System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting basic information of an own company; means for collecting work data of employees; means for analyzing the input and collected data and selecting an optimum software service; means for generating and displaying a candidate list of software services based on an analysis result; means for providing a tutorial for supporting introduction of the software services; and means for monitoring a use status of the introduced software services and providing an improvement plan or a replacement plan.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] Create a "Problem that the invention aims to solve" and a "Means for solving the problem."

[0005] In Japan, small and medium-sized enterprises (SMEs) need to increase efficiency with a small number of staff, given that improving productivity due to a declining population is a key issue. However, gathering information online or through sales representatives is inefficient, making it difficult to select the optimal software service (SaaS). In addition, verifying effectiveness after implementation and proposing replacements is time-consuming and labor-intensive. Given this background, there is a demand for a system that allows for the easy and quick selection of the optimal SaaS, and also includes post-implementation support. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems with a system that includes the following means: a means for inputting basic company information, a means for collecting employee work data, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating and displaying a list of candidate software services based on the analysis results, a means for providing tutorials to assist in the implementation of software services, and a means for monitoring the usage of implemented software services and providing improvement and replacement proposals. This system allows users to efficiently select the optimal SaaS for their company and optimize operations by monitoring the effectiveness even after implementation.

[0007] Understood. Below are definitions of important terms included in the claims.

[0008] "Means for entering basic information about your company" refers to an interface that allows users to enter information such as their company's industry, size, tools currently being used, and issues.

[0009] "Means for collecting employee business data" refers to tools and systems that monitor each employee's work activities and application usage and collect that data.

[0010] "Means of analyzing and selecting the most suitable software service" refers to the process of identifying the most suitable software service for a user based on collected data using machine learning algorithms, etc.

[0011] The "means for generating and displaying a list of candidate software services" is an interface that presents to the user in list form a plurality of software services that have been determined to be optimal based on the analysis results.

[0012] The "means for providing tutorials to support the introduction of software services" is a system that provides training materials and tutorials necessary for the effective introduction and use of selected software services.

[0013] "Means for monitoring the usage of introduced software services and providing improvement and replacement proposals" refers to a system that constantly monitors the usage of introduced software services, evaluates the effectiveness of their use, and presents improvement and replacement proposals to users as necessary. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] Understood. Below is the "Mode for carrying out the invention" based on the claims.

[0036] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness.

[0037] System Overview

[0038] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data, select the most suitable SaaS, assist with implementation, monitor its usage, and make suggestions for improvement.

[0039] Example of a system

[0040] Entering user information

[0041] 1. Users

[0042] Users enter basic information about their company (industry, size, current tools used, challenges) using a web form or dedicated application.

[0043] Collection of employee work data

[0044] 1. Users

[0045] Users install a dedicated work monitoring tool in each employee's work environment, which monitors and collects data on employee work activities and application usage.

[0046] 2. Server

[0047] The server receives the data sent from the business monitoring tool and stores it in a database.

[0048] Data analysis and optimal SaaS selection

[0049] 1. Server

[0050] The server uses machine learning algorithms to analyze the collected data and user-entered information, and then identifies the SaaS that best suits your company.

[0051] 2. Server

[0052] Based on the analysis results, a list of optimal SaaS candidates is generated, including details such as the benefits, expected impact, and implementation costs of each candidate.

[0053] Presenting a candidate list

[0054] 1. Terminal

[0055] The terminal displays the list of SaaS candidates sent from the server in a dashboard format to the user, who can then compare and consider each SaaS based on the provided information.

[0056] Providing onboarding support and tutorials

[0057] 1. Users

[0058] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[0059] 2. Server

[0060] The server provides tutorials on selected SaaS.

[0061] 3. Terminal

[0062] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[0063] Usage monitoring and improvement suggestions

[0064] 1. Server

[0065] The server constantly monitors the usage of the introduced SaaS and collects data.

[0066] 2. Server

[0067] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[0068] 3. Server

[0069] If necessary, the server will suggest improvements or replacements to the user.

[0070] Specific examples

[0071] The retail case

[0072] 1. Users

[0073] A retail business owner fills out a web form with basic information about his company (50 employees, inventory inefficiencies, Excel as the tool of choice).

[0074] 2. Users

[0075] Management installs performance monitoring tools to collect employee performance data.

[0076] 3. Server

[0077] The server receives the collected data and basic information and analyzes it using machine learning algorithms.

[0078] 4. Server

[0079] Based on the analysis results, a candidate list of SaaS specialized for inventory management (e.g., specific inventory management tools) is generated.

[0080] 5. Terminal

[0081] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[0082] 6. Server

[0083] Provide a tutorial for the inventory management tool that management has decided to implement.

[0084] 7. Terminal

[0085] Employees use the terminals to go through tutorials and learn how to use the tools.

[0086] 8. Server

[0087] The server monitors the usage of the introduced tools and, if necessary, proposes improvement ideas to management.

[0088] This system allows small and medium-sized enterprises to efficiently select and implement the SaaS that is best suited to their company, maximizing its effectiveness.

[0089] The processing flow will be explained below.

[0090] Understood. Below is a step-by-step explanation of the process.

[0091] Step 1:

[0092] Users enter basic information about their company (e.g., industry, size, tools currently being used, challenges) through a web form or a dedicated app.

[0093] Step 2:

[0094] Users install a dedicated work monitoring tool on each employee's PC and work environment, which automatically collects application usage data, work hours, and other data.

[0095] Step 3:

[0096] The server receives basic information sent from a web form or a dedicated app and stores it in a database.

[0097] Step 4:

[0098] The server receives business data periodically sent from the business monitoring tool and stores it in a database.

[0099] Step 5:

[0100] The server uses machine learning algorithms to analyze the collected basic information and business data, and identifies the software services (SaaS) needed to optimize the user's business processes.

[0101] Step 6:

[0102] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool.

[0103] Step 7:

[0104] The terminal displays the candidate list sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[0105] Step 8:

[0106] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[0107] Step 9:

[0108] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[0109] Step 10:

[0110] The terminal displays tutorial content to each employee, who then learns how to use the SaaS.

[0111] Step 11:

[0112] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[0113] Step 12:

[0114] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[0115] Step 13:

[0116] Based on the analysis results, the server will propose improvements or replacements to the user as needed, ensuring optimal operation at all times.

[0117] This process allows companies to efficiently select the SaaS that is best suited to their business and receive ongoing support to maximize its effectiveness even after implementation.

[0118] Example 1

[0119] 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."

[0120] The process for companies to select and implement the software service (SaaS) that is best suited to their business is complex and time-consuming. It is also difficult to continuously evaluate the effectiveness of implementation and propose improvements as needed. Small and medium-sized enterprises, in particular, often lack specialized knowledge and resources, making it difficult to efficiently select the optimal SaaS and maximize its effectiveness.

[0121] 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.

[0122] In this invention, the server includes means for inputting basic information about the company, means for collecting employee business data, means for analyzing the input and collected data and selecting the optimal software service, means for generating and displaying a list of candidate software services based on the analysis results, means for providing tutorials to assist in the introduction of software services, means for monitoring the usage of the introduced software service and providing improvement and replacement proposals, means for collecting usage data of the introduced software service and saving it in a database in real time, and means for analyzing the collected data with a machine learning algorithm. This enables companies to efficiently select the optimal SaaS for their company and continuously maximize its effectiveness even after introduction.

[0123] "Basic information about your company" refers to basic information such as your company's industry, size, tools you currently use, and challenges you face.

[0124] "Employee Business Data" refers to data about employee work activities and application usage.

[0125] "Analysis" refers to the process of analyzing collected data using machine learning algorithms and extracting useful information.

[0126] "Candidate list of software services" refers to a list of multiple software services that are deemed to be optimal for a company, obtained as a result of data analysis.

[0127] "Tutorial" means a means of providing guides and examples on how to install and use selected software services.

[0128] "Real-time" refers to a method in which data is collected, analyzed, and stored immediately, reflecting the latest information almost instantly.

[0129] "Database" refers to a system for centrally storing and managing collected data.

[0130] A "machine learning algorithm" refers to a computational method for training models based on large amounts of data to extract or predict patterns and trends.

[0131] "Improvement proposals" refer to specific methods and measures proposed to evaluate the usage of the implemented software service and to improve its effectiveness.

[0132] "Replacement proposal" refers to a proposal for an alternative software service when the current software service does not perform as expected.

[0133] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and continuously evaluate and optimize its effectiveness. This system involves a series of processes in which users input basic company information, collect and analyze employee work data, select the best SaaS, support the implementation, monitor its usage, and make improvement suggestions.

[0134] Entering user information

[0135] User

[0136] Users use dedicated web forms and applications to enter information about their company's industry, size, tools currently being used, challenges they are facing, etc. For example, when a user enters basic information about their company into a web form and presses the "Submit" button, this information is sent to the server.

[0137] Collection of employee work data

[0138] User

[0139] Users install a business monitoring tool into each employee's work environment. This tool monitors business activities and application usage and collects the data. The tool installed on each computer runs in the background and collects business data in real time.

[0140] Receiving and storing business data

[0141] server

[0142] The server periodically receives data sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in the storage. The receiving and storing process is automated and takes place in real time.

[0143] Data analysis and identification of optimal SaaS candidates

[0144] server

[0145] The server passes the stored data and basic information entered by the user to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. This analysis can then identify the best SaaS for the company.

[0146] Generate a list of potential SaaS

[0147] server

[0148] Based on the analysis results, the server generates a list of multiple SaaS candidates that are deemed most suitable for the company, including detailed information such as the characteristics, benefits, expected effects, and implementation costs of each SaaS.

[0149] View SaaS candidate list

[0150] Terminal

[0151] The terminal receives the candidate list from the server and displays it to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[0152] SaaS selection and implementation

[0153] User

[0154] The user selects the SaaS that is most suitable for their company from the displayed list of candidates and clicks the "Decide" button to decide on implementation. The selection results are sent to the server, and the system proceeds to the next step.

[0155] Providing tutorials

[0156] server

[0157] The server generates and serves tutorial content for selected SaaS services, including usage guides, implementation procedures, and best practices.

[0158] View tutorial

[0159] Terminal

[0160] The terminal displays the received tutorial content to the employee, who can then learn how to use the SaaS through the displayed tutorial.

[0161] Usage monitoring and data collection

[0162] server

[0163] The server monitors the usage of the installed SaaS in real time and collects usage data through APIs, which are then stored in storage.

[0164] Usage analysis and improvement suggestions

[0165] server

[0166] The server analyzes the collected usage data using machine learning algorithms to evaluate usage patterns and performance, and based on the analysis results, generates recommendations for improvements or replacement of other SaaS services as needed and notifies the user.

[0167] Examples of prompt statements

[0168] 1. "Please provide some basic information about your company. Please be specific about your industry, size, current tools you use, and challenges you face."

[0169] 2. "To learn how to install the business monitoring tool, please follow these steps."

[0170] 3. "View a dashboard with a list of the best SaaS candidates. See each candidate's benefits, expected impact, implementation costs, and more."

[0171] 4. "We will explain the steps to implement the selected SaaS and provide a tutorial for employees."

[0172] 5. "We will monitor the usage of the implemented SaaS and propose improvements or replacements."

[0173] In this way, this system enables companies to efficiently select and implement the SaaS that is best suited to their company, and continuously maximize its effectiveness.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The user enters basic information about their company. The user enters information such as their company's industry, company size, tools currently being used, and challenges they are facing into a web form or dedicated application, and then presses the "Submit" button. This operation sends the entered information to the server, which then stores the received basic information in a database.

[0177] Step 2:

[0178] The user installs the business monitoring tool. The user downloads the tool to each employee's computer and follows the installation wizard to complete the installation. After installation, the tool begins collecting business data in the background and prepares to send it to the server.

[0179] Step 3:

[0180] The server receives business data and stores it in a database. The server receives business activity and application usage data periodically sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in storage. This process is carried out in real time.

[0181] Step 4:

[0182] The server analyzes the collected data and identifies the best SaaS candidates. The server passes the stored business data and basic information to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. The analysis performed here identifies the best SaaS candidates for the company.

[0183] Step 5:

[0184] The server generates a list of SaaS candidates. Based on the analysis results, the server generates a list of multiple SaaS candidates that are considered to be most suitable for the company. This list includes the name, characteristics, advantages, expected effects, implementation costs, etc. of each SaaS. The generated list is sent to the terminal in the next step.

[0185] Step 6:

[0186] The terminal displays the list of SaaS candidates. The terminal displays the list of SaaS candidates received from the server to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[0187] Step 7:

[0188] The user selects the optimal SaaS and decides to implement it. The user selects the SaaS that is most suitable for their company from the displayed list of SaaS candidates and clicks the "Decide" button. This operation sends the selection results to the server, and the actual implementation procedure begins in the next step.

[0189] Step 8:

[0190] The server provides a tutorial for the introduced SaaS. The server generates detailed tutorial content (video, text, guidelines, etc.) for the SaaS selected by the user and transmits it to the user's device.

[0191] Step 9:

[0192] The terminal displays the tutorial. The terminal displays the tutorial content received from the server to the employee. Employees can learn and practice how to use the selected SaaS by following the on-screen guide.

[0193] Step 10:

[0194] The server monitors the usage of the SaaS and collects data. The server monitors the usage of the deployed SaaS in real time and stores the collected usage data in a database via API. This data is used for subsequent performance evaluation.

[0195] Step 11:

[0196] The server analyzes usage and makes improvement suggestions as needed. The server analyzes the stored usage data using machine learning algorithms to evaluate performance. Based on the results, it generates improvement suggestions or suggestions for replacing other SaaS as needed, and notifies the user. This notification is sent via the dashboard or email.

[0197] (Application example 1)

[0198] 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."

[0199] Content distribution companies face challenges in efficiently selecting and implementing the software services best suited to their company, and continuously evaluating and optimizing their effectiveness. Analyzing viewing data and selecting the optimal tool takes a significant amount of time and effort. It is also not easy to properly monitor the usage of the implemented tools and make necessary improvements or replacements. Furthermore, the explanations of the candidate lists provided are sometimes insufficient, so it is necessary to provide information in a format that makes it easy for users to understand and select.

[0200] 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.

[0201] In this invention, the server includes means for analyzing user information and collected viewing data and proposing optimal content distribution platforms and related tools, means for generating an explanation of the candidate list based on the analysis results using a generative AI model, and means for automatically generating prompts for the generative AI model. This enables content distribution companies to efficiently select the software services that are best suited to their company, easily compare and consider them, and appropriately monitor and optimize their usage even after implementation.

[0202] "Basic information about your company" refers to basic information for identifying your company, such as your company's industry, size, tools you currently use, and challenges.

[0203] "Employee business data" refers to data related to work, such as the tools used by employees, their usage status, work content, work hours, and viewing data.

[0204] "Software services" is a general term for services that provide software functions on a cloud basis and that users can use via the Internet.

[0205] "Analysis" is the process of analyzing input basic information and collected business data to derive meaningful information and patterns.

[0206] The "candidate list" is a list of optimal software services selected based on the analysis results.

[0207] A "tutorial" is a step-by-step instruction manual or guide that explains how to install and use a particular software service.

[0208] "Viewing data" refers to data collected when a user views content, including the viewing time of a particular piece of content, viewer feedback, and the like.

[0209] A "content distribution platform" is a service for distributing digital content such as video, audio, and text over the Internet.

[0210] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate responses or information in response to specific inputs.

[0211] A "prompt" is text that a generative AI model uses as input to generate a particular output.

[0212] This invention is a system that allows companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness. In particular, the system optimized for content distribution companies is shown below.

[0213] System configuration

[0214] The system includes the following elements:

[0215] 1. User Information and Business Data Collection Tools

[0216] A way to enter basic information about your company.

[0217] A means of collecting employee work data and viewing data.

[0218] 2. Data Analysis and SaaS Selection Tools

[0219] The collected data is analyzed and machine learning algorithms are used to select the most appropriate software services.

[0220] Based on collected user information and viewing data, we propose the most suitable content distribution platform and related tools.

[0221] 3. Viewing and Management Tools

[0222] A means of generating a list of software service candidates based on the analysis results and displaying them in a dashboard format.

[0223] A means of providing tutorials to assist with the adoption of software services.

[0224] A means of monitoring the usage of deployed software services and providing suggestions for improvement or replacement.

[0225] 4. Generative AI Models

[0226] A generative AI model to generate candidate list explanations based on analysis results.

[0227] A means of automatically generating prompts for generative AI models.

[0228] Example of a system

[0229] Collection of user information and viewing data

[0230] Users enter basic information about their company (industry, company size, tools currently used, challenges) using a web form or dedicated application, and also install a business monitoring tool in each employee's work environment to collect viewing data and work activities.

[0231] As a concrete example, a company operating in the content distribution business would enter its industry as "content distribution," its number of employees as "150," the tools it uses as "Vimeo, Google Analytics," and the challenges it faces as "low viewership, high server costs."

[0232] Data analysis and selection of optimal SaaS

[0233] The server uses a database containing the collected data to run machine learning algorithms to analyze the data and generate a list of content distribution platforms and related tools that are best suited to the company based on the collected viewing data and company information.

[0234] Software used includes Scikit-learn for running machine learning algorithms, Pandas and SQL databases for processing and storing data.

[0235] For example, the analysis results may result in a candidate list of "video encoding tools" and "advertising distribution platforms."

[0236] Presenting a candidate list

[0237] The server displays the generated candidate list in dashboard format on the user's device, and also generates prompt sentences that explain the analysis results using the generative AI model and displays the explanations on the dashboard.

[0238] For example, GPT is used as a generative AI model.

[0239] Providing onboarding support and tutorials

[0240] The user selects the most suitable software service from the list of candidates displayed on the dashboard and decides to implement it. The server then provides the corresponding tutorial and displays it on the employee's terminal.

[0241] Usage monitoring and improvement suggestions

[0242] The server monitors the usage of the installed software services, provides suggestions for improvement or replacement as needed, and evaluates the effectiveness of the software based on the viewing data and business data used.

[0243] Prompt Sentence Examples

[0244] For example, input the following prompt sentence into the generative AI model:

[0245] Based on your viewing data analysis and company information, please list the most suitable video encoding tools, analysis tools, and ad serving platforms. Please also provide information on the benefits, expected results, and implementation costs of each tool.

[0246] Industry: Content Distribution

[0247] Number of employees: 150

[0248] Current tools used: Vimeo, Google Analytics

[0249] Challenges: Low viewership, high server costs

[0250] This helps businesses effectively select software services by providing specific descriptions for each tool on the shortlist.

[0251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0252] Step 1:

[0253] Users enter basic information about their company into a web form or dedicated application. Specifically, they enter information such as the industry, company size, tools currently used, and challenges they are facing. The input data is sent to a server and stored in a database. A company profile is generated based on the input (basic company information).

[0254] Step 2:

[0255] The user installs a business process monitoring tool into each employee's work environment. This tool collects the software used by the employee, the work they are doing, and viewing data. The collected data is sent to a server and stored in a database. Based on the input (data collected by the business process monitoring tool), a profile of the business process data and viewing data is created.

[0256] Step 3:

[0257] The server runs a machine learning algorithm to analyze the collected company information and business data. Software such as Scikit-learn is used for this, and Pandas is used for data processing. As a result of the analysis, a candidate list of optimal software services (video encoding tools, ad distribution platforms, etc.) is generated. Analysis is performed based on the input (company information and business data), and a candidate list of software services is obtained as output.

[0258] Step 4:

[0259] The server uses a generative AI model to generate detailed descriptions of the candidate list. Specifically, a prompt sentence is input to a generative AI model such as GPT, and based on that, a description of each tool in the candidate list is generated, including its benefits, expected effects, and implementation costs. The generative AI model generates text based on the input (prompt sentence), and a detailed candidate list is obtained as output.

[0260] Step 5:

[0261] The server displays the generated candidate list and its detailed explanations on the user's device in the form of a dashboard. This dashboard contains detailed information about each candidate, allowing the user to select the most suitable software service based on that information. The dashboard is generated and displayed based on the input (detailed candidate list).

[0262] Step 6:

[0263] For each software service selected by the user, the server provides a tutorial to assist with implementation. The tutorial details how to install and use the software and is displayed on the employee's terminal to help the employee learn the new tool. Based on the input (the selected software service), an appropriate tutorial is generated and displayed as output on the user's terminal.

[0264] Step 7:

[0265] The server continuously monitors the usage of the installed software service. The monitoring results are used to evaluate whether the usage is producing the expected results based on the collected viewing data and business data. If necessary, the server proposes improvement or replacement proposals to the user. Analysis is performed based on the input (monitoring data), and improvement or replacement proposals are provided to the user as output.

[0266] 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.

[0267] Understood. Below, we will describe the "Mode for carrying out the invention" based on the scope of the patent claims of the invention combining an emotion engine.

[0268] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. In addition, by combining it with an emotion engine that recognizes user emotions, it is possible to further improve user satisfaction.

[0269] System Overview

[0270] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[0271] Example of a system

[0272] Entering user information

[0273] 1. Users

[0274] Using a web form or a dedicated application, users enter basic information about their company (e.g., industry, size, current tools used, challenges), as well as a simple questionnaire to assess the user's emotional state.

[0275] Collecting employee work and sentiment data

[0276] 1. Users

[0277] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment, which monitors and collects data on employee work activities and application usage.

[0278] The emotion engine analyzes emotions based on user operations and inputs and collects emotion data.

[0279] 2. Server

[0280] The server receives the data sent from the business monitoring tool and the emotion engine and stores it in a database.

[0281] Data analysis and optimal SaaS selection

[0282] 1. Server

[0283] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data, and identifies the SaaS that best suits the company.

[0284] 2. Server

[0285] Based on the analysis results, a list of optimal SaaS candidates is generated, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool. Sentiment data is also taken into consideration, and candidates that are likely to satisfy the user are presented with priority.

[0286] Presenting a candidate list

[0287] 1. Terminal

[0288] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[0289] Providing onboarding support and tutorials

[0290] 1. Users

[0291] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[0292] 2. Server

[0293] The server searches for and provides tutorial content related to the selected SaaS.

[0294] 3. Terminal

[0295] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[0296] Usage monitoring and improvement suggestions

[0297] 1. Server

[0298] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[0299] The emotion engine also monitors the user's emotional state, for example analyzing stress levels and satisfaction levels in real time while operating the device.

[0300] 2. Server

[0301] The server analyzes the collected usage data and sentiment data to evaluate whether the introduced SaaS is having the expected effect.

[0302] 3. Server

[0303] If necessary, the server will propose improvements or replacements to the user. Based on the emotion data, it will make suggestions to improve the user's satisfaction.

[0304] Specific examples

[0305] The retail case

[0306] 1. Users

[0307] A retail manager enters basic information about his or her company (50 employees, inventory inefficiencies, spreadsheet software used) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[0308] 2. Users

[0309] Management installs business monitoring tools and sentiment engines to collect business and sentiment data.

[0310] 3. Server

[0311] The server receives the collected data and analyzes it using machine learning algorithms.

[0312] 4. Server

[0313] Based on the analysis results, a list of candidates for SaaS specialized in inventory management (e.g., specific inventory management tools) is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[0314] 5. Terminal

[0315] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[0316] 6. Server

[0317] Provide tutorials for selected inventory management tools.

[0318] 7. Terminal

[0319] Employees receive tutorials via terminals to learn how to use the new tools.

[0320] 8. Server

[0321] The server monitors the usage of the introduced tools, evaluates user sentiment data, and, if necessary, proposes improvements or replacements to management.

[0322] This system allows small and medium-sized enterprises to efficiently select the SaaS that is best suited to their company, receive ongoing support to maximize its effectiveness even after implementation, and provides optimal support that takes user emotions into consideration.

[0323] The processing flow will be explained below.

[0324] Understood. Below I will explain the specific process step by step.

[0325] Step 1:

[0326] Users enter basic information about their company (e.g., industry, size, tools currently used, challenges) and emotional state information in the form of questions via a web form or a dedicated app.

[0327] Step 2:

[0328] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment. The work monitoring tool collects application usage data and work hours, while the emotion engine collects emotion data based on user operations and input.

[0329] Step 3:

[0330] The server receives basic information and emotional state data sent via a web form or a dedicated app and stores it in a database.

[0331] Step 4:

[0332] The server receives the business data and emotion data periodically sent from the business monitoring tool and emotion engine, and stores them in a database.

[0333] Step 5:

[0334] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotion data, and identifies SaaS solutions that will optimize business efficiency and user satisfaction.

[0335] Step 6:

[0336] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including details such as each tool's benefits, expected impact, implementation costs, and satisfaction predictions based on sentiment data.

[0337] Step 7:

[0338] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare and consider them.

[0339] Step 8:

[0340] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[0341] Step 9:

[0342] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[0343] Step 10:

[0344] The device displays the appropriate tutorial content for each employee, who then learns how to use the SaaS.

[0345] Step 11:

[0346] The server constantly monitors the usage of the implemented SaaS. Specifically, it collects data to evaluate the frequency of tool use, effectiveness, and any problems that arise. In addition, the emotion engine monitors and analyzes the user's emotional state (e.g., stress, satisfaction).

[0347] Step 12:

[0348] The server analyzes the collected usage and sentiment data to assess whether the SaaS is performing as expected.

[0349] Step 13:

[0350] The server then proposes improvements or replacements to the user based on the analysis results, and makes specific suggestions for improvement to increase user satisfaction based on the emotional data.

[0351] This process allows companies to efficiently select the SaaS that is best suited to their business, receive ongoing support to maximize effectiveness even after implementation, and provide optimal support that takes user emotions into consideration.

[0352] Example 2

[0353] 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."

[0354] Traditionally, it has been difficult for companies to select the optimal software service (SaaS) for their organization and continuously evaluate and optimize its effectiveness after implementation. In particular, software selection and optimization that takes into account not only employees' work efficiency but also their emotional state has not been practiced. This has led to a decline in the accuracy of software selection and post-use satisfaction in companies, which can ultimately have a negative impact on the efficiency and productivity of the entire organization.

[0355] 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 a means for inputting basic information about the company, a means for collecting work data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating a list of candidate software services based on the analysis results and displaying the list while taking the emotional data into consideration, a means for providing a tutorial to assist in the introduction of the software service, and a means for monitoring the usage status of the introduced software service and providing improvement or replacement suggestions. This enables a company to efficiently select the optimal software service for the company and to continuously optimize the software service while taking the emotions of employees into consideration even after the introduction.

[0356] "Basic information about your company" refers to basic company profile information, such as the company's industry, size, current tools used, and challenges.

[0357] "Employee Business Data" refers to data relating to the usage of applications and work progress used by employees in the course of their daily work.

[0358] "Emotional data" refers to data collected through real-time analysis of the emotional states displayed by employees while they are working.

[0359] "Analysis" refers to the process of analyzing data using machine learning algorithms based on collected basic information, business data, and sentiment data.

[0360] The "optimal software service" refers to the software service that is determined to be most suitable for the user based on the analysis results.

[0361] "Candidate List" refers to a list of multiple recommended software services generated based on the analysis results.

[0362] "Considering emotional data" refers to evaluating the user's emotional state and reflecting the results when selecting and proposing software services.

[0363] "Tutorial" means educational content that explains how to install and use selected Software Services.

[0364] "Monitoring" means the continuous monitoring of the usage of the Deployed Software Services.

[0365] "Improvement Suggestions" means suggestions for improving the current Software Services based on Usage Data and Sentiment Data.

[0366] "Replacement Proposal" means a proposal to replace a currently used software service with another software service.

[0367] "Dashboard format" refers to a set of interface formats that present information in an easy-to-visually organize manner.

[0368] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes employee emotions, it is possible to improve user satisfaction.

[0369] System Overview

[0370] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[0371] Entering user information

[0372] User

[0373] Using a dedicated web form or application, users enter basic information about their company (e.g., industry, size of employee base, current tools used, specific challenges, etc.) and also answer a questionnaire with sentiment assessment questions. This information serves as the basis for customization within the system.

[0374] Collecting employee work and sentiment data

[0375] User

[0376] Users install the task monitoring tool and emotion engine on employees' PCs and work environments, which monitor and collect data on employee activity (e.g., active windows, keyboard input, mouse movements) in real time.

[0377] The emotion engine analyzes and collects emotional data based on employees' reactions to operations and inputs.

[0378] server

[0379] The server receives data sent from the business monitoring tool and emotion engine and stores it in a database. The data is automatically transferred at regular intervals.

[0380] Data analysis and optimal SaaS selection

[0381] server

[0382] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data, and analyzes the data to determine business patterns, efficiency, and trends in emotional changes.

[0383] Based on the analysis results, a list of SaaS candidates optimal for the company is generated. The list includes detailed information about each software service (benefits, expected effects, implementation costs, and user satisfaction). In particular, sentiment data is taken into consideration, and candidates with high employee satisfaction are prioritized.

[0384] Presenting a candidate list

[0385] Terminal

[0386] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, visually organizing each tool's implementation cost, user ratings, and highlights of its main features.

[0387] User

[0388] Users can review the list of candidates through a dashboard and compare detailed information about each SaaS.

[0389] Providing onboarding support and tutorials

[0390] User

[0391] Users can select the SaaS they deem most suitable from the list of candidates and decide to implement it. They can start the implementation process for the selected SaaS with just one click.

[0392] server

[0393] The server searches for tutorial content related to the selected SaaS and provides an appropriate tutorial, such as an installation guide or initial setup manual.

[0394] Terminal

[0395] The terminal displays the provided tutorial on each employee's screen, allowing employees to learn how to use the SaaS by referring to the tutorial.

[0396] Usage monitoring and improvement suggestions

[0397] server

[0398] The server constantly monitors the usage of the introduced SaaS, specifically recording in detail the frequency of tool use, its effectiveness, and any problems that arise.

[0399] The emotion engine analyzes the user's emotional state (e.g., stress level and satisfaction level during operation) in real time.

[0400] server

[0401] Based on the collected usage and sentiment data, we evaluate whether the introduced SaaS is delivering the expected results. If necessary, we generate improvement or replacement proposals and present them to users. For example, we make specific proposals such as "proposing the addition of new functions to improve the operability of the current tool" or "proposing migration to another tool."

[0402] Specific examples

[0403] The retail case

[0404] 1. Users

[0405] A retail manager enters information about his company (50 employees, inventory management inefficiencies, current tool used: spreadsheet) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[0406] 2. Users

[0407] The manager installs a work monitoring tool and emotion engine on each employee's PC to collect work data and emotion data. After installation, the manager checks with the employee to see if the tools are working properly.

[0408] 3. Server

[0409] The server receives the collected data and analyzes it using machine learning algorithms, such as data on work efficiency and employee sentiment.

[0410] 4. Server

[0411] Based on the analysis results, a list of candidates for SaaS (specific inventory management tools) specialized for inventory management is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[0412] 5. Terminal

[0413] The candidate list is displayed in dashboard format on the manager's device, and the manager can check detailed information (implementation costs, benefits, user ratings, etc.) and select the tool to implement.

[0414] 6. Server

[0415] Provide managers with tutorials for selected inventory management tools, such as installation guides and initial setup instructions.

[0416] 7. Terminal

[0417] Employees learn how to use the new tools through tutorials delivered via the terminal.

[0418] 8. Server

[0419] The server monitors the usage of the introduced tools and evaluates user sentiment data. If necessary, it proposes improvements or replacements to management. For example, it may suggest adding new features or migrating to another tool.

[0420] Example prompts for generative AI models

[0421] We are developing a system to select the optimal SaaS. The system collects basic user information, employee work data, and emotional data, analyzes them using machine learning, and then proposes the optimal SaaS. Could you please explain each process step in detail?

[0422] This system allows companies to efficiently select the software services that are best suited to their business, and even after implementation, it enables continuous optimization that takes into account employee sentiment.

[0423] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0424] Step 1:

[0425] User

[0426] Users use a web form or a dedicated application to enter basic information about their company (industry type, size, tools currently used, challenges), and answer questionnaire-style questions for sentiment assessment. This information is used as input data for customizing the system. After input, it is sent to the server.

[0427] Step 2:

[0428] User

[0429] The user installs the work monitoring tool and emotion engine on each employee's PC and work environment. After installation, the user checks whether the tools are working properly. The work monitoring tool monitors employee activity (active windows, keyboard input, mouse movement) and collects data. The emotion engine analyzes and collects emotion data based on reactions to operations and inputs.

[0430] Step 3:

[0431] server

[0432] The server receives data (business data and emotion data) sent from the business monitoring tool and emotion engine and stores it in a database. Data transfer is performed automatically at regular intervals. This ensures that the latest data is always stored on the server and available for analysis.

[0433] Step 4:

[0434] server

[0435] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data. The analysis process includes analyzing business patterns, evaluating efficiency, and analyzing sentiment trends. This analysis provides data to identify the best SaaS for each company.

[0436] Step 5:

[0437] server

[0438] Based on the analysis results, the server generates a list of SaaS candidates that are optimal for the company. The generated candidate list includes detailed information about each software service (benefits, expected effectiveness, implementation costs, and user satisfaction). Emotional data is also taken into consideration, and candidates with high user satisfaction are prioritized in the list.

[0439] Step 6:

[0440] Terminal

[0441] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The dashboard visually organizes the software implementation costs, user ratings, and highlights of key features. The user can then use this information to make a detailed comparison.

[0442] Step 7:

[0443] User

[0444] Users can select the most suitable SaaS from a list of candidates through the dashboard and decide to implement it. The implementation process of the selected SaaS can be started with one click. When the selected SaaS is selected as input, related information is sent from the server.

[0445] Step 8:

[0446] server

[0447] The server searches for tutorial content related to the selected SaaS and provides the appropriate tutorial, which includes installation guides, initial setup instructions, and detailed usage instructions, helping employees smoothly get started with the new software.

[0448] Step 9:

[0449] Terminal

[0450] The terminal displays the provided tutorial on each employee's screen. Employees refer to the tutorial to learn how to use the SaaS and then actually operate it. The input in this step is the tutorial content, and the output is employee learning and skill improvement.

[0451] Step 10:

[0452] server

[0453] The server constantly monitors the usage of the introduced SaaS. Specifically, it records in detail the frequency of tool use, its effectiveness, and any problems that occur. The emotion engine also analyzes the user's emotional state in real time. This makes it possible to identify problems and areas for improvement during operation.

[0454] Step 11:

[0455] server

[0456] Based on the collected usage data and sentiment data, the introduced SaaS is evaluated to see if it is producing the expected results. If necessary, improvement or replacement proposals are generated and presented to the user. For example, specific proposals such as "proposals to add new functions to improve operability" or "proposals to migrate to other tools" can be presented. The input for this step is usage data and sentiment data, and the output is specific improvement proposals.

[0457] (Application example 2)

[0458] 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."

[0459] Conventional software service (SaaS) selection and implementation support systems are limited in their effectiveness because they only analyze a company's business data and do not take into account qualitative information such as employee emotions and satisfaction. Furthermore, when monitoring usage or proposing improvements after implementation, emotional data is not taken into account, leading to problems such as lower employee satisfaction and reduced work efficiency. However, by collecting and analyzing emotional data along with business data, it becomes possible to select the optimal SaaS with greater accuracy and continuously optimize its effectiveness even after implementation. Therefore, a comprehensive system that includes employee emotional data is needed.

[0460] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information about the company, a means for collecting business data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating and displaying a list of candidate software services based on the analysis results, a means for providing tutorials to assist in the introduction of software services, and a means for monitoring the usage of introduced software services and providing improvement and replacement proposals that include emotional data. This enables companies to select and introduce the optimal SaaS based on both business data and emotional data, and to continuously optimize its effectiveness.

[0461] Understood. Below are the definitions of important terms included in the patent claims, rewritten to fit the application example.

[0462] "Basic information" refers to initial data about a company or organization, such as its industry, size, tools in use, and challenges.

[0463] "Business data" refers to quantitative data such as task completion status, error rate, and working hours generated by employees through their daily work activities.

[0464] "Emotional data" refers to data related to an employee's emotional state, including qualitative data such as stress levels and satisfaction.

[0465] "Analysis" is the process of analyzing collected basic information, business data, and emotional data using statistical methods and machine learning algorithms to extract meaningful information.

[0466] "Software as a Service (SaaS)" is a software application that can be used over the Internet and is a tool that helps improve the efficiency and management of corporate operations.

[0467] A "candidate list" is a list of proposed software services generated based on the analysis results, detailing the features and benefits of each service.

[0468] The "dashboard format" is an interface format for intuitively displaying multiple pieces of information, and is a layout that makes extensive use of visual elements such as graphs and charts.

[0469] A "tutorial" is educational content that explains how to use the introduced software service and is a guide for users to efficiently utilize the tool.

[0470] "Monitoring" is the process of continuously observing and collecting data about the use of deployed software services.

[0471] "Improvement proposals" are specific proposals for improving the efficiency of use of software services and user satisfaction based on the results of monitoring and analysis.

[0472] A "machine learning algorithm" is an algorithm that automatically learns patterns and rules from large amounts of data and makes predictions and classifications based on new data.

[0473] An "emotion analysis engine" is a software module that analyzes an employee's emotional state based on their behavior and input data, and evaluates their stress and satisfaction.

[0474] These definitions clarify the technical scope of the invention and allow accurate understanding of the characteristics of the invention based on the claims.

[0475] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes user emotions, user satisfaction can be further improved. This article explains the main components and processing procedures of this system.

[0476] System Overview

[0477] 1. Enter your user information

[0478] Users enter basic information about their company (industry, size, tools currently used, challenges) using a web form or dedicated application, and are also provided with a questionnaire form to assess the user's emotional state, thereby collecting emotional data.

[0479] 2. Collecting employee work and sentiment data

[0480] Users install a dedicated work monitoring tool and emotion engine into each employee's work environment (PC and work environment). This tool monitors employees' work activities and application usage and collects that data. The emotion engine also analyzes emotions based on user operations and inputs and collects emotion data.

[0481] 3. Data analysis and selection of optimal SaaS

[0482] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data. This analysis identifies the SaaS that are best suited to the company and generates a candidate list. This candidate list includes detailed information such as the benefits, expected effectiveness, and implementation costs of each tool, and also takes emotional data into account to prioritize and present candidates that will satisfy the user.

[0483] 4. Presenting a candidate list

[0484] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The user can check detailed information about each SaaS and compare them through this dashboard. An index of user satisfaction based on emotional data is also displayed.

[0485] 5. Providing onboarding support and tutorials

[0486] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it. The server searches for and provides tutorial content related to the selected SaaS. The terminal displays the appropriate tutorial for each employee, and the employee learns how to use the SaaS.

[0487] 6. Monitoring usage and suggesting improvements

[0488] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc. The emotion engine also monitors the user's emotional state, analyzing, for example, stress levels and satisfaction during operation in real time. The server analyzes the collected usage data and emotion data to evaluate whether the introduced SaaS is delivering the expected results. If necessary, the server proposes improvement or replacement plans to the user. Based on the emotion data, it also makes improvement proposals to increase user satisfaction.

[0489] Specific examples

[0490] For example, consider the case where this system is implemented in a manufacturing factory with 50 employees. The manager of the factory enters basic information about the company (industry type, size, tools used, challenges) into a web form and also conducts a sentiment assessment. The manager also installs a business monitoring tool and sentiment engine on each employee's PC to collect business and sentiment data. The server analyzes the collected data using a machine learning algorithm and generates a list of candidate SaaS solutions, for example, specialized in inventory management. The dashboard also displays detailed information about each SaaS solution and a satisfaction index based on the sentiment data. The manager selects a SaaS solution based on this information and provides training to employees using tutorials. The server continues to monitor usage, analyzes sentiment data, and proposes improvement measures as needed.

[0491] Prompt Sentence Examples

[0492] "Select the optimal SaaS using employee data and sentiment data from within the factory. Business data:

[0493] Factory name: Manufacturing ABC

[0494] Industry: Manufacturing

[0495] Scale: Large

[0496] Tools in use: ERP, MES

[0497] Employee Data:

[0498] Employee ID: 1

[0499] Business data: { "tasks_completed": 10, "errors": 1}

[0500] Emotional data: { "happiness": 0.7, "stress": 0.3}

[0501] Please suggest the best SaaS candidates based on this information.

[0502] In this way, companies can select the most suitable software services based on both business and emotional data, and then effectively implement and utilize them. This system also improves employee satisfaction and achieves work efficiency.

[0503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0504] Step 1:

[0505] The user enters basic information about their company. Using a web form or a dedicated application, the user enters information such as the company's industry, size, tools currently being used, and challenges. This generates basic information data. As the user enters information, a questionnaire form is also provided to assess the user's emotional state, and emotional data is also collected. The basic information data and emotional data are sent as input to the server.

[0506] Step 2:

[0507] The user collects employee work data and emotional data. A dedicated work monitoring tool and emotional engine are installed on each employee's PC and work environment. This monitors and collects employees' work activities (task completion status, error rate, etc.) and emotional states (stress levels, satisfaction, etc.). The collected work data and emotional data are sent to a server.

[0508] Step 3:

[0509] The server analyzes the collected data and selects the optimal software service (SaaS). The server receives basic information data, business data, and emotion data as input and performs analysis using a machine learning algorithm. It analyzes the correlation between each data item and generates a list of optimal software service candidates. The analysis results in a list containing detailed information such as the benefits, expected effects, and implementation costs of each candidate. This list is generated as output and sent to the terminal.

[0510] Step 4:

[0511] The terminal displays the candidate list in a dashboard format. The terminal receives the candidate list sent from the server and displays it in a format that is intuitive to the user. A visual dashboard using graphs and charts displays detailed information about each SaaS and an index of user satisfaction based on emotional data. This allows the user to compare each candidate.

[0512] Step 5:

[0513] The user selects the optimal software service and decides to implement it. The user selects the SaaS they deem most suitable based on the candidate list displayed on the dashboard. After selection, the selection information is sent to the server, and the necessary tutorials are provided in the next phase.

[0514] Step 6:

[0515] The server searches for and provides tutorial content related to the selected software service. The server searches for content related to the target SaaS from the tutorial database and outputs it to the terminal. The terminal displays the received tutorial content to each employee, allowing them to learn how to use the SaaS.

[0516] Step 7:

[0517] The server monitors the usage of the installed software service and performs analysis, including emotional data. Data is collected to evaluate frequency of use, effectiveness, problems, etc., and an emotional engine is used to analyze the user's emotional state. For example, stress levels and satisfaction levels during operation are monitored in real time. The collected data is used to evaluate the effectiveness of the SaaS and generate improvement or replacement proposals as needed. Improvement proposals are output to the user, and measures to improve user satisfaction are proposed.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] 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. 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] In the smart glasses 214, 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.

[0533] 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."

[0534] Understood. Below is the "Mode for carrying out the invention" based on the claims.

[0535] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness.

[0536] System Overview

[0537] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data, select the most suitable SaaS, assist with implementation, monitor its usage, and make suggestions for improvement.

[0538] Example of a system

[0539] Entering user information

[0540] 1. Users

[0541] Users enter basic information about their company (industry, size, current tools used, challenges) using a web form or dedicated application.

[0542] Collection of employee work data

[0543] 1. Users

[0544] Users install a dedicated work monitoring tool in each employee's work environment, which monitors and collects data on employee work activities and application usage.

[0545] 2. Server

[0546] The server receives the data sent from the business monitoring tool and stores it in a database.

[0547] Data analysis and optimal SaaS selection

[0548] 1. Server

[0549] The server uses machine learning algorithms to analyze the collected data and user-entered information, and then identifies the SaaS that best suits your company.

[0550] 2. Server

[0551] Based on the analysis results, a list of optimal SaaS candidates is generated, including details such as the benefits, expected impact, and implementation costs of each candidate.

[0552] Presenting a candidate list

[0553] 1. Terminal

[0554] The terminal displays the list of SaaS candidates sent from the server in a dashboard format to the user, who can then compare and consider each SaaS based on the provided information.

[0555] Providing onboarding support and tutorials

[0556] 1. Users

[0557] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[0558] 2. Server

[0559] The server provides tutorials on selected SaaS.

[0560] 3. Terminal

[0561] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[0562] Usage monitoring and improvement suggestions

[0563] 1. Server

[0564] The server constantly monitors the usage of the introduced SaaS and collects data.

[0565] 2. Server

[0566] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[0567] 3. Server

[0568] If necessary, the server will suggest improvements or replacements to the user.

[0569] Specific examples

[0570] The retail case

[0571] 1. Users

[0572] A retail business owner fills out a web form with basic information about his company (50 employees, inventory inefficiencies, Excel as the tool of choice).

[0573] 2. Users

[0574] Management installs performance monitoring tools to collect employee performance data.

[0575] 3. Server

[0576] The server receives the collected data and basic information and analyzes it using machine learning algorithms.

[0577] 4. Server

[0578] Based on the analysis results, a candidate list of SaaS specialized for inventory management (e.g., specific inventory management tools) is generated.

[0579] 5. Terminal

[0580] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[0581] 6. Server

[0582] Provide a tutorial for the inventory management tool that management has decided to implement.

[0583] 7. Terminal

[0584] Employees use the terminals to go through tutorials and learn how to use the tools.

[0585] 8. Server

[0586] The server monitors the usage of the introduced tools and, if necessary, proposes improvement ideas to management.

[0587] This system allows small and medium-sized enterprises to efficiently select and implement the SaaS that is best suited to their company, maximizing its effectiveness.

[0588] The processing flow will be explained below.

[0589] Understood. Below is a step-by-step explanation of the process.

[0590] Step 1:

[0591] Users enter basic information about their company (e.g., industry, size, tools currently being used, challenges) through a web form or a dedicated app.

[0592] Step 2:

[0593] Users install a dedicated work monitoring tool on each employee's PC and work environment, which automatically collects application usage data, work hours, and other data.

[0594] Step 3:

[0595] The server receives basic information sent from a web form or a dedicated app and stores it in a database.

[0596] Step 4:

[0597] The server receives business data periodically sent from the business monitoring tool and stores it in a database.

[0598] Step 5:

[0599] The server uses machine learning algorithms to analyze the collected basic information and business data, and identifies the software services (SaaS) needed to optimize the user's business processes.

[0600] Step 6:

[0601] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool.

[0602] Step 7:

[0603] The terminal displays the candidate list sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[0604] Step 8:

[0605] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[0606] Step 9:

[0607] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[0608] Step 10:

[0609] The terminal displays tutorial content to each employee, who then learns how to use the SaaS.

[0610] Step 11:

[0611] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[0612] Step 12:

[0613] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[0614] Step 13:

[0615] Based on the analysis results, the server will propose improvements or replacements to the user as needed, ensuring optimal operation at all times.

[0616] This process allows companies to efficiently select the SaaS that is best suited to their business and receive ongoing support to maximize its effectiveness even after implementation.

[0617] Example 1

[0618] 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."

[0619] The process for companies to select and implement the software service (SaaS) that is best suited to their business is complex and time-consuming. It is also difficult to continuously evaluate the effectiveness of implementation and propose improvements as needed. Small and medium-sized enterprises, in particular, often lack specialized knowledge and resources, making it difficult to efficiently select the optimal SaaS and maximize its effectiveness.

[0620] 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.

[0621] In this invention, the server includes means for inputting basic information about the company, means for collecting employee business data, means for analyzing the input and collected data and selecting the optimal software service, means for generating and displaying a list of candidate software services based on the analysis results, means for providing tutorials to assist in the introduction of software services, means for monitoring the usage of the introduced software service and providing improvement and replacement proposals, means for collecting usage data of the introduced software service and saving it in a database in real time, and means for analyzing the collected data with a machine learning algorithm. This enables companies to efficiently select the optimal SaaS for their company and continuously maximize its effectiveness even after introduction.

[0622] "Basic information about your company" refers to basic information such as your company's industry, size, tools you currently use, and challenges you face.

[0623] "Employee Business Data" refers to data about employee work activities and application usage.

[0624] "Analysis" refers to the process of analyzing collected data using machine learning algorithms and extracting useful information.

[0625] "Candidate list of software services" refers to a list of multiple software services that are deemed to be optimal for a company, obtained as a result of data analysis.

[0626] "Tutorial" means a means of providing guides and examples on how to install and use selected software services.

[0627] "Real-time" refers to a method in which data is collected, analyzed, and stored immediately, reflecting the latest information almost instantly.

[0628] "Database" refers to a system for centrally storing and managing collected data.

[0629] A "machine learning algorithm" refers to a computational method for training models based on large amounts of data to extract or predict patterns and trends.

[0630] "Improvement proposals" refer to specific methods and measures proposed to evaluate the usage of the implemented software service and to improve its effectiveness.

[0631] "Replacement proposal" refers to a proposal for an alternative software service when the current software service does not perform as expected.

[0632] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and continuously evaluate and optimize its effectiveness. This system involves a series of processes in which users input basic company information, collect and analyze employee work data, select the best SaaS, support the implementation, monitor its usage, and make improvement suggestions.

[0633] Entering user information

[0634] User

[0635] Users use dedicated web forms and applications to enter information about their company's industry, size, tools currently being used, challenges they are facing, etc. For example, when a user enters basic information about their company into a web form and presses the "Submit" button, this information is sent to the server.

[0636] Collection of employee work data

[0637] User

[0638] Users install a business monitoring tool into each employee's work environment. This tool monitors business activities and application usage and collects the data. The tool installed on each computer runs in the background and collects business data in real time.

[0639] Receiving and storing business data

[0640] server

[0641] The server periodically receives data sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in the storage. The receiving and storing process is automated and takes place in real time.

[0642] Data analysis and identification of optimal SaaS candidates

[0643] server

[0644] The server passes the stored data and basic information entered by the user to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. This analysis can then identify the best SaaS for the company.

[0645] Generate a list of potential SaaS

[0646] server

[0647] Based on the analysis results, the server generates a list of multiple SaaS candidates that are deemed most suitable for the company, including detailed information such as the characteristics, benefits, expected effects, and implementation costs of each SaaS.

[0648] View SaaS candidate list

[0649] Terminal

[0650] The terminal receives the candidate list from the server and displays it to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[0651] SaaS selection and implementation

[0652] User

[0653] The user selects the SaaS that is most suitable for their company from the displayed list of candidates and clicks the "Decide" button to decide on implementation. The selection results are sent to the server, and the system proceeds to the next step.

[0654] Providing tutorials

[0655] server

[0656] The server generates and serves tutorial content for selected SaaS services, including usage guides, implementation procedures, and best practices.

[0657] View tutorial

[0658] Terminal

[0659] The terminal displays the received tutorial content to the employee, who can then learn how to use the SaaS through the displayed tutorial.

[0660] Usage monitoring and data collection

[0661] server

[0662] The server monitors the usage of the installed SaaS in real time and collects usage data through APIs, which are then stored in storage.

[0663] Usage analysis and improvement suggestions

[0664] server

[0665] The server analyzes the collected usage data using machine learning algorithms to evaluate usage patterns and performance, and based on the analysis results, generates recommendations for improvements or replacement of other SaaS services as needed and notifies the user.

[0666] Examples of prompt statements

[0667] 1. "Please provide some basic information about your company. Please be specific about your industry, size, current tools you use, and challenges you face."

[0668] 2. "To learn how to install the business monitoring tool, please follow these steps."

[0669] 3. "View a dashboard with a list of the best SaaS candidates. See each candidate's benefits, expected impact, implementation costs, and more."

[0670] 4. "We will explain the steps to implement the selected SaaS and provide a tutorial for employees."

[0671] 5. "We will monitor the usage of the implemented SaaS and propose improvements or replacements."

[0672] In this way, this system enables companies to efficiently select and implement the SaaS that is best suited to their company, and continuously maximize its effectiveness.

[0673] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0674] Step 1:

[0675] The user enters basic information about their company. The user enters information such as their company's industry, company size, tools currently being used, and challenges they are facing into a web form or dedicated application, and then presses the "Submit" button. This operation sends the entered information to the server, which then stores the received basic information in a database.

[0676] Step 2:

[0677] The user installs the business monitoring tool. The user downloads the tool to each employee's computer and follows the installation wizard to complete the installation. After installation, the tool begins collecting business data in the background and prepares to send it to the server.

[0678] Step 3:

[0679] The server receives business data and stores it in a database. The server receives business activity and application usage data periodically sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in storage. This process is carried out in real time.

[0680] Step 4:

[0681] The server analyzes the collected data and identifies the best SaaS candidates. The server passes the stored business data and basic information to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. The analysis performed here identifies the best SaaS candidates for the company.

[0682] Step 5:

[0683] The server generates a list of SaaS candidates. Based on the analysis results, the server generates a list of multiple SaaS candidates that are considered to be most suitable for the company. This list includes the name, characteristics, advantages, expected effects, implementation costs, etc. of each SaaS. The generated list is sent to the terminal in the next step.

[0684] Step 6:

[0685] The terminal displays the list of SaaS candidates. The terminal displays the list of SaaS candidates received from the server to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[0686] Step 7:

[0687] The user selects the optimal SaaS and decides to implement it. The user selects the SaaS that is most suitable for their company from the displayed list of SaaS candidates and clicks the "Decide" button. This operation sends the selection results to the server, and the actual implementation procedure begins in the next step.

[0688] Step 8:

[0689] The server provides a tutorial for the introduced SaaS. The server generates detailed tutorial content (video, text, guidelines, etc.) for the SaaS selected by the user and transmits it to the user's device.

[0690] Step 9:

[0691] The terminal displays the tutorial. The terminal displays the tutorial content received from the server to the employee. Employees can learn and practice how to use the selected SaaS by following the on-screen guide.

[0692] Step 10:

[0693] The server monitors the usage of the SaaS and collects data. The server monitors the usage of the deployed SaaS in real time and stores the collected usage data in a database via API. This data is used for subsequent performance evaluation.

[0694] Step 11:

[0695] The server analyzes usage and makes improvement suggestions as needed. The server analyzes the stored usage data using machine learning algorithms to evaluate performance. Based on the results, it generates improvement suggestions or suggestions for replacing other SaaS as needed, and notifies the user. This notification is sent via the dashboard or email.

[0696] (Application example 1)

[0697] 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."

[0698] Content distribution companies face challenges in efficiently selecting and implementing the software services best suited to their company, and continuously evaluating and optimizing their effectiveness. Analyzing viewing data and selecting the optimal tool takes a significant amount of time and effort. It is also not easy to properly monitor the usage of the implemented tools and make necessary improvements or replacements. Furthermore, the explanations of the candidate lists provided are sometimes insufficient, so it is necessary to provide information in a format that makes it easy for users to understand and select.

[0699] 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.

[0700] In this invention, the server includes means for analyzing user information and collected viewing data and proposing optimal content distribution platforms and related tools, means for generating an explanation of the candidate list based on the analysis results using a generative AI model, and means for automatically generating prompts for the generative AI model. This enables content distribution companies to efficiently select the software services that are best suited to their company, easily compare and consider them, and appropriately monitor and optimize their usage even after implementation.

[0701] "Basic information about your company" refers to basic information for identifying your company, such as your company's industry, size, tools you currently use, and challenges.

[0702] "Employee business data" refers to data related to work, such as the tools used by employees, their usage status, work content, work hours, and viewing data.

[0703] "Software services" is a general term for services that provide software functions on a cloud basis and that users can use via the Internet.

[0704] "Analysis" is the process of analyzing input basic information and collected business data to derive meaningful information and patterns.

[0705] The "candidate list" is a list of optimal software services selected based on the analysis results.

[0706] A "tutorial" is a step-by-step instruction manual or guide that explains how to install and use a particular software service.

[0707] "Viewing data" refers to data collected when a user views content, including the viewing time of a particular piece of content, viewer feedback, and the like.

[0708] A "content distribution platform" is a service for distributing digital content such as video, audio, and text over the Internet.

[0709] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate responses or information in response to specific inputs.

[0710] A "prompt" is text that a generative AI model uses as input to generate a particular output.

[0711] This invention is a system that allows companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness. In particular, the system optimized for content distribution companies is shown below.

[0712] System configuration

[0713] The system includes the following elements:

[0714] 1. User Information and Business Data Collection Tools

[0715] A way to enter basic information about your company.

[0716] A means of collecting employee work data and viewing data.

[0717] 2. Data Analysis and SaaS Selection Tools

[0718] The collected data is analyzed and machine learning algorithms are used to select the most appropriate software services.

[0719] Based on collected user information and viewing data, we propose the most suitable content distribution platform and related tools.

[0720] 3. Viewing and Management Tools

[0721] A means of generating a list of software service candidates based on the analysis results and displaying them in a dashboard format.

[0722] A means of providing tutorials to assist with the adoption of software services.

[0723] A means of monitoring the usage of deployed software services and providing suggestions for improvement or replacement.

[0724] 4. Generative AI Models

[0725] A generative AI model to generate candidate list explanations based on analysis results.

[0726] A means of automatically generating prompts for generative AI models.

[0727] Example of a system

[0728] Collection of user information and viewing data

[0729] Users enter basic information about their company (industry, company size, tools currently used, challenges) using a web form or dedicated application, and also install a business monitoring tool in each employee's work environment to collect viewing data and work activities.

[0730] As a concrete example, a company operating in the content distribution business would enter its industry as "content distribution," its number of employees as "150," the tools it uses as "Vimeo, Google Analytics," and the challenges it faces as "low viewership, high server costs."

[0731] Data analysis and selection of optimal SaaS

[0732] The server uses a database containing the collected data to run machine learning algorithms to analyze the data and generate a list of content distribution platforms and related tools that are best suited to the company based on the collected viewing data and company information.

[0733] Software used includes Scikit-learn for running machine learning algorithms, Pandas and SQL databases for processing and storing data.

[0734] For example, the analysis results may result in a candidate list of "video encoding tools" and "advertising distribution platforms."

[0735] Presenting a candidate list

[0736] The server displays the generated candidate list in dashboard format on the user's device, and also generates prompt sentences that explain the analysis results using the generative AI model and displays the explanations on the dashboard.

[0737] For example, GPT is used as a generative AI model.

[0738] Providing onboarding support and tutorials

[0739] The user selects the most suitable software service from the list of candidates displayed on the dashboard and decides to implement it. The server then provides the corresponding tutorial and displays it on the employee's terminal.

[0740] Usage monitoring and improvement suggestions

[0741] The server monitors the usage of the installed software services, provides suggestions for improvement or replacement as needed, and evaluates the effectiveness of the software based on the viewing data and business data used.

[0742] Prompt Sentence Examples

[0743] For example, input the following prompt sentence into the generative AI model:

[0744] Based on your viewing data analysis and company information, please list the most suitable video encoding tools, analysis tools, and ad serving platforms. Please also provide information on the benefits, expected results, and implementation costs of each tool.

[0745] Industry: Content Distribution

[0746] Number of employees: 150

[0747] Current tools used: Vimeo, Google Analytics

[0748] Challenges: Low viewership, high server costs

[0749] This helps businesses effectively select software services by providing specific descriptions for each tool on the shortlist.

[0750] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0751] Step 1:

[0752] Users enter basic information about their company into a web form or dedicated application. Specifically, they enter information such as the industry, company size, tools currently used, and challenges they are facing. The input data is sent to a server and stored in a database. A company profile is generated based on the input (basic company information).

[0753] Step 2:

[0754] The user installs a business process monitoring tool into each employee's work environment. This tool collects the software used by the employee, the work they are doing, and viewing data. The collected data is sent to a server and stored in a database. Based on the input (data collected by the business process monitoring tool), a profile of the business process data and viewing data is created.

[0755] Step 3:

[0756] The server runs a machine learning algorithm to analyze the collected company information and business data. Software such as Scikit-learn is used for this, and Pandas is used for data processing. As a result of the analysis, a candidate list of optimal software services (video encoding tools, ad distribution platforms, etc.) is generated. Analysis is performed based on the input (company information and business data), and a candidate list of software services is obtained as output.

[0757] Step 4:

[0758] The server uses a generative AI model to generate detailed descriptions of the candidate list. Specifically, a prompt sentence is input to a generative AI model such as GPT, and based on that, a description of each tool in the candidate list is generated, including its benefits, expected effects, and implementation costs. The generative AI model generates text based on the input (prompt sentence), and a detailed candidate list is obtained as output.

[0759] Step 5:

[0760] The server displays the generated candidate list and its detailed explanations on the user's device in the form of a dashboard. This dashboard contains detailed information about each candidate, allowing the user to select the most suitable software service based on that information. The dashboard is generated and displayed based on the input (detailed candidate list).

[0761] Step 6:

[0762] For each software service selected by the user, the server provides a tutorial to assist with implementation. The tutorial details how to install and use the software and is displayed on the employee's terminal to help the employee learn the new tool. Based on the input (the selected software service), an appropriate tutorial is generated and displayed as output on the user's terminal.

[0763] Step 7:

[0764] The server continuously monitors the usage of the installed software service. The monitoring results are used to evaluate whether the usage is producing the expected results based on the collected viewing data and business data. If necessary, the server proposes improvement or replacement proposals to the user. Analysis is performed based on the input (monitoring data), and improvement or replacement proposals are provided to the user as output.

[0765] 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.

[0766] Understood. Below, we will describe the "Mode for carrying out the invention" based on the scope of the patent claims of the invention combining an emotion engine.

[0767] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. In addition, by combining it with an emotion engine that recognizes user emotions, it is possible to further improve user satisfaction.

[0768] System Overview

[0769] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[0770] Example of a system

[0771] Entering user information

[0772] 1. Users

[0773] Using a web form or a dedicated application, users enter basic information about their company (e.g., industry, size, current tools used, challenges), as well as a simple questionnaire to assess the user's emotional state.

[0774] Collecting employee work and sentiment data

[0775] 1. Users

[0776] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment, which monitors and collects data on employee work activities and application usage.

[0777] The emotion engine analyzes emotions based on user operations and inputs and collects emotion data.

[0778] 2. Server

[0779] The server receives the data sent from the business monitoring tool and the emotion engine and stores it in a database.

[0780] Data analysis and optimal SaaS selection

[0781] 1. Server

[0782] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data, and identifies the SaaS that best suits the company.

[0783] 2. Server

[0784] Based on the analysis results, a list of optimal SaaS candidates is generated, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool. Sentiment data is also taken into consideration, and candidates that are likely to satisfy the user are presented with priority.

[0785] Presenting a candidate list

[0786] 1. Terminal

[0787] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[0788] Providing onboarding support and tutorials

[0789] 1. Users

[0790] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[0791] 2. Server

[0792] The server searches for and provides tutorial content related to the selected SaaS.

[0793] 3. Terminal

[0794] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[0795] Usage monitoring and improvement suggestions

[0796] 1. Server

[0797] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[0798] The emotion engine also monitors the user's emotional state, for example analyzing stress levels and satisfaction levels in real time while operating the device.

[0799] 2. Server

[0800] The server analyzes the collected usage data and sentiment data to evaluate whether the introduced SaaS is having the expected effect.

[0801] 3. Server

[0802] If necessary, the server will propose improvements or replacements to the user. Based on the emotion data, it will make suggestions to improve the user's satisfaction.

[0803] Specific examples

[0804] The retail case

[0805] 1. Users

[0806] A retail manager enters basic information about his or her company (50 employees, inventory inefficiencies, spreadsheet software used) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[0807] 2. Users

[0808] Management installs business monitoring tools and sentiment engines to collect business and sentiment data.

[0809] 3. Server

[0810] The server receives the collected data and analyzes it using machine learning algorithms.

[0811] 4. Server

[0812] Based on the analysis results, a list of candidates for SaaS specialized in inventory management (e.g., specific inventory management tools) is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[0813] 5. Terminal

[0814] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[0815] 6. Server

[0816] Provide tutorials for selected inventory management tools.

[0817] 7. Terminal

[0818] Employees receive tutorials via terminals to learn how to use the new tools.

[0819] 8. Server

[0820] The server monitors the usage of the introduced tools, evaluates user sentiment data, and, if necessary, proposes improvements or replacements to management.

[0821] This system allows small and medium-sized enterprises to efficiently select the SaaS that is best suited to their company, receive ongoing support to maximize its effectiveness even after implementation, and provides optimal support that takes user emotions into consideration.

[0822] The processing flow will be explained below.

[0823] Understood. Below I will explain the specific process step by step.

[0824] Step 1:

[0825] Users enter basic information about their company (e.g., industry, size, tools currently used, challenges) and emotional state information in the form of questions via a web form or a dedicated app.

[0826] Step 2:

[0827] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment. The work monitoring tool collects application usage data and work hours, while the emotion engine collects emotion data based on user operations and input.

[0828] Step 3:

[0829] The server receives basic information and emotional state data sent via a web form or a dedicated app and stores it in a database.

[0830] Step 4:

[0831] The server receives the business data and emotion data periodically sent from the business monitoring tool and emotion engine, and stores them in a database.

[0832] Step 5:

[0833] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotion data, and identifies SaaS solutions that will optimize business efficiency and user satisfaction.

[0834] Step 6:

[0835] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including details such as each tool's benefits, expected impact, implementation costs, and satisfaction predictions based on sentiment data.

[0836] Step 7:

[0837] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare and consider them.

[0838] Step 8:

[0839] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[0840] Step 9:

[0841] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[0842] Step 10:

[0843] The device displays the appropriate tutorial content for each employee, who then learns how to use the SaaS.

[0844] Step 11:

[0845] The server constantly monitors the usage of the implemented SaaS. Specifically, it collects data to evaluate the frequency of tool use, effectiveness, and any problems that arise. In addition, the emotion engine monitors and analyzes the user's emotional state (e.g., stress, satisfaction).

[0846] Step 12:

[0847] The server analyzes the collected usage and sentiment data to assess whether the SaaS is performing as expected.

[0848] Step 13:

[0849] The server then proposes improvements or replacements to the user based on the analysis results, and makes specific suggestions for improvement to increase user satisfaction based on the emotional data.

[0850] This process allows companies to efficiently select the SaaS that is best suited to their business, receive ongoing support to maximize effectiveness even after implementation, and provide optimal support that takes user emotions into consideration.

[0851] Example 2

[0852] 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."

[0853] Traditionally, it has been difficult for companies to select the optimal software service (SaaS) for their organization and continuously evaluate and optimize its effectiveness after implementation. In particular, software selection and optimization that takes into account not only employees' work efficiency but also their emotional state has not been practiced. This has led to a decline in the accuracy of software selection and post-use satisfaction in companies, which can ultimately have a negative impact on the efficiency and productivity of the entire organization.

[0854] 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 a means for inputting basic information about the company, a means for collecting work data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating a list of candidate software services based on the analysis results and displaying the list while taking the emotional data into consideration, a means for providing a tutorial to assist in the introduction of the software service, and a means for monitoring the usage status of the introduced software service and providing improvement or replacement suggestions. This enables a company to efficiently select the optimal software service for the company and to continuously optimize the software service while taking the emotions of employees into consideration even after the introduction.

[0855] "Basic information about your company" refers to basic company profile information, such as the company's industry, size, current tools used, and challenges.

[0856] "Employee Business Data" refers to data relating to the usage of applications and work progress used by employees in the course of their daily work.

[0857] "Emotional data" refers to data collected through real-time analysis of the emotional states displayed by employees while they are working.

[0858] "Analysis" refers to the process of analyzing data using machine learning algorithms based on collected basic information, business data, and sentiment data.

[0859] The "optimal software service" refers to the software service that is determined to be most suitable for the user based on the analysis results.

[0860] "Candidate List" refers to a list of multiple recommended software services generated based on the analysis results.

[0861] "Considering emotional data" refers to evaluating the user's emotional state and reflecting the results when selecting and proposing software services.

[0862] "Tutorial" means educational content that explains how to install and use selected Software Services.

[0863] "Monitoring" means the continuous monitoring of the usage of the Deployed Software Services.

[0864] "Improvement Suggestions" means suggestions for improving the current Software Services based on Usage Data and Sentiment Data.

[0865] "Replacement Proposal" means a proposal to replace a currently used software service with another software service.

[0866] "Dashboard format" refers to a set of interface formats that present information in an easy-to-visually organize manner.

[0867] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes employee emotions, it is possible to improve user satisfaction.

[0868] System Overview

[0869] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[0870] Entering user information

[0871] User

[0872] Using a dedicated web form or application, users enter basic information about their company (e.g., industry, size of employee base, current tools used, specific challenges, etc.) and also answer a questionnaire with sentiment assessment questions. This information serves as the basis for customization within the system.

[0873] Collecting employee work and sentiment data

[0874] User

[0875] Users install the task monitoring tool and emotion engine on employees' PCs and work environments, which monitor and collect data on employee activity (e.g., active windows, keyboard input, mouse movements) in real time.

[0876] The emotion engine analyzes and collects emotional data based on employees' reactions to operations and inputs.

[0877] server

[0878] The server receives data sent from the business monitoring tool and emotion engine and stores it in a database. The data is automatically transferred at regular intervals.

[0879] Data analysis and optimal SaaS selection

[0880] server

[0881] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data, and analyzes the data to determine business patterns, efficiency, and trends in emotional changes.

[0882] Based on the analysis results, a list of SaaS candidates optimal for the company is generated. The list includes detailed information about each software service (benefits, expected effects, implementation costs, and user satisfaction). In particular, sentiment data is taken into consideration, and candidates with high employee satisfaction are prioritized.

[0883] Presenting a candidate list

[0884] Terminal

[0885] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, visually organizing each tool's implementation cost, user ratings, and highlights of its main features.

[0886] User

[0887] Users can review the list of candidates through a dashboard and compare detailed information about each SaaS.

[0888] Providing onboarding support and tutorials

[0889] User

[0890] Users can select the SaaS they deem most suitable from the list of candidates and decide to implement it. They can start the implementation process for the selected SaaS with just one click.

[0891] server

[0892] The server searches for tutorial content related to the selected SaaS and provides an appropriate tutorial, such as an installation guide or initial setup manual.

[0893] Terminal

[0894] The terminal displays the provided tutorial on each employee's screen, allowing employees to learn how to use the SaaS by referring to the tutorial.

[0895] Usage monitoring and improvement suggestions

[0896] server

[0897] The server constantly monitors the usage of the introduced SaaS, specifically recording in detail the frequency of tool use, its effectiveness, and any problems that arise.

[0898] The emotion engine analyzes the user's emotional state (e.g., stress level and satisfaction level during operation) in real time.

[0899] server

[0900] Based on the collected usage and sentiment data, we evaluate whether the introduced SaaS is delivering the expected results. If necessary, we generate improvement or replacement proposals and present them to users. For example, we make specific proposals such as "proposing the addition of new functions to improve the operability of the current tool" or "proposing migration to another tool."

[0901] Specific examples

[0902] The retail case

[0903] 1. Users

[0904] A retail manager enters information about his company (50 employees, inventory management inefficiencies, current tool used: spreadsheet) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[0905] 2. Users

[0906] The manager installs a work monitoring tool and emotion engine on each employee's PC to collect work data and emotion data. After installation, the manager checks with the employee to see if the tools are working properly.

[0907] 3. Server

[0908] The server receives the collected data and analyzes it using machine learning algorithms, such as data on work efficiency and employee sentiment.

[0909] 4. Server

[0910] Based on the analysis results, a list of candidates for SaaS (specific inventory management tools) specialized for inventory management is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[0911] 5. Terminal

[0912] The candidate list is displayed in dashboard format on the manager's device, and the manager can check detailed information (implementation costs, benefits, user ratings, etc.) and select the tool to implement.

[0913] 6. Server

[0914] Provide managers with tutorials for selected inventory management tools, such as installation guides and initial setup instructions.

[0915] 7. Terminal

[0916] Employees learn how to use the new tools through tutorials delivered via the terminal.

[0917] 8. Server

[0918] The server monitors the usage of the introduced tools and evaluates user sentiment data. If necessary, it proposes improvements or replacements to management. For example, it may suggest adding new features or migrating to another tool.

[0919] Example prompts for generative AI models

[0920] We are developing a system to select the optimal SaaS. The system collects basic user information, employee work data, and emotional data, analyzes them using machine learning, and then proposes the optimal SaaS. Could you please explain each process step in detail?

[0921] This system allows companies to efficiently select the software services that are best suited to their business, and even after implementation, it enables continuous optimization that takes into account employee sentiment.

[0922] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0923] Step 1:

[0924] User

[0925] Users use a web form or a dedicated application to enter basic information about their company (industry type, size, tools currently used, challenges), and answer questionnaire-style questions for sentiment assessment. This information is used as input data for customizing the system. After input, it is sent to the server.

[0926] Step 2:

[0927] User

[0928] The user installs the work monitoring tool and emotion engine on each employee's PC and work environment. After installation, the user checks whether the tools are working properly. The work monitoring tool monitors employee activity (active windows, keyboard input, mouse movement) and collects data. The emotion engine analyzes and collects emotion data based on reactions to operations and inputs.

[0929] Step 3:

[0930] server

[0931] The server receives data (business data and emotion data) sent from the business monitoring tool and emotion engine and stores it in a database. Data transfer is performed automatically at regular intervals. This ensures that the latest data is always stored on the server and available for analysis.

[0932] Step 4:

[0933] server

[0934] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data. The analysis process includes analyzing business patterns, evaluating efficiency, and analyzing sentiment trends. This analysis provides data to identify the best SaaS for each company.

[0935] Step 5:

[0936] server

[0937] Based on the analysis results, the server generates a list of SaaS candidates that are optimal for the company. The generated candidate list includes detailed information about each software service (benefits, expected effectiveness, implementation costs, and user satisfaction). Emotional data is also taken into consideration, and candidates with high user satisfaction are prioritized in the list.

[0938] Step 6:

[0939] Terminal

[0940] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The dashboard visually organizes the software implementation costs, user ratings, and highlights of key features. The user can then use this information to make a detailed comparison.

[0941] Step 7:

[0942] User

[0943] Users can select the most suitable SaaS from a list of candidates through the dashboard and decide to implement it. The implementation process of the selected SaaS can be started with one click. When the selected SaaS is selected as input, related information is sent from the server.

[0944] Step 8:

[0945] server

[0946] The server searches for tutorial content related to the selected SaaS and provides the appropriate tutorial, which includes installation guides, initial setup instructions, and detailed usage instructions, helping employees smoothly get started with the new software.

[0947] Step 9:

[0948] Terminal

[0949] The terminal displays the provided tutorial on each employee's screen. Employees refer to the tutorial to learn how to use the SaaS and then actually operate it. The input in this step is the tutorial content, and the output is employee learning and skill improvement.

[0950] Step 10:

[0951] server

[0952] The server constantly monitors the usage of the introduced SaaS. Specifically, it records in detail the frequency of tool use, its effectiveness, and any problems that occur. The emotion engine also analyzes the user's emotional state in real time. This makes it possible to identify problems and areas for improvement during operation.

[0953] Step 11:

[0954] server

[0955] Based on the collected usage data and sentiment data, the introduced SaaS is evaluated to see if it is producing the expected results. If necessary, improvement or replacement proposals are generated and presented to the user. For example, specific proposals such as "proposals to add new functions to improve operability" or "proposals to migrate to other tools" can be presented. The input for this step is usage data and sentiment data, and the output is specific improvement proposals.

[0956] (Application example 2)

[0957] 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."

[0958] Conventional software service (SaaS) selection and implementation support systems are limited in their effectiveness because they only analyze a company's business data and do not take into account qualitative information such as employee emotions and satisfaction. Furthermore, when monitoring usage or proposing improvements after implementation, emotional data is not taken into account, leading to problems such as lower employee satisfaction and reduced work efficiency. However, by collecting and analyzing emotional data along with business data, it becomes possible to select the optimal SaaS with greater accuracy and continuously optimize its effectiveness even after implementation. Therefore, a comprehensive system that includes employee emotional data is needed.

[0959] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information about the company, a means for collecting business data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating and displaying a list of candidate software services based on the analysis results, a means for providing tutorials to assist in the introduction of software services, and a means for monitoring the usage of introduced software services and providing improvement and replacement proposals that include emotional data. This enables companies to select and introduce the optimal SaaS based on both business data and emotional data, and to continuously optimize its effectiveness.

[0960] Understood. Below are the definitions of important terms included in the patent claims, rewritten to fit the application example.

[0961] "Basic information" refers to initial data about a company or organization, such as its industry, size, tools in use, and challenges.

[0962] "Business data" refers to quantitative data such as task completion status, error rate, and working hours generated by employees through their daily work activities.

[0963] "Emotional data" refers to data related to an employee's emotional state, including qualitative data such as stress levels and satisfaction.

[0964] "Analysis" is the process of analyzing collected basic information, business data, and emotional data using statistical methods and machine learning algorithms to extract meaningful information.

[0965] "Software as a Service (SaaS)" is a software application that can be used over the Internet and is a tool that helps improve the efficiency and management of corporate operations.

[0966] A "candidate list" is a list of proposed software services generated based on the analysis results, detailing the features and benefits of each service.

[0967] The "dashboard format" is an interface format for intuitively displaying multiple pieces of information, and is a layout that makes extensive use of visual elements such as graphs and charts.

[0968] A "tutorial" is educational content that explains how to use the introduced software service and is a guide for users to efficiently utilize the tool.

[0969] "Monitoring" is the process of continuously observing and collecting data about the use of deployed software services.

[0970] "Improvement proposals" are specific proposals for improving the efficiency of use of software services and user satisfaction based on the results of monitoring and analysis.

[0971] A "machine learning algorithm" is an algorithm that automatically learns patterns and rules from large amounts of data and makes predictions and classifications based on new data.

[0972] An "emotion analysis engine" is a software module that analyzes an employee's emotional state based on their behavior and input data, and evaluates their stress and satisfaction.

[0973] These definitions clarify the technical scope of the invention and allow accurate understanding of the characteristics of the invention based on the claims.

[0974] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes user emotions, user satisfaction can be further improved. This article explains the main components and processing procedures of this system.

[0975] System Overview

[0976] 1. Enter your user information

[0977] Users enter basic information about their company (industry, size, tools currently used, challenges) using a web form or dedicated application, and are also provided with a questionnaire form to assess the user's emotional state, thereby collecting emotional data.

[0978] 2. Collecting employee work and sentiment data

[0979] Users install a dedicated work monitoring tool and emotion engine into each employee's work environment (PC and work environment). This tool monitors employees' work activities and application usage and collects that data. The emotion engine also analyzes emotions based on user operations and inputs and collects emotion data.

[0980] 3. Data analysis and selection of optimal SaaS

[0981] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data. This analysis identifies the SaaS that are best suited to the company and generates a candidate list. This candidate list includes detailed information such as the benefits, expected effectiveness, and implementation costs of each tool, and also takes emotional data into account to prioritize and present candidates that will satisfy the user.

[0982] 4. Presenting a candidate list

[0983] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The user can check detailed information about each SaaS and compare them through this dashboard. An index of user satisfaction based on emotional data is also displayed.

[0984] 5. Providing onboarding support and tutorials

[0985] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it. The server searches for and provides tutorial content related to the selected SaaS. The terminal displays the appropriate tutorial for each employee, and the employee learns how to use the SaaS.

[0986] 6. Monitoring usage and suggesting improvements

[0987] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc. The emotion engine also monitors the user's emotional state, analyzing, for example, stress levels and satisfaction during operation in real time. The server analyzes the collected usage data and emotion data to evaluate whether the introduced SaaS is delivering the expected results. If necessary, the server proposes improvement or replacement plans to the user. Based on the emotion data, it also makes improvement proposals to increase user satisfaction.

[0988] Specific examples

[0989] For example, consider the case where this system is implemented in a manufacturing factory with 50 employees. The manager of the factory enters basic information about the company (industry type, size, tools used, challenges) into a web form and also conducts a sentiment assessment. The manager also installs a business monitoring tool and sentiment engine on each employee's PC to collect business and sentiment data. The server analyzes the collected data using a machine learning algorithm and generates a list of candidate SaaS solutions, for example, specialized in inventory management. The dashboard also displays detailed information about each SaaS solution and a satisfaction index based on the sentiment data. The manager selects a SaaS solution based on this information and provides training to employees using tutorials. The server continues to monitor usage, analyzes sentiment data, and proposes improvement measures as needed.

[0990] Prompt Sentence Examples

[0991] "Select the optimal SaaS using employee data and sentiment data from within the factory. Business data:

[0992] Factory name: Manufacturing ABC

[0993] Industry: Manufacturing

[0994] Scale: Large

[0995] Tools in use: ERP, MES

[0996] Employee Data:

[0997] Employee ID: 1

[0998] Business data: { "tasks_completed": 10, "errors": 1}

[0999] Emotional data: { "happiness": 0.7, "stress": 0.3}

[1000] Please suggest the best SaaS candidates based on this information.

[1001] In this way, companies can select the most suitable software services based on both business and emotional data, and then effectively implement and utilize them. This system also improves employee satisfaction and achieves work efficiency.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1:

[1004] The user enters basic information about their company. Using a web form or a dedicated application, the user enters information such as the company's industry, size, tools currently being used, and challenges. This generates basic information data. As the user enters information, a questionnaire form is also provided to assess the user's emotional state, and emotional data is also collected. The basic information data and emotional data are sent as input to the server.

[1005] Step 2:

[1006] The user collects employee work data and emotional data. A dedicated work monitoring tool and emotional engine are installed on each employee's PC and work environment. This monitors and collects employees' work activities (task completion status, error rate, etc.) and emotional states (stress levels, satisfaction, etc.). The collected work data and emotional data are sent to a server.

[1007] Step 3:

[1008] The server analyzes the collected data and selects the optimal software service (SaaS). The server receives basic information data, business data, and emotion data as input and performs analysis using a machine learning algorithm. It analyzes the correlation between each data item and generates a list of optimal software service candidates. The analysis results in a list containing detailed information such as the benefits, expected effects, and implementation costs of each candidate. This list is generated as output and sent to the terminal.

[1009] Step 4:

[1010] The terminal displays the candidate list in a dashboard format. The terminal receives the candidate list sent from the server and displays it in a format that is intuitive to the user. A visual dashboard using graphs and charts displays detailed information about each SaaS and an index of user satisfaction based on emotional data. This allows the user to compare each candidate.

[1011] Step 5:

[1012] The user selects the optimal software service and decides to implement it. The user selects the SaaS they deem most suitable based on the candidate list displayed on the dashboard. After selection, the selection information is sent to the server, and the necessary tutorials are provided in the next phase.

[1013] Step 6:

[1014] The server searches for and provides tutorial content related to the selected software service. The server searches for content related to the target SaaS from the tutorial database and outputs it to the terminal. The terminal displays the received tutorial content to each employee, allowing them to learn how to use the SaaS.

[1015] Step 7:

[1016] The server monitors the usage of the installed software service and performs analysis, including emotional data. Data is collected to evaluate frequency of use, effectiveness, problems, etc., and an emotional engine is used to analyze the user's emotional state. For example, stress levels and satisfaction levels during operation are monitored in real time. The collected data is used to evaluate the effectiveness of the SaaS and generate improvement or replacement proposals as needed. Improvement proposals are output to the user, and measures to improve user satisfaction are proposed.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] [Third embodiment]

[1021] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1022] 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.

[1023] 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).

[1024] 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.

[1025] 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.

[1026] 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).

[1027] 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. 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.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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."

[1033] Understood. Below is the "Mode for carrying out the invention" based on the claims.

[1034] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness.

[1035] System Overview

[1036] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data, select the most suitable SaaS, assist with implementation, monitor its usage, and make suggestions for improvement.

[1037] Example of a system

[1038] Entering user information

[1039] 1. Users

[1040] Users enter basic information about their company (industry, size, current tools used, challenges) using a web form or dedicated application.

[1041] Collection of employee work data

[1042] 1. Users

[1043] Users install a dedicated work monitoring tool in each employee's work environment, which monitors and collects data on employee work activities and application usage.

[1044] 2. Server

[1045] The server receives the data sent from the business monitoring tool and stores it in a database.

[1046] Data analysis and optimal SaaS selection

[1047] 1. Server

[1048] The server uses machine learning algorithms to analyze the collected data and user-entered information, and then identifies the SaaS that best suits your company.

[1049] 2. Server

[1050] Based on the analysis results, a list of optimal SaaS candidates is generated, including details such as the benefits, expected impact, and implementation costs of each candidate.

[1051] Presenting a candidate list

[1052] 1. Terminal

[1053] The terminal displays the list of SaaS candidates sent from the server in a dashboard format to the user, who can then compare and consider each SaaS based on the provided information.

[1054] Providing onboarding support and tutorials

[1055] 1. Users

[1056] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[1057] 2. Server

[1058] The server provides tutorials on selected SaaS.

[1059] 3. Terminal

[1060] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[1061] Usage monitoring and improvement suggestions

[1062] 1. Server

[1063] The server constantly monitors the usage of the introduced SaaS and collects data.

[1064] 2. Server

[1065] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[1066] 3. Server

[1067] If necessary, the server will suggest improvements or replacements to the user.

[1068] Specific examples

[1069] The retail case

[1070] 1. Users

[1071] A retail business owner fills out a web form with basic information about his company (50 employees, inventory inefficiencies, Excel as the tool of choice).

[1072] 2. Users

[1073] Management installs performance monitoring tools to collect employee performance data.

[1074] 3. Server

[1075] The server receives the collected data and basic information and analyzes it using machine learning algorithms.

[1076] 4. Server

[1077] Based on the analysis results, a candidate list of SaaS specialized for inventory management (e.g., specific inventory management tools) is generated.

[1078] 5. Terminal

[1079] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[1080] 6. Server

[1081] Provide a tutorial for the inventory management tool that management has decided to implement.

[1082] 7. Terminal

[1083] Employees use the terminals to go through tutorials and learn how to use the tools.

[1084] 8. Server

[1085] The server monitors the usage of the introduced tools and, if necessary, proposes improvement ideas to management.

[1086] This system allows small and medium-sized enterprises to efficiently select and implement the SaaS that is best suited to their company, maximizing its effectiveness.

[1087] The processing flow will be explained below.

[1088] Understood. Below is a step-by-step explanation of the process.

[1089] Step 1:

[1090] Users enter basic information about their company (e.g., industry, size, tools currently being used, challenges) through a web form or a dedicated app.

[1091] Step 2:

[1092] Users install a dedicated work monitoring tool on each employee's PC and work environment, which automatically collects application usage data, work hours, and other data.

[1093] Step 3:

[1094] The server receives basic information sent from a web form or a dedicated app and stores it in a database.

[1095] Step 4:

[1096] The server receives business data periodically sent from the business monitoring tool and stores it in a database.

[1097] Step 5:

[1098] The server uses machine learning algorithms to analyze the collected basic information and business data, and identifies the software services (SaaS) needed to optimize the user's business processes.

[1099] Step 6:

[1100] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool.

[1101] Step 7:

[1102] The terminal displays the candidate list sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[1103] Step 8:

[1104] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[1105] Step 9:

[1106] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[1107] Step 10:

[1108] The terminal displays tutorial content to each employee, who then learns how to use the SaaS.

[1109] Step 11:

[1110] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[1111] Step 12:

[1112] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[1113] Step 13:

[1114] Based on the analysis results, the server will propose improvements or replacements to the user as needed, ensuring optimal operation at all times.

[1115] This process allows companies to efficiently select the SaaS that is best suited to their business and receive ongoing support to maximize its effectiveness even after implementation.

[1116] Example 1

[1117] 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."

[1118] The process for companies to select and implement the software service (SaaS) that is best suited to their business is complex and time-consuming. It is also difficult to continuously evaluate the effectiveness of implementation and propose improvements as needed. Small and medium-sized enterprises, in particular, often lack specialized knowledge and resources, making it difficult to efficiently select the optimal SaaS and maximize its effectiveness.

[1119] 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.

[1120] In this invention, the server includes means for inputting basic information about the company, means for collecting employee business data, means for analyzing the input and collected data and selecting the optimal software service, means for generating and displaying a list of candidate software services based on the analysis results, means for providing tutorials to assist in the introduction of software services, means for monitoring the usage of the introduced software service and providing improvement and replacement proposals, means for collecting usage data of the introduced software service and saving it in a database in real time, and means for analyzing the collected data with a machine learning algorithm. This enables companies to efficiently select the optimal SaaS for their company and continuously maximize its effectiveness even after introduction.

[1121] "Basic information about your company" refers to basic information such as your company's industry, size, tools you currently use, and challenges you face.

[1122] "Employee Business Data" refers to data about employee work activities and application usage.

[1123] "Analysis" refers to the process of analyzing collected data using machine learning algorithms and extracting useful information.

[1124] "Candidate list of software services" refers to a list of multiple software services that are deemed to be optimal for a company, obtained as a result of data analysis.

[1125] "Tutorial" means a means of providing guides and examples on how to install and use selected software services.

[1126] "Real-time" refers to a method in which data is collected, analyzed, and stored immediately, reflecting the latest information almost instantly.

[1127] "Database" refers to a system for centrally storing and managing collected data.

[1128] A "machine learning algorithm" refers to a computational method for training models based on large amounts of data to extract or predict patterns and trends.

[1129] "Improvement proposals" refer to specific methods and measures proposed to evaluate the usage of the implemented software service and to improve its effectiveness.

[1130] "Replacement proposal" refers to a proposal for an alternative software service when the current software service does not perform as expected.

[1131] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and continuously evaluate and optimize its effectiveness. This system involves a series of processes in which users input basic company information, collect and analyze employee work data, select the best SaaS, support the implementation, monitor its usage, and make improvement suggestions.

[1132] Entering user information

[1133] User

[1134] Users use dedicated web forms and applications to enter information about their company's industry, size, tools currently being used, challenges they are facing, etc. For example, when a user enters basic information about their company into a web form and presses the "Submit" button, this information is sent to the server.

[1135] Collection of employee work data

[1136] User

[1137] Users install a business monitoring tool into each employee's work environment. This tool monitors business activities and application usage and collects the data. The tool installed on each computer runs in the background and collects business data in real time.

[1138] Receiving and storing business data

[1139] server

[1140] The server periodically receives data sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in the storage. The receiving and storing process is automated and takes place in real time.

[1141] Data analysis and identification of optimal SaaS candidates

[1142] server

[1143] The server passes the stored data and basic information entered by the user to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. This analysis can then identify the best SaaS for the company.

[1144] Generate a list of potential SaaS

[1145] server

[1146] Based on the analysis results, the server generates a list of multiple SaaS candidates that are deemed most suitable for the company, including detailed information such as the characteristics, benefits, expected effects, and implementation costs of each SaaS.

[1147] View SaaS candidate list

[1148] Terminal

[1149] The terminal receives the candidate list from the server and displays it to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[1150] SaaS selection and implementation

[1151] User

[1152] The user selects the SaaS that is most suitable for their company from the displayed list of candidates and clicks the "Decide" button to decide on implementation. The selection results are sent to the server, and the system proceeds to the next step.

[1153] Providing tutorials

[1154] server

[1155] The server generates and serves tutorial content for selected SaaS services, including usage guides, implementation procedures, and best practices.

[1156] View tutorial

[1157] Terminal

[1158] The terminal displays the received tutorial content to the employee, who can then learn how to use the SaaS through the displayed tutorial.

[1159] Usage monitoring and data collection

[1160] server

[1161] The server monitors the usage of the installed SaaS in real time and collects usage data through APIs, which are then stored in storage.

[1162] Usage analysis and improvement suggestions

[1163] server

[1164] The server analyzes the collected usage data using machine learning algorithms to evaluate usage patterns and performance, and based on the analysis results, generates recommendations for improvements or replacement of other SaaS services as needed and notifies the user.

[1165] Examples of prompt statements

[1166] 1. "Please provide some basic information about your company. Please be specific about your industry, size, current tools you use, and challenges you face."

[1167] 2. "To learn how to install the business monitoring tool, please follow these steps."

[1168] 3. "View a dashboard with a list of the best SaaS candidates. See each candidate's benefits, expected impact, implementation costs, and more."

[1169] 4. "We will explain the steps to implement the selected SaaS and provide a tutorial for employees."

[1170] 5. "We will monitor the usage of the implemented SaaS and propose improvements or replacements."

[1171] In this way, this system enables companies to efficiently select and implement the SaaS that is best suited to their company, and continuously maximize its effectiveness.

[1172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] The user enters basic information about their company. The user enters information such as their company's industry, company size, tools currently being used, and challenges they are facing into a web form or dedicated application, and then presses the "Submit" button. This operation sends the entered information to the server, which then stores the received basic information in a database.

[1175] Step 2:

[1176] The user installs the business monitoring tool. The user downloads the tool to each employee's computer and follows the installation wizard to complete the installation. After installation, the tool begins collecting business data in the background and prepares to send it to the server.

[1177] Step 3:

[1178] The server receives business data and stores it in a database. The server receives business activity and application usage data periodically sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in storage. This process is carried out in real time.

[1179] Step 4:

[1180] The server analyzes the collected data and identifies the best SaaS candidates. The server passes the stored business data and basic information to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. The analysis performed here identifies the best SaaS candidates for the company.

[1181] Step 5:

[1182] The server generates a list of SaaS candidates. Based on the analysis results, the server generates a list of multiple SaaS candidates that are considered to be most suitable for the company. This list includes the name, characteristics, advantages, expected effects, implementation costs, etc. of each SaaS. The generated list is sent to the terminal in the next step.

[1183] Step 6:

[1184] The terminal displays the list of SaaS candidates. The terminal displays the list of SaaS candidates received from the server to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[1185] Step 7:

[1186] The user selects the optimal SaaS and decides to implement it. The user selects the SaaS that is most suitable for their company from the displayed list of SaaS candidates and clicks the "Decide" button. This operation sends the selection results to the server, and the actual implementation procedure begins in the next step.

[1187] Step 8:

[1188] The server provides a tutorial for the introduced SaaS. The server generates detailed tutorial content (video, text, guidelines, etc.) for the SaaS selected by the user and transmits it to the user's device.

[1189] Step 9:

[1190] The terminal displays the tutorial. The terminal displays the tutorial content received from the server to the employee. Employees can learn and practice how to use the selected SaaS by following the on-screen guide.

[1191] Step 10:

[1192] The server monitors the usage of the SaaS and collects data. The server monitors the usage of the deployed SaaS in real time and stores the collected usage data in a database via API. This data is used for subsequent performance evaluation.

[1193] Step 11:

[1194] The server analyzes usage and makes improvement suggestions as needed. The server analyzes the stored usage data using machine learning algorithms to evaluate performance. Based on the results, it generates improvement suggestions or suggestions for replacing other SaaS as needed, and notifies the user. This notification is sent via the dashboard or email.

[1195] (Application example 1)

[1196] 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."

[1197] Content distribution companies face challenges in efficiently selecting and implementing the software services best suited to their company, and continuously evaluating and optimizing their effectiveness. Analyzing viewing data and selecting the optimal tool takes a significant amount of time and effort. It is also not easy to properly monitor the usage of the implemented tools and make necessary improvements or replacements. Furthermore, the explanations of the candidate lists provided are sometimes insufficient, so it is necessary to provide information in a format that makes it easy for users to understand and select.

[1198] 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.

[1199] In this invention, the server includes means for analyzing user information and collected viewing data and proposing optimal content distribution platforms and related tools, means for generating an explanation of the candidate list based on the analysis results using a generative AI model, and means for automatically generating prompts for the generative AI model. This enables content distribution companies to efficiently select the software services that are best suited to their company, easily compare and consider them, and appropriately monitor and optimize their usage even after implementation.

[1200] "Basic information about your company" refers to basic information for identifying your company, such as your company's industry, size, tools you currently use, and challenges.

[1201] "Employee business data" refers to data related to work, such as the tools used by employees, their usage status, work content, work hours, and viewing data.

[1202] "Software services" is a general term for services that provide software functions on a cloud basis and that users can use via the Internet.

[1203] "Analysis" is the process of analyzing input basic information and collected business data to derive meaningful information and patterns.

[1204] The "candidate list" is a list of optimal software services selected based on the analysis results.

[1205] A "tutorial" is a step-by-step instruction manual or guide that explains how to install and use a particular software service.

[1206] "Viewing data" refers to data collected when a user views content, including the viewing time of a particular piece of content, viewer feedback, and the like.

[1207] A "content distribution platform" is a service for distributing digital content such as video, audio, and text over the Internet.

[1208] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate responses or information in response to specific inputs.

[1209] A "prompt" is text that a generative AI model uses as input to generate a particular output.

[1210] This invention is a system that allows companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness. In particular, the system optimized for content distribution companies is shown below.

[1211] System configuration

[1212] The system includes the following elements:

[1213] 1. User Information and Business Data Collection Tools

[1214] A way to enter basic information about your company.

[1215] A means of collecting employee work data and viewing data.

[1216] 2. Data Analysis and SaaS Selection Tools

[1217] The collected data is analyzed and machine learning algorithms are used to select the most appropriate software services.

[1218] Based on collected user information and viewing data, we propose the most suitable content distribution platform and related tools.

[1219] 3. Viewing and Management Tools

[1220] A means of generating a list of software service candidates based on the analysis results and displaying them in a dashboard format.

[1221] A means of providing tutorials to assist with the adoption of software services.

[1222] A means of monitoring the usage of deployed software services and providing suggestions for improvement or replacement.

[1223] 4. Generative AI Models

[1224] A generative AI model to generate candidate list explanations based on analysis results.

[1225] A means of automatically generating prompts for generative AI models.

[1226] Example of a system

[1227] Collection of user information and viewing data

[1228] Users enter basic information about their company (industry, company size, tools currently used, challenges) using a web form or dedicated application, and also install a business monitoring tool in each employee's work environment to collect viewing data and work activities.

[1229] As a concrete example, a company operating in the content distribution business would enter its industry as "content distribution," its number of employees as "150," the tools it uses as "Vimeo, Google Analytics," and the challenges it faces as "low viewership, high server costs."

[1230] Data analysis and selection of optimal SaaS

[1231] The server uses a database containing the collected data to run machine learning algorithms to analyze the data and generate a list of content distribution platforms and related tools that are best suited to the company based on the collected viewing data and company information.

[1232] Software used includes Scikit-learn for running machine learning algorithms, Pandas and SQL databases for processing and storing data.

[1233] For example, the analysis results may result in a candidate list of "video encoding tools" and "advertising distribution platforms."

[1234] Presenting a candidate list

[1235] The server displays the generated candidate list in dashboard format on the user's device, and also generates prompt sentences that explain the analysis results using the generative AI model and displays the explanations on the dashboard.

[1236] For example, GPT is used as a generative AI model.

[1237] Providing onboarding support and tutorials

[1238] The user selects the most suitable software service from the list of candidates displayed on the dashboard and decides to implement it. The server then provides the corresponding tutorial and displays it on the employee's terminal.

[1239] Usage monitoring and improvement suggestions

[1240] The server monitors the usage of the installed software services, provides suggestions for improvement or replacement as needed, and evaluates the effectiveness of the software based on the viewing data and business data used.

[1241] Prompt Sentence Examples

[1242] For example, input the following prompt sentence into the generative AI model:

[1243] Based on your viewing data analysis and company information, please list the most suitable video encoding tools, analysis tools, and ad serving platforms. Please also provide information on the benefits, expected results, and implementation costs of each tool.

[1244] Industry: Content Distribution

[1245] Number of employees: 150

[1246] Current tools used: Vimeo, Google Analytics

[1247] Challenges: Low viewership, high server costs

[1248] This helps businesses effectively select software services by providing specific descriptions for each tool on the shortlist.

[1249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1250] Step 1:

[1251] Users enter basic information about their company into a web form or dedicated application. Specifically, they enter information such as the industry, company size, tools currently used, and challenges they are facing. The input data is sent to a server and stored in a database. A company profile is generated based on the input (basic company information).

[1252] Step 2:

[1253] The user installs a business process monitoring tool into each employee's work environment. This tool collects the software used by the employee, the work they are doing, and viewing data. The collected data is sent to a server and stored in a database. Based on the input (data collected by the business process monitoring tool), a profile of the business process data and viewing data is created.

[1254] Step 3:

[1255] The server runs a machine learning algorithm to analyze the collected company information and business data. Software such as Scikit-learn is used for this, and Pandas is used for data processing. As a result of the analysis, a candidate list of optimal software services (video encoding tools, ad distribution platforms, etc.) is generated. Analysis is performed based on the input (company information and business data), and a candidate list of software services is obtained as output.

[1256] Step 4:

[1257] The server uses a generative AI model to generate detailed descriptions of the candidate list. Specifically, a prompt sentence is input to a generative AI model such as GPT, and based on that, a description of each tool in the candidate list is generated, including its benefits, expected effects, and implementation costs. The generative AI model generates text based on the input (prompt sentence), and a detailed candidate list is obtained as output.

[1258] Step 5:

[1259] The server displays the generated candidate list and its detailed explanations on the user's device in the form of a dashboard. This dashboard contains detailed information about each candidate, allowing the user to select the most suitable software service based on that information. The dashboard is generated and displayed based on the input (detailed candidate list).

[1260] Step 6:

[1261] For each software service selected by the user, the server provides a tutorial to assist with implementation. The tutorial details how to install and use the software and is displayed on the employee's terminal to help the employee learn the new tool. Based on the input (the selected software service), an appropriate tutorial is generated and displayed as output on the user's terminal.

[1262] Step 7:

[1263] The server continuously monitors the usage of the installed software service. The monitoring results are used to evaluate whether the usage is producing the expected results based on the collected viewing data and business data. If necessary, the server proposes improvement or replacement proposals to the user. Analysis is performed based on the input (monitoring data), and improvement or replacement proposals are provided to the user as output.

[1264] 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.

[1265] Understood. Below, we will describe the "Mode for carrying out the invention" based on the scope of the patent claims of the invention combining an emotion engine.

[1266] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. In addition, by combining it with an emotion engine that recognizes user emotions, it is possible to further improve user satisfaction.

[1267] System Overview

[1268] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[1269] Example of a system

[1270] Entering user information

[1271] 1. Users

[1272] Using a web form or a dedicated application, users enter basic information about their company (e.g., industry, size, current tools used, challenges), as well as a simple questionnaire to assess the user's emotional state.

[1273] Collecting employee work and sentiment data

[1274] 1. Users

[1275] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment, which monitors and collects data on employee work activities and application usage.

[1276] The emotion engine analyzes emotions based on user operations and inputs and collects emotion data.

[1277] 2. Server

[1278] The server receives the data sent from the business monitoring tool and the emotion engine and stores it in a database.

[1279] Data analysis and optimal SaaS selection

[1280] 1. Server

[1281] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data, and identifies the SaaS that best suits the company.

[1282] 2. Server

[1283] Based on the analysis results, a list of optimal SaaS candidates is generated, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool. Sentiment data is also taken into consideration, and candidates that are likely to satisfy the user are presented with priority.

[1284] Presenting a candidate list

[1285] 1. Terminal

[1286] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[1287] Providing onboarding support and tutorials

[1288] 1. Users

[1289] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[1290] 2. Server

[1291] The server searches for and provides tutorial content related to the selected SaaS.

[1292] 3. Terminal

[1293] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[1294] Usage monitoring and improvement suggestions

[1295] 1. Server

[1296] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[1297] The emotion engine also monitors the user's emotional state, for example analyzing stress levels and satisfaction levels in real time while operating the device.

[1298] 2. Server

[1299] The server analyzes the collected usage data and sentiment data to evaluate whether the introduced SaaS is having the expected effect.

[1300] 3. Server

[1301] If necessary, the server will propose improvements or replacements to the user. Based on the emotion data, it will make suggestions to improve the user's satisfaction.

[1302] Specific examples

[1303] The retail case

[1304] 1. Users

[1305] A retail manager enters basic information about his or her company (50 employees, inventory inefficiencies, spreadsheet software used) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[1306] 2. Users

[1307] Management installs business monitoring tools and sentiment engines to collect business and sentiment data.

[1308] 3. Server

[1309] The server receives the collected data and analyzes it using machine learning algorithms.

[1310] 4. Server

[1311] Based on the analysis results, a list of candidates for SaaS specialized in inventory management (e.g., specific inventory management tools) is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[1312] 5. Terminal

[1313] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[1314] 6. Server

[1315] Provide tutorials for selected inventory management tools.

[1316] 7. Terminal

[1317] Employees receive tutorials via terminals to learn how to use the new tools.

[1318] 8. Server

[1319] The server monitors the usage of the introduced tools, evaluates user sentiment data, and, if necessary, proposes improvements or replacements to management.

[1320] This system allows small and medium-sized enterprises to efficiently select the SaaS that is best suited to their company, receive ongoing support to maximize its effectiveness even after implementation, and provides optimal support that takes user emotions into consideration.

[1321] The processing flow will be explained below.

[1322] Understood. Below I will explain the specific process step by step.

[1323] Step 1:

[1324] Users enter basic information about their company (e.g., industry, size, tools currently used, challenges) and emotional state information in the form of questions via a web form or a dedicated app.

[1325] Step 2:

[1326] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment. The work monitoring tool collects application usage data and work hours, while the emotion engine collects emotion data based on user operations and input.

[1327] Step 3:

[1328] The server receives basic information and emotional state data sent via a web form or a dedicated app and stores it in a database.

[1329] Step 4:

[1330] The server receives the business data and emotion data periodically sent from the business monitoring tool and emotion engine, and stores them in a database.

[1331] Step 5:

[1332] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotion data, and identifies SaaS solutions that will optimize business efficiency and user satisfaction.

[1333] Step 6:

[1334] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including details such as each tool's benefits, expected impact, implementation costs, and satisfaction predictions based on sentiment data.

[1335] Step 7:

[1336] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare and consider them.

[1337] Step 8:

[1338] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[1339] Step 9:

[1340] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[1341] Step 10:

[1342] The device displays the appropriate tutorial content for each employee, who then learns how to use the SaaS.

[1343] Step 11:

[1344] The server constantly monitors the usage of the implemented SaaS. Specifically, it collects data to evaluate the frequency of tool use, effectiveness, and any problems that arise. In addition, the emotion engine monitors and analyzes the user's emotional state (e.g., stress, satisfaction).

[1345] Step 12:

[1346] The server analyzes the collected usage and sentiment data to assess whether the SaaS is performing as expected.

[1347] Step 13:

[1348] The server then proposes improvements or replacements to the user based on the analysis results, and makes specific suggestions for improvement to increase user satisfaction based on the emotional data.

[1349] This process allows companies to efficiently select the SaaS that is best suited to their business, receive ongoing support to maximize effectiveness even after implementation, and provide optimal support that takes user emotions into consideration.

[1350] Example 2

[1351] 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."

[1352] Traditionally, it has been difficult for companies to select the optimal software service (SaaS) for their organization and continuously evaluate and optimize its effectiveness after implementation. In particular, software selection and optimization that takes into account not only employees' work efficiency but also their emotional state has not been practiced. This has led to a decline in the accuracy of software selection and post-use satisfaction in companies, which can ultimately have a negative impact on the efficiency and productivity of the entire organization.

[1353] 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 a means for inputting basic information about the company, a means for collecting work data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating a list of candidate software services based on the analysis results and displaying the list while taking the emotional data into consideration, a means for providing a tutorial to assist in the introduction of the software service, and a means for monitoring the usage status of the introduced software service and providing improvement or replacement suggestions. This enables a company to efficiently select the optimal software service for the company and to continuously optimize the software service while taking the emotions of employees into consideration even after the introduction.

[1354] "Basic information about your company" refers to basic company profile information, such as the company's industry, size, current tools used, and challenges.

[1355] "Employee Business Data" refers to data relating to the usage of applications and work progress used by employees in the course of their daily work.

[1356] "Emotional data" refers to data collected through real-time analysis of the emotional states displayed by employees while they are working.

[1357] "Analysis" refers to the process of analyzing data using machine learning algorithms based on collected basic information, business data, and sentiment data.

[1358] The "optimal software service" refers to the software service that is determined to be most suitable for the user based on the analysis results.

[1359] "Candidate List" refers to a list of multiple recommended software services generated based on the analysis results.

[1360] "Considering emotional data" refers to evaluating the user's emotional state and reflecting the results when selecting and proposing software services.

[1361] "Tutorial" means educational content that explains how to install and use selected Software Services.

[1362] "Monitoring" means the continuous monitoring of the usage of the Deployed Software Services.

[1363] "Improvement Suggestions" means suggestions for improving the current Software Services based on Usage Data and Sentiment Data.

[1364] "Replacement Proposal" means a proposal to replace a currently used software service with another software service.

[1365] "Dashboard format" refers to a set of interface formats that present information in an easy-to-visually organize manner.

[1366] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes employee emotions, it is possible to improve user satisfaction.

[1367] System Overview

[1368] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[1369] Entering user information

[1370] User

[1371] Using a dedicated web form or application, users enter basic information about their company (e.g., industry, size of employee base, current tools used, specific challenges, etc.) and also answer a questionnaire with sentiment assessment questions. This information serves as the basis for customization within the system.

[1372] Collecting employee work and sentiment data

[1373] User

[1374] Users install the task monitoring tool and emotion engine on employees' PCs and work environments, which monitor and collect data on employee activity (e.g., active windows, keyboard input, mouse movements) in real time.

[1375] The emotion engine analyzes and collects emotional data based on employees' reactions to operations and inputs.

[1376] server

[1377] The server receives data sent from the business monitoring tool and emotion engine and stores it in a database. The data is automatically transferred at regular intervals.

[1378] Data analysis and optimal SaaS selection

[1379] server

[1380] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data, and analyzes the data to determine business patterns, efficiency, and trends in emotional changes.

[1381] Based on the analysis results, a list of SaaS candidates optimal for the company is generated. The list includes detailed information about each software service (benefits, expected effects, implementation costs, and user satisfaction). In particular, sentiment data is taken into consideration, and candidates with high employee satisfaction are prioritized.

[1382] Presenting a candidate list

[1383] Terminal

[1384] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, visually organizing each tool's implementation cost, user ratings, and highlights of its main features.

[1385] User

[1386] Users can review the list of candidates through a dashboard and compare detailed information about each SaaS.

[1387] Providing onboarding support and tutorials

[1388] User

[1389] Users can select the SaaS they deem most suitable from the list of candidates and decide to implement it. They can start the implementation process for the selected SaaS with just one click.

[1390] server

[1391] The server searches for tutorial content related to the selected SaaS and provides an appropriate tutorial, such as an installation guide or initial setup manual.

[1392] Terminal

[1393] The terminal displays the provided tutorial on each employee's screen, allowing employees to learn how to use the SaaS by referring to the tutorial.

[1394] Usage monitoring and improvement suggestions

[1395] server

[1396] The server constantly monitors the usage of the introduced SaaS, specifically recording in detail the frequency of tool use, its effectiveness, and any problems that arise.

[1397] The emotion engine analyzes the user's emotional state (e.g., stress level and satisfaction level during operation) in real time.

[1398] server

[1399] Based on the collected usage and sentiment data, we evaluate whether the introduced SaaS is delivering the expected results. If necessary, we generate improvement or replacement proposals and present them to users. For example, we make specific proposals such as "proposing the addition of new functions to improve the operability of the current tool" or "proposing migration to another tool."

[1400] Specific examples

[1401] The retail case

[1402] 1. Users

[1403] A retail manager enters information about his company (50 employees, inventory management inefficiencies, current tool used: spreadsheet) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[1404] 2. Users

[1405] The manager installs a work monitoring tool and emotion engine on each employee's PC to collect work data and emotion data. After installation, the manager checks with the employee to see if the tools are working properly.

[1406] 3. Server

[1407] The server receives the collected data and analyzes it using machine learning algorithms, such as data on work efficiency and employee sentiment.

[1408] 4. Server

[1409] Based on the analysis results, a list of candidates for SaaS (specific inventory management tools) specialized for inventory management is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[1410] 5. Terminal

[1411] The candidate list is displayed in dashboard format on the manager's device, and the manager can check detailed information (implementation costs, benefits, user ratings, etc.) and select the tool to implement.

[1412] 6. Server

[1413] Provide managers with tutorials for selected inventory management tools, such as installation guides and initial setup instructions.

[1414] 7. Terminal

[1415] Employees learn how to use the new tools through tutorials delivered via the terminal.

[1416] 8. Server

[1417] The server monitors the usage of the introduced tools and evaluates user sentiment data. If necessary, it proposes improvements or replacements to management. For example, it may suggest adding new features or migrating to another tool.

[1418] Example prompts for generative AI models

[1419] We are developing a system to select the optimal SaaS. The system collects basic user information, employee work data, and emotional data, analyzes them using machine learning, and then proposes the optimal SaaS. Could you please explain each process step in detail?

[1420] This system allows companies to efficiently select the software services that are best suited to their business, and even after implementation, it enables continuous optimization that takes into account employee sentiment.

[1421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1422] Step 1:

[1423] User

[1424] Users use a web form or a dedicated application to enter basic information about their company (industry type, size, tools currently used, challenges), and answer questionnaire-style questions for sentiment assessment. This information is used as input data for customizing the system. After input, it is sent to the server.

[1425] Step 2:

[1426] User

[1427] The user installs the work monitoring tool and emotion engine on each employee's PC and work environment. After installation, the user checks whether the tools are working properly. The work monitoring tool monitors employee activity (active windows, keyboard input, mouse movement) and collects data. The emotion engine analyzes and collects emotion data based on reactions to operations and inputs.

[1428] Step 3:

[1429] server

[1430] The server receives data (business data and emotion data) sent from the business monitoring tool and emotion engine and stores it in a database. Data transfer is performed automatically at regular intervals. This ensures that the latest data is always stored on the server and available for analysis.

[1431] Step 4:

[1432] server

[1433] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data. The analysis process includes analyzing business patterns, evaluating efficiency, and analyzing sentiment trends. This analysis provides data to identify the best SaaS for each company.

[1434] Step 5:

[1435] server

[1436] Based on the analysis results, the server generates a list of SaaS candidates that are optimal for the company. The generated candidate list includes detailed information about each software service (benefits, expected effectiveness, implementation costs, and user satisfaction). Emotional data is also taken into consideration, and candidates with high user satisfaction are prioritized in the list.

[1437] Step 6:

[1438] Terminal

[1439] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The dashboard visually organizes the software implementation costs, user ratings, and highlights of key features. The user can then use this information to make a detailed comparison.

[1440] Step 7:

[1441] User

[1442] Users can select the most suitable SaaS from a list of candidates through the dashboard and decide to implement it. The implementation process of the selected SaaS can be started with one click. When the selected SaaS is selected as input, related information is sent from the server.

[1443] Step 8:

[1444] server

[1445] The server searches for tutorial content related to the selected SaaS and provides the appropriate tutorial, which includes installation guides, initial setup instructions, and detailed usage instructions, helping employees smoothly get started with the new software.

[1446] Step 9:

[1447] Terminal

[1448] The terminal displays the provided tutorial on each employee's screen. Employees refer to the tutorial to learn how to use the SaaS and then actually operate it. The input in this step is the tutorial content, and the output is employee learning and skill improvement.

[1449] Step 10:

[1450] server

[1451] The server constantly monitors the usage of the introduced SaaS. Specifically, it records in detail the frequency of tool use, its effectiveness, and any problems that occur. The emotion engine also analyzes the user's emotional state in real time. This makes it possible to identify problems and areas for improvement during operation.

[1452] Step 11:

[1453] server

[1454] Based on the collected usage data and sentiment data, the introduced SaaS is evaluated to see if it is producing the expected results. If necessary, improvement or replacement proposals are generated and presented to the user. For example, specific proposals such as "proposals to add new functions to improve operability" or "proposals to migrate to other tools" can be presented. The input for this step is usage data and sentiment data, and the output is specific improvement proposals.

[1455] (Application example 2)

[1456] 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."

[1457] Conventional software service (SaaS) selection and implementation support systems are limited in their effectiveness because they only analyze a company's business data and do not take into account qualitative information such as employee emotions and satisfaction. Furthermore, when monitoring usage or proposing improvements after implementation, emotional data is not taken into account, leading to problems such as lower employee satisfaction and reduced work efficiency. However, by collecting and analyzing emotional data along with business data, it becomes possible to select the optimal SaaS with greater accuracy and continuously optimize its effectiveness even after implementation. Therefore, a comprehensive system that includes employee emotional data is needed.

[1458] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information about the company, a means for collecting business data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating and displaying a list of candidate software services based on the analysis results, a means for providing tutorials to assist in the introduction of software services, and a means for monitoring the usage of introduced software services and providing improvement and replacement proposals that include emotional data. This enables companies to select and introduce the optimal SaaS based on both business data and emotional data, and to continuously optimize its effectiveness.

[1459] Understood. Below are the definitions of important terms included in the patent claims, rewritten to fit the application example.

[1460] "Basic information" refers to initial data about a company or organization, such as its industry, size, tools in use, and challenges.

[1461] "Business data" refers to quantitative data such as task completion status, error rate, and working hours generated by employees through their daily work activities.

[1462] "Emotional data" refers to data related to an employee's emotional state, including qualitative data such as stress levels and satisfaction.

[1463] "Analysis" is the process of analyzing collected basic information, business data, and emotional data using statistical methods and machine learning algorithms to extract meaningful information.

[1464] "Software as a Service (SaaS)" is a software application that can be used over the Internet and is a tool that helps improve the efficiency and management of corporate operations.

[1465] A "candidate list" is a list of proposed software services generated based on the analysis results, detailing the features and benefits of each service.

[1466] The "dashboard format" is an interface format for intuitively displaying multiple pieces of information, and is a layout that makes extensive use of visual elements such as graphs and charts.

[1467] A "tutorial" is educational content that explains how to use the introduced software service and is a guide for users to efficiently utilize the tool.

[1468] "Monitoring" is the process of continuously observing and collecting data about the use of deployed software services.

[1469] "Improvement proposals" are specific proposals for improving the efficiency of use of software services and user satisfaction based on the results of monitoring and analysis.

[1470] A "machine learning algorithm" is an algorithm that automatically learns patterns and rules from large amounts of data and makes predictions and classifications based on new data.

[1471] An "emotion analysis engine" is a software module that analyzes an employee's emotional state based on their behavior and input data, and evaluates their stress and satisfaction.

[1472] These definitions clarify the technical scope of the invention and allow accurate understanding of the characteristics of the invention based on the claims.

[1473] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes user emotions, user satisfaction can be further improved. This article explains the main components and processing procedures of this system.

[1474] System Overview

[1475] 1. Enter your user information

[1476] Users enter basic information about their company (industry, size, tools currently used, challenges) using a web form or dedicated application, and are also provided with a questionnaire form to assess the user's emotional state, thereby collecting emotional data.

[1477] 2. Collecting employee work and sentiment data

[1478] Users install a dedicated work monitoring tool and emotion engine into each employee's work environment (PC and work environment). This tool monitors employees' work activities and application usage and collects that data. The emotion engine also analyzes emotions based on user operations and inputs and collects emotion data.

[1479] 3. Data analysis and selection of optimal SaaS

[1480] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data. This analysis identifies the SaaS that are best suited to the company and generates a candidate list. This candidate list includes detailed information such as the benefits, expected effectiveness, and implementation costs of each tool, and also takes emotional data into account to prioritize and present candidates that will satisfy the user.

[1481] 4. Presenting a candidate list

[1482] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The user can check detailed information about each SaaS and compare them through this dashboard. An index of user satisfaction based on emotional data is also displayed.

[1483] 5. Providing onboarding support and tutorials

[1484] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it. The server searches for and provides tutorial content related to the selected SaaS. The terminal displays the appropriate tutorial for each employee, and the employee learns how to use the SaaS.

[1485] 6. Monitoring usage and suggesting improvements

[1486] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc. The emotion engine also monitors the user's emotional state, analyzing, for example, stress levels and satisfaction during operation in real time. The server analyzes the collected usage data and emotion data to evaluate whether the introduced SaaS is delivering the expected results. If necessary, the server proposes improvement or replacement plans to the user. Based on the emotion data, it also makes improvement proposals to increase user satisfaction.

[1487] Specific examples

[1488] For example, consider the case where this system is implemented in a manufacturing factory with 50 employees. The manager of the factory enters basic information about the company (industry type, size, tools used, challenges) into a web form and also conducts a sentiment assessment. The manager also installs a business monitoring tool and sentiment engine on each employee's PC to collect business and sentiment data. The server analyzes the collected data using a machine learning algorithm and generates a list of candidate SaaS solutions, for example, specialized in inventory management. The dashboard also displays detailed information about each SaaS solution and a satisfaction index based on the sentiment data. The manager selects a SaaS solution based on this information and provides training to employees using tutorials. The server continues to monitor usage, analyzes sentiment data, and proposes improvement measures as needed.

[1489] Prompt Sentence Examples

[1490] "Select the optimal SaaS using employee data and sentiment data from within the factory. Business data:

[1491] Factory name: Manufacturing ABC

[1492] Industry: Manufacturing

[1493] Scale: Large

[1494] Tools in use: ERP, MES

[1495] Employee Data:

[1496] Employee ID: 1

[1497] Business data: { "tasks_completed": 10, "errors": 1}

[1498] Emotional data: { "happiness": 0.7, "stress": 0.3}

[1499] Please suggest the best SaaS candidates based on this information.

[1500] In this way, companies can select the most suitable software services based on both business and emotional data, and then effectively implement and utilize them. This system also improves employee satisfaction and achieves work efficiency.

[1501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1502] Step 1:

[1503] The user enters basic information about their company. Using a web form or a dedicated application, the user enters information such as the company's industry, size, tools currently being used, and challenges. This generates basic information data. As the user enters information, a questionnaire form is also provided to assess the user's emotional state, and emotional data is also collected. The basic information data and emotional data are sent as input to the server.

[1504] Step 2:

[1505] The user collects employee work data and emotional data. A dedicated work monitoring tool and emotional engine are installed on each employee's PC and work environment. This monitors and collects employees' work activities (task completion status, error rate, etc.) and emotional states (stress levels, satisfaction, etc.). The collected work data and emotional data are sent to a server.

[1506] Step 3:

[1507] The server analyzes the collected data and selects the optimal software service (SaaS). The server receives basic information data, business data, and emotion data as input and performs analysis using a machine learning algorithm. It analyzes the correlation between each data item and generates a list of optimal software service candidates. The analysis results in a list containing detailed information such as the benefits, expected effects, and implementation costs of each candidate. This list is generated as output and sent to the terminal.

[1508] Step 4:

[1509] The terminal displays the candidate list in a dashboard format. The terminal receives the candidate list sent from the server and displays it in a format that is intuitive to the user. A visual dashboard using graphs and charts displays detailed information about each SaaS and an index of user satisfaction based on emotional data. This allows the user to compare each candidate.

[1510] Step 5:

[1511] The user selects the optimal software service and decides to implement it. The user selects the SaaS they deem most suitable based on the candidate list displayed on the dashboard. After selection, the selection information is sent to the server, and the necessary tutorials are provided in the next phase.

[1512] Step 6:

[1513] The server searches for and provides tutorial content related to the selected software service. The server searches for content related to the target SaaS from the tutorial database and outputs it to the terminal. The terminal displays the received tutorial content to each employee, allowing them to learn how to use the SaaS.

[1514] Step 7:

[1515] The server monitors the usage of the installed software service and performs analysis, including emotional data. Data is collected to evaluate frequency of use, effectiveness, problems, etc., and an emotional engine is used to analyze the user's emotional state. For example, stress levels and satisfaction levels during operation are monitored in real time. The collected data is used to evaluate the effectiveness of the SaaS and generate improvement or replacement proposals as needed. Improvement proposals are output to the user, and measures to improve user satisfaction are proposed.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] [Fourth embodiment]

[1520] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1521] 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.

[1522] 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).

[1523] 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.

[1524] 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.

[1525] 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).

[1526] 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. 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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."

[1533] Understood. Below is the "Mode for carrying out the invention" based on the claims.

[1534] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness.

[1535] System Overview

[1536] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data, select the most suitable SaaS, assist with implementation, monitor its usage, and make suggestions for improvement.

[1537] Example of a system

[1538] Entering user information

[1539] 1. Users

[1540] Users enter basic information about their company (industry, size, current tools used, challenges) using a web form or dedicated application.

[1541] Collection of employee work data

[1542] 1. Users

[1543] Users install a dedicated work monitoring tool in each employee's work environment, which monitors and collects data on employee work activities and application usage.

[1544] 2. Server

[1545] The server receives the data sent from the business monitoring tool and stores it in a database.

[1546] Data analysis and optimal SaaS selection

[1547] 1. Server

[1548] The server uses machine learning algorithms to analyze the collected data and user-entered information, and then identifies the SaaS that best suits your company.

[1549] 2. Server

[1550] Based on the analysis results, a list of optimal SaaS candidates is generated, including details such as the benefits, expected impact, and implementation costs of each candidate.

[1551] Presenting a candidate list

[1552] 1. Terminal

[1553] The terminal displays the list of SaaS candidates sent from the server in a dashboard format to the user, who can then compare and consider each SaaS based on the provided information.

[1554] Providing onboarding support and tutorials

[1555] 1. Users

[1556] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[1557] 2. Server

[1558] The server provides tutorials on selected SaaS.

[1559] 3. Terminal

[1560] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[1561] Usage monitoring and improvement suggestions

[1562] 1. Server

[1563] The server constantly monitors the usage of the introduced SaaS and collects data.

[1564] 2. Server

[1565] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[1566] 3. Server

[1567] If necessary, the server will suggest improvements or replacements to the user.

[1568] Specific examples

[1569] The retail case

[1570] 1. Users

[1571] A retail business owner fills out a web form with basic information about his company (50 employees, inventory inefficiencies, Excel as the tool of choice).

[1572] 2. Users

[1573] Management installs performance monitoring tools to collect employee performance data.

[1574] 3. Server

[1575] The server receives the collected data and basic information and analyzes it using machine learning algorithms.

[1576] 4. Server

[1577] Based on the analysis results, a candidate list of SaaS specialized for inventory management (e.g., specific inventory management tools) is generated.

[1578] 5. Terminal

[1579] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[1580] 6. Server

[1581] Provide a tutorial for the inventory management tool that management has decided to implement.

[1582] 7. Terminal

[1583] Employees use the terminals to go through tutorials and learn how to use the tools.

[1584] 8. Server

[1585] The server monitors the usage of the introduced tools and, if necessary, proposes improvement ideas to management.

[1586] This system allows small and medium-sized enterprises to efficiently select and implement the SaaS that is best suited to their company, maximizing its effectiveness.

[1587] The processing flow will be explained below.

[1588] Understood. Below is a step-by-step explanation of the process.

[1589] Step 1:

[1590] Users enter basic information about their company (e.g., industry, size, tools currently being used, challenges) through a web form or a dedicated app.

[1591] Step 2:

[1592] Users install a dedicated work monitoring tool on each employee's PC and work environment, which automatically collects application usage data, work hours, and other data.

[1593] Step 3:

[1594] The server receives basic information sent from a web form or a dedicated app and stores it in a database.

[1595] Step 4:

[1596] The server receives business data periodically sent from the business monitoring tool and stores it in a database.

[1597] Step 5:

[1598] The server uses machine learning algorithms to analyze the collected basic information and business data, and identifies the software services (SaaS) needed to optimize the user's business processes.

[1599] Step 6:

[1600] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool.

[1601] Step 7:

[1602] The terminal displays the candidate list sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[1603] Step 8:

[1604] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[1605] Step 9:

[1606] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[1607] Step 10:

[1608] The terminal displays tutorial content to each employee, who then learns how to use the SaaS.

[1609] Step 11:

[1610] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[1611] Step 12:

[1612] The server analyzes usage data and evaluates whether the implemented SaaS is having the expected effect.

[1613] Step 13:

[1614] Based on the analysis results, the server will propose improvements or replacements to the user as needed, ensuring optimal operation at all times.

[1615] This process allows companies to efficiently select the SaaS that is best suited to their business and receive ongoing support to maximize its effectiveness even after implementation.

[1616] Example 1

[1617] 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."

[1618] The process for companies to select and implement the software service (SaaS) that is best suited to their business is complex and time-consuming. It is also difficult to continuously evaluate the effectiveness of implementation and propose improvements as needed. Small and medium-sized enterprises, in particular, often lack specialized knowledge and resources, making it difficult to efficiently select the optimal SaaS and maximize its effectiveness.

[1619] 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.

[1620] In this invention, the server includes means for inputting basic information about the company, means for collecting employee business data, means for analyzing the input and collected data and selecting the optimal software service, means for generating and displaying a list of candidate software services based on the analysis results, means for providing tutorials to assist in the introduction of software services, means for monitoring the usage of the introduced software service and providing improvement and replacement proposals, means for collecting usage data of the introduced software service and saving it in a database in real time, and means for analyzing the collected data with a machine learning algorithm. This enables companies to efficiently select the optimal SaaS for their company and continuously maximize its effectiveness even after introduction.

[1621] "Basic information about your company" refers to basic information such as your company's industry, size, tools you currently use, and challenges you face.

[1622] "Employee Business Data" refers to data about employee work activities and application usage.

[1623] "Analysis" refers to the process of analyzing collected data using machine learning algorithms and extracting useful information.

[1624] "Candidate list of software services" refers to a list of multiple software services that are deemed to be optimal for a company, obtained as a result of data analysis.

[1625] "Tutorial" means a means of providing guides and examples on how to install and use selected software services.

[1626] "Real-time" refers to a method in which data is collected, analyzed, and stored immediately, reflecting the latest information almost instantly.

[1627] "Database" refers to a system for centrally storing and managing collected data.

[1628] A "machine learning algorithm" refers to a computational method for training models based on large amounts of data to extract or predict patterns and trends.

[1629] "Improvement proposals" refer to specific methods and measures proposed to evaluate the usage of the implemented software service and to improve its effectiveness.

[1630] "Replacement proposal" refers to a proposal for an alternative software service when the current software service does not perform as expected.

[1631] This invention is a system that enables companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and continuously evaluate and optimize its effectiveness. This system involves a series of processes in which users input basic company information, collect and analyze employee work data, select the best SaaS, support the implementation, monitor its usage, and make improvement suggestions.

[1632] Entering user information

[1633] User

[1634] Users use dedicated web forms and applications to enter information about their company's industry, size, tools currently being used, challenges they are facing, etc. For example, when a user enters basic information about their company into a web form and presses the "Submit" button, this information is sent to the server.

[1635] Collection of employee work data

[1636] User

[1637] Users install a business monitoring tool into each employee's work environment. This tool monitors business activities and application usage and collects the data. The tool installed on each computer runs in the background and collects business data in real time.

[1638] Receiving and storing business data

[1639] server

[1640] The server periodically receives data sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in the storage. The receiving and storing process is automated and takes place in real time.

[1641] Data analysis and identification of optimal SaaS candidates

[1642] server

[1643] The server passes the stored data and basic information entered by the user to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. This analysis can then identify the best SaaS for the company.

[1644] Generate a list of potential SaaS

[1645] server

[1646] Based on the analysis results, the server generates a list of multiple SaaS candidates that are deemed most suitable for the company, including detailed information such as the characteristics, benefits, expected effects, and implementation costs of each SaaS.

[1647] View SaaS candidate list

[1648] Terminal

[1649] The terminal receives the candidate list from the server and displays it to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[1650] SaaS selection and implementation

[1651] User

[1652] The user selects the SaaS that is most suitable for their company from the displayed list of candidates and clicks the "Decide" button to decide on implementation. The selection results are sent to the server, and the system proceeds to the next step.

[1653] Providing tutorials

[1654] server

[1655] The server generates and serves tutorial content for selected SaaS services, including usage guides, implementation procedures, and best practices.

[1656] View tutorial

[1657] Terminal

[1658] The terminal displays the received tutorial content to the employee, who can then learn how to use the SaaS through the displayed tutorial.

[1659] Usage monitoring and data collection

[1660] server

[1661] The server monitors the usage of the installed SaaS in real time and collects usage data through APIs, which are then stored in storage.

[1662] Usage analysis and improvement suggestions

[1663] server

[1664] The server analyzes the collected usage data using machine learning algorithms to evaluate usage patterns and performance, and based on the analysis results, generates recommendations for improvements or replacement of other SaaS services as needed and notifies the user.

[1665] Examples of prompt statements

[1666] 1. "Please provide some basic information about your company. Please be specific about your industry, size, current tools you use, and challenges you face."

[1667] 2. "To learn how to install the business monitoring tool, please follow these steps."

[1668] 3. "View a dashboard with a list of the best SaaS candidates. See each candidate's benefits, expected impact, implementation costs, and more."

[1669] 4. "We will explain the steps to implement the selected SaaS and provide a tutorial for employees."

[1670] 5. "We will monitor the usage of the implemented SaaS and propose improvements or replacements."

[1671] In this way, this system enables companies to efficiently select and implement the SaaS that is best suited to their company, and continuously maximize its effectiveness.

[1672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1673] Step 1:

[1674] The user enters basic information about their company. The user enters information such as their company's industry, company size, tools currently being used, and challenges they are facing into a web form or dedicated application, and then presses the "Submit" button. This operation sends the entered information to the server, which then stores the received basic information in a database.

[1675] Step 2:

[1676] The user installs the business monitoring tool. The user downloads the tool to each employee's computer and follows the installation wizard to complete the installation. After installation, the tool begins collecting business data in the background and prepares to send it to the server.

[1677] Step 3:

[1678] The server receives business data and stores it in a database. The server receives business activity and application usage data periodically sent from the business monitoring tool via TCP / IP protocol and stores it in a database secured in storage. This process is carried out in real time.

[1679] Step 4:

[1680] The server analyzes the collected data and identifies the best SaaS candidates. The server passes the stored business data and basic information to a machine learning model implemented in Python, which analyzes patterns such as business performance, tool usage, and time efficiency. The analysis performed here identifies the best SaaS candidates for the company.

[1681] Step 5:

[1682] The server generates a list of SaaS candidates. Based on the analysis results, the server generates a list of multiple SaaS candidates that are considered to be most suitable for the company. This list includes the name, characteristics, advantages, expected effects, implementation costs, etc. of each SaaS. The generated list is sent to the terminal in the next step.

[1683] Step 6:

[1684] The terminal displays the list of SaaS candidates. The terminal displays the list of SaaS candidates received from the server to the user as a dashboard-style web page. The user can check the details of each candidate based on the provided list and compare them.

[1685] Step 7:

[1686] The user selects the optimal SaaS and decides to implement it. The user selects the SaaS that is most suitable for their company from the displayed list of SaaS candidates and clicks the "Decide" button. This operation sends the selection results to the server, and the actual implementation procedure begins in the next step.

[1687] Step 8:

[1688] The server provides a tutorial for the introduced SaaS. The server generates detailed tutorial content (video, text, guidelines, etc.) for the SaaS selected by the user and transmits it to the user's device.

[1689] Step 9:

[1690] The terminal displays the tutorial. The terminal displays the tutorial content received from the server to the employee. Employees can learn and practice how to use the selected SaaS by following the on-screen guide.

[1691] Step 10:

[1692] The server monitors the usage of the SaaS and collects data. The server monitors the usage of the deployed SaaS in real time and stores the collected usage data in a database via API. This data is used for subsequent performance evaluation.

[1693] Step 11:

[1694] The server analyzes usage and makes improvement suggestions as needed. The server analyzes the stored usage data using machine learning algorithms to evaluate performance. Based on the results, it generates improvement suggestions or suggestions for replacing other SaaS as needed, and notifies the user. This notification is sent via the dashboard or email.

[1695] (Application example 1)

[1696] 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."

[1697] Content distribution companies face challenges in efficiently selecting and implementing the software services best suited to their company, and continuously evaluating and optimizing their effectiveness. Analyzing viewing data and selecting the optimal tool takes a significant amount of time and effort. It is also not easy to properly monitor the usage of the implemented tools and make necessary improvements or replacements. Furthermore, the explanations of the candidate lists provided are sometimes insufficient, so it is necessary to provide information in a format that makes it easy for users to understand and select.

[1698] 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.

[1699] In this invention, the server includes means for analyzing user information and collected viewing data and proposing optimal content distribution platforms and related tools, means for generating an explanation of the candidate list based on the analysis results using a generative AI model, and means for automatically generating prompts for the generative AI model. This enables content distribution companies to efficiently select the software services that are best suited to their company, easily compare and consider them, and appropriately monitor and optimize their usage even after implementation.

[1700] "Basic information about your company" refers to basic information for identifying your company, such as your company's industry, size, tools you currently use, and challenges.

[1701] "Employee business data" refers to data related to work, such as the tools used by employees, their usage status, work content, work hours, and viewing data.

[1702] "Software services" is a general term for services that provide software functions on a cloud basis and that users can use via the Internet.

[1703] "Analysis" is the process of analyzing input basic information and collected business data to derive meaningful information and patterns.

[1704] The "candidate list" is a list of optimal software services selected based on the analysis results.

[1705] A "tutorial" is a step-by-step instruction manual or guide that explains how to install and use a particular software service.

[1706] "Viewing data" refers to data collected when a user views content, including the viewing time of a particular piece of content, viewer feedback, and the like.

[1707] A "content distribution platform" is a service for distributing digital content such as video, audio, and text over the Internet.

[1708] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate responses or information in response to specific inputs.

[1709] A "prompt" is text that a generative AI model uses as input to generate a particular output.

[1710] This invention is a system that allows companies to efficiently select and implement the software service (SaaS) that is best suited to their company, and also to continuously evaluate and optimize its effectiveness. In particular, the system optimized for content distribution companies is shown below.

[1711] System configuration

[1712] The system includes the following elements:

[1713] 1. User Information and Business Data Collection Tools

[1714] A way to enter basic information about your company.

[1715] A means of collecting employee work data and viewing data.

[1716] 2. Data Analysis and SaaS Selection Tools

[1717] The collected data is analyzed and machine learning algorithms are used to select the most appropriate software services.

[1718] Based on collected user information and viewing data, we propose the most suitable content distribution platform and related tools.

[1719] 3. Viewing and Management Tools

[1720] A means of generating a list of software service candidates based on the analysis results and displaying them in a dashboard format.

[1721] A means of providing tutorials to assist with the adoption of software services.

[1722] A means of monitoring the usage of deployed software services and providing suggestions for improvement or replacement.

[1723] 4. Generative AI Models

[1724] A generative AI model to generate candidate list explanations based on analysis results.

[1725] A means of automatically generating prompts for generative AI models.

[1726] Example of a system

[1727] Collection of user information and viewing data

[1728] Users enter basic information about their company (industry, company size, tools currently used, challenges) using a web form or dedicated application, and also install a business monitoring tool in each employee's work environment to collect viewing data and work activities.

[1729] As a concrete example, a company operating in the content distribution business would enter its industry as "content distribution," its number of employees as "150," the tools it uses as "Vimeo, Google Analytics," and the challenges it faces as "low viewership, high server costs."

[1730] Data analysis and selection of optimal SaaS

[1731] The server uses a database containing the collected data to run machine learning algorithms to analyze the data and generate a list of content distribution platforms and related tools that are best suited to the company based on the collected viewing data and company information.

[1732] Software used includes Scikit-learn for running machine learning algorithms, Pandas and SQL databases for processing and storing data.

[1733] For example, the analysis results may result in a candidate list of "video encoding tools" and "advertising distribution platforms."

[1734] Presenting a candidate list

[1735] The server displays the generated candidate list in dashboard format on the user's device, and also generates prompt sentences that explain the analysis results using the generative AI model and displays the explanations on the dashboard.

[1736] For example, GPT is used as a generative AI model.

[1737] Providing onboarding support and tutorials

[1738] The user selects the most suitable software service from the list of candidates displayed on the dashboard and decides to implement it. The server then provides the corresponding tutorial and displays it on the employee's terminal.

[1739] Usage monitoring and improvement suggestions

[1740] The server monitors the usage of the installed software services, provides suggestions for improvement or replacement as needed, and evaluates the effectiveness of the software based on the viewing data and business data used.

[1741] Prompt Sentence Examples

[1742] For example, input the following prompt sentence into the generative AI model:

[1743] Based on your viewing data analysis and company information, please list the most suitable video encoding tools, analysis tools, and ad serving platforms. Please also provide information on the benefits, expected results, and implementation costs of each tool.

[1744] Industry: Content Distribution

[1745] Number of employees: 150

[1746] Current tools used: Vimeo, Google Analytics

[1747] Challenges: Low viewership, high server costs

[1748] This helps businesses effectively select software services by providing specific descriptions for each tool on the shortlist.

[1749] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1750] Step 1:

[1751] Users enter basic information about their company into a web form or dedicated application. Specifically, they enter information such as the industry, company size, tools currently used, and challenges they are facing. The input data is sent to a server and stored in a database. A company profile is generated based on the input (basic company information).

[1752] Step 2:

[1753] The user installs a business process monitoring tool into each employee's work environment. This tool collects the software used by the employee, the work they are doing, and viewing data. The collected data is sent to a server and stored in a database. Based on the input (data collected by the business process monitoring tool), a profile of the business process data and viewing data is created.

[1754] Step 3:

[1755] The server runs a machine learning algorithm to analyze the collected company information and business data. Software such as Scikit-learn is used for this, and Pandas is used for data processing. As a result of the analysis, a candidate list of optimal software services (video encoding tools, ad distribution platforms, etc.) is generated. Analysis is performed based on the input (company information and business data), and a candidate list of software services is obtained as output.

[1756] Step 4:

[1757] The server uses a generative AI model to generate detailed descriptions of the candidate list. Specifically, a prompt sentence is input to a generative AI model such as GPT, and based on that, a description of each tool in the candidate list is generated, including its benefits, expected effects, and implementation costs. The generative AI model generates text based on the input (prompt sentence), and a detailed candidate list is obtained as output.

[1758] Step 5:

[1759] The server displays the generated candidate list and its detailed explanations on the user's device in the form of a dashboard. This dashboard contains detailed information about each candidate, allowing the user to select the most suitable software service based on that information. The dashboard is generated and displayed based on the input (detailed candidate list).

[1760] Step 6:

[1761] For each software service selected by the user, the server provides a tutorial to assist with implementation. The tutorial details how to install and use the software and is displayed on the employee's terminal to help the employee learn the new tool. Based on the input (the selected software service), an appropriate tutorial is generated and displayed as output on the user's terminal.

[1762] Step 7:

[1763] The server continuously monitors the usage of the installed software service. The monitoring results are used to evaluate whether the usage is producing the expected results based on the collected viewing data and business data. If necessary, the server proposes improvement or replacement proposals to the user. Analysis is performed based on the input (monitoring data), and improvement or replacement proposals are provided to the user as output.

[1764] 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.

[1765] Understood. Below, we will describe the "Mode for carrying out the invention" based on the scope of the patent claims of the invention combining an emotion engine.

[1766] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. In addition, by combining it with an emotion engine that recognizes user emotions, it is possible to further improve user satisfaction.

[1767] System Overview

[1768] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[1769] Example of a system

[1770] Entering user information

[1771] 1. Users

[1772] Using a web form or a dedicated application, users enter basic information about their company (e.g., industry, size, current tools used, challenges), as well as a simple questionnaire to assess the user's emotional state.

[1773] Collecting employee work and sentiment data

[1774] 1. Users

[1775] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment, which monitors and collects data on employee work activities and application usage.

[1776] The emotion engine analyzes emotions based on user operations and inputs and collects emotion data.

[1777] 2. Server

[1778] The server receives the data sent from the business monitoring tool and the emotion engine and stores it in a database.

[1779] Data analysis and optimal SaaS selection

[1780] 1. Server

[1781] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data, and identifies the SaaS that best suits the company.

[1782] 2. Server

[1783] Based on the analysis results, a list of optimal SaaS candidates is generated, including detailed information such as the benefits, expected effectiveness, and implementation costs of each tool. Sentiment data is also taken into consideration, and candidates that are likely to satisfy the user are presented with priority.

[1784] Presenting a candidate list

[1785] 1. Terminal

[1786] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare them.

[1787] Providing onboarding support and tutorials

[1788] 1. Users

[1789] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it.

[1790] 2. Server

[1791] The server searches for and provides tutorial content related to the selected SaaS.

[1792] 3. Terminal

[1793] The device will then display the appropriate tutorial for each employee, who will then learn how to use the SaaS.

[1794] Usage monitoring and improvement suggestions

[1795] 1. Server

[1796] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc.

[1797] The emotion engine also monitors the user's emotional state, for example analyzing stress levels and satisfaction levels in real time while operating the device.

[1798] 2. Server

[1799] The server analyzes the collected usage data and sentiment data to evaluate whether the introduced SaaS is having the expected effect.

[1800] 3. Server

[1801] If necessary, the server will propose improvements or replacements to the user. Based on the emotion data, it will make suggestions to improve the user's satisfaction.

[1802] Specific examples

[1803] The retail case

[1804] 1. Users

[1805] A retail manager enters basic information about his or her company (50 employees, inventory inefficiencies, spreadsheet software used) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[1806] 2. Users

[1807] Management installs business monitoring tools and sentiment engines to collect business and sentiment data.

[1808] 3. Server

[1809] The server receives the collected data and analyzes it using machine learning algorithms.

[1810] 4. Server

[1811] Based on the analysis results, a list of candidates for SaaS specialized in inventory management (e.g., specific inventory management tools) is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[1812] 5. Terminal

[1813] The candidate list is displayed in dashboard format on the manager's device, and the manager can review the detailed information and select the tool to be implemented.

[1814] 6. Server

[1815] Provide tutorials for selected inventory management tools.

[1816] 7. Terminal

[1817] Employees receive tutorials via terminals to learn how to use the new tools.

[1818] 8. Server

[1819] The server monitors the usage of the introduced tools, evaluates user sentiment data, and, if necessary, proposes improvements or replacements to management.

[1820] This system allows small and medium-sized enterprises to efficiently select the SaaS that is best suited to their company, receive ongoing support to maximize its effectiveness even after implementation, and provides optimal support that takes user emotions into consideration.

[1821] The processing flow will be explained below.

[1822] Understood. Below I will explain the specific process step by step.

[1823] Step 1:

[1824] Users enter basic information about their company (e.g., industry, size, tools currently used, challenges) and emotional state information in the form of questions via a web form or a dedicated app.

[1825] Step 2:

[1826] Users install a dedicated work monitoring tool and emotion engine on each employee's PC and work environment. The work monitoring tool collects application usage data and work hours, while the emotion engine collects emotion data based on user operations and input.

[1827] Step 3:

[1828] The server receives basic information and emotional state data sent via a web form or a dedicated app and stores it in a database.

[1829] Step 4:

[1830] The server receives the business data and emotion data periodically sent from the business monitoring tool and emotion engine, and stores them in a database.

[1831] Step 5:

[1832] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotion data, and identifies SaaS solutions that will optimize business efficiency and user satisfaction.

[1833] Step 6:

[1834] Based on the results of the data analysis, the server generates a list of optimal SaaS candidates, including details such as each tool's benefits, expected impact, implementation costs, and satisfaction predictions based on sentiment data.

[1835] Step 7:

[1836] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, allowing the user to check detailed information about each SaaS and compare and consider them.

[1837] Step 8:

[1838] Users select the SaaS they deem most suitable on the dashboard and decide to implement it.

[1839] Step 9:

[1840] The server retrieves and provides tutorial content for the selected SaaS, which provides detailed instructions on how to use the SaaS effectively.

[1841] Step 10:

[1842] The device displays the appropriate tutorial content for each employee, who then learns how to use the SaaS.

[1843] Step 11:

[1844] The server constantly monitors the usage of the implemented SaaS. Specifically, it collects data to evaluate the frequency of tool use, effectiveness, and any problems that arise. In addition, the emotion engine monitors and analyzes the user's emotional state (e.g., stress, satisfaction).

[1845] Step 12:

[1846] The server analyzes the collected usage and sentiment data to assess whether the SaaS is performing as expected.

[1847] Step 13:

[1848] The server then proposes improvements or replacements to the user based on the analysis results, and makes specific suggestions for improvement to increase user satisfaction based on the emotional data.

[1849] This process allows companies to efficiently select the SaaS that is best suited to their business, receive ongoing support to maximize effectiveness even after implementation, and provide optimal support that takes user emotions into consideration.

[1850] Example 2

[1851] 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."

[1852] Traditionally, it has been difficult for companies to select the optimal software service (SaaS) for their organization and continuously evaluate and optimize its effectiveness after implementation. In particular, software selection and optimization that takes into account not only employees' work efficiency but also their emotional state has not been practiced. This has led to a decline in the accuracy of software selection and post-use satisfaction in companies, which can ultimately have a negative impact on the efficiency and productivity of the entire organization.

[1853] 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 a means for inputting basic information about the company, a means for collecting work data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating a list of candidate software services based on the analysis results and displaying the list while taking the emotional data into consideration, a means for providing a tutorial to assist in the introduction of the software service, and a means for monitoring the usage status of the introduced software service and providing improvement or replacement suggestions. This enables a company to efficiently select the optimal software service for the company and to continuously optimize the software service while taking the emotions of employees into consideration even after the introduction.

[1854] "Basic information about your company" refers to basic company profile information, such as the company's industry, size, current tools used, and challenges.

[1855] "Employee Business Data" refers to data relating to the usage of applications and work progress used by employees in the course of their daily work.

[1856] "Emotional data" refers to data collected through real-time analysis of the emotional states displayed by employees while they are working.

[1857] "Analysis" refers to the process of analyzing data using machine learning algorithms based on collected basic information, business data, and sentiment data.

[1858] The "optimal software service" refers to the software service that is determined to be most suitable for the user based on the analysis results.

[1859] "Candidate List" refers to a list of multiple recommended software services generated based on the analysis results.

[1860] "Considering emotional data" refers to evaluating the user's emotional state and reflecting the results when selecting and proposing software services.

[1861] "Tutorial" means educational content that explains how to install and use selected Software Services.

[1862] "Monitoring" means the continuous monitoring of the usage of the Deployed Software Services.

[1863] "Improvement Suggestions" means suggestions for improving the current Software Services based on Usage Data and Sentiment Data.

[1864] "Replacement Proposal" means a proposal to replace a currently used software service with another software service.

[1865] "Dashboard format" refers to a set of interface formats that present information in an easy-to-visually organize manner.

[1866] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes employee emotions, it is possible to improve user satisfaction.

[1867] System Overview

[1868] The system involves a series of processes in which users input basic information about their company, collect and analyze employee work data and emotional data, select the most suitable SaaS, support its implementation, monitor its usage, and make suggestions for improvement.

[1869] Entering user information

[1870] User

[1871] Using a dedicated web form or application, users enter basic information about their company (e.g., industry, size of employee base, current tools used, specific challenges, etc.) and also answer a questionnaire with sentiment assessment questions. This information serves as the basis for customization within the system.

[1872] Collecting employee work and sentiment data

[1873] User

[1874] Users install the task monitoring tool and emotion engine on employees' PCs and work environments, which monitor and collect data on employee activity (e.g., active windows, keyboard input, mouse movements) in real time.

[1875] The emotion engine analyzes and collects emotional data based on employees' reactions to operations and inputs.

[1876] server

[1877] The server receives data sent from the business monitoring tool and emotion engine and stores it in a database. The data is automatically transferred at regular intervals.

[1878] Data analysis and optimal SaaS selection

[1879] server

[1880] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data, and analyzes the data to determine business patterns, efficiency, and trends in emotional changes.

[1881] Based on the analysis results, a list of SaaS candidates optimal for the company is generated. The list includes detailed information about each software service (benefits, expected effects, implementation costs, and user satisfaction). In particular, sentiment data is taken into consideration, and candidates with high employee satisfaction are prioritized.

[1882] Presenting a candidate list

[1883] Terminal

[1884] The terminal displays the list of SaaS candidates sent from the server in a dashboard format, visually organizing each tool's implementation cost, user ratings, and highlights of its main features.

[1885] User

[1886] Users can review the list of candidates through a dashboard and compare detailed information about each SaaS.

[1887] Providing onboarding support and tutorials

[1888] User

[1889] Users can select the SaaS they deem most suitable from the list of candidates and decide to implement it. They can start the implementation process for the selected SaaS with just one click.

[1890] server

[1891] The server searches for tutorial content related to the selected SaaS and provides an appropriate tutorial, such as an installation guide or initial setup manual.

[1892] Terminal

[1893] The terminal displays the provided tutorial on each employee's screen, allowing employees to learn how to use the SaaS by referring to the tutorial.

[1894] Usage monitoring and improvement suggestions

[1895] server

[1896] The server constantly monitors the usage of the introduced SaaS, specifically recording in detail the frequency of tool use, its effectiveness, and any problems that arise.

[1897] The emotion engine analyzes the user's emotional state (e.g., stress level and satisfaction level during operation) in real time.

[1898] server

[1899] Based on the collected usage and sentiment data, we evaluate whether the introduced SaaS is delivering the expected results. If necessary, we generate improvement or replacement proposals and present them to users. For example, we make specific proposals such as "proposing the addition of new functions to improve the operability of the current tool" or "proposing migration to another tool."

[1900] Specific examples

[1901] The retail case

[1902] 1. Users

[1903] A retail manager enters information about his company (50 employees, inventory management inefficiencies, current tool used: spreadsheet) into a web form. An emotional assessment is also conducted to record stress and expectations at the time of entering the information.

[1904] 2. Users

[1905] The manager installs a work monitoring tool and emotion engine on each employee's PC to collect work data and emotion data. After installation, the manager checks with the employee to see if the tools are working properly.

[1906] 3. Server

[1907] The server receives the collected data and analyzes it using machine learning algorithms, such as data on work efficiency and employee sentiment.

[1908] 4. Server

[1909] Based on the analysis results, a list of candidates for SaaS (specific inventory management tools) specialized for inventory management is generated. Emotional data is also taken into consideration to present candidates with high user satisfaction.

[1910] 5. Terminal

[1911] The candidate list is displayed in dashboard format on the manager's device, and the manager can check detailed information (implementation costs, benefits, user ratings, etc.) and select the tool to implement.

[1912] 6. Server

[1913] Provide managers with tutorials for selected inventory management tools, such as installation guides and initial setup instructions.

[1914] 7. Terminal

[1915] Employees learn how to use the new tools through tutorials delivered via the terminal.

[1916] 8. Server

[1917] The server monitors the usage of the introduced tools and evaluates user sentiment data. If necessary, it proposes improvements or replacements to management. For example, it may suggest adding new features or migrating to another tool.

[1918] Example prompts for generative AI models

[1919] We are developing a system to select the optimal SaaS. The system collects basic user information, employee work data, and emotional data, analyzes them using machine learning, and then proposes the optimal SaaS. Could you please explain each process step in detail?

[1920] This system allows companies to efficiently select the software services that are best suited to their business, and even after implementation, it enables continuous optimization that takes into account employee sentiment.

[1921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1922] Step 1:

[1923] User

[1924] Users use a web form or a dedicated application to enter basic information about their company (industry type, size, tools currently used, challenges), and answer questionnaire-style questions for sentiment assessment. This information is used as input data for customizing the system. After input, it is sent to the server.

[1925] Step 2:

[1926] User

[1927] The user installs the work monitoring tool and emotion engine on each employee's PC and work environment. After installation, the user checks whether the tools are working properly. The work monitoring tool monitors employee activity (active windows, keyboard input, mouse movement) and collects data. The emotion engine analyzes and collects emotion data based on reactions to operations and inputs.

[1928] Step 3:

[1929] server

[1930] The server receives data (business data and emotion data) sent from the business monitoring tool and emotion engine and stores it in a database. Data transfer is performed automatically at regular intervals. This ensures that the latest data is always stored on the server and available for analysis.

[1931] Step 4:

[1932] server

[1933] The server uses machine learning algorithms to analyze the collected basic information, business data, and sentiment data. The analysis process includes analyzing business patterns, evaluating efficiency, and analyzing sentiment trends. This analysis provides data to identify the best SaaS for each company.

[1934] Step 5:

[1935] server

[1936] Based on the analysis results, the server generates a list of SaaS candidates that are optimal for the company. The generated candidate list includes detailed information about each software service (benefits, expected effectiveness, implementation costs, and user satisfaction). Emotional data is also taken into consideration, and candidates with high user satisfaction are prioritized in the list.

[1937] Step 6:

[1938] Terminal

[1939] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The dashboard visually organizes the software implementation costs, user ratings, and highlights of key features. The user can then use this information to make a detailed comparison.

[1940] Step 7:

[1941] User

[1942] Users can select the most suitable SaaS from a list of candidates through the dashboard and decide to implement it. The implementation process of the selected SaaS can be started with one click. When the selected SaaS is selected as input, related information is sent from the server.

[1943] Step 8:

[1944] server

[1945] The server searches for tutorial content related to the selected SaaS and provides the appropriate tutorial, which includes installation guides, initial setup instructions, and detailed usage instructions, helping employees smoothly get started with the new software.

[1946] Step 9:

[1947] Terminal

[1948] The terminal displays the provided tutorial on each employee's screen. Employees refer to the tutorial to learn how to use the SaaS and then actually operate it. The input in this step is the tutorial content, and the output is employee learning and skill improvement.

[1949] Step 10:

[1950] server

[1951] The server constantly monitors the usage of the introduced SaaS. Specifically, it records in detail the frequency of tool use, its effectiveness, and any problems that occur. The emotion engine also analyzes the user's emotional state in real time. This makes it possible to identify problems and areas for improvement during operation.

[1952] Step 11:

[1953] server

[1954] Based on the collected usage data and sentiment data, the introduced SaaS is evaluated to see if it is producing the expected results. If necessary, improvement or replacement proposals are generated and presented to the user. For example, specific proposals such as "proposals to add new functions to improve operability" or "proposals to migrate to other tools" can be presented. The input for this step is usage data and sentiment data, and the output is specific improvement proposals.

[1955] (Application example 2)

[1956] 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."

[1957] Conventional software service (SaaS) selection and implementation support systems are limited in their effectiveness because they only analyze a company's business data and do not take into account qualitative information such as employee emotions and satisfaction. Furthermore, when monitoring usage or proposing improvements after implementation, emotional data is not taken into account, leading to problems such as lower employee satisfaction and reduced work efficiency. However, by collecting and analyzing emotional data along with business data, it becomes possible to select the optimal SaaS with greater accuracy and continuously optimize its effectiveness even after implementation. Therefore, a comprehensive system that includes employee emotional data is needed.

[1958] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting basic information about the company, a means for collecting business data and emotional data of employees, a means for analyzing the input and collected data and selecting the optimal software service, a means for generating and displaying a list of candidate software services based on the analysis results, a means for providing tutorials to assist in the introduction of software services, and a means for monitoring the usage of introduced software services and providing improvement and replacement proposals that include emotional data. This enables companies to select and introduce the optimal SaaS based on both business data and emotional data, and to continuously optimize its effectiveness.

[1959] Understood. Below are the definitions of important terms included in the patent claims, rewritten to fit the application example.

[1960] "Basic information" refers to initial data about a company or organization, such as its industry, size, tools in use, and challenges.

[1961] "Business data" refers to quantitative data such as task completion status, error rate, and working hours generated by employees through their daily work activities.

[1962] "Emotional data" refers to data related to an employee's emotional state, including qualitative data such as stress levels and satisfaction.

[1963] "Analysis" is the process of analyzing collected basic information, business data, and emotional data using statistical methods and machine learning algorithms to extract meaningful information.

[1964] "Software as a Service (SaaS)" is a software application that can be used over the Internet and is a tool that helps improve the efficiency and management of corporate operations.

[1965] A "candidate list" is a list of proposed software services generated based on the analysis results, detailing the features and benefits of each service.

[1966] The "dashboard format" is an interface format for intuitively displaying multiple pieces of information, and is a layout that makes extensive use of visual elements such as graphs and charts.

[1967] A "tutorial" is educational content that explains how to use the introduced software service and is a guide for users to efficiently utilize the tool.

[1968] "Monitoring" is the process of continuously observing and collecting data about the use of deployed software services.

[1969] "Improvement proposals" are specific proposals for improving the efficiency of use of software services and user satisfaction based on the results of monitoring and analysis.

[1970] A "machine learning algorithm" is an algorithm that automatically learns patterns and rules from large amounts of data and makes predictions and classifications based on new data.

[1971] An "emotion analysis engine" is a software module that analyzes an employee's emotional state based on their behavior and input data, and evaluates their stress and satisfaction.

[1972] These definitions clarify the technical scope of the invention and allow accurate understanding of the characteristics of the invention based on the claims.

[1973] This invention is a system that allows companies to efficiently select and implement the software services (SaaS) that are best suited to their company, and continuously evaluate and optimize their effectiveness. Furthermore, by combining it with an emotion engine that recognizes user emotions, user satisfaction can be further improved. This article explains the main components and processing procedures of this system.

[1974] System Overview

[1975] 1. Enter your user information

[1976] Users enter basic information about their company (industry, size, tools currently used, challenges) using a web form or dedicated application, and are also provided with a questionnaire form to assess the user's emotional state, thereby collecting emotional data.

[1977] 2. Collecting employee work and sentiment data

[1978] Users install a dedicated work monitoring tool and emotion engine into each employee's work environment (PC and work environment). This tool monitors employees' work activities and application usage and collects that data. The emotion engine also analyzes emotions based on user operations and inputs and collects emotion data.

[1979] 3. Data analysis and selection of optimal SaaS

[1980] The server uses machine learning algorithms to analyze the collected basic information, business data, and emotional data. This analysis identifies the SaaS that are best suited to the company and generates a candidate list. This candidate list includes detailed information such as the benefits, expected effectiveness, and implementation costs of each tool, and also takes emotional data into account to prioritize and present candidates that will satisfy the user.

[1981] 4. Presenting a candidate list

[1982] The terminal displays the list of SaaS candidates sent from the server in a dashboard format. The user can check detailed information about each SaaS and compare them through this dashboard. An index of user satisfaction based on emotional data is also displayed.

[1983] 5. Providing onboarding support and tutorials

[1984] The user selects the SaaS they deem most suitable from the list of candidates and decides to implement it. The server searches for and provides tutorial content related to the selected SaaS. The terminal displays the appropriate tutorial for each employee, and the employee learns how to use the SaaS.

[1985] 6. Monitoring usage and suggesting improvements

[1986] The server constantly monitors the usage of the introduced SaaS, collecting data to evaluate the frequency of tool use, effectiveness, problems, etc. The emotion engine also monitors the user's emotional state, analyzing, for example, stress levels and satisfaction during operation in real time. The server analyzes the collected usage data and emotion data to evaluate whether the introduced SaaS is delivering the expected results. If necessary, the server proposes improvement or replacement plans to the user. Based on the emotion data, it also makes improvement proposals to increase user satisfaction.

[1987] Specific examples

[1988] For example, consider the case where this system is implemented in a manufacturing factory with 50 employees. The manager of the factory enters basic information about the company (industry type, size, tools used, challenges) into a web form and also conducts a sentiment assessment. The manager also installs a business monitoring tool and sentiment engine on each employee's PC to collect business and sentiment data. The server analyzes the collected data using a machine learning algorithm and generates a list of candidate SaaS solutions, for example, specialized in inventory management. The dashboard also displays detailed information about each SaaS solution and a satisfaction index based on the sentiment data. The manager selects a SaaS solution based on this information and provides training to employees using tutorials. The server continues to monitor usage, analyzes sentiment data, and proposes improvement measures as needed.

[1989] Prompt Sentence Examples

[1990] "Select the optimal SaaS using employee data and sentiment data from within the factory. Business data:

[1991] Factory name: Manufacturing ABC

[1992] Industry: Manufacturing

[1993] Scale: Large

[1994] Tools in use: ERP, MES

[1995] Employee Data:

[1996] Employee ID: 1

[1997] Business data: { "tasks_completed": 10, "errors": 1}

[1998] Emotional data: { "happiness": 0.7, "stress": 0.3}

[1999] Please suggest the best SaaS candidates based on this information.

[2000] In this way, companies can select the most suitable software services based on both business and emotional data, and then effectively implement and utilize them. This system also improves employee satisfaction and achieves work efficiency.

[2001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2002] Step 1:

[2003] The user enters basic information about their company. Using a web form or a dedicated application, the user enters information such as the company's industry, size, tools currently being used, and challenges. This generates basic information data. As the user enters information, a questionnaire form is also provided to assess the user's emotional state, and emotional data is also collected. The basic information data and emotional data are sent as input to the server.

[2004] Step 2:

[2005] The user collects employee work data and emotional data. A dedicated work monitoring tool and emotional engine are installed on each employee's PC and work environment. This monitors and collects employees' work activities (task completion status, error rate, etc.) and emotional states (stress levels, satisfaction, etc.). The collected work data and emotional data are sent to a server.

[2006] Step 3:

[2007] The server analyzes the collected data and selects the optimal software service (SaaS). The server receives basic information data, business data, and emotion data as input and performs analysis using a machine learning algorithm. It analyzes the correlation between each data item and generates a list of optimal software service candidates. The analysis results in a list containing detailed information such as the benefits, expected effects, and implementation costs of each candidate. This list is generated as output and sent to the terminal.

[2008] Step 4:

[2009] The terminal displays the candidate list in a dashboard format. The terminal receives the candidate list sent from the server and displays it in a format that is intuitive to the user. A visual dashboard using graphs and charts displays detailed information about each SaaS and an index of user satisfaction based on emotional data. This allows the user to compare each candidate.

[2010] Step 5:

[2011] The user selects the optimal software service and decides to implement it. The user selects the SaaS they deem most suitable based on the candidate list displayed on the dashboard. After selection, the selection information is sent to the server, and the necessary tutorials are provided in the next phase.

[2012] Step 6:

[2013] The server searches for and provides tutorial content related to the selected software service. The server searches for content related to the target SaaS from the tutorial database and outputs it to the terminal. The terminal displays the received tutorial content to each employee, allowing them to learn how to use the SaaS.

[2014] Step 7:

[2015] The server monitors the usage of the installed software service and performs analysis, including emotional data. Data is collected to evaluate frequency of use, effectiveness, problems, etc., and an emotional engine is used to analyze the user's emotional state. For example, stress levels and satisfaction levels during operation are monitored in real time. The collected data is used to evaluate the effectiveness of the SaaS and generate improvement or replacement proposals as needed. Improvement proposals are output to the user, and measures to improve user satisfaction are proposed.

[2016] 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...

Claims

1. A way to enter basic information about your company, a means of collecting employee work data; A means for analyzing the input and collected data and selecting the most suitable software service; means for generating and displaying a list of candidate software services based on the analysis results; a means for providing tutorials to assist in the adoption of the software service; A means of monitoring the usage of deployed software services and providing suggestions for improvement or replacement; A system including:

2. The system of claim 1, wherein the software service analysis uses a machine learning algorithm.

3. 10. The system of claim 1, further comprising means for displaying the candidate list in a dashboard format.

Images