System

Abstract

An object of the system according to the embodiment is to propose and combine an optimum SaaS based on business contents and needs of a company.SOLUTION: A system according to an embodiment includes an operation content analysis unit, a SaaS proposal unit, and a combination optimization unit. The business contents analysis unit analyzes business contents and needs of a company. The SaaS proposing unit proposes an optimum SaaS based on the business contents and the needs analyzed by the business contents analyzing unit. The combination optimization unit optimizes the combination of the SaaS proposed by the SaaS proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to select and combine SaaS that was best suited to a company's business operations and needs.

[0005] The system according to the embodiment aims to propose and combine optimal SaaS services based on the business operations and needs of a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a business content analysis unit, a SaaS proposal unit, and a combination optimization unit. The business content analysis unit analyzes the business content and needs of a company. The SaaS proposal unit proposes optimal SaaS based on the business content and needs analyzed by the business content analysis unit. The combination optimization unit optimizes the SaaS combination proposed by the SaaS proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose and combine the most suitable SaaS based on the business operations and needs of a company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The SaaS selection support tool according to the embodiment of the present invention is a system that supports companies in improving business efficiency by combining and using SaaS that are optimal for their business operations. As a result, the SaaS selection support tool can propose optimal SaaS based on the business operations and needs of the company, and optimize the combination.

[0029] The SaaS selection support tool according to the embodiment includes a business content analysis unit, a SaaS proposal unit, and a combination optimization unit. The business content analysis unit analyzes a company's business content and needs. For example, the generation AI collects information on business flows, business processes, and currently used tools and systems provided by the company and performs analysis based on that information. The generation AI performs analysis based on prompts containing detailed information on the company's business content and needs. The SaaS proposal unit proposes optimal SaaS based on the business content and needs analyzed by the business content analysis unit. For example, the generation AI lists SaaS necessary for the company's business, such as project management tools, customer relationship management (CRM) systems, and accounting software, and explains the features and benefits of each. The generation AI selects the SaaS that best meets the company's needs and clearly explains the reasons for its selection. The combination optimization unit optimizes the combination of SaaS proposed by the SaaS proposal unit. For example, the generation AI proposes optimal combinations to ensure smooth collaboration and data integration between different SaaS. The generation AI analyzes the APIs and data formats of each SaaS and optimizes the integration method and data flow. As a result, the SaaS selection support tool according to the embodiment can support business efficiency by proposing the optimal SaaS based on the business content and needs of the company and optimizing the combination of these SaaS.

[0030] The business content analysis unit collects information on the business flows and business processes provided by a company, as well as the tools and systems currently in use, and can perform analysis based on that information. For example, in the business content analysis unit, the generation AI analyzes a company's business flows and evaluates the time and resource usage of each process. For example, if a particular task is taking an excessive amount of time, the generation AI identifies the cause and makes specific improvement proposals for efficiency. The generation AI also analyzes a company's business processes and automatically detects duplicate tasks and unnecessary steps. For example, if the same data needs to be entered multiple times, the generation AI makes proposals to automate that procedure. The generation AI also analyzes a company's business data and identifies bottlenecks. For example, if the approval process in a particular department is delayed, the generation AI makes proposals to simplify that process. This allows for a more accurate understanding of business content by performing analysis based on information on a company's business flows, business processes, and the tools and systems currently in use.

[0031] The SaaS proposal department can list the SaaS required for a company's business and explain the features and benefits of each. For example, the SaaS proposal department's generation AI analyzes a company's past business data and predicts seasonal fluctuations in workload. For example, if business is concentrated in a particular season, the generation AI can propose the resources needed for that period in advance. The generation AI can also analyze market trend data and identify trends that may affect the company's business. For example, when a new technology or product appears on the market, the generation AI can predict its impact and propose countermeasures. The generation AI can also integrate a company's business data with market data to dynamically predict future needs. For example, it can identify products and services that are likely to see a sudden increase in demand in advance and propose countermeasures. This makes it easier for companies to select the optimal SaaS by listing the SaaS required for a company's business and explaining the features and benefits of each.

[0032] The combinatorial optimization unit can propose optimal combinations to ensure smooth collaboration and data integration between different SaaS. For example, the combinatorial optimization unit uses the emotion estimation function to analyze the emotional data of a company's employees, identify parts of the business process where stress increases, and propose improvement measures. For example, the emotion estimation function can be used to collect employee emotional data in real time and identify parts of the business process where stress increases. For example, if a specific task or meeting causes stress, the unit can propose improvement measures. The combinatorial optimization unit can also analyze employee emotional data to identify the time of day or day of the week when stress increases. For example, if stress increases before the weekend or at the end of the month, the unit can propose ways to reduce workloads during those times. The combinatorial optimization unit can also propose specific improvement measures to reduce employee stress based on the emotion estimation data. For example, the unit can propose a relaxing environment or a training program to reduce stress. This allows the unit to propose optimal combinations to ensure smooth collaboration and data integration between different SaaS, thereby improving business efficiency.

[0033] The business content analysis unit analyzes a company's business flow, evaluates the time and resource usage of each process, identifies inefficient areas, and makes improvement proposals. For example, in the business content analysis unit, the generation AI analyzes a company's business flow and evaluates the time and resource usage of each process. For example, if a particular task is taking an excessive amount of time, it identifies the cause and makes specific improvement proposals for improving efficiency. The generation AI also analyzes a company's business processes and automatically detects duplicate tasks and unnecessary steps. For example, if the same data needs to be entered multiple times, it makes a proposal to automate that procedure. The generation AI also analyzes a company's business data and identifies bottlenecks. For example, if the approval process in a particular department is delayed, it makes a proposal to simplify the process. In this way, the company's business flow is analyzed, inefficient areas are identified, and improvement proposals are made, thereby improving business efficiency.

[0034] When analyzing a company's business operations, the business content analysis unit takes into account seasonal fluctuations in business operations and market trends, allowing it to dynamically predict needs. For example, in the business content analysis unit, the generation AI analyzes a company's past business data and predicts seasonal fluctuations in workload. For example, if business is concentrated in a particular season, the generation AI will propose in advance the resources needed for that period. The generation AI also analyzes market trend data and identifies trends that may affect the company's business operations. For example, when a new technology or product appears on the market, the generation AI predicts its impact and proposes countermeasures. The generation AI also integrates the company's business data with market data to dynamically predict future needs. For example, it identifies products or services that are likely to see a sudden increase in demand and proposes countermeasures. This allows the generation AI to take seasonal fluctuations and market trends into account when analyzing a company's business operations, dynamically predicting needs and enabling more appropriate proposals.

[0035] The business content analysis unit can analyze feedback and reviews from a company's customers and make business improvement proposals from the customer's perspective. In the business content analysis unit, for example, the generation AI analyzes feedback and reviews from a company's customers and makes business improvement proposals from the customer's perspective. For example, it identifies areas where customers are dissatisfied and proposes improvement measures. It also identifies areas for improvement in the company's products and services based on customer feedback data. For example, it makes proposals to add functions and services that customers want. The generation AI also analyzes customer reviews and proposes specific improvement measures to improve customer satisfaction. For example, it makes proposals to strengthen areas that customers rate highly and improve areas that they rate poorly. In this way, customer satisfaction is improved by analyzing customer feedback and reviews and making business improvement proposals from the customer's perspective.

[0036] The business content analysis unit analyzes a company's supply chain information and can improve the efficiency of the entire supply chain. In the business content analysis unit, for example, the generation AI analyzes a company's supply chain information and makes proposals to improve the efficiency of the entire supply chain. For example, it proposes optimization of inventory management and logistics. It also identifies inefficiencies that occur at each stage of the supply chain and proposes improvements. For example, it proposes specific measures to prevent delivery delays and excess inventory. The generation AI also proposes cost reduction measures for the entire supply chain based on the supply chain information. For example, it makes proposals to reduce transportation costs and improve inventory turnover. In this way, by analyzing supply chain information and improving the efficiency of the entire supply chain, business efficiency and cost reduction are achieved.

[0037] The SaaS proposal unit can select the optimal tool by taking into consideration the complexity and scale of a company's business processes. For example, the generation AI in the SaaS proposal unit analyzes the complexity of a company's business processes and proposes the optimal SaaS based on the results. For example, if complex project management is required, an advanced project management tool will be proposed. The SaaS proposal unit also selects the appropriate SaaS by taking into consideration the scale of the company's business. For example, it proposes highly scalable tools for large companies and cost-effective tools for small and medium-sized enterprises. The generation AI also analyzes the details of a company's business processes and selects the optimal SaaS based on the results. For example, it proposes tools specialized for specific business flows. In this way, the optimal tool is selected by taking into consideration the complexity and scale of a company's business processes, thereby improving business efficiency.

[0038] The SaaS proposal unit is able to select the optimal tool from a long-term perspective, taking into account the future scalability of a company's business processes. For example, the SaaS proposal unit uses a generation AI to analyze the future scalability of a company's business processes and propose the optimal SaaS based on the results. For example, if there is a possibility that business operations will expand in the future, it will propose a highly scalable tool. The SaaS proposal unit also selects the optimal SaaS from a long-term perspective, taking into account the company's growth plans. For example, it will propose a tool that allows new functions to be added in the future. The generation AI also predicts future changes in a company's business processes and selects the optimal SaaS based on the results. For example, if there is a possibility that business operations will change, it will propose a highly flexible tool. In this way, sustainable business efficiency can be achieved by selecting the optimal tool from a long-term perspective, taking into account the future scalability of a company's business processes.

[0039] The SaaS proposal unit can refer to the tools used by a company's competitors and propose tools to strengthen competitiveness. For example, the SaaS proposal unit uses a generation AI to analyze the tools used by a company's competitors and propose optimal SaaS based on the results. For example, it refers to tools that competitors have been successful with. It also proposes SaaS to strengthen the company's competitiveness based on the competitors' tool usage status. For example, it proposes new tools that competitors are not using. The generation AI also analyzes competitors' tool usage data and proposes optimal SaaS based on the results. For example, it makes proposals that take advantage of the advantages of tools used by competitors. In this way, it improves a company's competitiveness by referring to the tools used by competitors and proposing tools to strengthen competitiveness.

[0040] The SaaS proposal unit can consider the environmental impact of a company's business processes and propose eco-friendly tools. For example, the SaaS proposal unit uses a generation AI to analyze the environmental impact of a company's business processes and propose eco-friendly SaaS based on the results. For example, it selects tools that are highly energy-efficient. It also sets SaaS selection criteria that consider the environmental impact and proposes tools that are optimal for the company's business processes. For example, it selects tools with a low carbon footprint. It also analyzes environmental impact data of a company's business processes and proposes eco-friendly SaaS based on the results. For example, it selects tools that use recyclable materials. In this way, it considers the environmental impact of business processes and proposes eco-friendly tools, thereby reducing the company's environmental impact.

[0041] The combinatorial optimization unit automates data integration between each tool and can propose protocols for maintaining data consistency. For example, the generative AI in the combinatorial optimization unit analyzes the APIs of each SaaS and proposes protocols for automating data integration. For example, it optimizes data format conversion and the order of API calls. It also generates scripts and code for automating data integration between each tool and maintains data consistency. For example, it automates data synchronization and error handling. The generative AI also analyzes the data formats of each SaaS and proposes standard protocols for automating data integration. For example, it maintains data consistency by using a common data format. This automates data integration between each tool and proposes protocols for maintaining data consistency, improving data consistency and efficiency.

[0042] The combination optimization unit can monitor the frequency of use and performance of each tool in real time and dynamically adjust the optimal combination. For example, the generation AI in the combination optimization unit monitors the frequency of use and performance of each SaaS in real time and dynamically adjusts the optimal combination based on that data. For example, it prioritizes the combination of frequently used tools. It also analyzes the performance data of each tool and proposes the optimal combination. For example, it proposes replacing a tool with a deteriorating performance with another tool. The generation AI also monitors the usage status of each SaaS in real time and dynamically adjusts the optimal combination based on that data. For example, it removes tools that are used infrequently and adds necessary tools. In this way, the efficiency of the system is improved by monitoring the frequency of use and performance of each tool in real time and dynamically adjusting the optimal combination.

[0043] The combinatorial optimization unit can combine tools from different industries and fields to create new business processes. For example, the generative AI in the combinatorial optimization unit analyzes tools from different industries and fields and creates new business processes by combining them. For example, combining tools from the medical field with tools from the manufacturing field. Also, by combining tools from different industries, new business processes are proposed. For example, combining tools from the financial industry with tools from the retail industry. Also, the generative AI analyzes tools from different fields and creates new business processes by combining them. For example, combining tools from the education field with tools from the entertainment field. In this way, by combining tools from different industries and fields to create new business processes, business innovation and efficiency are achieved.

[0044] The combinatorial optimization unit can actively utilize open source tools to reduce costs. For example, the generative AI in the combinatorial optimization unit analyzes open source tools and makes proposals to reduce costs by combining them. For example, it may prioritize proposals for open source tools that can be used for free. Combining open source tools also reduces costs for companies. For example, it may propose replacing paid tools with open source tools. The generative AI also analyzes open source tools and combines them to reduce costs. For example, it may propose reducing license fees by using open source tools. This actively utilizes open source tools to reduce costs, thereby reducing the financial burden on companies.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The SaaS proposal department can select the optimal tool by taking into account the complexity and scale of a company's business processes. For example, the generation AI analyzes the complexity of a company's business processes and proposes the optimal SaaS based on the results. For example, if complex project management is required, it will propose an advanced project management tool. The generation AI also selects the appropriate SaaS by taking into account the scale of the company's business. For example, it will propose highly scalable tools for large companies and cost-effective tools for small and medium-sized enterprises. The generation AI also analyzes the details of a company's business processes and selects the optimal SaaS based on the results. For example, it will propose tools specialized for specific business flows. This allows the selection of the optimal tool by taking into account the complexity and scale of a company's business processes, thereby improving business efficiency.

[0047] The business content analysis unit can analyze feedback and reviews from a company's customers and make business improvement proposals from the customer's perspective. For example, the generation AI analyzes feedback and reviews from a company's customers and makes business improvement proposals from the customer's perspective. For example, it identifies areas where customers are dissatisfied and proposes improvement measures. It also identifies areas for improvement in a company's products and services based on customer feedback data. For example, it makes proposals to add functions and services that customers want. The generation AI also analyzes customer reviews and proposes specific improvement measures to improve customer satisfaction. For example, it makes proposals to strengthen areas that customers rate highly and improve areas that they rate poorly. In this way, customer satisfaction is improved by analyzing customer feedback and reviews and making business improvement proposals from the customer's perspective.

[0048] The combinatorial optimization unit can automate data exchange between each tool and propose protocols for maintaining data consistency. For example, the generation AI analyzes the APIs of each SaaS and proposes protocols for automating data exchange. For example, it optimizes data format conversion and the order of API calls. It also generates scripts and code for automating data exchange between each tool and maintaining data consistency. For example, it automates data synchronization and error handling. The generation AI also analyzes the data formats of each SaaS and proposes standard protocols for automating data exchange. For example, it maintains data consistency by using a common data format. This automates data exchange between each tool and proposes protocols for maintaining data consistency, improving data consistency and efficiency.

[0049] The business content analysis unit can analyze a company's supply chain information and improve the efficiency of the entire supply chain. For example, the generation AI analyzes a company's supply chain information and makes suggestions to improve the efficiency of the entire supply chain. For example, it proposes optimization of inventory management and logistics. It also identifies inefficiencies that occur at each stage of the supply chain and proposes improvements. For example, it proposes specific measures to prevent delivery delays and excess inventory. The generation AI also proposes cost-cutting measures for the entire supply chain based on the supply chain information. For example, it makes suggestions to reduce transportation costs and improve inventory turnover. In this way, by analyzing supply chain information and improving the efficiency of the entire supply chain, it is possible to achieve business efficiency and cost reduction.

[0050] The SaaS proposal unit can refer to the tools used by a company's competitors and propose tools to strengthen competitiveness. For example, the generation AI analyzes the tools used by a company's competitors and proposes the optimal SaaS based on the results. For example, it refers to the tools that competitors have been successful with. It also proposes SaaS to strengthen the company's competitiveness based on the competitors' tool usage status. For example, it proposes new tools that competitors are not using. The generation AI also analyzes competitors' tool usage data and proposes the optimal SaaS based on the results. For example, it makes proposals that take advantage of the advantages of the tools used by competitors. This improves a company's competitiveness by referring to the tools used by competitors and proposing tools to strengthen competitiveness.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The business content analysis unit analyzes the company's business content and needs. For example, the generation AI collects information provided by the company about business flows, business processes, and currently used tools and systems, and performs analysis based on that information. The generation AI performs analysis based on prompts containing detailed information about the company's business content and needs. Step 2: The SaaS proposal unit proposes the optimal SaaS based on the business content and needs analyzed by the business content analysis unit. For example, the generation AI will list SaaS necessary for a company's business, such as project management tools, customer relationship management (CRM) systems, and accounting software, and explain the features and benefits of each. The generation AI will select the SaaS that best suits the company's needs and clearly explain the reasons for its selection. Step 3: The combination optimization unit optimizes the combination of SaaS proposed by the SaaS proposal unit. For example, the generation AI proposes the optimal combination to ensure smooth collaboration and data integration between different SaaS. The generation AI analyzes the APIs and data formats of each SaaS and optimizes the collaboration method and data flow.

[0053] (Example 2) The SaaS selection support tool according to the embodiment of the present invention is a system that supports companies in improving business efficiency by combining and using SaaS that are optimal for their business operations. As a result, the SaaS selection support tool can propose optimal SaaS based on the business operations and needs of the company, and optimize the combination.

[0054] The SaaS selection support tool according to the embodiment includes a business content analysis unit, a SaaS proposal unit, and a combination optimization unit. The business content analysis unit analyzes a company's business content and needs. For example, the generation AI collects information on business flows, business processes, and currently used tools and systems provided by the company and performs analysis based on that information. The generation AI performs analysis based on prompts containing detailed information on the company's business content and needs. The SaaS proposal unit proposes optimal SaaS based on the business content and needs analyzed by the business content analysis unit. For example, the generation AI lists SaaS necessary for the company's business, such as project management tools, customer relationship management (CRM) systems, and accounting software, and explains the features and benefits of each. The generation AI selects the SaaS that best meets the company's needs and clearly explains the reasons for its selection. The combination optimization unit optimizes the combination of SaaS proposed by the SaaS proposal unit. For example, the generation AI proposes optimal combinations to ensure smooth collaboration and data integration between different SaaS. The generation AI analyzes the APIs and data formats of each SaaS and optimizes the integration method and data flow. As a result, the SaaS selection support tool according to the embodiment can support business efficiency by proposing the optimal SaaS based on the business content and needs of the company and optimizing the combination of these SaaS.

[0055] The business content analysis unit collects information on the business flows and business processes provided by a company, as well as the tools and systems currently in use, and can perform analysis based on that information. For example, in the business content analysis unit, the generation AI analyzes a company's business flows and evaluates the time and resource usage of each process. For example, if a particular task is taking an excessive amount of time, the generation AI identifies the cause and makes specific improvement proposals for efficiency. The generation AI also analyzes a company's business processes and automatically detects duplicate tasks and unnecessary steps. For example, if the same data needs to be entered multiple times, the generation AI makes proposals to automate that procedure. The generation AI also analyzes a company's business data and identifies bottlenecks. For example, if the approval process in a particular department is delayed, the generation AI makes proposals to simplify that process. This allows for a more accurate understanding of business content by performing analysis based on information on a company's business flows, business processes, and the tools and systems currently in use.

[0056] The SaaS proposal department can list the SaaS required for a company's business and explain the features and benefits of each. For example, the SaaS proposal department's generation AI analyzes a company's past business data and predicts seasonal fluctuations in workload. For example, if business is concentrated in a particular season, the generation AI can propose the resources needed for that period in advance. The generation AI can also analyze market trend data and identify trends that may affect the company's business. For example, when a new technology or product appears on the market, the generation AI can predict its impact and propose countermeasures. The generation AI can also integrate a company's business data with market data to dynamically predict future needs. For example, it can identify products and services that are likely to see a sudden increase in demand in advance and propose countermeasures. This makes it easier for companies to select the optimal SaaS by listing the SaaS required for a company's business and explaining the features and benefits of each.

[0057] The combinatorial optimization unit can propose optimal combinations to ensure smooth collaboration and data integration between different SaaS. For example, the combinatorial optimization unit uses the emotion estimation function to analyze the emotional data of a company's employees, identify parts of the business process where stress increases, and propose improvement measures. For example, the emotion estimation function can be used to collect employee emotional data in real time and identify parts of the business process where stress increases. For example, if a specific task or meeting causes stress, the unit can propose improvement measures. The combinatorial optimization unit can also analyze employee emotional data to identify the time of day or day of the week when stress increases. For example, if stress increases before the weekend or at the end of the month, the unit can propose ways to reduce workloads during those times. The combinatorial optimization unit can also propose specific improvement measures to reduce employee stress based on the emotion estimation data. For example, the unit can propose a relaxing environment or a training program to reduce stress. This allows the unit to propose optimal combinations to ensure smooth collaboration and data integration between different SaaS, thereby improving business efficiency.

[0058] The business content analysis unit analyzes a company's business flow, evaluates the time and resource usage of each process, identifies inefficient areas, and makes improvement proposals. For example, in the business content analysis unit, the generation AI analyzes a company's business flow and evaluates the time and resource usage of each process. For example, if a particular task is taking an excessive amount of time, it identifies the cause and makes specific improvement proposals for improving efficiency. The generation AI also analyzes a company's business processes and automatically detects duplicate tasks and unnecessary steps. For example, if the same data needs to be entered multiple times, it makes a proposal to automate that procedure. The generation AI also analyzes a company's business data and identifies bottlenecks. For example, if the approval process in a particular department is delayed, it makes a proposal to simplify the process. In this way, the company's business flow is analyzed, inefficient areas are identified, and improvement proposals are made, thereby improving business efficiency.

[0059] When analyzing a company's business operations, the business content analysis unit takes into account seasonal fluctuations in business operations and market trends, allowing it to dynamically predict needs. For example, in the business content analysis unit, the generation AI analyzes a company's past business data and predicts seasonal fluctuations in workload. For example, if business is concentrated in a particular season, the generation AI will propose in advance the resources needed for that period. The generation AI also analyzes market trend data and identifies trends that may affect the company's business operations. For example, when a new technology or product appears on the market, the generation AI predicts its impact and proposes countermeasures. The generation AI also integrates the company's business data with market data to dynamically predict future needs. For example, it identifies products or services that are likely to see a sudden increase in demand and proposes countermeasures. This allows the generation AI to take seasonal fluctuations and market trends into account when analyzing a company's business operations, dynamically predicting needs and enabling more appropriate proposals.

[0060] The work content analysis unit can use the emotion estimation function to analyze the emotional data of company employees, identify parts of the work process that cause stress, and propose improvement measures. For example, the work content analysis unit uses the emotion estimation function to collect employee emotional data in real time and identify parts of the work process that cause stress. For example, if a specific task or meeting is causing stress, the unit proposes improvement measures. The work content analysis unit also analyzes employee emotional data to identify times of day or days of the week when stress increases. For example, if stress increases before the weekend or at the end of the month, the unit proposes reducing the workload during those times. The work content analysis unit also proposes specific improvement measures to reduce employee stress based on the emotion estimation data. For example, the unit proposes providing a relaxing environment or a training program to reduce stress. In this way, by analyzing employee emotional data, identifying parts of the work process that cause stress, and proposing improvement measures, the unit reduces employee stress and improves work efficiency.

[0061] The business content analysis unit can analyze feedback and reviews from a company's customers and make business improvement proposals from the customer's perspective. In the business content analysis unit, for example, the generation AI analyzes feedback and reviews from a company's customers and makes business improvement proposals from the customer's perspective. For example, it identifies areas where customers are dissatisfied and proposes improvement measures. It also identifies areas for improvement in the company's products and services based on customer feedback data. For example, it makes proposals to add functions and services that customers want. The generation AI also analyzes customer reviews and proposes specific improvement measures to improve customer satisfaction. For example, it makes proposals to strengthen areas that customers rate highly and improve areas that they rate poorly. In this way, customer satisfaction is improved by analyzing customer feedback and reviews and making business improvement proposals from the customer's perspective.

[0062] The business content analysis unit analyzes a company's supply chain information and can improve the efficiency of the entire supply chain. In the business content analysis unit, for example, the generation AI analyzes a company's supply chain information and makes proposals to improve the efficiency of the entire supply chain. For example, it proposes optimization of inventory management and logistics. It also identifies inefficiencies that occur at each stage of the supply chain and proposes improvements. For example, it proposes specific measures to prevent delivery delays and excess inventory. The generation AI also proposes cost reduction measures for the entire supply chain based on the supply chain information. For example, it makes proposals to reduce transportation costs and improve inventory turnover. In this way, by analyzing supply chain information and improving the efficiency of the entire supply chain, business efficiency and cost reduction are achieved.

[0063] The business content analysis unit can use the emotion estimation function to analyze emotional data of the company's customers and make business improvement proposals to improve customer satisfaction. The business content analysis unit, for example, uses the emotion estimation function to collect customer emotional data in real time and make business improvement proposals to improve customer satisfaction. For example, it identifies areas where customers are dissatisfied and proposes improvement measures. It also analyzes customer emotional data and proposes specific improvement measures to improve customer satisfaction. For example, it makes proposals to enhance services and functions that customers find enjoyable. It also proposes training programs to improve customer satisfaction based on the emotion estimation data. For example, it provides training to improve customer service skills. In this way, customer satisfaction is improved by analyzing customer emotional data and making business improvement proposals to improve customer satisfaction.

[0064] The SaaS proposal unit can select the optimal tool by taking into consideration the complexity and scale of a company's business processes. For example, the generation AI in the SaaS proposal unit analyzes the complexity of a company's business processes and proposes the optimal SaaS based on the results. For example, if complex project management is required, an advanced project management tool will be proposed. The SaaS proposal unit also selects the appropriate SaaS by taking into consideration the scale of the company's business. For example, it proposes highly scalable tools for large companies and cost-effective tools for small and medium-sized enterprises. The generation AI also analyzes the details of a company's business processes and selects the optimal SaaS based on the results. For example, it proposes tools specialized for specific business flows. In this way, the optimal tool is selected by taking into consideration the complexity and scale of a company's business processes, thereby improving business efficiency.

[0065] The SaaS proposal unit is able to select the optimal tool from a long-term perspective, taking into account the future scalability of a company's business processes. For example, the SaaS proposal unit uses a generation AI to analyze the future scalability of a company's business processes and propose the optimal SaaS based on the results. For example, if there is a possibility that business operations will expand in the future, it will propose a highly scalable tool. The SaaS proposal unit also selects the optimal SaaS from a long-term perspective, taking into account the company's growth plans. For example, it will propose a tool that allows new functions to be added in the future. The generation AI also predicts future changes in a company's business processes and selects the optimal SaaS based on the results. For example, if there is a possibility that business operations will change, it will propose a highly flexible tool. In this way, sustainable business efficiency can be achieved by selecting the optimal tool from a long-term perspective, taking into account the future scalability of a company's business processes.

[0066] The SaaS proposal unit can use the emotion estimation function to analyze the emotion data of company employees and propose SaaS that employees find easy to use. The SaaS proposal unit, for example, uses the emotion estimation function to analyze employee emotion data and propose SaaS that employees find easy to use. For example, it selects a tool with an interface that employees find less stressful. Furthermore, based on employee emotion data, it proposes SaaS that emphasizes ease of use. For example, it selects a tool that allows intuitive operation. Furthermore, based on emotion estimation data, it proposes SaaS that will improve employee satisfaction. For example, it selects a tool that employees have positive emotions about. In this way, by analyzing employee emotion data and proposing SaaS that employees find easy to use, employee satisfaction and work efficiency are improved.

[0067] The SaaS proposal unit can refer to the tools used by a company's competitors and propose tools to strengthen competitiveness. For example, the SaaS proposal unit uses a generation AI to analyze the tools used by a company's competitors and propose optimal SaaS based on the results. For example, it refers to tools that competitors have been successful with. It also proposes SaaS to strengthen the company's competitiveness based on the competitors' tool usage status. For example, it proposes new tools that competitors are not using. The generation AI also analyzes competitors' tool usage data and proposes optimal SaaS based on the results. For example, it makes proposals that take advantage of the advantages of tools used by competitors. In this way, it improves a company's competitiveness by referring to the tools used by competitors and proposing tools to strengthen competitiveness.

[0068] The SaaS proposal unit can consider the environmental impact of a company's business processes and propose eco-friendly tools. For example, the SaaS proposal unit uses a generation AI to analyze the environmental impact of a company's business processes and propose eco-friendly SaaS based on the results. For example, it selects tools that are highly energy-efficient. It also sets SaaS selection criteria that consider the environmental impact and proposes tools that are optimal for the company's business processes. For example, it selects tools with a low carbon footprint. It also analyzes environmental impact data of a company's business processes and proposes eco-friendly SaaS based on the results. For example, it selects tools that use recyclable materials. In this way, it considers the environmental impact of business processes and proposes eco-friendly tools, thereby reducing the company's environmental impact.

[0069] The SaaS proposal unit can use the emotion estimation function to analyze emotion data of the company's customers and propose tools for improving customer satisfaction. The SaaS proposal unit, for example, uses the emotion estimation function to analyze customer emotion data and propose SaaS for improving customer satisfaction. For example, it selects tools that customers have positive emotions for. Furthermore, based on the customer emotion data, it proposes specific tools for improving customer satisfaction. For example, it selects tools with functions that make customers feel happy. Furthermore, based on the emotion estimation data, it proposes SaaS for improving customer satisfaction. For example, it selects tools with interfaces that customers have positive emotions for. In this way, customer satisfaction is improved by analyzing customer emotion data and proposing tools for improving customer satisfaction.

[0070] The combinatorial optimization unit automates data integration between each tool and can propose protocols for maintaining data consistency. For example, the generative AI in the combinatorial optimization unit analyzes the APIs of each SaaS and proposes protocols for automating data integration. For example, it optimizes data format conversion and the order of API calls. It also generates scripts and code for automating data integration between each tool and maintains data consistency. For example, it automates data synchronization and error handling. The generative AI also analyzes the data formats of each SaaS and proposes standard protocols for automating data integration. For example, it maintains data consistency by using a common data format. This automates data integration between each tool and proposes protocols for maintaining data consistency, improving data consistency and efficiency.

[0071] The combination optimization unit can monitor the frequency of use and performance of each tool in real time and dynamically adjust the optimal combination. For example, the generation AI in the combination optimization unit monitors the frequency of use and performance of each SaaS in real time and dynamically adjusts the optimal combination based on that data. For example, it prioritizes the combination of frequently used tools. It also analyzes the performance data of each tool and proposes the optimal combination. For example, it proposes replacing a tool with a deteriorating performance with another tool. The generation AI also monitors the usage status of each SaaS in real time and dynamically adjusts the optimal combination based on that data. For example, it removes tools that are used infrequently and adds necessary tools. In this way, the efficiency of the system is improved by monitoring the frequency of use and performance of each tool in real time and dynamically adjusting the optimal combination.

[0072] The combination optimization unit can use the emotion estimation function to analyze the emotion data of company employees and propose a combination of tools that will make employees feel less stressed. The combination optimization unit, for example, uses the emotion estimation function to analyze the emotion data of employees and propose a combination of tools that will make employees feel less stressed. For example, it prioritizes a combination of tools that employees feel positive about. It also proposes a combination of tools to reduce stress based on the emotion data of employees. For example, it combines tools that can be operated intuitively. It also proposes a combination of tools to reduce employee stress based on the emotion estimation data. For example, it combines tools that have an interface that makes employees feel positive about them. In this way, by analyzing the emotion data of employees and proposing a combination of tools that will make employees feel less stressed, employee stress is reduced and work efficiency is improved.

[0073] The combinatorial optimization unit can combine tools from different industries and fields to create new business processes. For example, the generative AI in the combinatorial optimization unit analyzes tools from different industries and fields and creates new business processes by combining them. For example, combining tools from the medical field with tools from the manufacturing field. Also, by combining tools from different industries, new business processes are proposed. For example, combining tools from the financial industry with tools from the retail industry. Also, the generative AI analyzes tools from different fields and creates new business processes by combining them. For example, combining tools from the education field with tools from the entertainment field. In this way, by combining tools from different industries and fields to create new business processes, business innovation and efficiency are achieved.

[0074] The combinatorial optimization unit can actively utilize open source tools to reduce costs. For example, the generative AI in the combinatorial optimization unit analyzes open source tools and makes proposals to reduce costs by combining them. For example, it may prioritize proposals for open source tools that can be used for free. Combining open source tools also reduces costs for companies. For example, it may propose replacing paid tools with open source tools. The generative AI also analyzes open source tools and combines them to reduce costs. For example, it may propose reducing license fees by using open source tools. This actively utilizes open source tools to reduce costs, thereby reducing the financial burden on companies.

[0075] The combinatorial optimization unit can use the emotion estimation function to analyze emotion data of a company's customers and propose a combination of tools to improve customer satisfaction. The combinatorial optimization unit, for example, uses the emotion estimation function to analyze customer emotion data and propose a combination of tools to improve customer satisfaction. For example, it prioritizes a combination of tools that customers have positive emotions for. Also, based on the customer emotion data, it proposes a specific combination of tools to improve customer satisfaction. For example, it combines tools that have functions that make customers feel happy. Also, based on the emotion estimation data, it proposes a combination of tools to improve customer satisfaction. For example, it combines tools that have interfaces that customers have positive emotions for. In this way, customer satisfaction is improved by analyzing customer emotion data and proposing a combination of tools to improve customer satisfaction.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The SaaS proposal department can select the optimal tool by taking into account the complexity and scale of a company's business processes. For example, the generation AI analyzes the complexity of a company's business processes and proposes the optimal SaaS based on the results. For example, if complex project management is required, it will propose an advanced project management tool. The generation AI also selects the appropriate SaaS by taking into account the scale of the company's business. For example, it will propose highly scalable tools for large companies and cost-effective tools for small and medium-sized enterprises. The generation AI also analyzes the details of a company's business processes and selects the optimal SaaS based on the results. For example, it will propose tools specialized for specific business flows. This allows the selection of the optimal tool by taking into account the complexity and scale of a company's business processes, thereby improving business efficiency.

[0078] The business content analysis unit can analyze feedback and reviews from a company's customers and make business improvement proposals from the customer's perspective. For example, the generation AI analyzes feedback and reviews from a company's customers and makes business improvement proposals from the customer's perspective. For example, it identifies areas where customers are dissatisfied and proposes improvement measures. It also identifies areas for improvement in a company's products and services based on customer feedback data. For example, it makes proposals to add functions and services that customers want. The generation AI also analyzes customer reviews and proposes specific improvement measures to improve customer satisfaction. For example, it makes proposals to strengthen areas that customers rate highly and improve areas that they rate poorly. In this way, customer satisfaction is improved by analyzing customer feedback and reviews and making business improvement proposals from the customer's perspective.

[0079] The combinatorial optimization unit can automate data exchange between each tool and propose protocols for maintaining data consistency. For example, the generation AI analyzes the APIs of each SaaS and proposes protocols for automating data exchange. For example, it optimizes data format conversion and the order of API calls. It also generates scripts and code for automating data exchange between each tool and maintaining data consistency. For example, it automates data synchronization and error handling. The generation AI also analyzes the data formats of each SaaS and proposes standard protocols for automating data exchange. For example, it maintains data consistency by using a common data format. This automates data exchange between each tool and proposes protocols for maintaining data consistency, improving data consistency and efficiency.

[0080] The business content analysis unit can analyze a company's supply chain information and improve the efficiency of the entire supply chain. For example, the generation AI analyzes a company's supply chain information and makes suggestions to improve the efficiency of the entire supply chain. For example, it proposes optimization of inventory management and logistics. It also identifies inefficiencies that occur at each stage of the supply chain and proposes improvements. For example, it proposes specific measures to prevent delivery delays and excess inventory. The generation AI also proposes cost-cutting measures for the entire supply chain based on the supply chain information. For example, it makes suggestions to reduce transportation costs and improve inventory turnover. In this way, by analyzing supply chain information and improving the efficiency of the entire supply chain, it is possible to achieve business efficiency and cost reduction.

[0081] The SaaS proposal unit can refer to the tools used by a company's competitors and propose tools to strengthen competitiveness. For example, the generation AI analyzes the tools used by a company's competitors and proposes the optimal SaaS based on the results. For example, it refers to the tools that competitors have been successful with. It also proposes SaaS to strengthen the company's competitiveness based on the competitors' tool usage status. For example, it proposes new tools that competitors are not using. The generation AI also analyzes competitors' tool usage data and proposes the optimal SaaS based on the results. For example, it makes proposals that take advantage of the advantages of the tools used by competitors. This improves a company's competitiveness by referring to the tools used by competitors and proposing tools to strengthen competitiveness.

[0082] The work content analysis unit can use the emotion estimation function to analyze the emotional data of company employees, identify parts of the work process that cause stress, and propose improvement measures. For example, the emotion estimation function can be used to collect employee emotional data in real time and identify parts of the work process that cause stress. For example, if a specific task or meeting is causing stress, improvement measures can be proposed. The work content analysis unit can also analyze employee emotional data to identify times of day or days of the week when stress increases. For example, if stress increases before the weekend or at the end of the month, a proposal can be made to reduce the workload at those times. The work content analysis unit can also propose specific improvement measures to reduce employee stress based on the emotion estimation data. For example, it can propose providing a relaxing environment or a training program to reduce stress. In this way, by analyzing employee emotional data, identifying parts of the work process that cause stress, and proposing improvement measures, employee stress can be reduced and work efficiency can be improved.

[0083] The SaaS proposal unit can use the emotion estimation function to analyze the emotional data of company employees and propose SaaS that employees find easy to use. For example, the emotion estimation function can be used to analyze employee emotional data and propose SaaS that employees find easy to use. For example, a tool with an interface that employees find less stressful can be selected. Furthermore, based on employee emotional data, SaaS that emphasizes ease of use can be proposed. For example, a tool that allows for intuitive operation can be selected. Furthermore, based on the emotion estimation data, SaaS that will improve employee satisfaction can be proposed. For example, a tool that employees have positive emotions about can be selected. In this way, by analyzing employee emotional data and proposing SaaS that employees find easy to use, employee satisfaction and work efficiency can be improved.

[0084] The combination optimization unit can use the emotion estimation function to analyze the emotional data of company employees and propose combinations of tools that make employees less likely to feel stressed. For example, the emotion estimation function can be used to analyze employee emotional data and propose combinations of tools that make employees less likely to feel stressed. For example, tools that evoke positive emotions in employees can be prioritized in combination. Furthermore, based on employee emotional data, a combination of tools to reduce stress can be proposed. For example, tools that can be intuitively operated can be combined. Furthermore, based on emotion estimation data, a combination of tools to reduce employee stress can be proposed. For example, tools with interfaces that evoke positive emotions in employees can be combined. In this way, by analyzing employee emotional data and proposing combinations of tools that make employees less likely to feel stressed, employee stress can be reduced and work efficiency can be improved.

[0085] The SaaS proposal unit can use the emotion estimation function to analyze the emotion data of the company's customers and propose tools to improve customer satisfaction. For example, the emotion estimation function is used to analyze customer emotion data and propose SaaS to improve customer satisfaction. For example, a tool that customers have positive emotions for is selected. Furthermore, specific tools to improve customer satisfaction are proposed based on the customer emotion data. For example, a tool with a function that makes customers feel happy is selected. Furthermore, SaaS to improve customer satisfaction is proposed based on the emotion estimation data. For example, a tool with an interface that customers have positive emotions for is selected. In this way, customer satisfaction is improved by analyzing customer emotion data and proposing tools to improve customer satisfaction.

[0086] The combination optimization unit can use the emotion estimation function to analyze emotional data of a company's customers and propose a combination of tools to improve customer satisfaction. For example, the emotion estimation function is used to analyze customer emotional data and propose a combination of tools to improve customer satisfaction. For example, tools that customers have positive emotions for are preferentially combined. Also, based on the customer emotional data, a specific combination of tools to improve customer satisfaction is proposed. For example, tools with functions that make customers feel happy are combined. Also, based on the emotion estimation data, a combination of tools to improve customer satisfaction is proposed. For example, tools with interfaces that customers have positive emotions for are combined. In this way, customer satisfaction is improved by analyzing customer emotional data and proposing a combination of tools to improve customer satisfaction.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The business content analysis unit analyzes the company's business content and needs. For example, the generation AI collects information provided by the company about business flows, business processes, and currently used tools and systems, and performs analysis based on that information. The generation AI performs analysis based on prompts containing detailed information about the company's business content and needs. Step 2: The SaaS proposal unit proposes the optimal SaaS based on the business content and needs analyzed by the business content analysis unit. For example, the generation AI will list SaaS necessary for a company's business, such as project management tools, customer relationship management (CRM) systems, and accounting software, and explain the features and benefits of each. The generation AI will select the SaaS that best suits the company's needs and clearly explain the reasons for its selection. Step 3: The combination optimization unit optimizes the combination of SaaS proposed by the SaaS proposal unit. For example, the generation AI proposes the optimal combination to ensure smooth collaboration and data integration between different SaaS. The generation AI analyzes the APIs and data formats of each SaaS and optimizes the collaboration method and data flow.

[0089] 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.

[0090] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A business content analysis department that analyzes the business content and needs of companies; a SaaS proposal unit that proposes an optimal SaaS based on the business content and needs analyzed by the business content analysis unit; a combination optimization unit that optimizes the combination of SaaS proposed by the SaaS proposal unit. A system characterized by:

2. The business content analysis unit Collect information on the business flow and business processes provided by the company, the tools currently being used, and the system, and use that information to conduct the analysis.

2. The system of claim 1.

3. The SaaS proposal unit List the SaaS required for the company's business and explain the features and benefits of each 2. The system of claim 1.

4. The business content analysis unit Analyze the company's supply chain information to improve the efficiency of the entire supply chain 2. The system of claim 1.

5. The SaaS proposal unit Select the most appropriate tool, taking into consideration the complexity and scale of the company's business processes.

2. The system of claim 1.

6. The combination optimization unit Propose a protocol to automate data exchange between each of the tools and maintain data consistency.

2. The system of claim 1.

7. The business content analysis unit Analyze the emotional data of the company's employees, identify areas of stress in the work process, and propose improvement measures.

2. The system of claim 1.

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