system

Abstract

The system according to the embodiment aims to calculate an estimate efficiently in a short period of time. [Solution] A system according to an embodiment includes a selection unit, a reference unit, a calculation unit, an input unit, and a display unit. The selection unit selects quotation conditions. The reference unit references past quotation data. The calculation unit calculates an estimate based on information obtained by the selection unit and the reference unit. The input unit inputs the quotation conditions of competitors. The display unit displays the quotation results calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of taking a lot of time when responding to requests for quotes in a short period of time or for multiple patterns of quotes.

[0005] The system according to the embodiment aims to calculate an estimate efficiently in a short period of time. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a reference unit, a calculation unit, an input unit, and a display unit. The selection unit selects estimation conditions. The reference unit references past estimation data. The calculation unit calculates an estimate based on information obtained by the selection unit and the reference unit. The input unit inputs the estimation conditions of competitors. The display unit displays the estimation results calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently calculate an estimate in a short period of time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A quotation system according to an embodiment of the present invention streamlines quotation work in groupware implementation. This quotation system allows a user to log in to the system using a dedicated ID and password and select quotation conditions on a GUI. The system automatically calculates a quotation based on previously submitted quotation terms and costs. Furthermore, by entering competitors' quotation terms and costs, a quotation tailored to the competitors' quotation terms and costs can be presented. This mechanism enables quick quotation requests and multiple quotation requests, thereby reducing wasted time. For example, a user logs in to the system using a dedicated ID and password. The user can access the system by entering a previously issued ID and password. For example, a company representative logs in to the system and begins quotation work. Next, the user selects quotation conditions on the GUI. For example, the user can select conditions such as the type and functions of the groupware to be implemented, the number of users, and the implementation period. This allows the user to easily set quotation conditions. The system automatically calculates a quotation based on previously submitted quotation terms and costs. For example, the system can calculate a current quotation based on quotation data submitted under similar conditions. This allows for quick and accurate quotation provision. Furthermore, by entering competitors' quotation terms and costs, a quotation tailored to the competitors' quotation terms and costs can be presented. For example, by entering the quotation conditions and costs of competitors, a quotation based on that can be presented. This allows for the provision of competitive quotation. This system makes it possible to respond to quotation requests in a short period of time or for multiple patterns, reducing wasted time. For example, a company's personnel can create multiple quotation patterns in a short time and quickly propose them. Furthermore, by streamlining the quotation process, they can concentrate on other tasks. This allows the quotation system to enable users to quickly and accurately create quotation and make competitive proposals.

[0029] The quotation system according to the embodiment includes a selection unit, a reference unit, a calculation unit, an input unit, and a display unit. The selection unit selects quotation conditions. The quotation conditions include, for example, the type and functions of the groupware to be implemented, the number of users to be used, and the implementation period, but are not limited to these examples. The selection unit, for example, allows a user to select quotation conditions on a GUI. The selection unit also has a function to transmit the conditions selected by the user to the system. The reference unit references past quotation data. The past quotation data includes, for example, previously submitted quotation terms and costs, but are not limited to these examples. The reference unit, for example, acquires past quotation data from a database and reflects the data in a current quotation. The calculation unit calculates an estimate based on information obtained by the selection unit and the reference unit. The calculation unit, for example, automatically calculates a current estimate based on the past quotation data. The calculation unit can also calculate an estimate based on the input of a competitor's quotation conditions and costs. The input unit inputs a competitor's quotation conditions. The input unit, for example, allows a user to input a competitor's quotation conditions and costs. The display unit displays the estimate result calculated by the calculation unit. The display unit can display the estimate result, for example, in text or graph form. This allows the estimate system according to the embodiment to enable the user to quickly and accurately create estimates and make competitive proposals.

[0030] The quotation system includes a storage unit that stores quotation results. The storage unit stores the quotation results. The quotation results include, for example, quotation conditions, calculated costs, and quotation conditions of competitors, but are not limited to these examples. The storage unit stores the quotation results in, for example, a database. The storage unit can also store the quotation results in a file format. For example, the storage unit can store the quotation results in PDF format or Excel format. By storing the quotation results, they can be referenced or reused later. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store the quotation results using an AI model that automatically classifies the quotation results and stores them in appropriate folders.

[0031] The quotation system includes a graph display unit that displays quotation results in a graph or a table. The graph display unit displays the quotation results in a graph or a table. Examples of graphs and tables include, but are not limited to, bar graphs, line graphs, pie charts, and table formats. The graph display unit can, for example, display the quotation results in a visually easy-to-understand manner. For example, the graph display unit can display the quotation results in a bar graph to compare the costs of each item. The graph display unit can also display the quotation results in a line graph to visually show fluctuations in costs. The graph display unit can also display the quotation results in a pie chart to show the proportion of the cost of each item. This allows the quotation results to be displayed in a visually easy-to-understand manner. Some or all of the above-described processing in the graph display unit may be performed, for example, using AI, or may be performed without using AI. For example, the graph display unit can display the quotation results using an AI model that automatically analyzes the quotation results and selects the optimal graph format.

[0032] The quotation system includes an update unit that periodically updates data. The update unit periodically updates the data. The data includes, but is not limited to, quotation conditions, past quotation data, and competitor quotation conditions. The update unit can update the data daily, weekly, monthly, or at other intervals. For example, the update unit can check the database daily and automatically update the data when new quotation data is added. The update unit can also check the database weekly, delete old data, and add new data. The update unit can also check the database monthly, verify data consistency, and correct the data as necessary. This allows the data to be kept up to date. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the data using an AI model that automatically adjusts the data update frequency.

[0033] The quotation system includes an analysis unit that analyzes data using AI. The analysis unit analyzes data using AI. Examples of AI include, but are not limited to, machine learning algorithms and deep learning algorithms. The analysis unit can analyze data such as quotation conditions, past quotation data, and competitors' quotation conditions to improve the accuracy of the quotation. For example, the analysis unit can use a machine learning algorithm to analyze past quotation data and reflect it in a current quotation. The analysis unit can also use a deep learning algorithm to analyze competitors' quotation conditions and calculate an optimal quotation. Furthermore, the analysis unit can use AI to analyze trends in quotation conditions and use the results for future quotation. This improves the accuracy of data analysis by using AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze trends in quotation conditions and use the results for future quotation.

[0034] The quotation system includes an authentication unit that performs two-step authentication. The authentication unit performs two-step authentication. Examples of two-step authentication include, but are not limited to, SMS authentication, email authentication, and biometric authentication. For example, when a user logs in to the system, the authentication unit requires the user to enter an SMS authentication code in addition to their ID and password. The authentication unit can also require the user to enter an email authentication code in addition to their ID and password when logging in to the system. Furthermore, the authentication unit can perform biometric authentication such as fingerprint authentication or facial authentication in addition to their ID and password when logging in to the system. This enhances security. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can use AI to analyze a user's login pattern and detect abnormal logins.

[0035] The estimation system includes a linking unit that links with other systems. The linking unit links with other systems. Linking includes, but is not limited to, API linking, database linking, and file linking, for example. The linking unit can exchange data with other systems using, for example, an API. The linking unit can also share data with other systems using a database. The linking unit can also exchange data with other systems using files. This facilitates data sharing and integration by linking with other systems. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can optimize data exchange with other systems using AI.

[0036] The selection unit can suggest optimal options by referring to the user's past selection history when making a selection. The selection unit can suggest optimal options by referring to the user's past selection history when making a selection. Past selection history includes, but is not limited to, selection history from the past year and history based on specific conditions. For example, the selection unit can automatically display conditions that the user has frequently selected in the past as candidates. The selection unit can also preferentially suggest conditions similar to conditions that the user has previously selected. Furthermore, the selection unit can predict and suggest conditions to be used in a specific time period based on the user's past selection history. In this way, optimal options can be suggested to the user by referring to the past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use AI to analyze the user's past selection history and suggest optimal options.

[0037] The selection unit can customize options based on the user's industry and business content at the time of selection. The selection unit customizes options based on the user's industry and business content at the time of selection. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the selection unit can prioritize displaying IT-related options. Furthermore, if the user belongs to the manufacturing industry, the selection unit can also display options specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the selection unit can prioritize displaying education-related options. This improves user convenience by providing options tailored to the user's industry and business content. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use AI to analyze the user's industry and business content and propose optimal options.

[0038] The selection unit can prioritize displaying highly relevant options in consideration of the user's geographical location information when making a selection. The selection unit can prioritize displaying highly relevant options in consideration of the user's geographical location information when making a selection. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the selection unit can prioritize displaying options related to that area. Furthermore, if the user is traveling, the selection unit can update options based on the user's current location in real time. Furthermore, if the user is in a specific city, the selection unit can prioritize displaying options related to that city. This improves user convenience by providing highly relevant options based on the geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can analyze the user's geographical location information and suggest optimal options using AI.

[0039] The selection unit may analyze the user's social media activity at the time of selection and suggest relevant options. The selection unit may analyze the user's social media activity at the time of selection and suggest relevant options. Social media activity may include, but is not limited to, post content, number of likes, follower activity, etc. The selection unit may suggest options related to topics frequently mentioned by the user on social media, for example. The selection unit may also suggest options based on criteria selected by the user's social media followers. Furthermore, the selection unit may analyze the content of the user's social media posts and suggest relevant options. This improves user convenience by providing relevant options based on social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may use AI to analyze the user's social media activity and suggest optimal options.

[0040] The reference unit can analyze past reference history and prioritize display of optimal data when referring to the data. The reference unit can analyze past reference history and prioritize display of optimal data when referring to the data. Past reference history includes, but is not limited to, reference history from the past year and history based on specific conditions. For example, the reference unit can automatically display data that the user frequently referenced in the past. The reference unit can also prioritize display of data similar to data the user referenced in the past. Furthermore, the reference unit can predict and display data that will be used in a specific time period based on the user's past reference history. This allows the user to be provided with optimal data by analyzing the past reference history. Some or all of the above-described processing in the reference unit can be performed, for example, using AI, or can be performed without using AI. For example, the reference unit can use AI to analyze the user's past reference history and display optimal data.

[0041] The reference unit can adjust the display order based on the reliability and importance of the data when referencing. The reference unit adjusts the display order based on the reliability and importance of the data when referencing. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the reference unit prioritizes displaying highly reliable data. The reference unit can also prioritize displaying highly important data. Furthermore, the reference unit can determine an optimal display order taking both reliability and importance into consideration. This improves user convenience by adjusting the display order based on the reliability and importance of the data. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can use AI to analyze the reliability and importance of the data and determine an optimal display order.

[0042] The reference unit can prioritize displaying highly relevant data based on the user's industry and business content during reference. The reference unit can prioritize displaying highly relevant data based on the user's industry and business content during reference. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the reference unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the reference unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the reference unit can prioritize displaying education-related data. This improves user convenience by providing data tailored to the industry and business content. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can use AI to analyze the user's industry and business content and display optimal data.

[0043] The reference unit can display related data by referencing the user's past project data during reference. The reference unit can display related data by referencing the user's past project data during reference. Past project data includes, but is not limited to, data on a specific project or project data from the past year. The reference unit can automatically display, for example, data on projects in which the user has been involved in the past. The reference unit can also prioritize displaying data similar to the user's past project data. Furthermore, the reference unit can analyze the user's past project data and display related data. This allows related data to be provided by referencing the past project data. Some or all of the above-described processing in the reference unit can be performed using, for example, AI, or without AI. For example, the reference unit can use AI to analyze the user's past project data and display optimal data.

[0044] The calculation unit can select the optimal algorithm by referring to past calculation results during calculation. The calculation unit can select the optimal algorithm by referring to past calculation results during calculation. Past calculation results include, but are not limited to, calculation results from the past year and results based on specific conditions. For example, the calculation unit preferentially uses an algorithm that has obtained the most accurate results in the past. The calculation unit can also analyze past calculation results and select the optimal algorithm. Furthermore, the calculation unit can compare past calculation results with current conditions and select the optimal algorithm. In this way, the optimal algorithm can be selected by referring to past calculation results. Some or all of the above-described processing in the calculation unit may be performed, for example, using AI or without using AI. For example, the calculation unit can analyze past calculation results and select the optimal algorithm using AI.

[0045] The calculation unit can improve accuracy by taking into account the interrelationships of data during calculation. The calculation unit improves accuracy by taking into account the interrelationships of data during calculation. Examples of the interrelationships of data include, but are not limited to, correlation analysis and regression analysis. For example, the calculation unit analyzes the interrelationships of data to obtain highly accurate calculation results. The calculation unit can also select an optimal calculation algorithm by taking into account the interrelationships of data. Furthermore, the calculation unit can provide highly accurate calculation results based on the interrelationships of data. As a result, calculation accuracy is improved by taking the interrelationships of data into account. Some or all of the above-described processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can analyze the interrelationships of data using AI and select an optimal calculation algorithm.

[0046] The calculation unit can select an optimal calculation method based on the user's industry and business content during calculation. The calculation unit selects an optimal calculation method based on the user's industry and business content during calculation. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the calculation unit can preferentially use an IT-related calculation method. Furthermore, if the user belongs to the manufacturing industry, the calculation unit can also use a calculation method specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the calculation unit can also preferentially use an education-related calculation method. This improves user convenience by providing an optimal calculation method according to the industry and business content. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can use AI to analyze the user's industry and business content and select an optimal calculation method.

[0047] The calculation unit may prioritize highly relevant data in consideration of the user's geographical location information during calculation. The calculation unit may prioritize highly relevant data in consideration of the user's geographical location information during calculation. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the calculation unit may prioritize data related to that area. Furthermore, if the user is moving, the calculation unit may also calculate data based on the user's current location in real time. Furthermore, if the user is in a specific city, the calculation unit may prioritize data related to that city. This improves user convenience by providing highly relevant data based on the geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may use AI to analyze the user's geographical location information and calculate optimal data.

[0048] The input unit can suggest the optimal input method by referring to past input history when inputting data. The input unit can suggest the optimal input method by referring to past input history when inputting data. Past input history includes, for example, input history from the past year, history based on specific conditions, etc., but is not limited to such examples. The input unit, for example, automatically displays conditions that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest conditions to be used in a specific time period from the user's past input history. In this way, by referring to the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the user's past input history and suggest the optimal input method.

[0049] The input unit can adjust the input order based on the reliability and importance of the data at the time of input. The input unit adjusts the input order based on the reliability and importance of the data at the time of input. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the input unit prioritizes input of highly reliable data. The input unit can also prioritize input of highly important data. Furthermore, the input unit can determine an optimal input order taking both reliability and importance into consideration. This improves user convenience by adjusting the input order based on the reliability and importance of the data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the reliability and importance of the data and determine an optimal input order.

[0050] The input unit can prioritize displaying highly relevant input items based on the user's industry and business content during input. The input unit can prioritize displaying highly relevant input items based on the user's industry and business content during input. Examples of industries and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the input unit can prioritize displaying IT-related input items. Furthermore, if the user belongs to the manufacturing industry, the input unit can also display input items specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the input unit can prioritize displaying education-related input items. This improves user convenience by providing input items according to the industry and business content. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the user's industry and business content and display optimal input items.

[0051] The input unit can analyze the user's social media activity at the time of input and suggest related input items. The input unit can analyze the user's social media activity at the time of input and suggest related input items. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and follower activity. The input unit can, for example, suggest input items related to topics frequently mentioned by the user on social media. The input unit can also suggest input items based on conditions selected by the user's social media followers. Furthermore, the input unit can analyze the content of the user's social media posts and suggest related input items. This improves user convenience by providing related input items based on social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can use AI to analyze the user's social media activity and suggest optimal input items.

[0052] The display unit can select the optimal display method by referring to past display history when displaying. The display unit can select the optimal display method by referring to past display history when displaying. Past display history includes, but is not limited to, display history from the past year, history based on specific conditions, etc. For example, the display unit automatically selects a display method that the user has frequently used in the past. The display unit can also analyze the user's past display history and select the optimal display method. Furthermore, the display unit can predict and select a display method to be used in a specific time period from the user's past display history. In this way, by referring to the past display history, the optimal display method can be provided to the user. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can use AI to analyze the user's past display history and select the optimal display method.

[0053] The display unit can adjust the display order based on the reliability and importance of the data when displaying the data. The display unit adjusts the display order based on the reliability and importance of the data when displaying the data. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the display unit can prioritize displaying highly reliable data. The display unit can also prioritize displaying highly important data. Furthermore, the display unit can determine an optimal display order taking both reliability and importance into consideration. This improves user convenience by adjusting the display order based on the reliability and importance of the data. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can use AI to analyze the reliability and importance of the data and determine an optimal display order.

[0054] The display unit can prioritize displaying highly relevant data based on the user's industry and business content when displaying the data. The display unit can prioritize displaying highly relevant data based on the user's industry and business content when displaying the data. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the display unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the display unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the display unit can prioritize displaying education-related data. This improves user convenience by providing data tailored to the user's industry and business content. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can use AI to analyze the user's industry and business content and display optimal data.

[0055] The display unit can prioritize displaying highly relevant data in consideration of the user's geographical location information when displaying the data. The display unit can prioritize displaying highly relevant data in consideration of the user's geographical location information when displaying the data. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the display unit can prioritize displaying data related to that area. Furthermore, when the user is moving, the display unit can also display data based on the user's current location in real time. Furthermore, when the user is in a specific city, the display unit can prioritize displaying data related to that city. This improves user convenience by providing highly relevant data based on the geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can analyze the user's geographical location information and display optimal data using AI.

[0056] The storage unit can select the optimal storage method by referring to past storage history when saving. The storage unit selects the optimal storage method by referring to past storage history when saving. Past storage history includes, for example, storage history from the past year, history based on specific conditions, etc., but is not limited to these examples. The storage unit, for example, automatically selects a storage method that the user has frequently used in the past. The storage unit can also analyze the user's past storage history and select the optimal storage method. Furthermore, the storage unit can predict and select a storage method to be used in a specific time period from the user's past storage history. In this way, by referring to the past storage history, the optimal storage method can be provided to the user. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can use AI to analyze the user's past storage history and select the optimal storage method.

[0057] The storage unit can prioritize saving highly relevant data based on the user's industry and business content when saving data. The storage unit prioritizes saving highly relevant data based on the user's industry and business content when saving data. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the storage unit can prioritize saving IT-related data. Furthermore, if the user belongs to the manufacturing industry, the storage unit can also prioritize saving data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the storage unit can prioritize saving education-related data. This prioritizes saving data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can use AI to analyze the user's industry and business content and save optimal data.

[0058] The graph display unit can select the optimal display method by referring to past display history when displaying a graph. The graph display unit can select the optimal display method by referring to past display history when displaying a graph. Past display history includes, but is not limited to, display history from the past year or history based on specific conditions, for example. The graph display unit, for example, automatically selects a graph display method that the user has frequently used in the past. The graph display unit can also analyze the user's past display history and select the optimal graph display method. Furthermore, the graph display unit can predict and select a graph display method to be used in a specific time period from the user's past display history. In this way, by referring to the past display history, the optimal graph display method can be provided to the user. Some or all of the above-described processing in the graph display unit may be performed, for example, using AI or without AI. For example, the graph display unit can use AI to analyze the user's past display history and select the optimal graph display method.

[0059] When displaying a graph, the graph display unit can prioritize displaying highly relevant data based on the user's industry and business content. When displaying a graph, the graph display unit can prioritize displaying highly relevant data based on the user's industry and business content. Examples of industries and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the graph display unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the graph display unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the graph display unit can prioritize displaying education-related data. This prioritizes displaying data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing in the graph display unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph display unit can use AI to analyze the user's industry and business content and display optimal data.

[0060] The update unit can select the optimal update method by referring to past update history during an update. The update unit can select the optimal update method by referring to past update history during an update. Past update history includes, but is not limited to, update history for the past year and history based on specific conditions, for example. The update unit, for example, automatically selects an update method that the user has frequently used in the past. The update unit can also analyze the user's past update history and select the optimal update method. Furthermore, the update unit can predict and select the update method to be used in a specific time period based on the user's past update history. In this way, by referring to the past update history, the optimal update method can be provided to the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can use AI to analyze the user's past update history and select the optimal update method.

[0061] The update unit can prioritize updating highly relevant data based on the user's industry and business content during an update. The update unit prioritizes updating highly relevant data based on the user's industry and business content during an update. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the update unit prioritizes updating IT-related data. Furthermore, if the user belongs to the manufacturing industry, the update unit can also update data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the update unit can also prioritize updating education-related data. This improves user convenience by prioritizing updating data according to the industry and business content. Some or all of the above-described processing by the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can use AI to analyze the user's industry and business content and update optimal data.

[0062] The analysis unit can select the optimal analysis method by referring to past analysis history during analysis. The analysis unit can select the optimal analysis method by referring to past analysis history during analysis. Past analysis history includes, for example, analysis history for the past year, history based on specific conditions, etc., but is not limited to such examples. The analysis unit, for example, preferentially uses the analysis method that has previously produced the most accurate results. The analysis unit can also analyze past analysis history and select the optimal analysis method. Furthermore, the analysis unit can compare past analysis results with current conditions and select the optimal analysis method. In this way, by referring to past analysis history, the optimal analysis method can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the user's past analysis history and select the optimal analysis method.

[0063] During analysis, the analysis unit can prioritize analysis of highly relevant data based on the user's industry and business content. During analysis, the analysis unit prioritizes analysis of highly relevant data based on the user's industry and business content. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the analysis unit prioritizes analysis of IT-related data. Furthermore, if the user belongs to the manufacturing industry, the analysis unit can analyze data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the analysis unit can prioritize analysis of education-related data. This prioritizes analysis of data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use AI to analyze the user's industry and business content and analyze optimal data.

[0064] The authentication unit can select the optimal authentication method by referring to past authentication history during authentication. The authentication unit selects the optimal authentication method by referring to past authentication history during authentication. Past authentication history includes, but is not limited to, authentication history from the past year or history based on specific conditions, for example. The authentication unit automatically selects, for example, an authentication method that the user has frequently used in the past. The authentication unit can also analyze the user's past authentication history and select the optimal authentication method. Furthermore, the authentication unit can predict and select an authentication method to be used in a specific time period based on the user's past authentication history. In this way, by referring to the past authentication history, the optimal authentication method can be provided to the user. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can use AI to analyze the user's past authentication history and select the optimal authentication method.

[0065] The authentication unit can select a highly relevant authentication method based on the user's industry and business content during authentication. The authentication unit can select a highly relevant authentication method based on the user's industry and business content during authentication. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the authentication unit can prioritize IT-related authentication methods. Furthermore, if the user belongs to the manufacturing industry, the authentication unit can also use authentication methods specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the authentication unit can also prioritize education-related authentication methods. This improves user convenience by providing authentication methods tailored to the industry and business content. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can use AI to analyze the user's industry and business content and select the optimal authentication method.

[0066] The collaboration unit can select the optimal collaboration method by referring to past collaboration history when collaborating. The collaboration unit selects the optimal collaboration method by referring to past collaboration history when collaborating. Past collaboration history includes, for example, collaboration history from the past year, history based on specific conditions, etc., but is not limited to these examples. The collaboration unit, for example, automatically selects a collaboration method that the user has frequently used in the past. The collaboration unit can also analyze the user's past collaboration history and select the optimal collaboration method. Furthermore, the collaboration unit can predict and select the collaboration method to be used in a specific time period from the user's past collaboration history. In this way, by referring to the past collaboration history, the optimal collaboration method can be provided to the user. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can use AI to analyze the user's past collaboration history and select the optimal collaboration method.

[0067] During linking, the linking unit can prioritize linking highly relevant data based on the user's industry and business content. During linking, the linking unit prioritizes linking highly relevant data based on the user's industry and business content. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the linking unit prioritizes linking IT-related data. Furthermore, if the user belongs to the manufacturing industry, the linking unit can also link data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the linking unit can also prioritize linking education-related data. This improves user convenience by prioritizing linking data according to the industry and business content. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without AI. For example, the linking unit can use AI to analyze the user's industry and business content and link optimal data.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The quotation system can analyze the user's past quotation history and automatically suggest optimal quotation conditions. For example, it can prioritize quotation conditions that the user has frequently selected in the past. It can also suggest conditions similar to those selected by the user in the past. Furthermore, it can predict and suggest conditions to be used in a specific time period based on the user's past quotation history. This allows it to suggest optimal quotation conditions to the user by referring to the user's past quotation history.

[0070] The quotation system can customize quotation conditions based on the user's industry and business content. For example, if the user is in the IT industry, it can prioritize displaying IT-related quotation conditions. If the user is in the manufacturing industry, it can also display quotation conditions specialized for the manufacturing industry. Furthermore, if the user is in the education industry, it can also prioritize displaying education-related quotation conditions. This improves user convenience by providing quotation conditions tailored to the user's industry and business content.

[0071] The quotation system can adjust quotation conditions taking into account the user's geographic location information. For example, if the user is in a specific area, quotation conditions related to that area can be displayed preferentially. Also, if the user is traveling, quotation conditions based on the user's current location can be updated in real time. Furthermore, if the user is in a specific city, quotation conditions related to that city can be displayed preferentially. This improves user convenience by providing quotation conditions that are highly relevant based on geographic location information.

[0072] The quotation system can analyze a user's social media activity and suggest relevant quotation terms. For example, it can suggest quotation terms related to topics that the user frequently mentions on social media. It can also suggest terms based on terms selected by the user's social media followers. It can also analyze the content of a user's social media posts and suggest relevant quotation terms. This improves user convenience by providing relevant quotation terms based on social media activity.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The selection unit selects the quotation conditions. The quotation conditions include the type and functions of the groupware to be introduced, the number of users to be used, and the implementation period. The selection unit allows the user to select the quotation conditions on the GUI and has the function of sending the selected conditions to the system. Step 2: The reference section references past quotation data. Past quotation data includes quotation terms and costs previously presented. The reference section retrieves the past quotation data from the database and reflects it in the current quotation. Step 3: The calculation unit calculates an estimate based on the information obtained by the selection unit and reference unit. The calculation unit automatically calculates the current estimate based on past estimate data. The calculation unit can also calculate an estimate that matches the estimate by inputting the estimate conditions and costs of competitors. Step 4: The input section inputs the quotation conditions of competitors. The input section allows the user to input the quotation conditions and costs of competitors. Step 5: The display unit displays the estimation result calculated by the calculation unit. The display unit can display the estimation result in text or graphs.

[0075] (Example 2) A quotation system according to an embodiment of the present invention streamlines quotation work in groupware implementation. This quotation system allows a user to log in to the system using a dedicated ID and password and select quotation conditions on a GUI. The system automatically calculates a quotation based on previously submitted quotation terms and costs. Furthermore, by entering competitors' quotation terms and costs, a quotation tailored to the competitors' quotation terms and costs can be presented. This mechanism enables quick quotation requests and multiple quotation requests, thereby reducing wasted time. For example, a user logs in to the system using a dedicated ID and password. The user can access the system by entering a previously issued ID and password. For example, a company representative logs in to the system and begins quotation work. Next, the user selects quotation conditions on the GUI. For example, the user can select conditions such as the type and functions of the groupware to be implemented, the number of users, and the implementation period. This allows the user to easily set quotation conditions. The system automatically calculates a quotation based on previously submitted quotation terms and costs. For example, the system can calculate a current quotation based on quotation data submitted under similar conditions. This allows for quick and accurate quotation provision. Furthermore, by entering competitors' quotation terms and costs, a quotation tailored to the competitors' quotation terms and costs can be presented. For example, by entering the quotation conditions and costs of competitors, a quotation based on that can be presented. This allows for the provision of competitive quotation. This system makes it possible to respond to quotation requests in a short period of time or for multiple patterns, reducing wasted time. For example, a company's personnel can create multiple quotation patterns in a short time and quickly propose them. Furthermore, by streamlining the quotation process, they can concentrate on other tasks. This allows the quotation system to enable users to quickly and accurately create quotation and make competitive proposals.

[0076] The quotation system according to the embodiment includes a selection unit, a reference unit, a calculation unit, an input unit, and a display unit. The selection unit selects quotation conditions. The quotation conditions include, for example, the type and functions of the groupware to be implemented, the number of users to be used, and the implementation period, but are not limited to these examples. The selection unit, for example, allows a user to select quotation conditions on a GUI. The selection unit also has a function to transmit the conditions selected by the user to the system. The reference unit references past quotation data. The past quotation data includes, for example, previously submitted quotation terms and costs, but are not limited to these examples. The reference unit, for example, acquires past quotation data from a database and reflects the data in a current quotation. The calculation unit calculates an estimate based on information obtained by the selection unit and the reference unit. The calculation unit, for example, automatically calculates a current estimate based on the past quotation data. The calculation unit can also calculate an estimate based on the input of a competitor's quotation conditions and costs. The input unit inputs a competitor's quotation conditions. The input unit, for example, allows a user to input a competitor's quotation conditions and costs. The display unit displays the estimate result calculated by the calculation unit. The display unit can display the estimate result, for example, in text or graph form. This allows the estimate system according to the embodiment to enable the user to quickly and accurately create estimates and make competitive proposals.

[0077] The quotation system includes a storage unit that stores quotation results. The storage unit stores the quotation results. The quotation results include, for example, quotation conditions, calculated costs, and quotation conditions of competitors, but are not limited to these examples. The storage unit stores the quotation results in, for example, a database. The storage unit can also store the quotation results in a file format. For example, the storage unit can store the quotation results in PDF format or Excel format. By storing the quotation results, they can be referenced or reused later. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can store the quotation results using an AI model that automatically classifies the quotation results and stores them in appropriate folders.

[0078] The quotation system includes a graph display unit that displays quotation results in a graph or a table. The graph display unit displays the quotation results in a graph or a table. Examples of graphs and tables include, but are not limited to, bar graphs, line graphs, pie charts, and table formats. The graph display unit can, for example, display the quotation results in a visually easy-to-understand manner. For example, the graph display unit can display the quotation results in a bar graph to compare the costs of each item. The graph display unit can also display the quotation results in a line graph to visually show fluctuations in costs. The graph display unit can also display the quotation results in a pie chart to show the proportion of the cost of each item. This allows the quotation results to be displayed in a visually easy-to-understand manner. Some or all of the above-described processing in the graph display unit may be performed, for example, using AI, or may be performed without using AI. For example, the graph display unit can display the quotation results using an AI model that automatically analyzes the quotation results and selects the optimal graph format.

[0079] The quotation system includes an update unit that periodically updates data. The update unit periodically updates the data. The data includes, but is not limited to, quotation conditions, past quotation data, and competitor quotation conditions. The update unit can update the data daily, weekly, monthly, or at other intervals. For example, the update unit can check the database daily and automatically update the data when new quotation data is added. The update unit can also check the database weekly, delete old data, and add new data. The update unit can also check the database monthly, verify data consistency, and correct the data as necessary. This allows the data to be kept up to date. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the data using an AI model that automatically adjusts the data update frequency.

[0080] The quotation system includes an analysis unit that analyzes data using AI. The analysis unit analyzes data using AI. Examples of AI include, but are not limited to, machine learning algorithms and deep learning algorithms. The analysis unit can analyze data such as quotation conditions, past quotation data, and competitors' quotation conditions to improve the accuracy of the quotation. For example, the analysis unit can use a machine learning algorithm to analyze past quotation data and reflect it in a current quotation. The analysis unit can also use a deep learning algorithm to analyze competitors' quotation conditions and calculate an optimal quotation. Furthermore, the analysis unit can use AI to analyze trends in quotation conditions and use the results for future quotation. This improves the accuracy of data analysis by using AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze trends in quotation conditions and use the results for future quotation.

[0081] The quotation system includes an authentication unit that performs two-step authentication. The authentication unit performs two-step authentication. Examples of two-step authentication include, but are not limited to, SMS authentication, email authentication, and biometric authentication. For example, when a user logs in to the system, the authentication unit requires the user to enter an SMS authentication code in addition to their ID and password. The authentication unit can also require the user to enter an email authentication code in addition to their ID and password when logging in to the system. Furthermore, the authentication unit can perform biometric authentication such as fingerprint authentication or facial authentication in addition to their ID and password when logging in to the system. This enhances security. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can use AI to analyze a user's login pattern and detect abnormal logins.

[0082] The estimation system includes a linking unit that links with other systems. The linking unit links with other systems. Linking includes, but is not limited to, API linking, database linking, and file linking, for example. The linking unit can exchange data with other systems using, for example, an API. The linking unit can also share data with other systems using a database. The linking unit can also exchange data with other systems using files. This facilitates data sharing and integration by linking with other systems. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can optimize data exchange with other systems using AI.

[0083] The selection unit estimates the user's emotion and adjusts the display order of options based on the estimated user emotion. The selection unit estimates the user's emotion and adjusts the display order of options based on the estimated user emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the selection unit displays the simplest and most intuitive options at the top. Furthermore, when the user is relaxed, the selection unit can display detailed options at the top, thereby increasing the degree of customization flexibility. Furthermore, when the user is in a hurry, the selection unit can display the quickest option at the top. This improves user convenience by adjusting the display order of options according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or without an AI. For example, the selection unit can use AI to analyze the user's emotions in real time and adjust the display order of options.

[0084] The selection unit can suggest optimal options by referring to the user's past selection history when making a selection. The selection unit can suggest optimal options by referring to the user's past selection history when making a selection. Past selection history includes, but is not limited to, selection history from the past year and history based on specific conditions. For example, the selection unit can automatically display conditions that the user has frequently selected in the past as candidates. The selection unit can also preferentially suggest conditions similar to conditions that the user has previously selected. Furthermore, the selection unit can predict and suggest conditions to be used in a specific time period based on the user's past selection history. In this way, optimal options can be suggested to the user by referring to the past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use AI to analyze the user's past selection history and suggest optimal options.

[0085] The selection unit can customize options based on the user's industry and business content at the time of selection. The selection unit customizes options based on the user's industry and business content at the time of selection. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the selection unit can prioritize displaying IT-related options. Furthermore, if the user belongs to the manufacturing industry, the selection unit can also display options specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the selection unit can prioritize displaying education-related options. This improves user convenience by providing options tailored to the user's industry and business content. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use AI to analyze the user's industry and business content and propose optimal options.

[0086] The selection unit can estimate the user's emotion and adjust the number of options based on the estimated user emotion. The selection unit can estimate the user's emotion and adjust the number of options based on the estimated user emotion. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the selection unit can reduce the number of options and provide a simple interface. Furthermore, when the user is relaxed, the selection unit can increase the number of options and enable detailed customization. Furthermore, when the user is in a hurry, the selection unit can display only the most important options to encourage quick selection. This improves user convenience by adjusting the number of options according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can use AI to analyze the user's emotions in real time and adjust the number of options.

[0087] The selection unit can prioritize displaying highly relevant options in consideration of the user's geographical location information when making a selection. The selection unit can prioritize displaying highly relevant options in consideration of the user's geographical location information when making a selection. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the selection unit can prioritize displaying options related to that area. Furthermore, if the user is traveling, the selection unit can update options based on the user's current location in real time. Furthermore, if the user is in a specific city, the selection unit can prioritize displaying options related to that city. This improves user convenience by providing highly relevant options based on the geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can analyze the user's geographical location information and suggest optimal options using AI.

[0088] The selection unit may analyze the user's social media activity at the time of selection and suggest relevant options. The selection unit may analyze the user's social media activity at the time of selection and suggest relevant options. Social media activity may include, but is not limited to, post content, number of likes, follower activity, etc. The selection unit may suggest options related to topics frequently mentioned by the user on social media, for example. The selection unit may also suggest options based on criteria selected by the user's social media followers. Furthermore, the selection unit may analyze the content of the user's social media posts and suggest relevant options. This improves user convenience by providing relevant options based on social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may use AI to analyze the user's social media activity and suggest optimal options.

[0089] The reference unit can estimate the user's emotion and adjust the display method of the reference data based on the estimated user emotion. The reference unit can estimate the user's emotion and adjust the display method of the reference data based on the estimated user emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the reference unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the reference unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the reference unit can provide a display method that focuses on the main points. This improves user convenience by adjusting the display method of the reference data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reference unit can be performed using, for example, AI, or without AI. For example, the reference unit can use AI to analyze user emotions in real time and adjust how reference data is displayed.

[0090] The reference unit can analyze past reference history and prioritize display of optimal data when referring to the data. The reference unit can analyze past reference history and prioritize display of optimal data when referring to the data. Past reference history includes, but is not limited to, reference history from the past year and history based on specific conditions. For example, the reference unit can automatically display data that the user frequently referenced in the past. The reference unit can also prioritize display of data similar to data the user referenced in the past. Furthermore, the reference unit can predict and display data that will be used in a specific time period based on the user's past reference history. This allows the user to be provided with optimal data by analyzing the past reference history. Some or all of the above-described processing in the reference unit can be performed, for example, using AI, or can be performed without using AI. For example, the reference unit can use AI to analyze the user's past reference history and display optimal data.

[0091] The reference unit can adjust the display order based on the reliability and importance of the data when referencing. The reference unit adjusts the display order based on the reliability and importance of the data when referencing. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the reference unit prioritizes displaying highly reliable data. The reference unit can also prioritize displaying highly important data. Furthermore, the reference unit can determine an optimal display order taking both reliability and importance into consideration. This improves user convenience by adjusting the display order based on the reliability and importance of the data. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can use AI to analyze the reliability and importance of the data and determine an optimal display order.

[0092] The reference unit can estimate the user's emotion and adjust the level of detail of the reference data based on the estimated user's emotion. The reference unit can estimate the user's emotion and adjust the level of detail of the reference data based on the estimated user's emotion. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the reference unit can display concise and to-the-point data. Furthermore, when the user is relaxed, the reference unit can display detailed data. Furthermore, when the user is in a hurry, the reference unit can display only the most important information. This improves user convenience by adjusting the level of detail of the reference data according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reference unit can be performed using, for example, an AI or without an AI. For example, the reference unit can use AI to analyze user emotions in real time and adjust the level of detail of the reference data.

[0093] The reference unit can prioritize displaying highly relevant data based on the user's industry and business content during reference. The reference unit can prioritize displaying highly relevant data based on the user's industry and business content during reference. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the reference unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the reference unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the reference unit can prioritize displaying education-related data. This improves user convenience by providing data tailored to the industry and business content. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can use AI to analyze the user's industry and business content and display optimal data.

[0094] The reference unit can display related data by referencing the user's past project data during reference. The reference unit can display related data by referencing the user's past project data during reference. Past project data includes, but is not limited to, data on a specific project or project data from the past year. The reference unit can automatically display, for example, data on projects in which the user has been involved in the past. The reference unit can also prioritize displaying data similar to the user's past project data. Furthermore, the reference unit can analyze the user's past project data and display related data. This allows related data to be provided by referencing the past project data. Some or all of the above-described processing in the reference unit can be performed using, for example, AI, or without AI. For example, the reference unit can use AI to analyze the user's past project data and display optimal data.

[0095] The calculation unit can estimate the user's emotion and adjust the calculation algorithm based on the estimated user's emotion. The calculation unit can estimate the user's emotion and adjust the calculation algorithm based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the calculation unit uses a simple and fast calculation algorithm. Furthermore, when the user is relaxed, the calculation unit can use a calculation algorithm that takes detailed data into account. Furthermore, when the user is in a hurry, the calculation unit can use a calculation algorithm that can obtain results most quickly. This improves user convenience by adjusting the calculation algorithm according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or without AI. For example, the calculation unit can use AI to analyze the user's emotion in real time and adjust the calculation algorithm.

[0096] The calculation unit can select the optimal algorithm by referring to past calculation results during calculation. The calculation unit can select the optimal algorithm by referring to past calculation results during calculation. Past calculation results include, but are not limited to, calculation results from the past year and results based on specific conditions. For example, the calculation unit preferentially uses an algorithm that has obtained the most accurate results in the past. The calculation unit can also analyze past calculation results and select the optimal algorithm. Furthermore, the calculation unit can compare past calculation results with current conditions and select the optimal algorithm. In this way, the optimal algorithm can be selected by referring to past calculation results. Some or all of the above-described processing in the calculation unit may be performed, for example, using AI or without using AI. For example, the calculation unit can analyze past calculation results and select the optimal algorithm using AI.

[0097] The calculation unit can improve accuracy by taking into account the interrelationships of data during calculation. The calculation unit improves accuracy by taking into account the interrelationships of data during calculation. Examples of the interrelationships of data include, but are not limited to, correlation analysis and regression analysis. For example, the calculation unit analyzes the interrelationships of data to obtain highly accurate calculation results. The calculation unit can also select an optimal calculation algorithm by taking into account the interrelationships of data. Furthermore, the calculation unit can provide highly accurate calculation results based on the interrelationships of data. As a result, calculation accuracy is improved by taking the interrelationships of data into account. Some or all of the above-described processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can analyze the interrelationships of data using AI and select an optimal calculation algorithm.

[0098] The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the estimated user's emotion. The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the calculation unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the calculation unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the calculation unit can provide a display method that focuses on the main points. This improves user convenience by adjusting the display method of the calculation result according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or without using an AI. For example, the calculation unit can use AI to analyze the user's emotions in real time and adjust the way the calculation results are displayed.

[0099] The calculation unit can select an optimal calculation method based on the user's industry and business content during calculation. The calculation unit selects an optimal calculation method based on the user's industry and business content during calculation. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the calculation unit can preferentially use an IT-related calculation method. Furthermore, if the user belongs to the manufacturing industry, the calculation unit can also use a calculation method specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the calculation unit can also preferentially use an education-related calculation method. This improves user convenience by providing an optimal calculation method according to the industry and business content. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can use AI to analyze the user's industry and business content and select an optimal calculation method.

[0100] The calculation unit may prioritize highly relevant data in consideration of the user's geographical location information during calculation. The calculation unit may prioritize highly relevant data in consideration of the user's geographical location information during calculation. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the calculation unit may prioritize data related to that area. Furthermore, if the user is moving, the calculation unit may also calculate data based on the user's current location in real time. Furthermore, if the user is in a specific city, the calculation unit may prioritize data related to that city. This improves user convenience by providing highly relevant data based on the geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may use AI to analyze the user's geographical location information and calculate optimal data.

[0101] The input unit can estimate a user's emotion and adjust the input interface based on the estimated user's emotion. The input unit can estimate a user's emotion and adjust the input interface based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the input unit can provide a simple and intuitive input interface when the user is stressed. The input unit can also provide detailed input options when the user is relaxed. Furthermore, the input unit can provide an interface that allows quick input when the user is in a hurry. This improves user convenience by adjusting the input interface according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or without AI. For example, the input unit can use AI to analyze the user's emotion in real time and adjust the input interface.

[0102] The input unit can suggest the optimal input method by referring to past input history when inputting data. The input unit can suggest the optimal input method by referring to past input history when inputting data. Past input history includes, for example, input history from the past year, history based on specific conditions, etc., but is not limited to such examples. The input unit, for example, automatically displays conditions that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest conditions to be used in a specific time period from the user's past input history. In this way, by referring to the past input history, the optimal input method can be suggested to the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the user's past input history and suggest the optimal input method.

[0103] The input unit can adjust the input order based on the reliability and importance of the data at the time of input. The input unit adjusts the input order based on the reliability and importance of the data at the time of input. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the input unit prioritizes input of highly reliable data. The input unit can also prioritize input of highly important data. Furthermore, the input unit can determine an optimal input order taking both reliability and importance into consideration. This improves user convenience by adjusting the input order based on the reliability and importance of the data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the reliability and importance of the data and determine an optimal input order.

[0104] The input unit can estimate the user's emotions and adjust the number of input items based on the estimated user emotions. The input unit can estimate the user's emotions and adjust the number of input items based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the input unit can reduce the number of input items and provide a simple interface. Furthermore, when the user is relaxed, the input unit can increase the number of input items and enable detailed customization. Furthermore, when the user is in a hurry, the input unit can display only the most important input items to encourage quick input. This improves user convenience by adjusting the number of input items according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can use AI to analyze the user's emotions in real time and adjust the number of input items.

[0105] The input unit can prioritize displaying highly relevant input items based on the user's industry and business content during input. The input unit can prioritize displaying highly relevant input items based on the user's industry and business content during input. Examples of industries and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the input unit can prioritize displaying IT-related input items. Furthermore, if the user belongs to the manufacturing industry, the input unit can also display input items specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the input unit can prioritize displaying education-related input items. This improves user convenience by providing input items according to the industry and business content. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can use AI to analyze the user's industry and business content and display optimal input items.

[0106] The input unit can analyze the user's social media activity at the time of input and suggest related input items. The input unit can analyze the user's social media activity at the time of input and suggest related input items. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and follower activity. The input unit can, for example, suggest input items related to topics frequently mentioned by the user on social media. The input unit can also suggest input items based on conditions selected by the user's social media followers. Furthermore, the input unit can analyze the content of the user's social media posts and suggest related input items. This improves user convenience by providing related input items based on social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can use AI to analyze the user's social media activity and suggest optimal input items.

[0107] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the display unit can provide a simple, highly visible display method. When the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This improves user convenience by adjusting the display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can use AI to analyze the user's emotion in real time and adjust the display method.

[0108] The display unit can select the optimal display method by referring to past display history when displaying. The display unit can select the optimal display method by referring to past display history when displaying. Past display history includes, but is not limited to, display history from the past year, history based on specific conditions, etc. For example, the display unit automatically selects a display method that the user has frequently used in the past. The display unit can also analyze the user's past display history and select the optimal display method. Furthermore, the display unit can predict and select a display method to be used in a specific time period from the user's past display history. In this way, by referring to the past display history, the optimal display method can be provided to the user. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can use AI to analyze the user's past display history and select the optimal display method.

[0109] The display unit can adjust the display order based on the reliability and importance of the data when displaying the data. The display unit adjusts the display order based on the reliability and importance of the data when displaying the data. Examples of the reliability and importance of the data include, but are not limited to, the reliability of the data source and the update frequency of the data. For example, the display unit can prioritize displaying highly reliable data. The display unit can also prioritize displaying highly important data. Furthermore, the display unit can determine an optimal display order taking both reliability and importance into consideration. This improves user convenience by adjusting the display order based on the reliability and importance of the data. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can use AI to analyze the reliability and importance of the data and determine an optimal display order.

[0110] The display unit can estimate the user's emotion and adjust the level of detail of the display content based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the level of detail of the display content based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the display unit can provide concise and to-the-point display content. Furthermore, when the user is relaxed, the display unit can also provide display content including detailed information. Furthermore, when the user is in a hurry, the display unit can display only the most important information. This improves user convenience by adjusting the level of detail of the display content according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI or without an AI. For example, the display unit can use AI to analyze the user's emotions in real time and adjust the level of detail of the displayed content.

[0111] The display unit can prioritize displaying highly relevant data based on the user's industry and business content when displaying the data. The display unit can prioritize displaying highly relevant data based on the user's industry and business content when displaying the data. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the display unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the display unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the display unit can prioritize displaying education-related data. This improves user convenience by providing data tailored to the user's industry and business content. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can use AI to analyze the user's industry and business content and display optimal data.

[0112] The display unit can prioritize displaying highly relevant data in consideration of the user's geographical location information when displaying the data. The display unit can prioritize displaying highly relevant data in consideration of the user's geographical location information when displaying the data. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the display unit can prioritize displaying data related to that area. Furthermore, when the user is moving, the display unit can also display data based on the user's current location in real time. Furthermore, when the user is in a specific city, the display unit can prioritize displaying data related to that city. This improves user convenience by providing highly relevant data based on the geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can analyze the user's geographical location information and display optimal data using AI.

[0113] The storage unit can estimate the user's emotions and adjust the storage method based on the estimated user emotions. The storage unit can estimate the user's emotions and adjust the storage method based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the storage unit can provide a simple and quick storage method when the user is stressed. The storage unit can also provide detailed storage options when the user is relaxed. Furthermore, the storage unit can provide the quickest storage method when the user is in a hurry. This improves user convenience by adjusting the storage method according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can use AI to analyze the user's emotions in real time and adjust the storage method.

[0114] The storage unit can select the optimal storage method by referring to past storage history when saving. The storage unit selects the optimal storage method by referring to past storage history when saving. Past storage history includes, for example, storage history from the past year, history based on specific conditions, etc., but is not limited to these examples. The storage unit, for example, automatically selects a storage method that the user has frequently used in the past. The storage unit can also analyze the user's past storage history and select the optimal storage method. Furthermore, the storage unit can predict and select a storage method to be used in a specific time period from the user's past storage history. In this way, by referring to the past storage history, the optimal storage method can be provided to the user. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can use AI to analyze the user's past storage history and select the optimal storage method.

[0115] The storage unit can estimate the user's emotions and prioritize the stored data based on the estimated user emotions. The storage unit can estimate the user's emotions and prioritize the stored data based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the storage unit can prioritize saving the most important data. Furthermore, when the user is relaxed, the storage unit can prioritize saving detailed data. Furthermore, when the user is in a hurry, the storage unit can prioritize saving data that can be saved most quickly. In this way, by prioritizing the stored data according to the user's emotions, important data can be saved preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can use AI to analyze user emotions in real time and determine the priority of stored data.

[0116] The storage unit can prioritize saving highly relevant data based on the user's industry and business content when saving data. The storage unit prioritizes saving highly relevant data based on the user's industry and business content when saving data. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the storage unit can prioritize saving IT-related data. Furthermore, if the user belongs to the manufacturing industry, the storage unit can also prioritize saving data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the storage unit can prioritize saving education-related data. This prioritizes saving data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can use AI to analyze the user's industry and business content and save optimal data.

[0117] The graph display unit can estimate the user's emotion and adjust the graph display method based on the estimated user's emotion. The graph display unit can estimate the user's emotion and adjust the graph display method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the graph display unit can provide a simple, highly visible graph. Furthermore, when the user is relaxed, the graph display unit can provide a graph with detailed information. Furthermore, when the user is in a hurry, the graph display unit can provide a graph that focuses on the main points. This improves user convenience by adjusting the graph display method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graph display unit can be performed using, for example, AI, or without AI. For example, the graph display unit can use AI to analyze the user's emotions in real time and adjust the way the graph is displayed.

[0118] The graph display unit can select the optimal display method by referring to past display history when displaying a graph. The graph display unit can select the optimal display method by referring to past display history when displaying a graph. Past display history includes, but is not limited to, display history from the past year or history based on specific conditions, for example. The graph display unit, for example, automatically selects a graph display method that the user has frequently used in the past. The graph display unit can also analyze the user's past display history and select the optimal graph display method. Furthermore, the graph display unit can predict and select a graph display method to be used in a specific time period from the user's past display history. In this way, by referring to the past display history, the optimal graph display method can be provided to the user. Some or all of the above-described processing in the graph display unit may be performed, for example, using AI or without AI. For example, the graph display unit can use AI to analyze the user's past display history and select the optimal graph display method.

[0119] The graph display unit can estimate the user's emotion and adjust the type of graph based on the estimated user's emotion. The graph display unit can estimate the user's emotion and adjust the type of graph based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the graph display unit can provide a simple bar graph. If the user is relaxed, the graph display unit can also provide a pie chart with detailed information. Furthermore, if the user is in a hurry, the graph display unit can also provide a line graph that highlights the main points. This improves user convenience by adjusting the type of graph based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graph display unit can be performed using, for example, AI, or without AI. For example, the graph display unit can use AI to analyze the user's emotions in real time and adjust the type of graph.

[0120] When displaying a graph, the graph display unit can prioritize displaying highly relevant data based on the user's industry and business content. When displaying a graph, the graph display unit can prioritize displaying highly relevant data based on the user's industry and business content. Examples of industries and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the graph display unit can prioritize displaying IT-related data. Furthermore, if the user belongs to the manufacturing industry, the graph display unit can also display data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the graph display unit can prioritize displaying education-related data. This prioritizes displaying data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing in the graph display unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph display unit can use AI to analyze the user's industry and business content and display optimal data.

[0121] The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the update unit can reduce the update frequency to provide a stable environment. Furthermore, when the user is relaxed, the update unit can increase the update frequency to provide the latest information. Furthermore, when the user is in a hurry, the update unit can provide the fastest update method. This improves user convenience by adjusting the update frequency according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or without AI. For example, the update unit can use AI to analyze the user's emotion in real time and adjust the update frequency.

[0122] The update unit can select the optimal update method by referring to past update history during an update. The update unit can select the optimal update method by referring to past update history during an update. Past update history includes, but is not limited to, update history for the past year and history based on specific conditions, for example. The update unit, for example, automatically selects an update method that the user has frequently used in the past. The update unit can also analyze the user's past update history and select the optimal update method. Furthermore, the update unit can predict and select the update method to be used in a specific time period based on the user's past update history. In this way, by referring to the past update history, the optimal update method can be provided to the user. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can use AI to analyze the user's past update history and select the optimal update method.

[0123] The update unit can estimate the user's emotions and determine the priority of update data based on the estimated user emotions. The update unit can estimate the user's emotions and determine the priority of update data based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the update unit can prioritize updating the most important data. Furthermore, when the user is relaxed, the update unit can prioritize updating detailed data. Furthermore, when the user is in a hurry, the update unit can prioritize updating data that can be updated most quickly. In this way, by determining the priority of update data according to the user's emotions, important data can be updated preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the update unit can be performed, for example, using AI or without AI. For example, the update unit can use AI to analyze user emotions in real time and determine the priority of update data.

[0124] The update unit can prioritize updating highly relevant data based on the user's industry and business content during an update. The update unit prioritizes updating highly relevant data based on the user's industry and business content during an update. Examples of industry and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the update unit prioritizes updating IT-related data. Furthermore, if the user belongs to the manufacturing industry, the update unit can also update data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the update unit can also prioritize updating education-related data. This improves user convenience by prioritizing updating data according to the industry and business content. Some or all of the above-described processing by the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can use AI to analyze the user's industry and business content and update optimal data.

[0125] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the analysis unit can use a simple and fast analysis algorithm. Furthermore, if the user is relaxed, the analysis unit can use an analysis algorithm that takes detailed data into account. Furthermore, if the user is in a hurry, the analysis unit can use an analysis algorithm that can obtain results most quickly. This improves user convenience by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the user's emotions in real time and adjust the analysis algorithm.

[0126] The analysis unit can select the optimal analysis method by referring to past analysis history during analysis. The analysis unit can select the optimal analysis method by referring to past analysis history during analysis. Past analysis history includes, for example, analysis history for the past year, history based on specific conditions, etc., but is not limited to such examples. The analysis unit, for example, preferentially uses the analysis method that has previously produced the most accurate results. The analysis unit can also analyze past analysis history and select the optimal analysis method. Furthermore, the analysis unit can compare past analysis results with current conditions and select the optimal analysis method. In this way, by referring to past analysis history, the optimal analysis method can be provided to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the user's past analysis history and select the optimal analysis method.

[0127] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves user convenience by adjusting the display method of the analysis results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can use AI to analyze the user's emotions in real time and adjust how the analysis results are displayed.

[0128] During analysis, the analysis unit can prioritize analysis of highly relevant data based on the user's industry and business content. During analysis, the analysis unit prioritizes analysis of highly relevant data based on the user's industry and business content. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the analysis unit prioritizes analysis of IT-related data. Furthermore, if the user belongs to the manufacturing industry, the analysis unit can analyze data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the analysis unit can prioritize analysis of education-related data. This prioritizes analysis of data according to the industry and business content, thereby improving user convenience. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use AI to analyze the user's industry and business content and analyze optimal data.

[0129] The authentication unit can estimate the user's emotions and adjust the authentication method based on the estimated user emotions. The authentication unit can estimate the user's emotions and adjust the authentication method based on the estimated user emotions. Emotion estimation can include, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the authentication unit can provide a simple and quick authentication method. If the user is relaxed, the authentication unit can also provide detailed authentication options. Furthermore, if the user is in a hurry, the authentication unit can provide the quickest authentication method. This improves user convenience by adjusting the authentication method according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the authentication unit can be performed using AI, for example, or without AI. For example, the authentication unit can use AI to analyze the user's emotions in real time and adjust the authentication method.

[0130] The authentication unit can select the optimal authentication method by referring to past authentication history during authentication. The authentication unit selects the optimal authentication method by referring to past authentication history during authentication. Past authentication history includes, but is not limited to, authentication history from the past year or history based on specific conditions, for example. The authentication unit automatically selects, for example, an authentication method that the user has frequently used in the past. The authentication unit can also analyze the user's past authentication history and select the optimal authentication method. Furthermore, the authentication unit can predict and select an authentication method to be used in a specific time period based on the user's past authentication history. In this way, by referring to the past authentication history, the optimal authentication method can be provided to the user. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can use AI to analyze the user's past authentication history and select the optimal authentication method.

[0131] The authentication unit can estimate the user's emotions and adjust the authentication procedure based on the estimated user emotions. The authentication unit can estimate the user's emotions and adjust the authentication procedure based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the authentication unit can provide a simple and quick authentication procedure. Furthermore, if the user is relaxed, the authentication unit can provide a detailed authentication procedure. Furthermore, if the user is in a hurry, the authentication unit can provide the quickest authentication procedure. This improves user convenience by adjusting the authentication procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can use AI to analyze the user's emotions in real time and adjust the authentication procedure.

[0132] The authentication unit can select a highly relevant authentication method based on the user's industry and business content during authentication. The authentication unit can select a highly relevant authentication method based on the user's industry and business content during authentication. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the authentication unit can prioritize IT-related authentication methods. Furthermore, if the user belongs to the manufacturing industry, the authentication unit can also use authentication methods specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the authentication unit can also prioritize education-related authentication methods. This improves user convenience by providing authentication methods tailored to the industry and business content. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can use AI to analyze the user's industry and business content and select the optimal authentication method.

[0133] The collaboration unit can estimate the user's emotion and adjust the collaboration method based on the estimated user's emotion. The collaboration unit can estimate the user's emotion and adjust the collaboration method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the collaboration unit can provide a simple and quick collaboration method. Furthermore, when the user is relaxed, the collaboration unit can provide detailed collaboration options. Furthermore, when the user is in a hurry, the collaboration unit can provide the quickest collaboration method. This improves user convenience by adjusting the collaboration method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can use AI to analyze the user's emotion in real time and adjust the collaboration method.

[0134] The collaboration unit can select the optimal collaboration method by referring to past collaboration history when collaborating. The collaboration unit selects the optimal collaboration method by referring to past collaboration history when collaborating. Past collaboration history includes, for example, collaboration history from the past year, history based on specific conditions, etc., but is not limited to these examples. The collaboration unit, for example, automatically selects a collaboration method that the user has frequently used in the past. The collaboration unit can also analyze the user's past collaboration history and select the optimal collaboration method. Furthermore, the collaboration unit can predict and select the collaboration method to be used in a specific time period from the user's past collaboration history. In this way, by referring to the past collaboration history, the optimal collaboration method can be provided to the user. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can use AI to analyze the user's past collaboration history and select the optimal collaboration method.

[0135] The linking unit can estimate the user's emotions and determine the priority of linked data based on the estimated user emotions. The linking unit can estimate the user's emotions and determine the priority of linked data based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the linking unit can prioritize linking the most important data. Furthermore, when the user is relaxed, the linking unit can prioritize linking detailed data. Furthermore, when the user is in a hurry, the linking unit can prioritize linking data that can be linked most quickly. In this way, by determining the priority of linked data according to the user's emotions, important data can be prioritized for linking. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, an AI, or without using an AI. For example, the collaboration unit can use AI to analyze user emotions in real time and determine the priority of the linked data.

[0136] During linking, the linking unit can prioritize linking highly relevant data based on the user's industry and business content. During linking, the linking unit prioritizes linking highly relevant data based on the user's industry and business content. Industry types and business content include, but are not limited to, manufacturing, service, and specific business processes. For example, if the user belongs to the IT industry, the linking unit prioritizes linking IT-related data. Furthermore, if the user belongs to the manufacturing industry, the linking unit can also link data specialized for the manufacturing industry. Furthermore, if the user belongs to the education industry, the linking unit can also prioritize linking education-related data. This improves user convenience by prioritizing linking data according to the industry and business content. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without AI. For example, the linking unit can use AI to analyze the user's industry and business content and link optimal data. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, reference unit, calculation unit, input unit, display unit, storage unit, graph display unit, update unit, analysis unit, authentication unit, linking unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and allows a user to select quotation conditions on a GUI. The reference unit is implemented by the specific processing unit 290 of the data processing device 12 and refers to past quotation data. The calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically calculates quotation results. The input unit is implemented by the control unit 46A of the smart device 14 and inputs competitor quotation conditions. The display unit is implemented by the output device 40 of the smart device 14 and displays the quotation results. The storage unit is implemented by the database 24 of the data processing device 12 and stores the quotation results. The graph display unit is implemented by the output device 40 of the smart device 14 and displays the quotation results in a graph or table. The update unit is realized by the specific processing unit 290 of the data processing device 12 and periodically updates data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. The authentication unit is realized by the control unit 46A of the smart device 14 and performs two-step authentication. The collaboration unit is realized by the communication I / F 26 of the data processing device 12 and collaborates with other systems. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the display order of options. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, reference unit, calculation unit, input unit, display unit, storage unit, graph display unit, update unit, analysis unit, authentication unit, linking unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and enables a user to select quotation conditions on a GUI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to past quotation data. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically calculates quotation results. The input unit is realized by the control unit 46A of the smart glasses 214 and inputs quotation conditions of competitors. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the quotation results. The storage unit is realized by the database 24 of the data processing device 12 and stores the quotation results. The graph display unit is realized by the speaker 240 of the smart glasses 214 and displays the quotation results in a graph or table. The update unit is realized by the specific processing unit 290 of the data processing device 12 and periodically updates data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. The authentication unit is realized by the control unit 46A of the smart glasses 214 and performs two-step authentication. The collaboration unit is realized by the communication I / F 26 of the data processing device 12 and collaborates with other systems. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the display order of options. === Hard Collateral 1-3 === Each of the multiple elements, including the selection unit, reference unit, calculation unit, input unit, display unit, storage unit, graph display unit, update unit, analysis unit, authentication unit, linking unit, and emotion estimation function, is implemented, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the headset type terminal 314 and allows a user to select quotation conditions on a GUI. The reference unit is implemented by the specific processing unit 290 of the data processing device 12 and refers to past quotation data. The calculation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically calculates quotation results. The input unit is implemented by the control unit 46A of the headset type terminal 314 and inputs quotation conditions of competitors. The display unit is implemented by the display 343 of the headset type terminal 314 and displays the quotation results. The storage unit is implemented by the database 24 of the data processing device 12 and stores the quotation results. The graph display unit is implemented by the display 343 of the headset type terminal 314 and displays the quotation results in a graph or table. The update unit is realized by the specific processing unit 290 of the data processing device 12 and periodically updates data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. The authentication unit is realized by the control unit 46A of the headset type terminal 314 and performs two-step authentication. The collaboration unit is realized by the communication I / F 26 of the data processing device 12 and collaborates with other systems. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the display order of options. === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, reference unit, calculation unit, input unit, display unit, storage unit, graph display unit, update unit, analysis unit, authentication unit, linking unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and allows a user to select quotation conditions on a GUI. The reference unit is realized by the specific processing unit 290 of the data processing device 12 and refers to past quotation data. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically calculates quotation results. The input unit is realized by the control unit 46A of the robot 414 and inputs quotation conditions of competitors. The display unit is realized by the speaker 240 of the robot 414 and displays the quotation results. The storage unit is realized by the database 24 of the data processing device 12 and stores the quotation results. The graph display unit is realized by the speaker 240 of the robot 414 and displays the quotation results in a graph or table. The update unit is realized by the specific processing unit 290 of the data processing device 12 and periodically updates data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data using AI. The authentication unit is realized by the control unit 46A of the robot 414 and performs two-step authentication. The collaboration unit is realized by the communication I / F 26 of the data processing device 12 and collaborates with other systems. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the display order of options.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The quotation system can analyze the user's past quotation history and automatically suggest optimal quotation conditions. For example, it can prioritize quotation conditions that the user has frequently selected in the past. It can also suggest conditions similar to those selected by the user in the past. Furthermore, it can predict and suggest conditions to be used in a specific time period based on the user's past quotation history. This allows it to suggest optimal quotation conditions to the user by referring to the user's past quotation history.

[0139] The quotation system can customize quotation conditions based on the user's industry and business content. For example, if the user is in the IT industry, it can prioritize displaying IT-related quotation conditions. If the user is in the manufacturing industry, it can also display quotation conditions specialized for the manufacturing industry. Furthermore, if the user is in the education industry, it can also prioritize displaying education-related quotation conditions. This improves user convenience by providing quotation conditions tailored to the user's industry and business content.

[0140] The quotation system can adjust quotation conditions taking into account the user's geographic location information. For example, if the user is in a specific area, quotation conditions related to that area can be displayed preferentially. Also, if the user is traveling, quotation conditions based on the user's current location can be updated in real time. Furthermore, if the user is in a specific city, quotation conditions related to that city can be displayed preferentially. This improves user convenience by providing quotation conditions that are highly relevant based on geographic location information.

[0141] The quotation system can analyze a user's social media activity and suggest relevant quotation terms. For example, it can suggest quotation terms related to topics that the user frequently mentions on social media. It can also suggest terms based on terms selected by the user's social media followers. It can also analyze the content of a user's social media posts and suggest relevant quotation terms. This improves user convenience by providing relevant quotation terms based on social media activity.

[0142] The quotation system can estimate the user's emotions and adjust the display order of quotation conditions based on the estimated user's emotions. For example, if the user is feeling stressed, the simplest and most intuitive quotation conditions can be displayed at the top. Also, if the user is relaxed, detailed quotation conditions can be displayed at the top, increasing the degree of freedom for customization. Furthermore, if the user is in a hurry, quotation conditions that can be selected most quickly can be displayed at the top. In this way, user convenience is improved by adjusting the display order of quotation conditions according to the user's emotions.

[0143] The quotation system can estimate the user's emotions and adjust the number of quotation conditions based on the estimated user emotions. For example, if the user is feeling stressed, the system can reduce the number of quotation conditions and provide a simple interface. Alternatively, if the user is relaxed, the system can increase the number of quotation conditions and enable detailed customization. Furthermore, if the user is in a hurry, the system can display only the most important quotation conditions to encourage quick selection. In this way, user convenience is improved by adjusting the number of quotation conditions according to the user's emotions.

[0144] The quotation system can estimate the user's emotions and adjust the display method of the quotation results based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, user convenience is improved by adjusting the display method of the quotation results according to the user's emotions.

[0145] The quotation system can estimate the user's emotions and adjust the level of detail in the quotation results based on the estimated user's emotions. For example, if the user is feeling stressed, a concise and to-the-point quotation result can be displayed. If the user is relaxed, a detailed quotation result can be displayed. Furthermore, if the user is in a hurry, only the most important information can be displayed. In this way, user convenience is improved by adjusting the level of detail in the quotation results according to the user's emotions.

[0146] The quotation system can estimate the user's emotions and adjust the method for saving the quotation results based on the estimated user's emotions. For example, if the user is stressed, a simple and quick saving method can be provided. If the user is relaxed, more detailed saving options can be provided. Furthermore, if the user is in a hurry, the quickest saving method can be provided. This improves user convenience by adjusting the saving method according to the user's emotions.

[0147] The quotation system can estimate the user's emotions and determine the priority of quotation conditions based on the estimated user's emotions. For example, if the user is feeling stressed, the most important quotation conditions can be displayed with priority. Also, if the user is relaxed, detailed quotation conditions can be displayed with priority. Furthermore, if the user is in a hurry, quotation conditions that can be selected most quickly can be displayed with priority. In this way, by determining the priority of quotation conditions according to the user's emotions, important quotation conditions can be displayed with priority.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The selection unit selects the quotation conditions. The quotation conditions include the type and functions of the groupware to be introduced, the number of users to be used, and the implementation period. The selection unit allows the user to select the quotation conditions on the GUI and has the function of sending the selected conditions to the system. Step 2: The reference section references past quotation data. Past quotation data includes quotation terms and costs previously presented. The reference section retrieves the past quotation data from the database and reflects it in the current quotation. Step 3: The calculation unit calculates an estimate based on the information obtained by the selection unit and reference unit. The calculation unit automatically calculates the current estimate based on past estimate data. The calculation unit can also calculate an estimate that matches the estimate by inputting the estimate conditions and costs of competitors. Step 4: The input section inputs the quotation conditions of competitors. The input section allows the user to input the quotation conditions and costs of competitors. Step 5: The display unit displays the estimation result calculated by the calculation unit. The display unit can display the estimation result in text or graphs.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

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

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

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0168] The data processing system 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.

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

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] 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.

[0199] 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.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0201] 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.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on 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.

[0208] 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."

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Explanation of symbols]

[0222] 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 selection section for selecting estimation conditions; A reference section that references past estimate data; a calculation unit that calculates an estimate based on the information obtained by the selection unit and the reference unit; an input section for inputting the quotation conditions of competitors; a display unit that displays the estimate result calculated by the calculation unit; Equipped with A system characterized by:

2. Equipped with a storage unit to save the estimation results 2. The system of claim 1.

3. Equipped with a graph display section that displays estimate results in graphs and tables 2. The system of claim 1.

4. Equipped with an update unit that periodically updates data 2. The system of claim 1.

5. Equipped with an analysis unit that uses AI to analyze data 2. The system of claim 1.

6. Equipped with an authentication unit that performs two-step authentication 2. The system of claim 1.

7. Equipped with a linking section that links with other systems 2. The system of claim 1.

8. The selection unit Inferring user emotions and adjusting the display order of options based on the estimated user emotions 2. The system of claim 1.

9. The selection unit When selecting, the system refers to the user's past selection history to suggest the best option.

2. The system of claim 1.

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