.) from the collected data, and outputs the passenger's emotional state as numerical data.

[0441] Input: facial expression data, voice data

[0442] Output: Emotion recognition data (e.g., stress = 0.8, joy = 0.2)

[0443] Step 3:

[0444] Interpretation and response generation by generative AI

[0445] The server sends the emotion recognition data from the emotion engine to the generation AI, which then generates appropriate responses and suggestions based on the passenger's emotions. For example, if stress levels are high, it can suggest relaxing music. The generation AI generates responses using prompt sentences based on past data and the analysis results of the emotion engine.

[0446] Input: Emotion recognition data

[0447] Output: Response data (e.g., music playback instruction)

[0448] Step 4:

[0449] Response execution

[0450] The server customizes the in-car environment and information provision based on the response data generated by the AI. Specifically, this includes music playback, lighting and temperature control in the car, etc. In this step, the server works in conjunction with the vehicle's internal control system to execute the instructions.

[0451] Input: Response data

[0452] Output: Changes made to the car environment (e.g., music playback, temperature adjustment)

[0453] Step 5:

[0454] Feedback and Improvements

[0455] After the server responds, it again monitors passenger reactions using cameras and microphones and feeds the resulting data back to the generative AI and emotion engine, improving the quality of the next response and further enhancing passenger comfort and safety.

[0456] Input: Passenger response data

[0457] Output: Feedback data (used for next analysis and response generation)

[0458] In this way, by processing and analyzing input data at each step and obtaining optimal output, a system is created that responds appropriately based on the passenger's emotional state, thereby improving passenger comfort and safety and enabling efficient service provision.

[0459] 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.

[0460] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0461] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 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.

[0465] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0466] 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.

[0467] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0468] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0469] 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.

[0470] 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.

[0471] 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 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.

[0472] 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.

[0473] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0474] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0475] This invention is a system that efficiently provides IT solutions to small businesses and startups. Specifically, it collects information from users through a website that utilizes generative AI, and then automates the entire process of proposing and implementing optimal IT solutions based on that information, all the way through to customer success.

[0476] System program and processing flow

[0477] 1. Collecting User Information

[0478] Users access the website and enter company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0479] The terminal transmits the input information to the server.

[0480] 2. Solution proposal

[0481] The server receives the information sent from the device and requests the generating AI to analyze it.

[0482] The generative AI analyzes the information input by the user and proposes the optimal IT solution (e.g., a cloud-based remote desktop solution).

[0483] The server displays this proposal to the user, who then confirms the details.

[0484] 3. Solution Selection and Implementation

[0485] The user selects the best solution from the proposed solutions.

[0486] The terminal transmits the selection result to the server.

[0487] The server generates an implementation plan based on the selection results and automatically executes specific configuration and installation procedures.

[0488] 4. Supporting Customer Success

[0489] The server monitors the system in real time after installation, monitoring performance and usage.

[0490] If users have any questions or problems, they can contact us through the website.

[0491] The server sends the query to the generation AI and requests analysis.

[0492] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[0493] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0494] Specific examples

[0495] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[0496] 1. Collecting User Information

[0497] An IT professional at TechStartup visits the website and enters "Optimizing the remote work environment" as an assignment.

[0498] The terminal transmits the information to the server.

[0499] 2. Solution proposal

[0500] The server receives the information and asks the generating AI to analyze it.

[0501] The generative AI generates a proposal called a "cloud-based remote desktop solution" and sends it back to the server.

[0502] The server displays this suggestion to the IT staff.

[0503] 3. Solution Selection and Implementation

[0504] The IT staff reviews the proposed solutions and selects one.

[0505] The terminal transmits the selection result to the server.

[0506] The server generates a deployment plan and automatically configures and installs the software.

[0507] 4. Supporting Customer Success

[0508] The server monitors the system in real time after installation.

[0509] If IT staff notice a problem, they can contact the website.

[0510] The server sends the query to the generating AI, requesting analysis and an answer.

[0511] The generation AI generates an answer, and the server notifies the person in charge.

[0512] The server periodically analyzes system usage and suggests improvements.

[0513] In this way, the system of the present invention provides an environment in which users can easily introduce IT solutions and also automates post-operation support, thereby helping small businesses efficiently develop and operate their IT infrastructure.

[0514] The processing flow will be explained below.

[0515] Step 1:

[0516] The user accesses the website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0517] Step 2:

[0518] The terminal transmits the input information to the server.

[0519] Step 3:

[0520] The server receives the information sent from the terminal and stores it in a database.

[0521] Step 4:

[0522] The server converts the received data into a format that is easy for the generation AI to analyze, and requests the generation AI to analyze it.

[0523] Step 5:

[0524] Generative AI analyzes company information and issues, and generates multiple optimal IT solutions.

[0525] Step 6:

[0526] The generation AI sends the generated proposals to the server.

[0527] Step 7:

[0528] The server converts the proposed content into a format that is easy for the user to view, and transmits it to the terminal for display.

[0529] Step 8:

[0530] The user selects the best solution from the proposed solutions.

[0531] Step 9:

[0532] The terminal transmits the user's selection result to the server.

[0533] Step 10:

[0534] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0535] Step 11:

[0536] The server will automatically perform the configuration and installation to complete the solution deployment.

[0537] Step 12:

[0538] The server monitors the system in real time after installation, monitoring performance and usage.

[0539] Step 13:

[0540] If a user has any questions or problems, they can contact us through the website.

[0541] Step 14:

[0542] The server receives an inquiry from the user and requests the generation AI to analyze it.

[0543] Step 15:

[0544] The generation AI analyzes the inquiry and generates an appropriate answer.

[0545] Step 16:

[0546] The server sends the answer from the generated AI to the user and displays it.

[0547] Step 17:

[0548] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0549] In this way, servers, terminals, and users work together at each step to efficiently realize the entire process from proposing IT solutions to implementation and customer success.

[0550] Example 1

[0551] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0552] For today's small businesses and startups, the efficient implementation and operation of information technology solutions is crucial. However, these businesses often have limited technical resources and expertise, and therefore spend a great deal of time and money selecting, implementing, and supporting the appropriate solutions. Therefore, there is a need for a system that can simplify and automate these processes.

[0553] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0554] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generative artificial intelligence model; a means for the generative AI model to propose an optimal information technology solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the terminal to send the selection results to the server; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system in real time after implementation and provide ongoing support; a means for the generative artificial intelligence model to generate answers to inquiries from the user; a means for the server to notify the user of the answers; and a means for the server to periodically analyze system usage and notify the user of additional suggestions and improvements, thereby enabling users to easily select, implement, and operate appropriate information technology solutions.

[0555] "User" refers to a company or individual who uses the system.

[0556] A "terminal" refers to a computer device operated by a user, and is a means for inputting information and transmitting it to a server.

[0557] "Server" refers to a central computer system that processes information sent from users and devices and works with generative AI models to propose and implement solutions.

[0558] "Generative artificial intelligence model" refers to an AI system that analyzes information provided by users and generates optimal information technology solutions.

[0559] "Information" refers to company information and current issues that users input into the system.

[0560] "Analysis" refers to the process by which a generative artificial intelligence model derives the optimal solution based on information provided by the user.

[0561] "Proposal" refers to the optimal information technology solution generated by the generative artificial intelligence model based on the analysis results.

[0562] "Implementation Plan" means a document describing the configuration and installation steps required to implement the proposed Solution.

[0563] "Monitoring" refers to the process of monitoring a system's performance and usage in real time after implementation.

[0564] "Inquiry" refers to a question or problem report that a user makes to the system.

[0565] "Answer" refers to the appropriate solution or information generated by a generative artificial intelligence model in response to a query.

[0566] "Usage status" refers to the performance and operational status of the system after its implementation, and refers to information that is periodically analyzed by the server.

[0567] This invention is a system for efficiently providing information technology solutions to small businesses and startups. Specifically, it collects information from users through a website using a generative artificial intelligence model, and then automates the entire process of proposing and implementing optimal information technology solutions based on that information, leading to customer success.

[0568] First, the user accesses a dedicated website via a browser. The user enters company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). To do this, the user uses a device such as a PC, tablet, or smartphone. The device then sends the entered information to the server in JSON format.

[0569] The server receives the information sent from the device and requests a generative artificial intelligence model, such as OpenAI's GPT-3, to analyze the data. At this time, the server converts the collected information into a format that is easy for the generative artificial intelligence model to analyze. The generative artificial intelligence model generates an appropriate information technology solution based on the user's input information. For example, a "cloud-based remote desktop solution" may be proposed. The server then converts the generated analysis results into HTML format and displays them on a website in a user-friendly format.

[0570] Next, the user reviews the displayed proposals and selects the optimal solution from multiple options. The selection results are then sent back to the server from the device. The server then automatically generates a deployment plan based on the selection results, and performs instance configuration and service provisioning using, for example, AWS cloud services or Microsoft Azure. Specific configuration and installation procedures are performed by automated scripts.

[0571] After implementation, the server uses monitoring tools such as Datadog and New Relic to monitor system performance and usage in real time. When users have questions or problems, they can make inquiries via the website. The server sends the inquiry to the generative AI model for analysis and response. The generative AI model generates an appropriate response, and the server notifies the user of the response. In addition, the server regularly analyzes system usage and uses the generative AI model to generate additional suggestions and improvements, which are then notified to the user.

[0572] As a concrete example, consider the case where the company TechStartup wants to implement a new remote work environment. The IT staff at TechStartup accesses the website and inputs the challenge of "optimizing the remote work environment." The generative AI then suggests a "cloud-based remote desktop solution," and the IT staff selects this solution. The server configures the AWS cloud-based remote desktop and automatically installs it. After implementation, the system is monitored using Datadog, and if the IT staff notices a problem, they can inquire on the website and the generative AI will provide an appropriate response.

[0573] An example of a prompt to be input to the generative AI model might be something like, "The user wants to 'optimize their remote work environment.' The company information is as follows: Company name: TechStartup, Number of employees: 50, Industry: Technology, Current challenge: To establish secure access from outside the company. Please propose the optimal information technology solution."

[0574] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0575] Step 1:

[0576] The user opens a browser and accesses a dedicated website. The user enters company information (company name, number of employees, industry) and current issues (optimizing the remote work environment). The input data here might be, for example, "TechStartup, 50, technology, optimizing the remote work environment." The entered information is sent from the device to the server in JSON format. At this point, the input is the user's company information and issues, and the output is the information sent to the server.

[0577] Step 2:

[0578] The server converts the received information into a format that is easy to parse. Specifically, the server converts JSON-formatted data into a parseable format and passes it to a generative AI model such as OpenAI's GPT-3. For example, converting JSON data into text format. The input of this step is the information sent from the device, and the output is an analysis request to the generative AI model.

[0579] Step 3:

[0580] The generative AI model analyzes the received information and generates the optimal information technology solution. The data calculations performed here are a process of evaluating various solutions based on the user's company information and challenges, and deriving the optimal solution. Specifically, it generates a proposal such as a "cloud-based remote desktop solution." The input to this step is the analysis request passed to the generative AI model, and the output is a proposed solution as the analysis result.

[0581] Step 4:

[0582] The server converts the suggestions received from the generative AI model into HTML format for display to the user. The output is a web page that is displayed in the user's browser. The input to this step is the suggestions from the generative AI model, and the output is an HTML page that is displayed to the user.

[0583] Step 5:

[0584] The user checks the displayed proposals and selects the optimal solution from the multiple options. The solution selected by the user is sent back to the server from the device. The input at this point is the user's selection result, and the output is the selection data sent to the server.

[0585] Step 6:

[0586] The server receives the selection results and automatically generates a deployment plan based on that information. Specifically, it configures instances and provisions services using AWS cloud services or Microsoft Azure. The server runs automated scripts to perform the necessary configuration and installation steps. The input for this step is the user's selection results, and the output is the specific steps in the deployment plan.

[0587] Step 7:

[0588] To monitor the system after the server is deployed, monitoring tools such as Datadog and New Relic are used to monitor the system's performance and usage in real time. The input of this step is data from the monitoring tool, and the output is system status information.

[0589] Step 8:

[0590] When a user has a question or problem with the system, they make an inquiry through the website. The inquiry is sent from the terminal to the server. The input of this step is the inquiry information from the user, and the output is the inquiry data sent to the server.

[0591] Step 9:

[0592] The server sends the query to the generative AI model, requesting analysis and an answer. The generative AI model generates an appropriate answer, and the server notifies the user of the answer. The input to this step is the query information from the user and an analysis request to the generative AI model, and the output is the answer data.

[0593] Step 10:

[0594] The server periodically analyzes system usage and generates additional suggestions and improvements using a generative AI model. It notifies the user of these suggestions and improvements. The input for this step is system usage data, and the output is notifications of suggestions and improvements.

[0595] (Application example 1)

[0596] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0597] Operators of small brick-and-mortar stores face challenges such as increasing sales and streamlining inventory management. However, it is difficult for each store to independently find and implement the optimal IT solution, requiring a great deal of time and resources. Furthermore, selecting the right marketing strategy and inventory management system requires specialized knowledge, and many store operators lack the means to solve these challenges. Against this backdrop, there is a need for a system that proposes efficient and optimal IT solutions for brick-and-mortar stores and automates everything from implementation to operational support.

[0598] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0599] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from a generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to inquiries from users; a means for the server to notify the users of the responses; a means for the server to periodically analyze system usage and notify additional suggestions and improvements; a means for collecting information about the operation of a physical store via an application installed on a smartphone and proposing a marketing strategy and an inventory management system based on the analysis results; and a means for sending information input by the user via the application to the generation AI and presenting the analysis results to the store operator. This enables physical store operators to easily introduce optimal IT solutions, improve sales, and streamline inventory management.

[0600] The "means for users to input information" refers to an interface that allows operators of physical stores to input basic information about their stores and the challenges they are currently facing.

[0601] "Means by which the device transmits input information to the server" refers to the communications protocols and infrastructure that allow smartphones and other devices to transmit user-entered information to a central server.

[0602] "Means by which the server receives information and requests analysis from the generation AI" refers to a series of processes in which the server sends the received user information to the generation AI system and requests analysis.

[0603] "Means for the generative AI to propose optimal solutions based on the analysis results" refers to the function in which the generative AI analyzes information entered by the user and generates appropriate IT solutions and operational improvement measures.

[0604] The "means by which the server displays the proposed content to the user" is a mechanism for displaying the solution generated as the analysis result on the user's terminal via the server.

[0605] The "means for the user to select a proposed solution" is an interface that allows the user to select the most suitable solution from the multiple solutions displayed.

[0606] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to a system that formulates an implementation plan based on the solution selected by the user and automatically performs the necessary settings and installation according to that plan.

[0607] "Means for the server to monitor the system after installation and provide ongoing support" is a mechanism that monitors installed systems in real time, analyzes their operational status, and automatically provides the necessary support.

[0608] "Means by which the generation AI generates answers to inquiries from users" refers to a function that analyzes the content of inquiries received from users and automatically generates appropriate answers.

[0609] The "means by which the server notifies the user of the answer" is a mechanism for promptly notifying the user of the generated answer.

[0610] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to a mechanism that periodically analyzes system usage data, automatically generates new suggestions and improvements, and notifies users.

[0611] "A means of collecting information about the operation of physical stores via an application installed on a smartphone and proposing marketing strategies and inventory management systems based on the analysis results" refers to a function that allows physical store operators to input information via a smartphone app and then proposes appropriate marketing strategies and inventory management systems based on that information.

[0612] "A means of sending information entered by the user through an application to a generation AI and presenting the analysis results to the store operator" refers to a system in which information entered via a smartphone app is sent to a generation AI and the analysis results are displayed to the store operator.

[0613] The present invention relates to a system for efficiently providing IT solutions to operators of small brick-and-mortar stores through a smartphone application that utilizes generative AI. The present invention is embodied in the following specific forms.

[0614] System Program

[0615] This system involves a series of processes in which users input information and the generative AI proposes optimal solutions based on that information. Specifically, users use a smartphone application to input the operational status and issues of their physical store, and send that data to a server. The server then requests the generative AI to analyze the received data and proposes appropriate marketing strategies and inventory management systems based on the analysis results.

[0616] Processing Description

[0617] Collecting user information

[0618] The user (physical store operator) first uses a smartphone application to enter information about their store, such as the store name, number of employees, type of business, and current operational challenges (e.g., improving inventory management efficiency, increasing sales, etc.). The hardware used is a smartphone, and the software is a dedicated application (e.g., an application developed with React Native).

[0619] Sending information from the device to the server

[0620] The device (smartphone) sends the entered information to the server. The communication technology and protocol used is HTTP or HTTPS, and communication is carried out with security in mind.

[0621] Data processing on the server

[0622] The server converts the information received from the user into a format that is easy to analyze. This includes processing such as data shaping and filtering. The software used is AWS Lambda and Node.js.

[0623] Analysis and solution proposals using generative AI

[0624] The server sends the formatted data to a generation AI (e.g., GPT-3) for analysis. The generation AI generates the optimal solution based on the user's input data and returns it to the server.

[0625] Display and selection of proposals

[0626] The server converts the analysis results received from the generation AI into a format that is easy for the user to view, and presents them to the user via a smartphone application. The user can then select the optimal solution from the presented solutions.

[0627] Generate and execute an implementation plan

[0628] Based on the selected solution, the server generates a deployment plan and automatically performs the necessary configuration and installation, including cloud-based resource configuration and automatic software installation.

[0629] Customer Success Support

[0630] The server monitors the system in real time after installation, notifying users if any problems occur and providing assistance. It also periodically analyzes system usage and notifies users of additional suggestions and improvements.

[0631] Examples of specific examples and prompts

[0632] For example, let's say a brick-and-mortar store called TechStore wants to streamline inventory management. The operator uses a smartphone application to enter the following information:

[0633] Store name: TechStore

[0634] Number of employees: 15

[0635] Industry: Home appliance sales

[0636] Challenge: Streamlining inventory management

[0637] This prompt is sent to the AI ​​generator, which then proposes the introduction of a barcode inventory management system as the analysis result. The server processes the analysis result and displays it to the user, who can then confirm and select the system, which will then be automatically introduced.

[0638] According to the present invention, operators of physical stores can easily introduce optimal IT solutions and improve operational efficiency, even without specialized knowledge.

[0639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0640] Step 1:

[0641] The user launches the smartphone application and enters store information (e.g., store name, number of employees, industry, issues, etc.).

[0642] Input: Store information (store name, number of employees, industry, issues)

[0643] Output: Store information data is saved in the device.

[0644] Step 2:

[0645] The terminal transmits the input store information to the server.

[0646] Input: Store information data stored in the device

[0647] Output: Store information data sent to the server

[0648] Step 3:

[0649] The server converts the received store information data into a format that is easy to analyze.

[0650] Input: Store information data sent to the server

[0651] Output: Store information data converted into a parsable format

[0652] Step 4:

[0653] The server sends analyzable store information data to the generation AI and requests it to analyze it.

[0654] Input: Store information data converted into a parsable format

[0655] Output: Analysis request data sent to the generation AI

[0656] Step 5:

[0657] Generative AI analyzes input data and generates optimal solutions (e.g., marketing strategies, inventory management systems, etc.).

[0658] Input: Analysis request data sent to the generation AI

[0659] Output: Generated optimal solution data

[0660] Step 6:

[0661] The server converts the solution data received from the generated AI into a format that is easy for the user to understand and displays it to the user through a smartphone application.

[0662] Input: Solution data received from the generation AI

[0663] Output: The solution data displayed to the user in a user-friendly format

[0664] Step 7:

[0665] The user selects the best solution from the presented solutions.

[0666] Input: User-friendly solution data displayed

[0667] Output: Selected solution data

[0668] Step 8:

[0669] The server generates a deployment plan based on the selected solution and automatically configures and installs according to that plan.

[0670] Input: Selected solution data

[0671] Output: Automatic configuration and installation steps

[0672] Step 9:

[0673] The server monitors the system in real time after installation, monitoring performance and usage.

[0674] Input: Settings and post-installation operational data

[0675] Output: Monitoring data collected in real time

[0676] Step 10:

[0677] If a user has any questions or problems, they can contact the company through the smartphone application.

[0678] Input: User inquiry

[0679] Output: Query data sent to the server

[0680] Step 11:

[0681] The server sends the query data to the generation AI, requesting analysis and an answer.

[0682] Input: Query data sent to the server

[0683] Output: Analysis request data sent to the generation AI

[0684] Step 12:

[0685] The generation AI analyzes the inquiry and generates an appropriate answer.

[0686] Input: Analysis request data sent to the generation AI

[0687] Output: Generated response data

[0688] Step 13:

[0689] The server notifies the user of the answer data received from the generating AI.

[0690] Input: Answer data received from the generation AI

[0691] Output: Answer data notified to the user

[0692] Step 14:

[0693] The server periodically analyzes system usage and notifies users of additional suggestions and improvements.

[0694] Input: Monitoring and usage data

[0695] Output: Additional suggestions and improvement notification data

[0696] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0697] This invention relates to a system for efficiently providing IT solutions to small businesses and startups. In particular, it is a system that combines generative AI and an emotion engine to recognize user emotions and propose more appropriate solutions. This system collects information from users via a website, and automates the entire process from proposing and implementing optimal IT solutions based on that information and emotions, to ensuring customer success.

[0698] System program and processing flow

[0699] 1. Collecting User Information

[0700] Users access the website and enter their company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, an emotion engine that recognizes the user's emotions is also running, and analyzes the user's emotions from the input information.

[0701] 2. Emotion Recognition by Emotion Engine

[0702] The emotion engine analyzes the user's input information and past usage history to recognize their emotions, which are then taken into consideration when proposing solutions.

[0703] 3. Solution proposal

[0704] The terminal transmits the input information and emotion information to the server.

[0705] The server receives the information and emotion data and requests the generative AI to analyze it. The generative AI generates optimal IT solutions based on the company information, issues, and recognized emotions.

[0706] The suggestions are adjusted based on the analysis results of the emotion engine and are displayed at the most appropriate time and in the most appropriate way for the user.

[0707] 4. Solution Selection and Implementation

[0708] The user selects the best solution from the proposed solutions.

[0709] The terminal transmits the selection result to the server.

[0710] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0711] The server will automatically perform the configuration and installation to complete the solution deployment.

[0712] 5. Supporting Customer Success

[0713] The server monitors the system in real time after installation, monitoring performance and usage.

[0714] If a user has any questions or problems, they can contact us through the website.

[0715] The server sends the query to the generating AI, requesting analysis and an answer.

[0716] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[0717] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0718] Specific examples

[0719] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[0720] 1. Collecting User Information

[0721] An IT professional accesses the website and enters "optimizing the remote work environment" as a task. At this time, the emotion engine recognizes emotions such as stress and anticipation from the IT professional's input.

[0722] 2. Emotion Recognition by Emotion Engine

[0723] The emotion engine analyzes input data and past usage history to recognize "high expectations" and "slight stress."

[0724] 3. Solution proposal

[0725] The device transmits the information and emotion data to the server.

[0726] The server requests the AI ​​to analyze the data, and the AI ​​generates a proposal called a "cloud-based remote desktop solution." The AI ​​uses the results of the emotion engine as a reference and displays the proposal to the user in a way that reduces stress.

[0727] 4. Solution Selection and Implementation

[0728] The IT staff member reviews the proposed solutions and selects one. At this time, the emotion engine also analyzes the staff member's reactions and uses this information in the next proposal.

[0729] The terminal transmits the selection result to the server.

[0730] The server generates an implementation plan based on the selection results and automatically configures and installs the system.

[0731] 5. Supporting Customer Success

[0732] The server monitors the system after installation.

[0733] If IT staff notice a problem, they can contact the website.

[0734] The server requests a query from the generation AI and notifies the person in charge of the appropriate answer.

[0735] The server periodically analyzes the system and proposes improvements, while an emotion engine analyzes the responses of the agents to improve the quality of support.

[0736] In this way, the system of the present invention incorporates the user's emotions to provide optimal IT solutions, and is capable of efficiently carrying out everything from implementation to support, providing an environment in which small businesses can make the most of their resources.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] A user accesses a website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, the input form includes questions about emotions (e.g., feelings about the current issues), and the user also enters answers to those questions.

[0740] Step 2:

[0741] The terminal transmits the input company information, issue, and emotion data to the server.

[0742] Step 3:

[0743] The server receives the information sent from the terminal, stores it in a database, and has the emotion engine analyze the input data.

[0744] Step 4:

[0745] The emotion engine analyzes the user's input information and emotional data to recognize specific emotions such as "stress" or "expectation." The analysis results are stored in an emotion information database.

[0746] Step 5:

[0747] The server provides the received company information and emotional information to the generation AI and requests it to analyze it.

[0748] Step 6:

[0749] Generative AI generates optimal IT solutions based on company information, challenges, and perceived sentiment, for example, proposing multiple "cloud-based remote desktop solutions" or "team collaboration tools."

[0750] Step 7:

[0751] The server adjusts the suggestions received from the generation AI based on the results of the emotion engine and displays them in the most appropriate format and at the most appropriate time for the user. For example, a user feeling stressed will be shown a concise and reassuring explanation.

[0752] Step 8:

[0753] The user selects the best solution from the displayed suggestions.

[0754] Step 9:

[0755] The terminal transmits the user's selection result to the server.

[0756] Step 10:

[0757] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0758] Step 11:

[0759] The server will automatically perform the configuration and installation to complete the solution deployment.

[0760] Step 12:

[0761] The server monitors the system in real time after installation, monitoring performance and usage.

[0762] Step 13:

[0763] If a user has a problem with the system or has a question, they can contact the system via the website.

[0764] Step 14:

[0765] The server receives an inquiry from the user and requests the generation AI to analyze it.

[0766] Step 15:

[0767] The generation AI analyzes the inquiry and generates an appropriate answer.

[0768] Step 16:

[0769] The server sends the answer from the generated AI to the user and displays it.

[0770] Step 17:

[0771] The server periodically analyzes system usage and, if necessary, provides additional suggestions or improvement measures. At this time, the emotion engine analyzes the user's reactions and uses them to improve the next suggestions and responses.

[0772] In this way, at each step, the server, terminal, emotion engine, generative AI, and user work together to realize a system that efficiently introduces and supports IT solutions while reflecting the user's emotions.

[0773] Example 2

[0774] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0775] Conventional IT solution proposal systems rarely provide appropriate proposals that take user emotions into consideration, making it difficult to select and implement efficient solutions. Furthermore, there was also the issue of insufficient ongoing support after implementation, making it difficult to improve user satisfaction.

[0776] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means including an emotion engine that analyzes user input information and generates emotion data, means for reflecting the emotion data generated by the emotion engine in a solution proposal, and means for requesting analysis from a generative AI model. This makes it possible to propose an optimal solution that takes the user's emotions into consideration, thereby realizing efficient implementation and continuous support.

[0777] "User" refers to an end user who uses the system to input information and propose or select solutions.

[0778] A "terminal" is a computing device that a user uses to enter information and send information to a server.

[0779] A "server" is a computing device that receives information sent by users, analyzes it, proposes solutions, and monitors the system.

[0780] A "generative AI model" is an artificial intelligence algorithm that analyzes input information from users and generates optimal solutions.

[0781] The "emotion engine" is a system component that analyzes user input information and generates user emotion data.

[0782] "Emotion data" is data that indicates the user's emotional state, generated by the emotion engine.

[0783] A "solution" is a technical or operational proposal that is suitable for solving a user's problem.

[0784] An "implementation plan" is a document that outlines the specific steps for implementing the selected solution.

[0785] "Continuous support" is a service that provides regular monitoring and suggests improvements to ensure that the implemented solution functions properly.

[0786] An "inquiry" is an action in which a user reports a question or problem to the system and requests a solution.

[0787] A "prompt sentence" is a sentence used as input to a generative AI model, and includes analysis instructions and questions.

[0788] This invention is a system that efficiently provides IT solutions that take user emotions into consideration for small businesses and startups. The system begins when a user accesses the system using a web browser and enters information. The server, terminal, and generative AI model work together to analyze the user's input, propose appropriate solutions, and consistently automate the process from implementation to support.

[0789] When entering information, users use a web browser (e.g., Google Chrome or Firefox) to access a website built with HTML, CSS, and JavaScript. Users enter their company information (company name, number of employees, industry) and current challenges (e.g., optimizing the remote work environment) into a form. The input data is sent in real time to an emotion engine (e.g., Azure Emotion API), which analyzes the user's emotions.

[0790] The emotion engine uses NLP technology to analyze the input text data and generate emotion data (e.g., "expectation" or "stress"). The generated emotion data is sent from the device to a server. The server receives the company information, issues, and emotion data sent by the user, and sends the data to a generative AI model (e.g., OpenAI's GPT-3) for analysis.

[0791] The generative AI model analyzes the received prompt (e.g., "A software development company with 50 employees wants to optimize its remote work environment. The user's emotions are 'high expectations' and 'slight stress'. Please propose the optimal IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution"). At this time, the model also takes into account the results of the emotion engine and adjusts the proposal content to use the most appropriate wording for the user.

[0792] The server displays the generated solution proposals to the user, who then selects the optimal solution. The selection results are sent from the terminal to the server, which then generates a specific deployment plan. The plan includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts). The server then executes the automated scripts to complete the deployment of the solution.

[0793] After deployment, the server uses agent software (e.g., Prometheus) to monitor the system in real time. When a user inquires about a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer. The generative AI model generates an appropriate answer, which the server notifies the user. The server also periodically analyzes system usage, evaluates user reactions using an emotion engine, and appropriately notifies users of additional suggestions and improvements.

[0794] In this way, by efficiently and automatically providing optimal IT solutions that take user emotions into consideration and providing consistent support from implementation to support, we provide an environment where small companies and startups can make the most of their resources.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1: Gather user information

[0797] The user accesses the system's website using a web browser (e.g., Google Chrome or Firefox) and enters data into a form to enter company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0798] Input data is sent to the emotion engine in real time. The input is text data entered by the user, and the output is emotion data.

[0799] Step 2: Emotion recognition by the emotion engine

[0800] The emotion engine analyzes the received text data using natural language processing (NLP) technology to extract the user's emotions.

[0801] The extracted emotional data (e.g., "expectation" or "stress") is returned to the device and then transmitted from the device to the server.

[0802] The input is text data entered by the user, and the output is generated emotion data.

[0803] Step 3: Propose a solution

[0804] The server receives the company information, the task, and the emotion data transmitted from the terminal.

[0805] The server sends the received data to the generative AI model and requests it to analyze it.

[0806] The generative AI model analyzes prompt statements (e.g., "A software development company with 50 employees wants to optimize its remote work environment. User emotions are 'high expectations' and 'slight stress'. Please suggest the best IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution").

[0807] The inputs are company information, challenges, and sentiment data, and the output is the generated solution proposal.

[0808] Step 4: Select a solution

[0809] Users can review the proposed solutions through a web interface and select the most suitable one.

[0810] The selection result is sent from the terminal to the server.

[0811] The input is the generated solution proposal and the output is the user-selected solution.

[0812] Step 5: Generate and execute a deployment plan

[0813] The server generates a specific deployment plan based on the selection results, which includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts).

[0814] The server runs automated scripts to automate configuration and installation tasks and complete the solution deployment.

[0815] The input is the user-selected solution, and the output is the completed configuration and installation using an automated script.

[0816] Step 6: Post-implementation system monitoring and support

[0817] The server uses agent software (e.g., Prometheus) to monitor the deployed system in real time, collecting and analyzing performance data and usage.

[0818] When a user submits a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer.

[0819] The generative AI model generates an appropriate answer, which the server notifies the user.

[0820] It regularly analyzes system usage, evaluates user reactions using an emotion engine, and provides additional suggestions and improvements as needed.

[0821] The inputs are system performance data and user queries, and the outputs are analysis results and answers from the generative AI model, as well as additional suggestions and improvements.

[0822] (Application example 2)

[0823] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0824] It is difficult for small companies and startups to efficiently implement IT solutions while maximizing their resources, and there is no system in place to accurately recognize the emotions of passengers in autonomous vehicles and respond appropriately to ensure a comfortable experience. This leads to problems such as reduced passenger comfort and safety, and makes it difficult for companies to carry out their business efficiently.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0826] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to user inquiries; a means for the server to notify the user of the responses; a means for the server to periodically analyze system usage and notify the user of additional suggestions or improvements; a means for an emotion engine to analyze user emotions; a means for collecting passenger emotions using a camera and a microphone inside the autonomous vehicle; a means for the server to send passenger emotion data to the generation AI and generate a response; and a means for executing the generated response and customizing the in-vehicle environment and information provision. This enables autonomous vehicles to recognize passenger emotions in real time and provide a comfortable and safe environment.

[0827] "Means for users to input information" refers to the interface or device that users use to input their own information or tasks into the terminal.

[0828] The "means for transmitting information input by the terminal to the server" refers to a system or device for transmitting information input by the user to the server via a network.

[0829] "Means by which the server receives information and requests the generation AI to analyze it" refers to the process by which the server sends the information it receives to the generation AI and requests it to analyze it.

[0830] "Means by which generative AI proposes optimal solutions based on analysis results" refers to the process by which generative AI proposes IT solutions suitable for users based on the data analyzed.

[0831] "Means by which the server displays the proposal content to the user" refers to the means by which the server presents the proposal content obtained from the generation AI to the user in an easy-to-understand manner.

[0832] "Means for users to select proposed solutions" refers to the interface or process by which users select the most appropriate solution from the presented solutions.

[0833] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to the process by which the server creates a specific implementation plan based on the user's selection and configures and implements the system accordingly.

[0834] "Means for the server to monitor the system after implementation and provide ongoing support" refers to the process of monitoring whether the implemented system is operating properly and providing support or improvements as necessary.

[0835] "Means by which the generative AI generates answers to user inquiries" refers to the process by which the AI ​​receives and analyzes the content of the user's inquiry and generates an appropriate answer.

[0836] "Means for the server to notify the user of the answer" refers to the means by which the generation AI communicates the answer provided to the user.

[0837] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to the process of periodically analyzing system usage data and notifying users of new suggestions and improvements.

[0838] The "means by which the emotion engine analyzes the user's emotions" is an engine for analyzing the user's emotions from input information and past usage history.

[0839] "Means for collecting passenger emotions using cameras and microphones inside an autonomous vehicle" is a system that collects passengers' facial expressions and voices using cameras and microphones installed inside the vehicle.

[0840] "Means for the server to send passenger emotional data to the generation AI and generate a response" refers to the process of sending collected emotional data to the generation AI and generating an appropriate response.

[0841] "Means for executing the generated response and customizing the in-car environment and information provision" refers to means for adjusting the in-car environment and information provision based on the response content created by the generation AI.

[0842] This invention is a system that combines an emotion engine and generative AI to recognize user emotions and provide optimal solutions based on them. This system is primarily designed for small businesses and startups, but as an application example, we will introduce a passenger emotion response system in an autonomous vehicle.

[0843] In this embodiment, the system operates in the following manner.

[0844] 1. Hardware and Software

[0845] Hardware

[0846] Camera (to recognize passengers' facial expressions)

[0847] Microphone (to collect passenger voices)

[0848] Computer inside the vehicle (for data processing)

[0849] software

[0850] Emotion Engine

[0851] Generative AI (e.g., GPT-4)

[0852] Data analysis tools (e.g., TensorFlow)

[0853] Voice recognition software (e.g., Google Speech-to-Text)

[0854] 2. Data Collection

[0855] Using cameras and microphones inside the vehicle, facial expressions and voices of passengers are collected in real time, and the collected data is important for identifying passenger emotions.

[0856] 3. Emotion recognition

[0857] The collected data is sent to an Emotion Engine to analyze the passenger's emotions (e.g., stress, anxiety, joy, etc.), which allows the passenger's current emotional state to be identified.

[0858] 4. Interpretation and response generation by generative AI

[0859] The recognized emotion data is fed into generative AI, which then generates appropriate responses and suggestions accordingly – for example, if a passenger is feeling stressed, it will suggest playing relaxing music.

[0860] 5. Response Execution

[0861] Based on the generated responses, the in-car environment and information provision can be customized, for example by adjusting the lighting and temperature or playing relaxing music.

[0862] 6. Feedback and Improvement

[0863] The passenger's reaction to the generated response is monitored again using a camera and microphone, and the results are fed back to the generating AI to improve the quality of the next response.

[0864] Specific examples

[0865] For example, consider a case where a passenger feels stressed in a self-driving vehicle. Cameras and microphones capture the passenger's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine detects "stress" and sends the data to the generative AI. The generative AI then generates suggestions to reduce the "sense of stress" and instructs the car to play relaxing music. This entire process is automated.

[0866] Prompt Sentence Examples

[0867] For example, the prompt text to be input to the generative AI model is:

[0868] Based on the passenger's emotion analysis, generate an appropriate response to reduce the passenger's stress. For example, suggest playing relaxing music. Also consider the passenger's other emotions (happiness, anxiety, etc.) to generate the optimal response.

[0869] In this way, the system can improve passenger comfort and safety by combining emotion recognition and generative AI. The same method can also be applied to providing IT solutions to businesses, making optimal suggestions based on user emotions.

[0870] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0871] Step 1:

[0872] Data collection

[0873] The server uses cameras and microphones inside the autonomous vehicle to collect passenger facial expressions and voices in real time. The camera captures the passenger's facial features, and the microphone records the passenger's voice, forming a dataset that reflects the passenger's current emotions.

[0874] Input: Camera video data, audio data

[0875] Output: Collected facial expression and voice data

[0876] Step 2:

[0877] emotion recognition

[0878] The server sends the collected facial and voice data to the emotion engine for analysis. The emotion engine uses machine learning algorithms to identify passenger emotions (stress, anxiety, joy, etc.) from the collected data, and outputs the passenger's emotional state as numerical data.

[0879] Input: facial expression data, voice data

[0880] Output: Emotion recognition data (e.g., stress = 0.8, joy = 0.2)

[0881] Step 3:

[0882] Interpretation and response generation by generative AI

[0883] The server sends the emotion recognition data from the emotion engine to the generation AI, which then generates appropriate responses and suggestions based on the passenger's emotions. For example, if stress levels are high, it can suggest relaxing music. The generation AI generates responses using prompt sentences based on past data and the analysis results of the emotion engine.

[0884] Input: Emotion recognition data

[0885] Output: Response data (e.g., music playback instruction)

[0886] Step 4:

[0887] Response execution

[0888] The server customizes the in-car environment and information provision based on the response data generated by the AI. Specifically, this includes music playback, lighting and temperature control in the car, etc. In this step, the server works in conjunction with the vehicle's internal control system to execute the instructions.

[0889] Input: Response data

[0890] Output: Changes made to the car environment (e.g., music playback, temperature adjustment)

[0891] Step 5:

[0892] Feedback and Improvements

[0893] After the server responds, it again monitors passenger reactions using cameras and microphones and feeds the resulting data back to the generative AI and emotion engine, improving the quality of the next response and further enhancing passenger comfort and safety.

[0894] Input: Passenger response data

[0895] Output: Feedback data (used for next analysis and response generation)

[0896] In this way, by processing and analyzing input data at each step and obtaining optimal output, a system is created that responds appropriately based on the passenger's emotional state, thereby improving passenger comfort and safety and enabling efficient service provision.

[0897] 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.

[0898] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0899] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0900] [Third embodiment]

[0901] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0902] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0903] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0904] 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.

[0905] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0906] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0907] 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.

[0908] 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.

[0909] 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 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.

[0910] 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.

[0911] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0912] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0913] This invention is a system that efficiently provides IT solutions to small businesses and startups. Specifically, it collects information from users through a website that utilizes generative AI, and then automates the entire process of proposing and implementing optimal IT solutions based on that information, all the way through to customer success.

[0914] System program and processing flow

[0915] 1. Collecting User Information

[0916] Users access the website and enter company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0917] The terminal transmits the input information to the server.

[0918] 2. Solution proposal

[0919] The server receives the information sent from the device and requests the generating AI to analyze it.

[0920] The generative AI analyzes the information input by the user and proposes the optimal IT solution (e.g., a cloud-based remote desktop solution).

[0921] The server displays this proposal to the user, who then confirms the details.

[0922] 3. Solution Selection and Implementation

[0923] The user selects the best solution from the proposed solutions.

[0924] The terminal transmits the selection result to the server.

[0925] The server generates an implementation plan based on the selection results and automatically executes specific configuration and installation procedures.

[0926] 4. Supporting Customer Success

[0927] The server monitors the system in real time after installation, monitoring performance and usage.

[0928] If users have any questions or problems, they can contact us through the website.

[0929] The server sends the query to the generation AI and requests analysis.

[0930] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[0931] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0932] Specific examples

[0933] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[0934] 1. Collecting User Information

[0935] An IT professional at TechStartup visits the website and enters "Optimizing the remote work environment" as an assignment.

[0936] The terminal transmits the information to the server.

[0937] 2. Solution proposal

[0938] The server receives the information and asks the generating AI to analyze it.

[0939] The generative AI generates a proposal called a "cloud-based remote desktop solution" and sends it back to the server.

[0940] The server displays this suggestion to the IT staff.

[0941] 3. Solution Selection and Implementation

[0942] The IT staff reviews the proposed solutions and selects one.

[0943] The terminal transmits the selection result to the server.

[0944] The server generates a deployment plan and automatically configures and installs the software.

[0945] 4. Supporting Customer Success

[0946] The server monitors the system in real time after installation.

[0947] If IT staff notice a problem, they can contact the website.

[0948] The server sends the query to the generating AI, requesting analysis and an answer.

[0949] The generation AI generates an answer, and the server notifies the person in charge.

[0950] The server periodically analyzes system usage and suggests improvements.

[0951] In this way, the system of the present invention provides an environment in which users can easily introduce IT solutions and also automates post-operation support, thereby helping small businesses efficiently develop and operate their IT infrastructure.

[0952] The processing flow will be explained below.

[0953] Step 1:

[0954] The user accesses the website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[0955] Step 2:

[0956] The terminal transmits the input information to the server.

[0957] Step 3:

[0958] The server receives the information sent from the terminal and stores it in a database.

[0959] Step 4:

[0960] The server converts the received data into a format that is easy for the generation AI to analyze, and requests the generation AI to analyze it.

[0961] Step 5:

[0962] Generative AI analyzes company information and issues, and generates multiple optimal IT solutions.

[0963] Step 6:

[0964] The generation AI sends the generated proposals to the server.

[0965] Step 7:

[0966] The server converts the proposed content into a format that is easy for the user to view, and transmits it to the terminal for display.

[0967] Step 8:

[0968] The user selects the best solution from the proposed solutions.

[0969] Step 9:

[0970] The terminal transmits the user's selection result to the server.

[0971] Step 10:

[0972] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[0973] Step 11:

[0974] The server will automatically perform the configuration and installation to complete the solution deployment.

[0975] Step 12:

[0976] The server monitors the system in real time after installation, monitoring performance and usage.

[0977] Step 13:

[0978] If a user has any questions or problems, they can contact us through the website.

[0979] Step 14:

[0980] The server receives an inquiry from the user and requests the generation AI to analyze it.

[0981] Step 15:

[0982] The generation AI analyzes the inquiry and generates an appropriate answer.

[0983] Step 16:

[0984] The server sends the answer from the generated AI to the user and displays it.

[0985] Step 17:

[0986] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[0987] In this way, servers, terminals, and users work together at each step to efficiently realize the entire process from proposing IT solutions to implementation and customer success.

[0988] Example 1

[0989] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0990] For today's small businesses and startups, the efficient implementation and operation of information technology solutions is crucial. However, these businesses often have limited technical resources and expertise, and therefore spend a great deal of time and money selecting, implementing, and supporting the appropriate solutions. Therefore, there is a need for a system that can simplify and automate these processes.

[0991] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0992] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generative artificial intelligence model; a means for the generative AI model to propose an optimal information technology solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the terminal to send the selection results to the server; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system in real time after implementation and provide ongoing support; a means for the generative artificial intelligence model to generate answers to inquiries from the user; a means for the server to notify the user of the answers; and a means for the server to periodically analyze system usage and notify the user of additional suggestions and improvements, thereby enabling users to easily select, implement, and operate appropriate information technology solutions.

[0993] "User" refers to a company or individual who uses the system.

[0994] A "terminal" refers to a computer device operated by a user, and is a means for inputting information and transmitting it to a server.

[0995] "Server" refers to a central computer system that processes information sent from users and devices and works with generative AI models to propose and implement solutions.

[0996] "Generative artificial intelligence model" refers to an AI system that analyzes information provided by users and generates optimal information technology solutions.

[0997] "Information" refers to company information and current issues that users input into the system.

[0998] "Analysis" refers to the process by which a generative artificial intelligence model derives the optimal solution based on information provided by the user.

[0999] "Proposal" refers to the optimal information technology solution generated by the generative artificial intelligence model based on the analysis results.

[1000] "Implementation Plan" means a document describing the configuration and installation steps required to implement the proposed Solution.

[1001] "Monitoring" refers to the process of monitoring a system's performance and usage in real time after implementation.

[1002] "Inquiry" refers to a question or problem report that a user makes to the system.

[1003] "Answer" refers to the appropriate solution or information generated by a generative artificial intelligence model in response to a query.

[1004] "Usage status" refers to the performance and operational status of the system after its implementation, and refers to information that is periodically analyzed by the server.

[1005] This invention is a system for efficiently providing information technology solutions to small businesses and startups. Specifically, it collects information from users through a website using a generative artificial intelligence model, and then automates the entire process of proposing and implementing optimal information technology solutions based on that information, leading to customer success.

[1006] First, the user accesses a dedicated website via a browser. The user enters company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). To do this, the user uses a device such as a PC, tablet, or smartphone. The device then sends the entered information to the server in JSON format.

[1007] The server receives the information sent from the device and requests a generative artificial intelligence model, such as OpenAI's GPT-3, to analyze the data. At this time, the server converts the collected information into a format that is easy for the generative artificial intelligence model to analyze. The generative artificial intelligence model generates an appropriate information technology solution based on the user's input information. For example, a "cloud-based remote desktop solution" may be proposed. The server then converts the generated analysis results into HTML format and displays them on a website in a user-friendly format.

[1008] Next, the user reviews the displayed proposals and selects the optimal solution from multiple options. The selection results are then sent back to the server from the device. The server then automatically generates a deployment plan based on the selection results, and performs instance configuration and service provisioning using, for example, AWS cloud services or Microsoft Azure. Specific configuration and installation procedures are performed by automated scripts.

[1009] After implementation, the server uses monitoring tools such as Datadog and New Relic to monitor system performance and usage in real time. When users have questions or problems, they can make inquiries via the website. The server sends the inquiry to the generative AI model for analysis and response. The generative AI model generates an appropriate response, and the server notifies the user of the response. In addition, the server regularly analyzes system usage and uses the generative AI model to generate additional suggestions and improvements, which are then notified to the user.

[1010] As a concrete example, consider the case where the company TechStartup wants to implement a new remote work environment. The IT staff at TechStartup accesses the website and inputs the challenge of "optimizing the remote work environment." The generative AI then suggests a "cloud-based remote desktop solution," and the IT staff selects this solution. The server configures the AWS cloud-based remote desktop and automatically installs it. After implementation, the system is monitored using Datadog, and if the IT staff notices a problem, they can inquire on the website and the generative AI will provide an appropriate response.

[1011] An example of a prompt to be input to the generative AI model might be something like, "The user wants to 'optimize their remote work environment.' The company information is as follows: Company name: TechStartup, Number of employees: 50, Industry: Technology, Current challenge: To establish secure access from outside the company. Please propose the optimal information technology solution."

[1012] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1013] Step 1:

[1014] The user opens a browser and accesses a dedicated website. The user enters company information (company name, number of employees, industry) and current issues (optimizing the remote work environment). The input data here might be, for example, "TechStartup, 50, technology, optimizing the remote work environment." The entered information is sent from the device to the server in JSON format. At this point, the input is the user's company information and issues, and the output is the information sent to the server.

[1015] Step 2:

[1016] The server converts the received information into a format that is easy to parse. Specifically, the server converts JSON-formatted data into a parseable format and passes it to a generative AI model such as OpenAI's GPT-3. For example, converting JSON data into text format. The input of this step is the information sent from the device, and the output is an analysis request to the generative AI model.

[1017] Step 3:

[1018] The generative AI model analyzes the received information and generates the optimal information technology solution. The data calculations performed here are a process of evaluating various solutions based on the user's company information and challenges, and deriving the optimal solution. Specifically, it generates a proposal such as a "cloud-based remote desktop solution." The input to this step is the analysis request passed to the generative AI model, and the output is a proposed solution as the analysis result.

[1019] Step 4:

[1020] The server converts the suggestions received from the generative AI model into HTML format for display to the user. The output is a web page that is displayed in the user's browser. The input to this step is the suggestions from the generative AI model, and the output is an HTML page that is displayed to the user.

[1021] Step 5:

[1022] The user checks the displayed proposals and selects the optimal solution from the multiple options. The solution selected by the user is sent back to the server from the device. The input at this point is the user's selection result, and the output is the selection data sent to the server.

[1023] Step 6:

[1024] The server receives the selection results and automatically generates a deployment plan based on that information. Specifically, it configures instances and provisions services using AWS cloud services or Microsoft Azure. The server runs automated scripts to perform the necessary configuration and installation steps. The input for this step is the user's selection results, and the output is the specific steps in the deployment plan.

[1025] Step 7:

[1026] To monitor the system after the server is deployed, monitoring tools such as Datadog and New Relic are used to monitor the system's performance and usage in real time. The input of this step is data from the monitoring tool, and the output is system status information.

[1027] Step 8:

[1028] When a user has a question or problem with the system, they make an inquiry through the website. The inquiry is sent from the terminal to the server. The input of this step is the inquiry information from the user, and the output is the inquiry data sent to the server.

[1029] Step 9:

[1030] The server sends the query to the generative AI model, requesting analysis and an answer. The generative AI model generates an appropriate answer, and the server notifies the user of the answer. The input to this step is the query information from the user and an analysis request to the generative AI model, and the output is the answer data.

[1031] Step 10:

[1032] The server periodically analyzes system usage and generates additional suggestions and improvements using a generative AI model. It notifies the user of these suggestions and improvements. The input for this step is system usage data, and the output is notifications of suggestions and improvements.

[1033] (Application example 1)

[1034] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1035] Operators of small brick-and-mortar stores face challenges such as increasing sales and streamlining inventory management. However, it is difficult for each store to independently find and implement the optimal IT solution, requiring a great deal of time and resources. Furthermore, selecting the right marketing strategy and inventory management system requires specialized knowledge, and many store operators lack the means to solve these challenges. Against this backdrop, there is a need for a system that proposes efficient and optimal IT solutions for brick-and-mortar stores and automates everything from implementation to operational support.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1037] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from a generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to inquiries from users; a means for the server to notify the users of the responses; a means for the server to periodically analyze system usage and notify additional suggestions and improvements; a means for collecting information about the operation of a physical store via an application installed on a smartphone and proposing a marketing strategy and an inventory management system based on the analysis results; and a means for sending information input by the user via the application to the generation AI and presenting the analysis results to the store operator. This enables physical store operators to easily introduce optimal IT solutions, improve sales, and streamline inventory management.

[1038] The "means for users to input information" refers to an interface that allows operators of physical stores to input basic information about their stores and the challenges they are currently facing.

[1039] "Means by which the device transmits input information to the server" refers to the communications protocols and infrastructure that allow smartphones and other devices to transmit user-entered information to a central server.

[1040] "Means by which the server receives information and requests analysis from the generation AI" refers to a series of processes in which the server sends the received user information to the generation AI system and requests analysis.

[1041] "Means for the generative AI to propose optimal solutions based on the analysis results" refers to the function in which the generative AI analyzes information entered by the user and generates appropriate IT solutions and operational improvement measures.

[1042] The "means by which the server displays the proposed content to the user" is a mechanism for displaying the solution generated as the analysis result on the user's terminal via the server.

[1043] The "means for the user to select a proposed solution" is an interface that allows the user to select the most suitable solution from the multiple solutions displayed.

[1044] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to a system that formulates an implementation plan based on the solution selected by the user and automatically performs the necessary settings and installation according to that plan.

[1045] "Means for the server to monitor the system after installation and provide ongoing support" is a mechanism that monitors installed systems in real time, analyzes their operational status, and automatically provides the necessary support.

[1046] "Means by which the generation AI generates answers to inquiries from users" refers to a function that analyzes the content of inquiries received from users and automatically generates appropriate answers.

[1047] The "means by which the server notifies the user of the answer" is a mechanism for promptly notifying the user of the generated answer.

[1048] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to a mechanism that periodically analyzes system usage data, automatically generates new suggestions and improvements, and notifies users.

[1049] "A means of collecting information about the operation of physical stores via an application installed on a smartphone and proposing marketing strategies and inventory management systems based on the analysis results" refers to a function that allows physical store operators to input information via a smartphone app and then proposes appropriate marketing strategies and inventory management systems based on that information.

[1050] "A means of sending information entered by the user through an application to a generation AI and presenting the analysis results to the store operator" refers to a system in which information entered via a smartphone app is sent to a generation AI and the analysis results are displayed to the store operator.

[1051] The present invention relates to a system for efficiently providing IT solutions to operators of small brick-and-mortar stores through a smartphone application that utilizes generative AI. The present invention is embodied in the following specific forms.

[1052] System Program

[1053] This system involves a series of processes in which users input information and the generative AI proposes optimal solutions based on that information. Specifically, users use a smartphone application to input the operational status and issues of their physical store, and send that data to a server. The server then requests the generative AI to analyze the received data and proposes appropriate marketing strategies and inventory management systems based on the analysis results.

[1054] Processing Description

[1055] Collecting user information

[1056] The user (physical store operator) first uses a smartphone application to enter information about their store, such as the store name, number of employees, type of business, and current operational challenges (e.g., improving inventory management efficiency, increasing sales, etc.). The hardware used is a smartphone, and the software is a dedicated application (e.g., an application developed with React Native).

[1057] Sending information from the device to the server

[1058] The device (smartphone) sends the entered information to the server. The communication technology and protocol used is HTTP or HTTPS, and communication is carried out with security in mind.

[1059] Data processing on the server

[1060] The server converts the information received from the user into a format that is easy to analyze. This includes processing such as data shaping and filtering. The software used is AWS Lambda and Node.js.

[1061] Analysis and solution proposals using generative AI

[1062] The server sends the formatted data to a generation AI (e.g., GPT-3) for analysis. The generation AI generates the optimal solution based on the user's input data and returns it to the server.

[1063] Display and selection of proposals

[1064] The server converts the analysis results received from the generation AI into a format that is easy for the user to view, and presents them to the user via a smartphone application. The user can then select the optimal solution from the presented solutions.

[1065] Generate and execute an implementation plan

[1066] Based on the selected solution, the server generates a deployment plan and automatically performs the necessary configuration and installation, including cloud-based resource configuration and automatic software installation.

[1067] Customer Success Support

[1068] The server monitors the system in real time after installation, notifying users if any problems occur and providing assistance. It also periodically analyzes system usage and notifies users of additional suggestions and improvements.

[1069] Examples of specific examples and prompts

[1070] For example, let's say a brick-and-mortar store called TechStore wants to streamline inventory management. The operator uses a smartphone application to enter the following information:

[1071] Store name: TechStore

[1072] Number of employees: 15

[1073] Industry: Home appliance sales

[1074] Challenge: Streamlining inventory management

[1075] This prompt is sent to the AI ​​generator, which then proposes the introduction of a barcode inventory management system as the analysis result. The server processes the analysis result and displays it to the user, who can then confirm and select the system, which will then be automatically introduced.

[1076] According to the present invention, operators of physical stores can easily introduce optimal IT solutions and improve operational efficiency, even without specialized knowledge.

[1077] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1078] Step 1:

[1079] The user launches the smartphone application and enters store information (e.g., store name, number of employees, industry, issues, etc.).

[1080] Input: Store information (store name, number of employees, industry, issues)

[1081] Output: Store information data is saved in the device.

[1082] Step 2:

[1083] The terminal transmits the input store information to the server.

[1084] Input: Store information data stored in the device

[1085] Output: Store information data sent to the server

[1086] Step 3:

[1087] The server converts the received store information data into a format that is easy to analyze.

[1088] Input: Store information data sent to the server

[1089] Output: Store information data converted into a parsable format

[1090] Step 4:

[1091] The server sends analyzable store information data to the generation AI and requests it to analyze it.

[1092] Input: Store information data converted into a parsable format

[1093] Output: Analysis request data sent to the generation AI

[1094] Step 5:

[1095] Generative AI analyzes input data and generates optimal solutions (e.g., marketing strategies, inventory management systems, etc.).

[1096] Input: Analysis request data sent to the generation AI

[1097] Output: Generated optimal solution data

[1098] Step 6:

[1099] The server converts the solution data received from the generated AI into a format that is easy for the user to understand and displays it to the user through a smartphone application.

[1100] Input: Solution data received from the generation AI

[1101] Output: The solution data displayed to the user in a user-friendly format

[1102] Step 7:

[1103] The user selects the best solution from the presented solutions.

[1104] Input: User-friendly solution data displayed

[1105] Output: Selected solution data

[1106] Step 8:

[1107] The server generates a deployment plan based on the selected solution and automatically configures and installs according to that plan.

[1108] Input: Selected solution data

[1109] Output: Automatic configuration and installation steps

[1110] Step 9:

[1111] The server monitors the system in real time after installation, monitoring performance and usage.

[1112] Input: Settings and post-installation operational data

[1113] Output: Monitoring data collected in real time

[1114] Step 10:

[1115] If a user has any questions or problems, they can contact the company through the smartphone application.

[1116] Input: User inquiry

[1117] Output: Query data sent to the server

[1118] Step 11:

[1119] The server sends the query data to the generation AI, requesting analysis and an answer.

[1120] Input: Query data sent to the server

[1121] Output: Analysis request data sent to the generation AI

[1122] Step 12:

[1123] The generation AI analyzes the inquiry and generates an appropriate answer.

[1124] Input: Analysis request data sent to the generation AI

[1125] Output: Generated response data

[1126] Step 13:

[1127] The server notifies the user of the answer data received from the generating AI.

[1128] Input: Answer data received from the generation AI

[1129] Output: Answer data notified to the user

[1130] Step 14:

[1131] The server periodically analyzes system usage and notifies users of additional suggestions and improvements.

[1132] Input: Monitoring and usage data

[1133] Output: Additional suggestions and improvement notification data

[1134] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1135] This invention relates to a system for efficiently providing IT solutions to small businesses and startups. In particular, it is a system that combines generative AI and an emotion engine to recognize user emotions and propose more appropriate solutions. This system collects information from users via a website, and automates the entire process from proposing and implementing optimal IT solutions based on that information and emotions, to ensuring customer success.

[1136] System program and processing flow

[1137] 1. Collecting User Information

[1138] Users access the website and enter their company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, an emotion engine that recognizes the user's emotions is also running, and analyzes the user's emotions from the input information.

[1139] 2. Emotion Recognition by Emotion Engine

[1140] The emotion engine analyzes the user's input information and past usage history to recognize their emotions, which are then taken into consideration when proposing solutions.

[1141] 3. Solution proposal

[1142] The terminal transmits the input information and emotion information to the server.

[1143] The server receives the information and emotion data and requests the generative AI to analyze it. The generative AI generates optimal IT solutions based on the company information, issues, and recognized emotions.

[1144] The suggestions are adjusted based on the analysis results of the emotion engine and are displayed at the most appropriate time and in the most appropriate way for the user.

[1145] 4. Solution Selection and Implementation

[1146] The user selects the best solution from the proposed solutions.

[1147] The terminal transmits the selection result to the server.

[1148] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[1149] The server will automatically perform the configuration and installation to complete the solution deployment.

[1150] 5. Supporting Customer Success

[1151] The server monitors the system in real time after installation, monitoring performance and usage.

[1152] If a user has any questions or problems, they can contact us through the website.

[1153] The server sends the query to the generating AI, requesting analysis and an answer.

[1154] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[1155] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[1156] Specific examples

[1157] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[1158] 1. Collecting User Information

[1159] An IT professional accesses the website and enters "optimizing the remote work environment" as a task. At this time, the emotion engine recognizes emotions such as stress and anticipation from the IT professional's input.

[1160] 2. Emotion Recognition by Emotion Engine

[1161] The emotion engine analyzes input data and past usage history to recognize "high expectations" and "slight stress."

[1162] 3. Solution proposal

[1163] The device transmits the information and emotion data to the server.

[1164] The server requests the AI ​​to analyze the data, and the AI ​​generates a proposal called a "cloud-based remote desktop solution." The AI ​​uses the results of the emotion engine as a reference and displays the proposal to the user in a way that reduces stress.

[1165] 4. Solution Selection and Implementation

[1166] The IT staff member reviews the proposed solutions and selects one. At this time, the emotion engine also analyzes the staff member's reactions and uses this information in the next proposal.

[1167] The terminal transmits the selection result to the server.

[1168] The server generates an implementation plan based on the selection results and automatically configures and installs the system.

[1169] 5. Supporting Customer Success

[1170] The server monitors the system after installation.

[1171] If IT staff notice a problem, they can contact the website.

[1172] The server requests a query from the generation AI and notifies the person in charge of the appropriate answer.

[1173] The server periodically analyzes the system and proposes improvements, while an emotion engine analyzes the responses of the agents to improve the quality of support.

[1174] In this way, the system of the present invention incorporates the user's emotions to provide optimal IT solutions, and is capable of efficiently carrying out everything from implementation to support, providing an environment in which small businesses can make the most of their resources.

[1175] The processing flow will be explained below.

[1176] Step 1:

[1177] A user accesses a website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, the input form includes questions about emotions (e.g., feelings about the current issues), and the user also enters answers to those questions.

[1178] Step 2:

[1179] The terminal transmits the input company information, issue, and emotion data to the server.

[1180] Step 3:

[1181] The server receives the information sent from the terminal, stores it in a database, and has the emotion engine analyze the input data.

[1182] Step 4:

[1183] The emotion engine analyzes the user's input information and emotional data to recognize specific emotions such as "stress" or "expectation." The analysis results are stored in an emotion information database.

[1184] Step 5:

[1185] The server provides the received company information and emotional information to the generation AI and requests it to analyze it.

[1186] Step 6:

[1187] Generative AI generates optimal IT solutions based on company information, challenges, and perceived sentiment, for example, proposing multiple "cloud-based remote desktop solutions" or "team collaboration tools."

[1188] Step 7:

[1189] The server adjusts the suggestions received from the generation AI based on the results of the emotion engine and displays them in the most appropriate format and at the most appropriate time for the user. For example, a user feeling stressed will be shown a concise and reassuring explanation.

[1190] Step 8:

[1191] The user selects the best solution from the displayed suggestions.

[1192] Step 9:

[1193] The terminal transmits the user's selection result to the server.

[1194] Step 10:

[1195] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[1196] Step 11:

[1197] The server will automatically perform the configuration and installation to complete the solution deployment.

[1198] Step 12:

[1199] The server monitors the system in real time after installation, monitoring performance and usage.

[1200] Step 13:

[1201] If a user has a problem with the system or has a question, they can contact the system via the website.

[1202] Step 14:

[1203] The server receives an inquiry from the user and requests the generation AI to analyze it.

[1204] Step 15:

[1205] The generation AI analyzes the inquiry and generates an appropriate answer.

[1206] Step 16:

[1207] The server sends the answer from the generated AI to the user and displays it.

[1208] Step 17:

[1209] The server periodically analyzes system usage and, if necessary, provides additional suggestions or improvement measures. At this time, the emotion engine analyzes the user's reactions and uses them to improve the next suggestions and responses.

[1210] In this way, at each step, the server, terminal, emotion engine, generative AI, and user work together to realize a system that efficiently introduces and supports IT solutions while reflecting the user's emotions.

[1211] Example 2

[1212] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1213] Conventional IT solution proposal systems rarely provide appropriate proposals that take user emotions into consideration, making it difficult to select and implement efficient solutions. Furthermore, there was also the issue of insufficient ongoing support after implementation, making it difficult to improve user satisfaction.

[1214] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means including an emotion engine that analyzes user input information and generates emotion data, means for reflecting the emotion data generated by the emotion engine in a solution proposal, and means for requesting analysis from a generative AI model. This makes it possible to propose an optimal solution that takes the user's emotions into consideration, thereby realizing efficient implementation and continuous support.

[1215] "User" refers to an end user who uses the system to input information and propose or select solutions.

[1216] A "terminal" is a computing device that a user uses to enter information and send information to a server.

[1217] A "server" is a computing device that receives information sent by users, analyzes it, proposes solutions, and monitors the system.

[1218] A "generative AI model" is an artificial intelligence algorithm that analyzes input information from users and generates optimal solutions.

[1219] The "emotion engine" is a system component that analyzes user input information and generates user emotion data.

[1220] "Emotion data" is data that indicates the user's emotional state, generated by the emotion engine.

[1221] A "solution" is a technical or operational proposal that is suitable for solving a user's problem.

[1222] An "implementation plan" is a document that outlines the specific steps for implementing the selected solution.

[1223] "Continuous support" is a service that provides regular monitoring and suggests improvements to ensure that the implemented solution functions properly.

[1224] An "inquiry" is an action in which a user reports a question or problem to the system and requests a solution.

[1225] A "prompt sentence" is a sentence used as input to a generative AI model, and includes analysis instructions and questions.

[1226] This invention is a system that efficiently provides IT solutions that take user emotions into consideration for small businesses and startups. The system begins when a user accesses the system using a web browser and enters information. The server, terminal, and generative AI model work together to analyze the user's input, propose appropriate solutions, and consistently automate the process from implementation to support.

[1227] When entering information, users use a web browser (e.g., Google Chrome or Firefox) to access a website built with HTML, CSS, and JavaScript. Users enter their company information (company name, number of employees, industry) and current challenges (e.g., optimizing the remote work environment) into a form. The input data is sent in real time to an emotion engine (e.g., Azure Emotion API), which analyzes the user's emotions.

[1228] The emotion engine uses NLP technology to analyze the input text data and generate emotion data (e.g., "expectation" or "stress"). The generated emotion data is sent from the device to a server. The server receives the company information, issues, and emotion data sent by the user, and sends the data to a generative AI model (e.g., OpenAI's GPT-3) for analysis.

[1229] The generative AI model analyzes the received prompt (e.g., "A software development company with 50 employees wants to optimize its remote work environment. The user's emotions are 'high expectations' and 'slight stress'. Please propose the optimal IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution"). At this time, the model also takes into account the results of the emotion engine and adjusts the proposal content to use the most appropriate wording for the user.

[1230] The server displays the generated solution proposals to the user, who then selects the optimal solution. The selection results are sent from the terminal to the server, which then generates a specific deployment plan. The plan includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts). The server then executes the automated scripts to complete the deployment of the solution.

[1231] After deployment, the server uses agent software (e.g., Prometheus) to monitor the system in real time. When a user inquires about a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer. The generative AI model generates an appropriate answer, which the server notifies the user. The server also periodically analyzes system usage, evaluates user reactions using an emotion engine, and appropriately notifies users of additional suggestions and improvements.

[1232] In this way, by efficiently and automatically providing optimal IT solutions that take user emotions into consideration and providing consistent support from implementation to support, we provide an environment where small companies and startups can make the most of their resources.

[1233] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1234] Step 1: Gather user information

[1235] The user accesses the system's website using a web browser (e.g., Google Chrome or Firefox) and enters data into a form to enter company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[1236] Input data is sent to the emotion engine in real time. The input is text data entered by the user, and the output is emotion data.

[1237] Step 2: Emotion recognition by the emotion engine

[1238] The emotion engine analyzes the received text data using natural language processing (NLP) technology to extract the user's emotions.

[1239] The extracted emotional data (e.g., "expectation" or "stress") is returned to the device and then transmitted from the device to the server.

[1240] The input is text data entered by the user, and the output is generated emotion data.

[1241] Step 3: Propose a solution

[1242] The server receives the company information, the task, and the emotion data transmitted from the terminal.

[1243] The server sends the received data to the generative AI model and requests it to analyze it.

[1244] The generative AI model analyzes prompt statements (e.g., "A software development company with 50 employees wants to optimize its remote work environment. User emotions are 'high expectations' and 'slight stress'. Please suggest the best IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution").

[1245] The inputs are company information, challenges, and sentiment data, and the output is the generated solution proposal.

[1246] Step 4: Select a solution

[1247] Users can review the proposed solutions through a web interface and select the most suitable one.

[1248] The selection result is sent from the terminal to the server.

[1249] The input is the generated solution proposal and the output is the user-selected solution.

[1250] Step 5: Generate and execute a deployment plan

[1251] The server generates a specific deployment plan based on the selection results, which includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts).

[1252] The server runs automated scripts to automate configuration and installation tasks and complete the solution deployment.

[1253] The input is the user-selected solution, and the output is the completed configuration and installation using an automated script.

[1254] Step 6: Post-implementation system monitoring and support

[1255] The server uses agent software (e.g., Prometheus) to monitor the deployed system in real time, collecting and analyzing performance data and usage.

[1256] When a user submits a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer.

[1257] The generative AI model generates an appropriate answer, which the server notifies the user.

[1258] It regularly analyzes system usage, evaluates user reactions using an emotion engine, and provides additional suggestions and improvements as needed.

[1259] The inputs are system performance data and user queries, and the outputs are analysis results and answers from the generative AI model, as well as additional suggestions and improvements.

[1260] (Application example 2)

[1261] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1262] It is difficult for small companies and startups to efficiently implement IT solutions while maximizing their resources, and there is no system in place to accurately recognize the emotions of passengers in autonomous vehicles and respond appropriately to ensure a comfortable experience. This leads to problems such as reduced passenger comfort and safety, and makes it difficult for companies to carry out their business efficiently.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1264] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to user inquiries; a means for the server to notify the user of the responses; a means for the server to periodically analyze system usage and notify the user of additional suggestions or improvements; a means for an emotion engine to analyze user emotions; a means for collecting passenger emotions using a camera and a microphone inside the autonomous vehicle; a means for the server to send passenger emotion data to the generation AI and generate a response; and a means for executing the generated response and customizing the in-vehicle environment and information provision. This enables autonomous vehicles to recognize passenger emotions in real time and provide a comfortable and safe environment.

[1265] "Means for users to input information" refers to the interface or device that users use to input their own information or tasks into the terminal.

[1266] The "means for transmitting information input by the terminal to the server" refers to a system or device for transmitting information input by the user to the server via a network.

[1267] "Means by which the server receives information and requests the generation AI to analyze it" refers to the process by which the server sends the information it receives to the generation AI and requests it to analyze it.

[1268] "Means by which generative AI proposes optimal solutions based on analysis results" refers to the process by which generative AI proposes IT solutions suitable for users based on the data analyzed.

[1269] "Means by which the server displays the proposal content to the user" refers to the means by which the server presents the proposal content obtained from the generation AI to the user in an easy-to-understand manner.

[1270] "Means for users to select proposed solutions" refers to the interface or process by which users select the most appropriate solution from the presented solutions.

[1271] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to the process by which the server creates a specific implementation plan based on the user's selection and configures and implements the system accordingly.

[1272] "Means for the server to monitor the system after implementation and provide ongoing support" refers to the process of monitoring whether the implemented system is operating properly and providing support or improvements as necessary.

[1273] "Means by which the generative AI generates answers to user inquiries" refers to the process by which the AI ​​receives and analyzes the content of the user's inquiry and generates an appropriate answer.

[1274] "Means for the server to notify the user of the answer" refers to the means by which the generation AI communicates the answer provided to the user.

[1275] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to the process of periodically analyzing system usage data and notifying users of new suggestions and improvements.

[1276] The "means by which the emotion engine analyzes the user's emotions" is an engine for analyzing the user's emotions from input information and past usage history.

[1277] "Means for collecting passenger emotions using cameras and microphones inside an autonomous vehicle" is a system that collects passengers' facial expressions and voices using cameras and microphones installed inside the vehicle.

[1278] "Means for the server to send passenger emotional data to the generation AI and generate a response" refers to the process of sending collected emotional data to the generation AI and generating an appropriate response.

[1279] "Means for executing the generated response and customizing the in-car environment and information provision" refers to means for adjusting the in-car environment and information provision based on the response content created by the generation AI.

[1280] This invention is a system that combines an emotion engine and generative AI to recognize user emotions and provide optimal solutions based on them. This system is primarily designed for small businesses and startups, but as an application example, we will introduce a passenger emotion response system in an autonomous vehicle.

[1281] In this embodiment, the system operates in the following manner.

[1282] 1. Hardware and Software

[1283] Hardware

[1284] Camera (to recognize passengers' facial expressions)

[1285] Microphone (to collect passenger voices)

[1286] Computer inside the vehicle (for data processing)

[1287] software

[1288] Emotion Engine

[1289] Generative AI (e.g., GPT-4)

[1290] Data analysis tools (e.g., TensorFlow)

[1291] Voice recognition software (e.g., Google Speech-to-Text)

[1292] 2. Data Collection

[1293] Using cameras and microphones inside the vehicle, facial expressions and voices of passengers are collected in real time, and the collected data is important for identifying passenger emotions.

[1294] 3. Emotion recognition

[1295] The collected data is sent to an Emotion Engine to analyze the passenger's emotions (e.g., stress, anxiety, joy, etc.), which allows the passenger's current emotional state to be identified.

[1296] 4. Interpretation and response generation by generative AI

[1297] The recognized emotion data is fed into generative AI, which then generates appropriate responses and suggestions accordingly – for example, if a passenger is feeling stressed, it will suggest playing relaxing music.

[1298] 5. Response Execution

[1299] Based on the generated responses, the in-car environment and information provision can be customized, for example by adjusting the lighting and temperature or playing relaxing music.

[1300] 6. Feedback and Improvement

[1301] The passenger's reaction to the generated response is monitored again using a camera and microphone, and the results are fed back to the generating AI to improve the quality of the next response.

[1302] Specific examples

[1303] For example, consider a case where a passenger feels stressed in a self-driving vehicle. Cameras and microphones capture the passenger's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine detects "stress" and sends the data to the generative AI. The generative AI then generates suggestions to reduce the "sense of stress" and instructs the car to play relaxing music. This entire process is automated.

[1304] Prompt Sentence Examples

[1305] For example, the prompt text to be input to the generative AI model is:

[1306] Based on the passenger's emotion analysis, generate an appropriate response to reduce the passenger's stress. For example, suggest playing relaxing music. Also consider the passenger's other emotions (happiness, anxiety, etc.) to generate the optimal response.

[1307] In this way, the system can improve passenger comfort and safety by combining emotion recognition and generative AI. The same method can also be applied to providing IT solutions to businesses, making optimal suggestions based on user emotions.

[1308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1309] Step 1:

[1310] Data collection

[1311] The server uses cameras and microphones inside the autonomous vehicle to collect passenger facial expressions and voices in real time. The camera captures the passenger's facial features, and the microphone records the passenger's voice, forming a dataset that reflects the passenger's current emotions.

[1312] Input: Camera video data, audio data

[1313] Output: Collected facial expression and voice data

[1314] Step 2:

[1315] emotion recognition

[1316] The server sends the collected facial and voice data to the emotion engine for analysis. The emotion engine uses machine learning algorithms to identify passenger emotions (stress, anxiety, joy, etc.) from the collected data, and outputs the passenger's emotional state as numerical data.

[1317] Input: facial expression data, voice data

[1318] Output: Emotion recognition data (e.g., stress = 0.8, joy = 0.2)

[1319] Step 3:

[1320] Interpretation and response generation by generative AI

[1321] The server sends the emotion recognition data from the emotion engine to the generation AI, which then generates appropriate responses and suggestions based on the passenger's emotions. For example, if stress levels are high, it can suggest relaxing music. The generation AI generates responses using prompt sentences based on past data and the analysis results of the emotion engine.

[1322] Input: Emotion recognition data

[1323] Output: Response data (e.g., music playback instruction)

[1324] Step 4:

[1325] Response execution

[1326] The server customizes the in-car environment and information provision based on the response data generated by the AI. Specifically, this includes music playback, lighting and temperature control in the car, etc. In this step, the server works in conjunction with the vehicle's internal control system to execute the instructions.

[1327] Input: Response data

[1328] Output: Changes made to the car environment (e.g., music playback, temperature adjustment)

[1329] Step 5:

[1330] Feedback and Improvements

[1331] After the server responds, it again monitors passenger reactions using cameras and microphones and feeds the resulting data back to the generative AI and emotion engine, improving the quality of the next response and further enhancing passenger comfort and safety.

[1332] Input: Passenger response data

[1333] Output: Feedback data (used for next analysis and response generation)

[1334] In this way, by processing and analyzing input data at each step and obtaining optimal output, a system is created that responds appropriately based on the passenger's emotional state, thereby improving passenger comfort and safety and enabling efficient service provision.

[1335] 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.

[1336] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1337] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1338] [Fourth embodiment]

[1339] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1340] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1341] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1342] 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.

[1343] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1344] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1345] 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.

[1346] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1347] 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.

[1348] 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 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.

[1349] 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.

[1350] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1351] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1352] This invention is a system that efficiently provides IT solutions to small businesses and startups. Specifically, it collects information from users through a website that utilizes generative AI, and then automates the entire process of proposing and implementing optimal IT solutions based on that information, all the way through to customer success.

[1353] System program and processing flow

[1354] 1. Collecting User Information

[1355] Users access the website and enter company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[1356] The terminal transmits the input information to the server.

[1357] 2. Solution proposal

[1358] The server receives the information sent from the device and requests the generating AI to analyze it.

[1359] The generative AI analyzes the information input by the user and proposes the optimal IT solution (e.g., a cloud-based remote desktop solution).

[1360] The server displays this proposal to the user, who then confirms the details.

[1361] 3. Solution Selection and Implementation

[1362] The user selects the best solution from the proposed solutions.

[1363] The terminal transmits the selection result to the server.

[1364] The server generates an implementation plan based on the selection results and automatically executes specific configuration and installation procedures.

[1365] 4. Supporting Customer Success

[1366] The server monitors the system in real time after installation, monitoring performance and usage.

[1367] If users have any questions or problems, they can contact us through the website.

[1368] The server sends the query to the generation AI and requests analysis.

[1369] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[1370] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[1371] Specific examples

[1372] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[1373] 1. Collecting User Information

[1374] An IT professional at TechStartup visits the website and enters "Optimizing the remote work environment" as an assignment.

[1375] The terminal transmits the information to the server.

[1376] 2. Solution proposal

[1377] The server receives the information and asks the generating AI to analyze it.

[1378] The generative AI generates a proposal called a "cloud-based remote desktop solution" and sends it back to the server.

[1379] The server displays this suggestion to the IT staff.

[1380] 3. Solution Selection and Implementation

[1381] The IT staff reviews the proposed solutions and selects one.

[1382] The terminal transmits the selection result to the server.

[1383] The server generates a deployment plan and automatically configures and installs the software.

[1384] 4. Supporting Customer Success

[1385] The server monitors the system in real time after installation.

[1386] If IT staff notice a problem, they can contact the website.

[1387] The server sends the query to the generating AI, requesting analysis and an answer.

[1388] The generation AI generates an answer, and the server notifies the person in charge.

[1389] The server periodically analyzes system usage and suggests improvements.

[1390] In this way, the system of the present invention provides an environment in which users can easily introduce IT solutions and also automates post-operation support, thereby helping small businesses efficiently develop and operate their IT infrastructure.

[1391] The processing flow will be explained below.

[1392] Step 1:

[1393] The user accesses the website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[1394] Step 2:

[1395] The terminal transmits the input information to the server.

[1396] Step 3:

[1397] The server receives the information sent from the terminal and stores it in a database.

[1398] Step 4:

[1399] The server converts the received data into a format that is easy for the generation AI to analyze, and requests the generation AI to analyze it.

[1400] Step 5:

[1401] Generative AI analyzes company information and issues, and generates multiple optimal IT solutions.

[1402] Step 6:

[1403] The generation AI sends the generated proposals to the server.

[1404] Step 7:

[1405] The server converts the proposed content into a format that is easy for the user to view, and transmits it to the terminal for display.

[1406] Step 8:

[1407] The user selects the best solution from the proposed solutions.

[1408] Step 9:

[1409] The terminal transmits the user's selection result to the server.

[1410] Step 10:

[1411] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[1412] Step 11:

[1413] The server will automatically perform the configuration and installation to complete the solution deployment.

[1414] Step 12:

[1415] The server monitors the system in real time after installation, monitoring performance and usage.

[1416] Step 13:

[1417] If a user has any questions or problems, they can contact us through the website.

[1418] Step 14:

[1419] The server receives an inquiry from the user and requests the generation AI to analyze it.

[1420] Step 15:

[1421] The generation AI analyzes the inquiry and generates an appropriate answer.

[1422] Step 16:

[1423] The server sends the answer from the generated AI to the user and displays it.

[1424] Step 17:

[1425] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[1426] In this way, servers, terminals, and users work together at each step to efficiently realize the entire process from proposing IT solutions to implementation and customer success.

[1427] Example 1

[1428] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] For today's small businesses and startups, the efficient implementation and operation of information technology solutions is crucial. However, these businesses often have limited technical resources and expertise, and therefore spend a great deal of time and money selecting, implementing, and supporting the appropriate solutions. Therefore, there is a need for a system that can simplify and automate these processes.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1431] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generative artificial intelligence model; a means for the generative AI model to propose an optimal information technology solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the terminal to send the selection results to the server; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system in real time after implementation and provide ongoing support; a means for the generative artificial intelligence model to generate answers to inquiries from the user; a means for the server to notify the user of the answers; and a means for the server to periodically analyze system usage and notify the user of additional suggestions and improvements, thereby enabling users to easily select, implement, and operate appropriate information technology solutions.

[1432] "User" refers to a company or individual who uses the system.

[1433] A "terminal" refers to a computer device operated by a user, and is a means for inputting information and transmitting it to a server.

[1434] "Server" refers to a central computer system that processes information sent from users and devices and works with generative AI models to propose and implement solutions.

[1435] "Generative artificial intelligence model" refers to an AI system that analyzes information provided by users and generates optimal information technology solutions.

[1436] "Information" refers to company information and current issues that users input into the system.

[1437] "Analysis" refers to the process by which a generative artificial intelligence model derives the optimal solution based on information provided by the user.

[1438] "Proposal" refers to the optimal information technology solution generated by the generative artificial intelligence model based on the analysis results.

[1439] "Implementation Plan" means a document describing the configuration and installation steps required to implement the proposed Solution.

[1440] "Monitoring" refers to the process of monitoring a system's performance and usage in real time after implementation.

[1441] "Inquiry" refers to a question or problem report that a user makes to the system.

[1442] "Answer" refers to the appropriate solution or information generated by a generative artificial intelligence model in response to a query.

[1443] "Usage status" refers to the performance and operational status of the system after its implementation, and refers to information that is periodically analyzed by the server.

[1444] This invention is a system for efficiently providing information technology solutions to small businesses and startups. Specifically, it collects information from users through a website using a generative artificial intelligence model, and then automates the entire process of proposing and implementing optimal information technology solutions based on that information, leading to customer success.

[1445] First, the user accesses a dedicated website via a browser. The user enters company information (e.g., company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). To do this, the user uses a device such as a PC, tablet, or smartphone. The device then sends the entered information to the server in JSON format.

[1446] The server receives the information sent from the device and requests a generative artificial intelligence model, such as OpenAI's GPT-3, to analyze the data. At this time, the server converts the collected information into a format that is easy for the generative artificial intelligence model to analyze. The generative artificial intelligence model generates an appropriate information technology solution based on the user's input information. For example, a "cloud-based remote desktop solution" may be proposed. The server then converts the generated analysis results into HTML format and displays them on a website in a user-friendly format.

[1447] Next, the user reviews the displayed proposals and selects the optimal solution from multiple options. The selection results are then sent back to the server from the device. The server then automatically generates a deployment plan based on the selection results, and performs instance configuration and service provisioning using, for example, AWS cloud services or Microsoft Azure. Specific configuration and installation procedures are performed by automated scripts.

[1448] After implementation, the server uses monitoring tools such as Datadog and New Relic to monitor system performance and usage in real time. When users have questions or problems, they can make inquiries via the website. The server sends the inquiry to the generative AI model for analysis and response. The generative AI model generates an appropriate response, and the server notifies the user of the response. In addition, the server regularly analyzes system usage and uses the generative AI model to generate additional suggestions and improvements, which are then notified to the user.

[1449] As a concrete example, consider the case where the company TechStartup wants to implement a new remote work environment. The IT staff at TechStartup accesses the website and inputs the challenge of "optimizing the remote work environment." The generative AI then suggests a "cloud-based remote desktop solution," and the IT staff selects this solution. The server configures the AWS cloud-based remote desktop and automatically installs it. After implementation, the system is monitored using Datadog, and if the IT staff notices a problem, they can inquire on the website and the generative AI will provide an appropriate response.

[1450] An example of a prompt to be input to the generative AI model might be something like, "The user wants to 'optimize their remote work environment.' The company information is as follows: Company name: TechStartup, Number of employees: 50, Industry: Technology, Current challenge: To establish secure access from outside the company. Please propose the optimal information technology solution."

[1451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user opens a browser and accesses a dedicated website. The user enters company information (company name, number of employees, industry) and current issues (optimizing the remote work environment). The input data here might be, for example, "TechStartup, 50, technology, optimizing the remote work environment." The entered information is sent from the device to the server in JSON format. At this point, the input is the user's company information and issues, and the output is the information sent to the server.

[1454] Step 2:

[1455] The server converts the received information into a format that is easy to parse. Specifically, the server converts JSON-formatted data into a parseable format and passes it to a generative AI model such as OpenAI's GPT-3. For example, converting JSON data into text format. The input of this step is the information sent from the device, and the output is an analysis request to the generative AI model.

[1456] Step 3:

[1457] The generative AI model analyzes the received information and generates the optimal information technology solution. The data calculations performed here are a process of evaluating various solutions based on the user's company information and challenges, and deriving the optimal solution. Specifically, it generates a proposal such as a "cloud-based remote desktop solution." The input to this step is the analysis request passed to the generative AI model, and the output is a proposed solution as the analysis result.

[1458] Step 4:

[1459] The server converts the suggestions received from the generative AI model into HTML format for display to the user. The output is a web page that is displayed in the user's browser. The input to this step is the suggestions from the generative AI model, and the output is an HTML page that is displayed to the user.

[1460] Step 5:

[1461] The user checks the displayed proposals and selects the optimal solution from the multiple options. The solution selected by the user is sent back to the server from the device. The input at this point is the user's selection result, and the output is the selection data sent to the server.

[1462] Step 6:

[1463] The server receives the selection results and automatically generates a deployment plan based on that information. Specifically, it configures instances and provisions services using AWS cloud services or Microsoft Azure. The server runs automated scripts to perform the necessary configuration and installation steps. The input for this step is the user's selection results, and the output is the specific steps in the deployment plan.

[1464] Step 7:

[1465] To monitor the system after the server is deployed, monitoring tools such as Datadog and New Relic are used to monitor the system's performance and usage in real time. The input of this step is data from the monitoring tool, and the output is system status information.

[1466] Step 8:

[1467] When a user has a question or problem with the system, they make an inquiry through the website. The inquiry is sent from the terminal to the server. The input of this step is the inquiry information from the user, and the output is the inquiry data sent to the server.

[1468] Step 9:

[1469] The server sends the query to the generative AI model, requesting analysis and an answer. The generative AI model generates an appropriate answer, and the server notifies the user of the answer. The input to this step is the query information from the user and an analysis request to the generative AI model, and the output is the answer data.

[1470] Step 10:

[1471] The server periodically analyzes system usage and generates additional suggestions and improvements using a generative AI model. It notifies the user of these suggestions and improvements. The input for this step is system usage data, and the output is notifications of suggestions and improvements.

[1472] (Application example 1)

[1473] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1474] Operators of small brick-and-mortar stores face challenges such as increasing sales and streamlining inventory management. However, it is difficult for each store to independently find and implement the optimal IT solution, requiring a great deal of time and resources. Furthermore, selecting the right marketing strategy and inventory management system requires specialized knowledge, and many store operators lack the means to solve these challenges. Against this backdrop, there is a need for a system that proposes efficient and optimal IT solutions for brick-and-mortar stores and automates everything from implementation to operational support.

[1475] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1476] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from a generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to inquiries from users; a means for the server to notify the users of the responses; a means for the server to periodically analyze system usage and notify additional suggestions and improvements; a means for collecting information about the operation of a physical store via an application installed on a smartphone and proposing a marketing strategy and an inventory management system based on the analysis results; and a means for sending information input by the user via the application to the generation AI and presenting the analysis results to the store operator. This enables physical store operators to easily introduce optimal IT solutions, improve sales, and streamline inventory management.

[1477] The "means for users to input information" refers to an interface that allows operators of physical stores to input basic information about their stores and the challenges they are currently facing.

[1478] "Means by which the device transmits input information to the server" refers to the communications protocols and infrastructure that allow smartphones and other devices to transmit user-entered information to a central server.

[1479] "Means by which the server receives information and requests analysis from the generation AI" refers to a series of processes in which the server sends the received user information to the generation AI system and requests analysis.

[1480] "Means for the generative AI to propose optimal solutions based on the analysis results" refers to the function in which the generative AI analyzes information entered by the user and generates appropriate IT solutions and operational improvement measures.

[1481] The "means by which the server displays the proposed content to the user" is a mechanism for displaying the solution generated as the analysis result on the user's terminal via the server.

[1482] The "means for the user to select a proposed solution" is an interface that allows the user to select the most suitable solution from the multiple solutions displayed.

[1483] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to a system that formulates an implementation plan based on the solution selected by the user and automatically performs the necessary settings and installation according to that plan.

[1484] "Means for the server to monitor the system after installation and provide ongoing support" is a mechanism that monitors installed systems in real time, analyzes their operational status, and automatically provides the necessary support.

[1485] "Means by which the generation AI generates answers to inquiries from users" refers to a function that analyzes the content of inquiries received from users and automatically generates appropriate answers.

[1486] The "means by which the server notifies the user of the answer" is a mechanism for promptly notifying the user of the generated answer.

[1487] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to a mechanism that periodically analyzes system usage data, automatically generates new suggestions and improvements, and notifies users.

[1488] "A means of collecting information about the operation of physical stores via an application installed on a smartphone and proposing marketing strategies and inventory management systems based on the analysis results" refers to a function that allows physical store operators to input information via a smartphone app and then proposes appropriate marketing strategies and inventory management systems based on that information.

[1489] "A means of sending information entered by the user through an application to a generation AI and presenting the analysis results to the store operator" refers to a system in which information entered via a smartphone app is sent to a generation AI and the analysis results are displayed to the store operator.

[1490] The present invention relates to a system for efficiently providing IT solutions to operators of small brick-and-mortar stores through a smartphone application that utilizes generative AI. The present invention is embodied in the following specific forms.

[1491] System Program

[1492] This system involves a series of processes in which users input information and the generative AI proposes optimal solutions based on that information. Specifically, users use a smartphone application to input the operational status and issues of their physical store, and send that data to a server. The server then requests the generative AI to analyze the received data and proposes appropriate marketing strategies and inventory management systems based on the analysis results.

[1493] Processing Description

[1494] Collecting user information

[1495] The user (physical store operator) first uses a smartphone application to enter information about their store, such as the store name, number of employees, type of business, and current operational challenges (e.g., improving inventory management efficiency, increasing sales, etc.). The hardware used is a smartphone, and the software is a dedicated application (e.g., an application developed with React Native).

[1496] Sending information from the device to the server

[1497] The device (smartphone) sends the entered information to the server. The communication technology and protocol used is HTTP or HTTPS, and communication is carried out with security in mind.

[1498] Data processing on the server

[1499] The server converts the information received from the user into a format that is easy to analyze. This includes processing such as data shaping and filtering. The software used is AWS Lambda and Node.js.

[1500] Analysis and solution proposals using generative AI

[1501] The server sends the formatted data to a generation AI (e.g., GPT-3) for analysis. The generation AI generates the optimal solution based on the user's input data and returns it to the server.

[1502] Display and selection of proposals

[1503] The server converts the analysis results received from the generation AI into a format that is easy for the user to view, and presents them to the user via a smartphone application. The user can then select the optimal solution from the presented solutions.

[1504] Generate and execute an implementation plan

[1505] Based on the selected solution, the server generates a deployment plan and automatically performs the necessary configuration and installation, including cloud-based resource configuration and automatic software installation.

[1506] Customer Success Support

[1507] The server monitors the system in real time after installation, notifying users if any problems occur and providing assistance. It also periodically analyzes system usage and notifies users of additional suggestions and improvements.

[1508] Examples of specific examples and prompts

[1509] For example, let's say a brick-and-mortar store called TechStore wants to streamline inventory management. The operator uses a smartphone application to enter the following information:

[1510] Store name: TechStore

[1511] Number of employees: 15

[1512] Industry: Home appliance sales

[1513] Challenge: Streamlining inventory management

[1514] This prompt is sent to the AI ​​generator, which then proposes the introduction of a barcode inventory management system as the analysis result. The server processes the analysis result and displays it to the user, who can then confirm and select the system, which will then be automatically introduced.

[1515] According to the present invention, operators of physical stores can easily introduce optimal IT solutions and improve operational efficiency, even without specialized knowledge.

[1516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1517] Step 1:

[1518] The user launches the smartphone application and enters store information (e.g., store name, number of employees, industry, issues, etc.).

[1519] Input: Store information (store name, number of employees, industry, issues)

[1520] Output: Store information data is saved in the device.

[1521] Step 2:

[1522] The terminal transmits the input store information to the server.

[1523] Input: Store information data stored in the device

[1524] Output: Store information data sent to the server

[1525] Step 3:

[1526] The server converts the received store information data into a format that is easy to analyze.

[1527] Input: Store information data sent to the server

[1528] Output: Store information data converted into a parsable format

[1529] Step 4:

[1530] The server sends analyzable store information data to the generation AI and requests it to analyze it.

[1531] Input: Store information data converted into a parsable format

[1532] Output: Analysis request data sent to the generation AI

[1533] Step 5:

[1534] Generative AI analyzes input data and generates optimal solutions (e.g., marketing strategies, inventory management systems, etc.).

[1535] Input: Analysis request data sent to the generation AI

[1536] Output: Generated optimal solution data

[1537] Step 6:

[1538] The server converts the solution data received from the generated AI into a format that is easy for the user to understand and displays it to the user through a smartphone application.

[1539] Input: Solution data received from the generation AI

[1540] Output: The solution data displayed to the user in a user-friendly format

[1541] Step 7:

[1542] The user selects the best solution from the presented solutions.

[1543] Input: User-friendly solution data displayed

[1544] Output: Selected solution data

[1545] Step 8:

[1546] The server generates a deployment plan based on the selected solution and automatically configures and installs according to that plan.

[1547] Input: Selected solution data

[1548] Output: Automatic configuration and installation steps

[1549] Step 9:

[1550] The server monitors the system in real time after installation, monitoring performance and usage.

[1551] Input: Settings and post-installation operational data

[1552] Output: Monitoring data collected in real time

[1553] Step 10:

[1554] If a user has any questions or problems, they can contact the company through the smartphone application.

[1555] Input: User inquiry

[1556] Output: Query data sent to the server

[1557] Step 11:

[1558] The server sends the query data to the generation AI, requesting analysis and an answer.

[1559] Input: Query data sent to the server

[1560] Output: Analysis request data sent to the generation AI

[1561] Step 12:

[1562] The generation AI analyzes the inquiry and generates an appropriate answer.

[1563] Input: Analysis request data sent to the generation AI

[1564] Output: Generated response data

[1565] Step 13:

[1566] The server notifies the user of the answer data received from the generating AI.

[1567] Input: Answer data received from the generation AI

[1568] Output: Answer data notified to the user

[1569] Step 14:

[1570] The server periodically analyzes system usage and notifies users of additional suggestions and improvements.

[1571] Input: Monitoring and usage data

[1572] Output: Additional suggestions and improvement notification data

[1573] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1574] This invention relates to a system for efficiently providing IT solutions to small businesses and startups. In particular, it is a system that combines generative AI and an emotion engine to recognize user emotions and propose more appropriate solutions. This system collects information from users via a website, and automates the entire process from proposing and implementing optimal IT solutions based on that information and emotions, to ensuring customer success.

[1575] System program and processing flow

[1576] 1. Collecting User Information

[1577] Users access the website and enter their company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, an emotion engine that recognizes the user's emotions is also running, and analyzes the user's emotions from the input information.

[1578] 2. Emotion Recognition by Emotion Engine

[1579] The emotion engine analyzes the user's input information and past usage history to recognize their emotions, which are then taken into consideration when proposing solutions.

[1580] 3. Solution proposal

[1581] The terminal transmits the input information and emotion information to the server.

[1582] The server receives the information and emotion data and requests the generative AI to analyze it. The generative AI generates optimal IT solutions based on the company information, issues, and recognized emotions.

[1583] The suggestions are adjusted based on the analysis results of the emotion engine and are displayed at the most appropriate time and in the most appropriate way for the user.

[1584] 4. Solution Selection and Implementation

[1585] The user selects the best solution from the proposed solutions.

[1586] The terminal transmits the selection result to the server.

[1587] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[1588] The server will automatically perform the configuration and installation to complete the solution deployment.

[1589] 5. Supporting Customer Success

[1590] The server monitors the system in real time after installation, monitoring performance and usage.

[1591] If a user has any questions or problems, they can contact us through the website.

[1592] The server sends the query to the generating AI, requesting analysis and an answer.

[1593] The generation AI generates an appropriate answer, and the server notifies the user of that answer.

[1594] The server periodically analyzes system usage and provides additional suggestions and improvements as needed.

[1595] Specific examples

[1596] For example, consider the case where a company called TechStartup wants to introduce a new remote work environment.

[1597] 1. Collecting User Information

[1598] An IT professional accesses the website and enters "optimizing the remote work environment" as a task. At this time, the emotion engine recognizes emotions such as stress and anticipation from the IT professional's input.

[1599] 2. Emotion Recognition by Emotion Engine

[1600] The emotion engine analyzes input data and past usage history to recognize "high expectations" and "slight stress."

[1601] 3. Solution proposal

[1602] The device transmits the information and emotion data to the server.

[1603] The server requests the AI ​​to analyze the data, and the AI ​​generates a proposal called a "cloud-based remote desktop solution." The AI ​​uses the results of the emotion engine as a reference and displays the proposal to the user in a way that reduces stress.

[1604] 4. Solution Selection and Implementation

[1605] The IT staff member reviews the proposed solutions and selects one. At this time, the emotion engine also analyzes the staff member's reactions and uses this information in the next proposal.

[1606] The terminal transmits the selection result to the server.

[1607] The server generates an implementation plan based on the selection results and automatically configures and installs the system.

[1608] 5. Supporting Customer Success

[1609] The server monitors the system after installation.

[1610] If IT staff notice a problem, they can contact the website.

[1611] The server requests a query from the generation AI and notifies the person in charge of the appropriate answer.

[1612] The server periodically analyzes the system and proposes improvements, while an emotion engine analyzes the responses of the agents to improve the quality of support.

[1613] In this way, the system of the present invention incorporates the user's emotions to provide optimal IT solutions, and is capable of efficiently carrying out everything from implementation to support, providing an environment in which small businesses can make the most of their resources.

[1614] The processing flow will be explained below.

[1615] Step 1:

[1616] A user accesses a website and enters company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment). At this time, the input form includes questions about emotions (e.g., feelings about the current issues), and the user also enters answers to those questions.

[1617] Step 2:

[1618] The terminal transmits the input company information, issue, and emotion data to the server.

[1619] Step 3:

[1620] The server receives the information sent from the terminal, stores it in a database, and has the emotion engine analyze the input data.

[1621] Step 4:

[1622] The emotion engine analyzes the user's input information and emotional data to recognize specific emotions such as "stress" or "expectation." The analysis results are stored in an emotion information database.

[1623] Step 5:

[1624] The server provides the received company information and emotional information to the generation AI and requests it to analyze it.

[1625] Step 6:

[1626] Generative AI generates optimal IT solutions based on company information, challenges, and perceived sentiment, for example, proposing multiple "cloud-based remote desktop solutions" or "team collaboration tools."

[1627] Step 7:

[1628] The server adjusts the suggestions received from the generation AI based on the results of the emotion engine and displays them in the most appropriate format and at the most appropriate time for the user. For example, a user feeling stressed will be shown a concise and reassuring explanation.

[1629] Step 8:

[1630] The user selects the best solution from the displayed suggestions.

[1631] Step 9:

[1632] The terminal transmits the user's selection result to the server.

[1633] Step 10:

[1634] The server generates an implementation plan based on the selection results and creates specific settings and installation procedures.

[1635] Step 11:

[1636] The server will automatically perform the configuration and installation to complete the solution deployment.

[1637] Step 12:

[1638] The server monitors the system in real time after installation, monitoring performance and usage.

[1639] Step 13:

[1640] If a user has a problem with the system or has a question, they can contact the system via the website.

[1641] Step 14:

[1642] The server receives an inquiry from the user and requests the generation AI to analyze it.

[1643] Step 15:

[1644] The generation AI analyzes the inquiry and generates an appropriate answer.

[1645] Step 16:

[1646] The server sends the answer from the generated AI to the user and displays it.

[1647] Step 17:

[1648] The server periodically analyzes system usage and, if necessary, provides additional suggestions or improvement measures. At this time, the emotion engine analyzes the user's reactions and uses them to improve the next suggestions and responses.

[1649] In this way, at each step, the server, terminal, emotion engine, generative AI, and user work together to realize a system that efficiently introduces and supports IT solutions while reflecting the user's emotions.

[1650] Example 2

[1651] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1652] Conventional IT solution proposal systems rarely provide appropriate proposals that take user emotions into consideration, making it difficult to select and implement efficient solutions. Furthermore, there was also the issue of insufficient ongoing support after implementation, making it difficult to improve user satisfaction.

[1653] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means including an emotion engine that analyzes user input information and generates emotion data, means for reflecting the emotion data generated by the emotion engine in a solution proposal, and means for requesting analysis from a generative AI model. This makes it possible to propose an optimal solution that takes the user's emotions into consideration, thereby realizing efficient implementation and continuous support.

[1654] "User" refers to an end user who uses the system to input information and propose or select solutions.

[1655] A "terminal" is a computing device that a user uses to enter information and send information to a server.

[1656] A "server" is a computing device that receives information sent by users, analyzes it, proposes solutions, and monitors the system.

[1657] A "generative AI model" is an artificial intelligence algorithm that analyzes input information from users and generates optimal solutions.

[1658] The "emotion engine" is a system component that analyzes user input information and generates user emotion data.

[1659] "Emotion data" is data that indicates the user's emotional state, generated by the emotion engine.

[1660] A "solution" is a technical or operational proposal that is suitable for solving a user's problem.

[1661] An "implementation plan" is a document that outlines the specific steps for implementing the selected solution.

[1662] "Continuous support" is a service that provides regular monitoring and suggests improvements to ensure that the implemented solution functions properly.

[1663] An "inquiry" is an action in which a user reports a question or problem to the system and requests a solution.

[1664] A "prompt sentence" is a sentence used as input to a generative AI model, and includes analysis instructions and questions.

[1665] This invention is a system that efficiently provides IT solutions that take user emotions into consideration for small businesses and startups. The system begins when a user accesses the system using a web browser and enters information. The server, terminal, and generative AI model work together to analyze the user's input, propose appropriate solutions, and consistently automate the process from implementation to support.

[1666] When entering information, users use a web browser (e.g., Google Chrome or Firefox) to access a website built with HTML, CSS, and JavaScript. Users enter their company information (company name, number of employees, industry) and current challenges (e.g., optimizing the remote work environment) into a form. The input data is sent in real time to an emotion engine (e.g., Azure Emotion API), which analyzes the user's emotions.

[1667] The emotion engine uses NLP technology to analyze the input text data and generate emotion data (e.g., "expectation" or "stress"). The generated emotion data is sent from the device to a server. The server receives the company information, issues, and emotion data sent by the user, and sends the data to a generative AI model (e.g., OpenAI's GPT-3) for analysis.

[1668] The generative AI model analyzes the received prompt (e.g., "A software development company with 50 employees wants to optimize its remote work environment. The user's emotions are 'high expectations' and 'slight stress'. Please propose the optimal IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution"). At this time, the model also takes into account the results of the emotion engine and adjusts the proposal content to use the most appropriate wording for the user.

[1669] The server displays the generated solution proposals to the user, who then selects the optimal solution. The selection results are sent from the terminal to the server, which then generates a specific deployment plan. The plan includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts). The server then executes the automated scripts to complete the deployment of the solution.

[1670] After deployment, the server uses agent software (e.g., Prometheus) to monitor the system in real time. When a user inquires about a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer. The generative AI model generates an appropriate answer, which the server notifies the user. The server also periodically analyzes system usage, evaluates user reactions using an emotion engine, and appropriately notifies users of additional suggestions and improvements.

[1671] In this way, by efficiently and automatically providing optimal IT solutions that take user emotions into consideration and providing consistent support from implementation to support, we provide an environment where small companies and startups can make the most of their resources.

[1672] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1673] Step 1: Gather user information

[1674] The user accesses the system's website using a web browser (e.g., Google Chrome or Firefox) and enters data into a form to enter company information (company name, number of employees, industry) and current issues (e.g., optimizing the remote work environment).

[1675] Input data is sent to the emotion engine in real time. The input is text data entered by the user, and the output is emotion data.

[1676] Step 2: Emotion recognition by the emotion engine

[1677] The emotion engine analyzes the received text data using natural language processing (NLP) technology to extract the user's emotions.

[1678] The extracted emotional data (e.g., "expectation" or "stress") is returned to the device and then transmitted from the device to the server.

[1679] The input is text data entered by the user, and the output is generated emotion data.

[1680] Step 3: Propose a solution

[1681] The server receives the company information, the task, and the emotion data transmitted from the terminal.

[1682] The server sends the received data to the generative AI model and requests it to analyze it.

[1683] The generative AI model analyzes prompt statements (e.g., "A software development company with 50 employees wants to optimize its remote work environment. User emotions are 'high expectations' and 'slight stress'. Please suggest the best IT solution for this company.") and generates the optimal solution (e.g., "a cloud-based remote desktop solution").

[1684] The inputs are company information, challenges, and sentiment data, and the output is the generated solution proposal.

[1685] Step 4: Select a solution

[1686] Users can review the proposed solutions through a web interface and select the most suitable one.

[1687] The selection result is sent from the terminal to the server.

[1688] The input is the generated solution proposal and the output is the user-selected solution.

[1689] Step 5: Generate and execute a deployment plan

[1690] The server generates a specific deployment plan based on the selection results, which includes configuration and installation procedures using automated scripts (e.g., Ansible, shell scripts).

[1691] The server runs automated scripts to automate configuration and installation tasks and complete the solution deployment.

[1692] The input is the user-selected solution, and the output is the completed configuration and installation using an automated script.

[1693] Step 6: Post-implementation system monitoring and support

[1694] The server uses agent software (e.g., Prometheus) to monitor the deployed system in real time, collecting and analyzing performance data and usage.

[1695] When a user submits a question or problem through the website, the server sends the query to the generative AI model, which analyzes and provides an answer.

[1696] The generative AI model generates an appropriate answer, which the server notifies the user.

[1697] It regularly analyzes system usage, evaluates user reactions using an emotion engine, and provides additional suggestions and improvements as needed.

[1698] The inputs are system performance data and user queries, and the outputs are analysis results and answers from the generative AI model, as well as additional suggestions and improvements.

[1699] (Application example 2)

[1700] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1701] It is difficult for small companies and startups to efficiently implement IT solutions while maximizing their resources, and there is no system in place to accurately recognize the emotions of passengers in autonomous vehicles and respond appropriately to ensure a comfortable experience. This leads to problems such as reduced passenger comfort and safety, and makes it difficult for companies to carry out their business efficiently.

[1702] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1703] In this invention, the server includes: a means for a user to input information; a means for a terminal to send the input information to the server; a means for the server to receive the information and request an analysis from the generation AI; a means for the generation AI to propose an optimal solution based on the analysis results; a means for the server to display the proposal to the user; a means for the user to select the proposed solution; a means for the server to generate an implementation plan based on the selection results and execute the implementation procedure; a means for the server to monitor the system after implementation and provide ongoing support; a means for the generation AI to generate responses to user inquiries; a means for the server to notify the user of the responses; a means for the server to periodically analyze system usage and notify the user of additional suggestions or improvements; a means for an emotion engine to analyze user emotions; a means for collecting passenger emotions using a camera and a microphone inside the autonomous vehicle; a means for the server to send passenger emotion data to the generation AI and generate a response; and a means for executing the generated response and customizing the in-vehicle environment and information provision. This enables autonomous vehicles to recognize passenger emotions in real time and provide a comfortable and safe environment.

[1704] "Means for users to input information" refers to the interface or device that users use to input their own information or tasks into the terminal.

[1705] The "means for transmitting information input by the terminal to the server" refers to a system or device for transmitting information input by the user to the server via a network.

[1706] "Means by which the server receives information and requests the generation AI to analyze it" refers to the process by which the server sends the information it receives to the generation AI and requests it to analyze it.

[1707] "Means by which generative AI proposes optimal solutions based on analysis results" refers to the process by which generative AI proposes IT solutions suitable for users based on the data analyzed.

[1708] "Means by which the server displays the proposal content to the user" refers to the means by which the server presents the proposal content obtained from the generation AI to the user in an easy-to-understand manner.

[1709] "Means for users to select proposed solutions" refers to the interface or process by which users select the most appropriate solution from the presented solutions.

[1710] "Means for the server to generate an implementation plan based on the selection results and execute the implementation procedures" refers to the process by which the server creates a specific implementation plan based on the user's selection and configures and implements the system accordingly.

[1711] "Means for the server to monitor the system after implementation and provide ongoing support" refers to the process of monitoring whether the implemented system is operating properly and providing support or improvements as necessary.

[1712] "Means by which the generative AI generates answers to user inquiries" refers to the process by which the AI ​​receives and analyzes the content of the user's inquiry and generates an appropriate answer.

[1713] "Means for the server to notify the user of the answer" refers to the means by which the generation AI communicates the answer provided to the user.

[1714] "Means for the server to periodically analyze system usage and notify users of additional suggestions and improvements" refers to the process of periodically analyzing system usage data and notifying users of new suggestions and improvements.

[1715] The "means by which the emotion engine analyzes the user's emotions" is an engine for analyzing the user's emotions from input information and past usage history.

[1716] "Means for collecting passenger emotions using cameras and microphones inside an autonomous vehicle" is a system that collects passengers' facial expressions and voices using cameras and microphones installed inside the vehicle.

[1717] "Means for the server to send passenger emotional data to the generation AI and generate a response" refers to the process of sending collected emotional data to the generation AI and generating an appropriate response.

[1718] "Means for executing the generated response and customizing the in-car environment and information provision" refers to means for adjusting the in-car environment and information provision based on the response content created by the generation AI.

[1719] This invention is a system that combines an emotion engine and generative AI to recognize user emotions and provide optimal solutions based on them. This system is primarily designed for small businesses and startups, but as an application example, we will introduce a passenger emotion response system in an autonomous vehicle.

[1720] In this embodiment, the system operates in the following manner.

[1721] 1. Hardware and Software

[1722] Hardware

[1723] Camera (to recognize passengers' facial expressions)

[1724] Microphone (to collect passenger voices)

[1725] Computer inside the vehicle (for data processing)

[1726] software

[1727] Emotion Engine

[1728] Generative AI (e.g., GPT-4)

[1729] Data analysis tools (e.g., TensorFlow)

[1730] Voice recognition software (e.g., Google Speech-to-Text)

[1731] 2. Data Collection

[1732] Using cameras and microphones inside the vehicle, facial expressions and voices of passengers are collected in real time, and the collected data is important for identifying passenger emotions.

[1733] 3. Emotion recognition

[1734] The collected data is sent to an Emotion Engine to analyze the passenger's emotions (e.g., stress, anxiety, joy, etc.), which allows the passenger's current emotional state to be identified.

[1735] 4. Interpretation and response generation by generative AI

[1736] The recognized emotion data is fed into generative AI, which then generates appropriate responses and suggestions accordingly – for example, if a passenger is feeling stressed, it will suggest playing relaxing music.

[1737] 5. Response Execution

[1738] Based on the generated responses, the in-car environment and information provision can be customized, for example by adjusting the lighting and temperature or playing relaxing music.

[1739] 6. Feedback and Improvement

[1740] The passenger's reaction to the generated response is monitored again using a camera and microphone, and the results are fed back to the generating AI to improve the quality of the next response.

[1741] Specific examples

[1742] For example, consider a case where a passenger feels stressed in a self-driving vehicle. Cameras and microphones capture the passenger's facial expressions and voice, which are then analyzed by the emotion engine. The emotion engine detects "stress" and sends the data to the generative AI. The generative AI then generates suggestions to reduce the "sense of stress" and instructs the car to play relaxing music. This entire process is automated.

[1743] Prompt Sentence Examples

[1744] For example, the prompt text to be input to the generative AI model is:

[1745] Based on the passenger's emotion analysis, generate an appropriate response to reduce the passenger's stress. For example, suggest playing relaxing music. Also consider the passenger's other emotions (happiness, anxiety, etc.) to generate the optimal response.

[1746] In this way, the system can improve passenger comfort and safety by combining emotion recognition and generative AI. The same method can also be applied to providing IT solutions to businesses, making optimal suggestions based on user emotions.

[1747] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1748] Step 1:

[1749] Data collection

[1750] The server uses cameras and microphones inside the autonomous vehicle to collect passenger facial expressions and voices in real time. The camera captures the passenger's facial features, and the microphone records the passenger's voice, forming a dataset that reflects the passenger's current emotions.

[1751] Input: Camera video data, audio data

[1752] Output: Collected facial expression and voice data

[1753] Step 2:

[1754] emotion recognition

[1755] The server sends the collected facial and voice data to the emotion engine for analysis. The emotion engine uses machine learning algorithms to identify passenger emotions (stress, anxiety, joy, etc.) from the collected data, and outputs the passenger's emotional state as numerical data.

[1756] Input: facial expression data, voice data

[1757] Output: Emotion recognition data (e.g., stress = 0.8, joy = 0.2)

[1758] Step 3:

[1759] Interpretation and response generation by generative AI

[1760] The server sends the emotion recognition data from the emotion engine to the generation AI, which then generates appropriate responses and suggestions based on the passenger's emotions. For example, if stress levels are high, it can suggest relaxing music. The generation AI generates responses using prompt sentences based on past data and the analysis results of the emotion engine.

[1761] Input: Emotion recognition data

[1762] Output: Response data (e.g., music playback instruction)

[1763] Step 4:

[1764] Response execution

[1765] The server customizes the in-car environment and information provision based on the response data generated by the AI. Specifically, this includes music playback, lighting and temperature control in the car, etc. In this step, the server works in conjunction with the vehicle's internal control system to execute the instructions.

[1766] Input: Response data

[1767] Output: Changes made to the car environment (e.g., music playback, temperature adjustment)

[1768] Step 5:

[1769] Feedback and Improvements

[1770] After the server responds, it again monitors passenger reactions using cameras and microphones and feeds the resulting data back to the generative AI and emotion engine, improving the quality of the next response and further enhancing passenger comfort and safety.

[1771] Input: Passenger response data

[1772] Output: Feedback data (used for next analysis and response generation)

[1773] In this way, by processing and analyzing input data at each step and obtaining optimal output, a system is created that responds appropriately based on the passenger's emotional state, thereby improving passenger comfort and safety and enabling efficient service provision.

[1774] 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.

[1775] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1777] 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.

[1778] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1779] 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.

[1780] 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).

[1781] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1782] 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."

[1783] 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.

[1784] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1785] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1790] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] The following is further disclosed regarding the above embodiment.

[1796] (Claim 1)

[1797] a means for a user to input information;

[1798] A means for transmitting input information from the terminal to a server;

[1799] The server receives the information and requests the generating AI to analyze it.

[1800] A means for generative AI to propose optimal solutions based on the analysis results,

[1801] a means for the server to display the suggestions to the user;

[1802] a means for a user to select a proposed solution;

[1803] A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure;

[1804] A means for the server to monitor the system after deployment and provide ongoing support;

[1805] A means for the generation AI to generate answers to user inquiries;

[1806] A means for the server to notify the user of the answer;

[1807] The server periodically analyzes system usage and provides additional suggestions and improvements.

[1808] A system including:

[1809] (Claim 2)

[1810] The system according to claim 1, further comprising means for converting the information collected by the server into a format that is easy for the generating AI to analyze.

[1811] (Claim 3)

[1812] The system according to claim 1, further comprising means for converting the analysis results from the generated AI into a format that is easy for the user to view and display the results.

[1813] "Example 1"

[1814] (Claim 1)

[1815] a means for a user to input information;

[1816] A means for transmitting input information from the terminal to a server;

[1817] A means for the server to receive the information and request the generative artificial intelligence model to analyze it;

[1818] A means for the generative artificial intelligence model to propose optimal information technology solutions based on the analysis results;

[1819] a means for the server to display the suggestions to the user;

[1820] a means for a user to select a proposed solution;

[1821] A means for the terminal to transmit the selection result to the server;

[1822] A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure;

[1823] The server provides real-time monitoring of the system after deployment and provides ongoing support.

[1824] A means for the generative artificial intelligence model to generate an answer to a user's inquiry;

[1825] A means for the server to notify the user of the answer;

[1826] The server periodically analyzes system usage and provides additional suggestions and improvements.

[1827] A system including:

[1828] (Claim 2)

[1829] The system of claim 1, further comprising means for converting the information collected by the server into a format that is easy for the generating artificial intelligence model to analyze.

[1830] (Claim 3)

[1831] 2. The system according to claim 1, wherein the server includes means for converting the analysis results from the generated artificial intelligence model into a format that is easy for the user to view and displaying the results.

[1832] "Application Example 1"

[1833] (Claim 1)

[1834] a means for a user to input information;

[1835] A means for transmitting input information from the terminal to a server;

[1836] The server receives the information and requests the generating AI to analyze it.

[1837] A means for generative AI to propose optimal solutions based on the analysis results,

[1838] a means for the server to display the suggestions to the user;

[1839] a means for a user to select a proposed solution;

[1840] A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure;

[1841] A means for the server to monitor the system after deployment and provide ongoing support;

[1842] A means for the generation AI to generate answers to user inquiries;

[1843] A means for the server to notify the user of the answer;

[1844] The server periodically analyzes system usage and provides additional suggestions and improvements.

[1845] A means to collect information on the operation of physical stores through an application installed on a smartphone and propose marketing strategies and inventory management systems based on the analysis results.

[1846] A means to send the information entered by the user through the application to the generation AI and present the analysis results to the store operator;

[1847] A system including:

[1848] (Claim 2)

[1849] The system according to claim 1, further comprising means for converting the information collected by the server into a format that is easy for the generating AI to analyze.

[1850] (Claim 3)

[1851] The system according to claim 1, further comprising means for converting the analysis results from the generated AI into a format that is easy for the user to view and display the results.

[1852] "Example 2: Combining Emotion Engines"

[1853] (Claim 1)

[1854] a means for a user to input information;

[1855] A means for transmitting input information from the terminal to a server;

[1856] A means for the server to receive information and request analysis from the generative AI model;

[1857] A means for the generative AI model to propose optimal solutions based on the analysis results,

[1858] a means for the server to display the suggestions to the user;

[1859] a means for a user to select a proposed solution;

[1860] A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure;

[1861] A means for the server to monitor the system after deployment and provide ongoing support;

[1862] A means for the generative AI model to generate answers to user inquiries;

[1863] A means for the server to notify the user of the answer;

[1864] The server periodically analyzes system usage and provides additional suggestions and improvements.

[1865] means including an emotion engine that analyzes user input information and generates emotion data;

[1866] A means for reflecting the emotional data generated by the emotion engine in solution proposals;

[1867] A system including:

[1868] (Claim 2)

[1869] The system of claim 1, further comprising means for converting the information collected by the server into a format that is easy for the generative AI model to analyze.

[1870] (Claim 3)

[1871] The system according to claim 1, wherein the server includes means for converting the analysis results from the generated AI model into a format that is easy for the user to view and displaying the results.

[1872] "Application example 2 when combining emotion engines"

[1873] (Claim 1)

[1874] a means for a user to input information;

[1875] A means for transmitting input information from the terminal to a server;

[1876] The server receives the information and requests the generating AI to analyze it.

[1877] A means for generative AI to propose optimal solutions based on the analysis results,

[1878] a means for the server to display the suggestions to the user;

[1879] a means for a user to select a proposed solution;

[1880] A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure;

[1881] A means for the server to monitor the system after deployment and provide ongoing support;

[1882] A means for the generation AI to generate answers to user inquiries;

[1883] A means for the server to notify the user of the answer;

[1884] The server periodically analyzes system usage and provides additional suggestions and improvements.

[1885] a means for the emotion engine to analyze the user's emotion;

[1886] A means for collecting passenger emotions using cameras and microphones inside the autonomous vehicle;

[1887] A means for the server to transmit passenger emotion data to the generation AI and generate a response;

[1888] A means for executing the generated responses to customize the in-vehicle environment and information provision; an...

Claims

1. a means for a user to input information; A means for transmitting input information from the terminal to a server; The server receives the information and requests the generating AI to analyze it. A means for generative AI to propose optimal solutions based on the analysis results, a means for the server to display the suggestions to the user; a means for a user to select a proposed solution; A means for the server to generate an implementation plan based on the selection result and execute an implementation procedure; A means for the server to monitor the system after deployment and provide ongoing support; A means for the generation AI to generate answers to user inquiries; A means for the server to notify the user of the answer; The server periodically analyzes system usage and provides additional suggestions and improvements. A system including:

2. The system according to claim 1, further comprising means for converting the information collected by the server into a format that is easy for the generating AI to analyze.

3. The system according to claim 1, further comprising means for converting the analysis results from the generated AI into a format that is easy for the user to view and displaying the results.

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