system

Abstract

The system according to the embodiment aims to automate and efficiently select products and generate estimates. [Solution] A system according to an embodiment includes a storage unit, a reception unit, a selection unit, and a generation unit. The storage unit stores information about products. The reception unit receives input from a user. The selection unit uses AI to select products based on the information received by the reception unit. The generation unit generates an estimate based on the products selected by the selection 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 techniques require manual selection of products and creation of quotes, which can be inefficient and prone to errors.

[0005] The system according to the embodiment aims to automate and efficiently select products and generate estimates. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, a reception unit, a selection unit, and a generation unit. The storage unit stores information about products. The reception unit receives input from a user. The selection unit uses AI to select products based on the information received by the reception unit. The generation unit generates an estimate based on the products selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate and efficiently select products and generate estimates. [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) An automatic quote generation system according to an embodiment of the present invention stores product terms and conditions and costs (unit prices) and automatically generates a composite quote based on the size of the company and other conditions. This system stores the product terms and conditions and costs (unit prices) in a database. A user inputs the company's size and other conditions, and the system selects an optimal product combination and automatically generates a composite quote. For example, when storing product terms and conditions and costs (unit prices) in the database, detailed information about each product is entered and saved in the database. This allows the system to accurately grasp the product information. Next, the user inputs the company's size and other conditions, such as the number of employees, annual sales, and quantity of products required. This information is entered into the system and used to select products. The system selects the optimal product combination based on the input information. For example, a larger company requires a larger quantity of products, so the system selects products that meet these conditions. The system also optimizes the product combination based on specific conditions. For example, if a specific product is eligible for a discount, the system prioritizes the selection of that product. Finally, the system automatically generates a composite estimate. For example, it generates an estimate that includes the total price of the selected products and detailed information about each product. This estimate is provided to the user, improving the efficiency of the estimate process. This improves the efficiency of the estimate process and provides accurate estimates quickly. Users can leave the complex estimate process to the system, thereby improving work efficiency. As a result, the automatic estimate generation system accurately stores product information, selects the optimal products based on user input, and automatically generates estimates, thereby improving the efficiency and accuracy of the estimate process.

[0029] The automatic estimate generation system according to the embodiment includes a storage unit, a reception unit, a selection unit, and a generation unit. The storage unit stores information about products. The product information includes, but is not limited to, product information, service information, and price information. The storage unit stores, for example, terms and conditions information and cost (unit price) of the products in a database. For example, the terms and conditions information and cost (unit price) of product A are input and stored in the database. This allows the system to accurately grasp the product information. The reception unit receives input from a user. The user input includes, but is not limited to, text input, option input, and numerical input. The reception unit receives, for example, company size and other conditions as input. For example, the number of employees, annual sales, and quantity of products required are input. The selection unit uses AI to select an optimal combination of products based on the information received by the reception unit. The AI ​​may use, for example, machine learning, deep learning, natural language processing, or other technologies, but is not limited to these examples. For example, a larger company requires a larger quantity of merchandise, so the selection unit selects merchandise that meets those conditions. The selection unit also optimizes the combination of merchandise based on specific conditions. For example, if a specific merchandise is eligible for a discount, the selection unit prioritizes the selection of that merchandise. The generation unit generates an estimate based on the merchandise selected by the selection unit. The estimate may include, but is not limited to, a price calculation method, an estimate format, and the like. The generation unit generates an estimate that includes, for example, the total price of the selected merchandise and detailed information about each merchandise. As a result, the automatic estimate generation system according to the embodiment accurately stores merchandise information, selects optimal merchandise based on user input, and automatically generates an estimate, thereby improving the efficiency and accuracy of the estimate process.

[0030] The memory unit can store the terms and conditions information and cost price of the merchandise. The memory unit, for example, stores the terms and conditions information and cost price (unit price) of the merchandise in a database. The terms and conditions information includes, for example, contract conditions, terms of use, warranty details, etc., but is not limited to these examples. The cost price includes, for example, material costs, labor costs, and indirect costs, but is not limited to these examples. The memory unit, for example, inputs the terms and conditions information and cost price (unit price) of merchandise A and stores them in the database. This allows the system to accurately grasp the merchandise information. As a result, storing the terms and conditions information and cost price (unit price) of the merchandise can provide basic data for generating accurate estimates.

[0031] The reception unit can receive the company size and specific conditions as input. The reception unit receives, for example, the company size and other conditions as input. The company size includes, for example, but is not limited to, the number of employees, sales, and capital. The specific conditions include, for example, but are not limited to, the industry, region, and transaction history. The reception unit inputs, for example, the number of employees, annual sales, and quantity of required products of the company. By receiving the company size and other conditions as input, it is possible to generate an estimate that meets the user's needs.

[0032] The selection unit can use AI to select an optimal combination of products based on the information received by the reception unit. The selection unit, for example, uses AI to select an optimal combination of products based on the information received by the reception unit. AI may use technologies such as, but not limited to, machine learning, deep learning, and natural language processing. For example, if a company is large, a larger quantity of products will be required, so the selection unit selects products that meet those conditions. The selection unit also optimizes the combination of products based on specific conditions. For example, if a specific product is eligible for discount, the selection unit prioritizes that product. Thus, by using AI, the optimal combination of products can be selected quickly and accurately.

[0033] The generation unit can generate a composite estimate based on the products selected by the selection unit. The generation unit generates a composite estimate based on, for example, the products selected by the selection unit. Composite estimates include, but are not limited to, total estimates for multiple products and the addition of options. The generation unit generates an estimate that includes, for example, the total price of the selected products and detailed information about each product. By generating a composite estimate based on the selected products, an accurate estimate can be provided to the user.

[0034] The memory unit can store information on the supplier and delivery date of the product in addition to the product's terms and conditions information and cost price. The memory unit stores, for example, information on the supplier and delivery date of the product in addition to the product's terms and conditions information and cost price (unit price). Suppliers include, for example, manufacturers, suppliers, distributors, etc., but are not limited to these examples. Delivery date information includes, for example, delivery date, lead time, delivery schedule, etc., but are not limited to these examples. The memory unit stores, for example, the terms and conditions information, cost price (unit price), supplier, and delivery date information of product A. The memory unit can also store information on product B and product C in a similar manner. By storing information on the supplier and delivery date of the product, more detailed estimate information can be provided.

[0035] The memory unit stores the past transaction history of the product and can utilize it for future estimates. The memory unit, for example, stores the past transaction history of the product and utilizes it for future estimates. Past transaction history includes, for example, transaction date, transaction amount, transaction content, etc., but is not limited to these examples. The memory unit, for example, stores the past transaction history of product A and utilizes it for future estimates. It can also store the past transaction history of product B and product C in the same way and utilize it for future estimates. In this way, by storing past transaction history, the accuracy of future estimates can be improved.

[0036] The memory unit can also store environmental impact information and eco-label information of the commercial materials. The memory unit stores, for example, environmental impact information and eco-label information of the commercial materials. Examples of environmental impact information include, but are not limited to, CO2 emissions, energy consumption, and recycling rates. Examples of eco-label information include, but are not limited to, Eco Mark, Green Label, and Energy Star. The memory unit stores, for example, environmental impact information and eco-label information of commercial material A. It can also store information similarly for commercial materials B and C. By storing environmental impact information and eco-label information of commercial materials, it is possible to provide environmentally friendly estimates.

[0037] The memory unit can store market price trends of merchandise in real time. The memory unit, for example, stores market price trends of merchandise in real time. Market price trends include, for example, price fluctuation data, real-time price information, price forecasts, etc., but are not limited to these examples. The memory unit, for example, stores market price trends of merchandise A in real time. It can also store market price trends of merchandise B and merchandise C in the same way. In this way, by storing market price trends in real time, it is possible to provide estimates based on the latest price information.

[0038] The reception unit can provide input assistance by referring to the user's past input history. The reception unit provides input assistance by, for example, referring to the user's past input history. Input assistance includes, but is not limited to, an auto-complete function, presentation of input candidates, and display of the past input history. The reception unit can, for example, automatically display information that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to make the user's input work more efficient by referring to the past input history.

[0039] The reception unit may add a function for accepting voice input or gesture input. The reception unit adds a function for accepting, for example, voice input or gesture input. Voice input includes, for example, but is not limited to, voice recognition technology, how to use a microphone, and types of voice commands. Gesture input includes, for example, but is not limited to, gesture recognition technology, how to use a camera, and types of gestures. The reception unit may, for example, enable a user to input the size of the company or other conditions by voice. Alternatively, the reception unit may enable a user to input the size of the company or other conditions by gesture. Alternatively, the reception unit may enable a user to input by a combination of both voice and gesture. In this way, by accepting voice input or gesture input, the user's options for input methods can be expanded.

[0040] The reception unit can customize the input content taking into account the geographical location information of the user. The reception unit, for example, customizes the input content taking into account the geographical location information of the user. Geographical location information includes, but is not limited to, for example, GPS data, IP address, location information services, etc. For example, when the user is in a specific area, the reception unit preferentially accepts input content related to that area. Furthermore, when the user is moving, the reception unit can also customize the input content based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially accept input content related to that location. In this way, by taking into account the geographical location information, it is possible to provide input content according to the user's current location.

[0041] The reception unit can provide an optimal input method by taking into account device information of the user. The reception unit provides an optimal input method by taking into account, for example, the device information of the user. Device information includes, but is not limited to, for example, the device type, OS version, browser information, etc. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide an input method optimized for keyboard input. In this way, by taking into account the device information, it is possible to provide an input method that is optimized for the device used by the user.

[0042] The selection unit can select the optimal combination by taking into consideration the interrelationships between the products. The selection unit, for example, selects the optimal combination by taking into consideration the interrelationships between the products. Interrelationships between the products include, but are not limited to, compatibility, complementarity, and competitiveness. For example, the selection unit selects the optimal combination by taking into consideration the interrelationship between products A and B. The selection unit can also select the optimal combination by taking into consideration the interrelationship between products C and D. The selection unit can also select the optimal combination by taking into consideration the interrelationship between products E and F. In this way, it is possible to provide the optimal combination of products by taking into consideration the interrelationships between the products.

[0043] The selection unit can make a selection taking into consideration the reliability and past transaction history of the supplier of the product. The selection unit makes a selection taking into consideration, for example, the reliability and past transaction history of the supplier of the product. The reliability of the supplier includes, for example, past transaction history, customer evaluation, authentication information, etc., but is not limited to these examples. The selection unit makes a selection taking into consideration, for example, the reliability and past transaction history of the supplier of product A. The selection unit can also make a similar selection for product B and product C. In this way, by taking into consideration the reliability and past transaction history of the supplier, it is possible to select highly reliable products.

[0044] The selection unit can make a selection taking into consideration the geographical distribution of the merchandise. The selection unit makes a selection taking into consideration, for example, the geographical distribution of the merchandise. Geographical distribution includes, but is not limited to, regional supply conditions, logistics costs, and regional characteristics. The selection unit makes a selection taking into consideration, for example, the geographical distribution of merchandise A. The selection unit can also make a similar selection for merchandise B and merchandise C. In this way, by taking geographical distribution into consideration, merchandise suitable for a region can be selected.

[0045] The selection unit can improve the accuracy of the selection by referring to literature and reviews related to the product. The selection unit improves the accuracy of the selection by, for example, referring to literature and reviews related to the product. Examples of related literature and reviews include, but are not limited to, academic papers, customer reviews, and expert evaluations. The selection unit improves the accuracy of the selection by, for example, referring to literature and reviews related to product A. The selection unit can also improve the accuracy of the selection by similarly referring to literature and reviews related to product B and product C. In this way, the accuracy of the selection can be improved by referring to related literature and reviews.

[0046] The generation unit can adjust the level of detail of the estimate in accordance with a user request. For example, the generation unit adjusts the level of detail of the estimate in accordance with a user request. The level of detail of the estimate includes, but is not limited to, the level of detail of items, price breakdown, and display of options. For example, when a user requests a detailed estimate, the generation unit provides an estimate that includes detailed information about each product. Furthermore, when a user requests a simple estimate, the generation unit can provide an estimate that covers the main points. Furthermore, when a user is interested in a specific item, the generation unit can provide an estimate that focuses on that item. In this way, by adjusting the level of detail of the estimate in accordance with a user request, an estimate that meets the user's needs can be provided.

[0047] The generation unit can record the estimate history and use it for future estimates. The generation unit, for example, records the estimate history and uses it for future estimates. The estimate history includes, for example, but is not limited to, the estimate date, the estimated amount, and the estimate content. The generation unit, for example, records the past estimate history and uses it for future estimates. The generation unit can also analyze the past estimate history to improve the accuracy of estimates. The generation unit can also refer to the past estimate history to quickly generate similar estimates. In this way, by recording the estimate history, the accuracy of future estimates can be improved.

[0048] The generation unit can generate an optimal estimate by taking into account the geographical conditions of the estimate. The generation unit generates an optimal estimate by taking into account, for example, the geographical conditions of the estimate. Geographical conditions include, but are not limited to, regional supply conditions, logistics costs, and regional characteristics. For example, if the user is in a specific region, the generation unit can prioritize generating an estimate related to that region. Furthermore, if the user is traveling, the generation unit can customize the estimate based on the user's current location. Furthermore, if the user is in a specific location, the generation unit can prioritize generating an estimate related to that location. In this way, by taking into account the geographical conditions, an estimate appropriate for the region can be provided.

[0049] The generation unit can improve the accuracy of the estimate by referring to literature related to the estimate and market data. The generation unit improves the accuracy of the estimate by referring to literature related to the estimate and market data, for example. Market data includes, but is not limited to, price trend data, demand forecast data, and competitive information. The generation unit improves the accuracy of the estimate by referring to literature related to the estimate, for example. The generation unit can also improve the accuracy of the estimate by referring to market data. The generation unit can also improve the accuracy of the estimate by referring to past estimate data. In this way, the accuracy of the estimate can be improved by referring to related literature and market data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can provide input assistance by referring to the user's past input history. For example, it can automatically display information that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to make the user's input work more efficient by referring to the past input history.

[0052] The storage unit can also store environmental impact information and eco-label information for commercial materials. For example, it stores environmental impact information and eco-label information for commercial material A. It can also store information similarly for commercial materials B and C. By storing environmental impact information and eco-label information for commercial materials, it is possible to provide environmentally friendly estimates.

[0053] The selection unit can select the optimal combination by taking into consideration the interrelationships between the products. For example, the selection unit selects the optimal combination by taking into consideration the interrelationship between product A and product B. The selection unit can also select the optimal combination by taking into consideration the interrelationship between product C and product D. Furthermore, the selection unit can also select the optimal combination by taking into consideration the interrelationships between products E and F. In this way, the optimal combination of products can be provided by taking into consideration the interrelationships between the products.

[0054] The generation unit can record the history of estimates and use it for future estimates. For example, the generation unit can record the history of past estimates and use it for future estimates. The generation unit can also analyze the history of past estimates to improve the accuracy of estimates. Furthermore, the generation unit can refer to the history of past estimates to quickly generate similar estimates. In this way, by recording the history of estimates, the accuracy of future estimates can be improved.

[0055] The reception unit can add a function to receive voice input or gesture input. For example, the reception unit can be configured to allow the user to input the size of the company or other conditions by voice. Alternatively, the reception unit can be configured to allow the user to input the size of the company or other conditions by gesture. Furthermore, the reception unit can be configured to allow the user to input by a combination of voice and gesture. This allows the reception of voice input or gesture input to broaden the options for the user's input method.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The storage unit stores information about the product. The product information includes product information, service information, price information, terms and conditions information, cost (unit price), etc. For example, the terms and conditions information and cost (unit price) of product A are stored in the database. Step 2: The reception unit receives input from the user. User input includes text input, selection input, numerical input, etc. For example, the size of the company, number of employees, annual sales, quantity of required products, etc. are received as input. Step 3: The selection unit uses AI to select the optimal product combination based on the information received by the reception unit. AI uses technologies such as machine learning, deep learning, and natural language processing. For example, if the company is large, it will select products that meet its requirements and optimize the product combination based on specific conditions. Step 4: The generation unit generates an estimate based on the products selected by the selection unit. The estimate includes the price calculation method and the estimate format. For example, an estimate is generated that includes the total price of the selected products and detailed information about each product.

[0058] (Example 2) An automatic quote generation system according to an embodiment of the present invention stores product terms and conditions and costs (unit prices) and automatically generates a composite quote based on the size of the company and other conditions. This system stores the product terms and conditions and costs (unit prices) in a database. A user inputs the company's size and other conditions, and the system selects an optimal product combination and automatically generates a composite quote. For example, when storing product terms and conditions and costs (unit prices) in the database, detailed information about each product is entered and saved in the database. This allows the system to accurately grasp the product information. Next, the user inputs the company's size and other conditions, such as the number of employees, annual sales, and quantity of products required. This information is entered into the system and used to select products. The system selects the optimal product combination based on the input information. For example, a larger company requires a larger quantity of products, so the system selects products that meet these conditions. The system also optimizes the product combination based on specific conditions. For example, if a specific product is eligible for a discount, the system prioritizes the selection of that product. Finally, the system automatically generates a composite estimate. For example, it generates an estimate that includes the total price of the selected products and detailed information about each product. This estimate is provided to the user, improving the efficiency of the estimate process. This improves the efficiency of the estimate process and provides accurate estimates quickly. Users can leave the complex estimate process to the system, thereby improving work efficiency. As a result, the automatic estimate generation system accurately stores product information, selects the optimal products based on user input, and automatically generates estimates, thereby improving the efficiency and accuracy of the estimate process.

[0059] The automatic estimate generation system according to the embodiment includes a storage unit, a reception unit, a selection unit, and a generation unit. The storage unit stores information about products. The product information includes, but is not limited to, product information, service information, and price information. The storage unit stores, for example, terms and conditions information and cost (unit price) of the products in a database. For example, the terms and conditions information and cost (unit price) of product A are input and stored in the database. This allows the system to accurately grasp the product information. The reception unit receives input from a user. The user input includes, but is not limited to, text input, option input, and numerical input. The reception unit receives, for example, company size and other conditions as input. For example, the number of employees, annual sales, and quantity of products required are input. The selection unit uses AI to select an optimal combination of products based on the information received by the reception unit. The AI ​​may use, for example, machine learning, deep learning, natural language processing, or other technologies, but is not limited to these examples. For example, a larger company requires a larger quantity of merchandise, so the selection unit selects merchandise that meets those conditions. The selection unit also optimizes the combination of merchandise based on specific conditions. For example, if a specific merchandise is eligible for a discount, the selection unit prioritizes the selection of that merchandise. The generation unit generates an estimate based on the merchandise selected by the selection unit. The estimate may include, but is not limited to, a price calculation method, an estimate format, and the like. The generation unit generates an estimate that includes, for example, the total price of the selected merchandise and detailed information about each merchandise. As a result, the automatic estimate generation system according to the embodiment accurately stores merchandise information, selects optimal merchandise based on user input, and automatically generates an estimate, thereby improving the efficiency and accuracy of the estimate process.

[0060] The memory unit can store the terms and conditions information and cost price of the merchandise. The memory unit, for example, stores the terms and conditions information and cost price (unit price) of the merchandise in a database. The terms and conditions information includes, for example, contract conditions, terms of use, warranty details, etc., but is not limited to these examples. The cost price includes, for example, material costs, labor costs, and indirect costs, but is not limited to these examples. The memory unit, for example, inputs the terms and conditions information and cost price (unit price) of merchandise A and stores them in the database. This allows the system to accurately grasp the merchandise information. As a result, storing the terms and conditions information and cost price (unit price) of the merchandise can provide basic data for generating accurate estimates.

[0061] The reception unit can receive the company size and specific conditions as input. The reception unit receives, for example, the company size and other conditions as input. The company size includes, for example, but is not limited to, the number of employees, sales, and capital. The specific conditions include, for example, but are not limited to, the industry, region, and transaction history. The reception unit inputs, for example, the number of employees, annual sales, and quantity of required products of the company. By receiving the company size and other conditions as input, it is possible to generate an estimate that meets the user's needs.

[0062] The selection unit can use AI to select an optimal combination of products based on the information received by the reception unit. The selection unit, for example, uses AI to select an optimal combination of products based on the information received by the reception unit. AI may use technologies such as, but not limited to, machine learning, deep learning, and natural language processing. For example, if a company is large, a larger quantity of products will be required, so the selection unit selects products that meet those conditions. The selection unit also optimizes the combination of products based on specific conditions. For example, if a specific product is eligible for discount, the selection unit prioritizes that product. Thus, by using AI, the optimal combination of products can be selected quickly and accurately.

[0063] The generation unit can generate a composite estimate based on the products selected by the selection unit. The generation unit generates a composite estimate based on, for example, the products selected by the selection unit. Composite estimates include, but are not limited to, total estimates for multiple products and the addition of options. The generation unit generates an estimate that includes, for example, the total price of the selected products and detailed information about each product. By generating a composite estimate based on the selected products, an accurate estimate can be provided to the user.

[0064] The storage unit can estimate a user's emotion and determine the priority of the product information to be stored based on the estimated user emotion. The storage unit, for example, estimates a user's emotion and determines the priority of the product information to be stored based on the estimated user emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and urgency. For example, when the user is stressed, the storage unit can prioritize storing product information of high importance. Furthermore, when the user is relaxed, the storage unit can prioritize storing detailed product information. Furthermore, when the user is in a hurry, the storage unit can prioritize storing product information that is immediately needed. Thus, by determining the priority of product information based on the user's emotion, information management according to the user's needs becomes possible. The emotion estimation is performed using, for example, an emotion analysis algorithm or user feedback. The priority is determined taking into account, for example, importance, urgency, and user interest.

[0065] The memory unit can store information on the supplier and delivery date of the product in addition to the product's terms and conditions information and cost price. The memory unit stores, for example, information on the supplier and delivery date of the product in addition to the product's terms and conditions information and cost price (unit price). Suppliers include, for example, manufacturers, suppliers, distributors, etc., but are not limited to these examples. Delivery date information includes, for example, delivery date, lead time, delivery schedule, etc., but are not limited to these examples. The memory unit stores, for example, the terms and conditions information, cost price (unit price), supplier, and delivery date information of product A. The memory unit can also store information on product B and product C in a similar manner. By storing information on the supplier and delivery date of the product, more detailed estimate information can be provided.

[0066] The memory unit stores the past transaction history of the product and can utilize it for future estimates. The memory unit, for example, stores the past transaction history of the product and utilizes it for future estimates. Past transaction history includes, for example, transaction date, transaction amount, transaction content, etc., but is not limited to these examples. The memory unit, for example, stores the past transaction history of product A and utilizes it for future estimates. It can also store the past transaction history of product B and product C in the same way and utilize it for future estimates. In this way, by storing past transaction history, the accuracy of future estimates can be improved.

[0067] The storage unit can estimate a user's emotion and adjust the update frequency of the stored product information based on the estimated user emotion. The storage unit, for example, estimates a user's emotion and adjusts the update frequency of the stored product information based on the estimated user emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and urgency. For example, the storage unit can decrease the update frequency of the product information when the user is stressed. Furthermore, the storage unit can increase the update frequency of the product information when the user is relaxed. Furthermore, the storage unit can update the product information immediately when the user is in a hurry. This allows information management according to user needs by adjusting the update frequency of the product information based on the user's emotion. The emotion estimation is performed using, for example, an emotion analysis algorithm or user feedback. The update frequency can be determined by taking into account, for example, periodic updates, event-based updates, updates based on user requests, and the like.

[0068] The memory unit can also store environmental impact information and eco-label information of the commercial materials. The memory unit stores, for example, environmental impact information and eco-label information of the commercial materials. Examples of environmental impact information include, but are not limited to, CO2 emissions, energy consumption, and recycling rates. Examples of eco-label information include, but are not limited to, Eco Mark, Green Label, and Energy Star. The memory unit stores, for example, environmental impact information and eco-label information of commercial material A. It can also store information similarly for commercial materials B and C. By storing environmental impact information and eco-label information of commercial materials, it is possible to provide environmentally friendly estimates.

[0069] The memory unit can store market price trends of merchandise in real time. The memory unit, for example, stores market price trends of merchandise in real time. Market price trends include, for example, price fluctuation data, real-time price information, price forecasts, etc., but are not limited to these examples. The memory unit, for example, stores market price trends of merchandise A in real time. It can also store market price trends of merchandise B and merchandise C in the same way. In this way, by storing market price trends in real time, it is possible to provide estimates based on the latest price information.

[0070] The reception unit can estimate a user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the reception unit estimates a user's emotion and adjusts the display method of the input interface based on the estimated user emotion. User emotions include, but are not limited to, tension, excitement, fatigue, and the like. For example, the reception unit can provide an interface with subdued colors when the user is nervous. Alternatively, the reception unit can provide an interface with bright colors when the user is enjoying themselves. Alternatively, the reception unit can provide a simple, highly visible interface when the user is tired. This allows for an easy-to-use interface to be provided by adjusting the display method of the input interface based on the user's emotion. The emotion estimation is performed using, for example, an emotion analysis algorithm or user feedback. The adjustment of the display method of the input interface can take into account, for example, changing the color, changing the layout, adjusting the font size, and the like.

[0071] The reception unit can provide input assistance by referring to the user's past input history. The reception unit provides input assistance by, for example, referring to the user's past input history. Input assistance includes, but is not limited to, an auto-complete function, presentation of input candidates, and display of the past input history. The reception unit can, for example, automatically display information that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to make the user's input work more efficient by referring to the past input history.

[0072] The reception unit may add a function for accepting voice input or gesture input. The reception unit adds a function for accepting, for example, voice input or gesture input. Voice input includes, for example, but is not limited to, voice recognition technology, how to use a microphone, and types of voice commands. Gesture input includes, for example, but is not limited to, gesture recognition technology, how to use a camera, and types of gestures. The reception unit may, for example, enable a user to input the size of the company or other conditions by voice. Alternatively, the reception unit may enable a user to input the size of the company or other conditions by gesture. Alternatively, the reception unit may enable a user to input by a combination of both voice and gesture. In this way, by accepting voice input or gesture input, the user's options for input methods can be expanded.

[0073] The reception unit can estimate the user's emotion and determine the priority of input content based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input content based on the estimated user emotion. User emotions include, but are not limited to, stress, relaxation, and urgency. For example, when the user is stressed, the reception unit can prioritize receiving input content with high importance. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed input content. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving input content that is immediately required. In this way, by determining the priority of input content based on the user's emotion, input management according to the user's needs is possible. Emotion estimation is performed, for example, using an emotion analysis algorithm, user feedback, etc. The priority of input content is determined taking into account, for example, importance, urgency, and user interest.

[0074] The reception unit can customize the input content taking into account the geographical location information of the user. The reception unit, for example, customizes the input content taking into account the geographical location information of the user. Geographical location information includes, but is not limited to, for example, GPS data, IP address, location information services, etc. For example, when the user is in a specific area, the reception unit preferentially accepts input content related to that area. Furthermore, when the user is moving, the reception unit can also customize the input content based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially accept input content related to that location. In this way, by taking into account the geographical location information, it is possible to provide input content according to the user's current location.

[0075] The reception unit can provide an optimal input method by taking into account device information of the user. The reception unit provides an optimal input method by taking into account, for example, the device information of the user. Device information includes, but is not limited to, for example, the device type, OS version, browser information, etc. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide an input method optimized for keyboard input. In this way, by taking into account the device information, it is possible to provide an input method that is optimized for the device used by the user.

[0076] The selection unit can estimate a user's emotion and adjust product selection criteria based on the estimated user emotion. The selection unit, for example, estimates a user's emotion and adjusts the product selection criteria based on the estimated user emotion. Examples of user emotions include, but are not limited to, stress, relaxation, and urgency. For example, the selection unit uses simple selection criteria when the user is stressed. Alternatively, the selection unit can use detailed selection criteria when the user is relaxed. Alternatively, the selection unit can prioritize products that are needed immediately when the user is in a hurry. This allows product selection according to the user's needs by adjusting the product selection criteria based on the user's emotion. Emotion estimation is performed using, for example, a sentiment analysis algorithm or user feedback. The product selection criteria can be adjusted based on, for example, price, quality, and supplier reliability.

[0077] The selection unit can select the optimal combination by taking into consideration the interrelationships between the products. The selection unit, for example, selects the optimal combination by taking into consideration the interrelationships between the products. Interrelationships between the products include, but are not limited to, compatibility, complementarity, and competitiveness. For example, the selection unit selects the optimal combination by taking into consideration the interrelationship between products A and B. The selection unit can also select the optimal combination by taking into consideration the interrelationship between products C and D. The selection unit can also select the optimal combination by taking into consideration the interrelationship between products E and F. In this way, it is possible to provide the optimal combination of products by taking into consideration the interrelationships between the products.

[0078] The selection unit can make a selection taking into consideration the reliability and past transaction history of the supplier of the product. The selection unit makes a selection taking into consideration, for example, the reliability and past transaction history of the supplier of the product. The reliability of the supplier includes, for example, past transaction history, customer evaluation, authentication information, etc., but is not limited to these examples. The selection unit makes a selection taking into consideration, for example, the reliability and past transaction history of the supplier of product A. The selection unit can also make a similar selection for product B and product C. In this way, by taking into consideration the reliability and past transaction history of the supplier, it is possible to select highly reliable products.

[0079] The selection unit can estimate the user's emotion and adjust the display method of the selection results based on the estimated user emotion. For example, the selection unit can estimate the user's emotion and adjust the display method of the selection results based on the estimated user emotion. Examples of user emotions include, but are not limited to, tension, relaxation, and urgency. For example, if the user is tensioned, the selection unit can provide a simple, highly visible display method. For example, if the user is relaxed, the selection unit can provide a display method including detailed information. For example, if the user is hurried, the selection unit can provide a display method that focuses on the main points. By adjusting the display method of the selection results based on the user's emotion, it is possible to provide a display that is easy for the user to view. The emotion estimation is performed using, for example, an emotion analysis algorithm or user feedback. The adjustment of the display method of the selection results can take into account, for example, changing the color, changing the layout, adjusting the font size, etc.

[0080] The selection unit can make a selection taking into consideration the geographical distribution of the merchandise. The selection unit makes a selection taking into consideration, for example, the geographical distribution of the merchandise. Geographical distribution includes, but is not limited to, regional supply conditions, logistics costs, and regional characteristics. The selection unit makes a selection taking into consideration, for example, the geographical distribution of merchandise A. The selection unit can also make a similar selection for merchandise B and merchandise C. In this way, by taking geographical distribution into consideration, merchandise suitable for a region can be selected.

[0081] The selection unit can improve the accuracy of the selection by referring to literature and reviews related to the product. The selection unit improves the accuracy of the selection by, for example, referring to literature and reviews related to the product. Examples of related literature and reviews include, but are not limited to, academic papers, customer reviews, and expert evaluations. The selection unit improves the accuracy of the selection by, for example, referring to literature and reviews related to product A. The selection unit can also improve the accuracy of the selection by similarly referring to literature and reviews related to product B and product C. In this way, the accuracy of the selection can be improved by referring to related literature and reviews.

[0082] The generation unit can estimate the user's emotion and adjust the display method of the estimate based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the display method of the estimate based on the estimated user's emotion. User emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the user is tensioned, the generation unit can provide a simple, highly visible display method. If the user is relaxed, the generation unit can provide a display method including detailed information. If the user is in a hurry, the generation unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the estimate based on the user's emotion, it is possible to provide an estimate that is easy for the user to read. Emotion estimation is performed, for example, using an emotion analysis algorithm, user feedback, etc. Adjusting the display method of the estimate can take into account, for example, changing the color, changing the layout, adjusting the font size, etc.

[0083] The generation unit can adjust the level of detail of the estimate in accordance with a user request. For example, the generation unit adjusts the level of detail of the estimate in accordance with a user request. The level of detail of the estimate includes, but is not limited to, the level of detail of items, price breakdown, and display of options. For example, when a user requests a detailed estimate, the generation unit provides an estimate that includes detailed information about each product. Furthermore, when a user requests a simple estimate, the generation unit can provide an estimate that covers the main points. Furthermore, when a user is interested in a specific item, the generation unit can provide an estimate that focuses on that item. In this way, by adjusting the level of detail of the estimate in accordance with a user request, an estimate that meets the user's needs can be provided.

[0084] The generation unit can record the estimate history and use it for future estimates. The generation unit, for example, records the estimate history and uses it for future estimates. The estimate history includes, for example, but is not limited to, the estimate date, the estimated amount, and the estimate content. The generation unit, for example, records the past estimate history and uses it for future estimates. The generation unit can also analyze the past estimate history to improve the accuracy of estimates. The generation unit can also refer to the past estimate history to quickly generate similar estimates. In this way, by recording the estimate history, the accuracy of future estimates can be improved.

[0085] The generation unit can estimate the user's emotions and determine the priority of estimates based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and determine the priority of estimates based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and urgency. For example, when the user is stressed, the generation unit can prioritize generating estimates with high importance. Furthermore, when the user is relaxed, the generation unit can prioritize generating detailed estimates. Furthermore, when the user is in a hurry, the generation unit can prioritize generating estimates that are needed immediately. Thus, by prioritizing estimates based on the user's emotions, estimates that meet the user's needs can be provided. The emotion estimation can be performed using, for example, an emotion analysis algorithm or user feedback. The priority of estimates can be determined based on, for example, importance, urgency, and user interest.

[0086] The generation unit can generate an optimal estimate by taking into account the geographical conditions of the estimate. The generation unit generates an optimal estimate by taking into account, for example, the geographical conditions of the estimate. Geographical conditions include, but are not limited to, regional supply conditions, logistics costs, and regional characteristics. For example, if the user is in a specific region, the generation unit can prioritize generating an estimate related to that region. Furthermore, if the user is traveling, the generation unit can customize the estimate based on the user's current location. Furthermore, if the user is in a specific location, the generation unit can prioritize generating an estimate related to that location. In this way, by taking into account the geographical conditions, an estimate appropriate for the region can be provided.

[0087] The generation unit can improve the accuracy of the estimate by referring to literature related to the estimate and market data. The generation unit improves the accuracy of the estimate by referring to literature related to the estimate and market data, for example. Market data includes, but is not limited to, price trend data, demand forecast data, and competitive information. The generation unit improves the accuracy of the estimate by referring to literature related to the estimate, for example. The generation unit can also improve the accuracy of the estimate by referring to market data. The generation unit can also improve the accuracy of the estimate by referring to past estimate data. In this way, the accuracy of the estimate can be improved by referring to related literature and market data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned storage unit, reception unit, selection unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit stores terms and conditions information and cost (unit price) of the products in the database 24 of the data processing device 12. The reception unit receives input from a user using the reception device 38 of the smart device 14. The selection unit selects an optimal combination of products using AI with the specific processing unit 290 of the data processing device 12. The generation unit generates an estimate based on the products selected by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned storage unit, reception unit, selection unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit stores terms and conditions information and cost (unit price) of the products in the database 24 of the data processing device 12. The reception unit receives input from the user using the microphone 238 of the smart glasses 214. The selection unit selects an optimal combination of products using AI by the specific processing unit 290 of the data processing device 12. The generation unit generates an estimate based on the products selected by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned storage unit, reception unit, selection unit, and generation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the storage unit stores product clause information and cost (unit price) in the database 24 of the data processing device 12. The reception unit receives input from the user using the microphone 238 of the headset terminal 314. The selection unit selects an optimal combination of products using AI by the specification processing unit 290 of the data processing device 12. The generation unit generates an estimate based on the products selected by the specification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned storage unit, reception unit, selection unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit stores terms and conditions information and cost (unit price) of the products in the database 24 of the data processing device 12. The reception unit receives input from the user using the microphone 238 of the robot 414. The selection unit selects an optimal combination of products using AI by the specific processing unit 290 of the data processing device 12. The generation unit generates an estimate based on the products selected by the specific processing unit 290 of the data processing device 12.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can provide input assistance by referring to the user's past input history. For example, it can automatically display information that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to make the user's input work more efficient by referring to the past input history.

[0090] The storage unit can also store environmental impact information and eco-label information for commercial materials. For example, it stores environmental impact information and eco-label information for commercial material A. It can also store information similarly for commercial materials B and C. By storing environmental impact information and eco-label information for commercial materials, it is possible to provide environmentally friendly estimates.

[0091] The selection unit can select the optimal combination by taking into consideration the interrelationships between the products. For example, the selection unit selects the optimal combination by taking into consideration the interrelationship between product A and product B. The selection unit can also select the optimal combination by taking into consideration the interrelationship between product C and product D. Furthermore, the selection unit can also select the optimal combination by taking into consideration the interrelationships between products E and F. In this way, the optimal combination of products can be provided by taking into consideration the interrelationships between the products.

[0092] The generation unit can record the history of estimates and use it for future estimates. For example, the generation unit can record the history of past estimates and use it for future estimates. The generation unit can also analyze the history of past estimates to improve the accuracy of estimates. Furthermore, the generation unit can refer to the history of past estimates to quickly generate similar estimates. In this way, by recording the history of estimates, the accuracy of future estimates can be improved.

[0093] The reception unit can add a function to receive voice input or gesture input. For example, the reception unit can be configured to allow the user to input the size of the company or other conditions by voice. Alternatively, the reception unit can be configured to allow the user to input the size of the company or other conditions by gesture. Furthermore, the reception unit can be configured to allow the user to input by a combination of voice and gesture. This allows the reception of voice input or gesture input to broaden the options for the user's input method.

[0094] The storage unit can estimate the user's emotions and determine the priority of merchandise information to be stored based on the estimated user emotions. For example, if the user is feeling stressed, it can store more important merchandise information with higher priority. Also, if the user is relaxed, it can store more detailed merchandise information with higher priority. Furthermore, if the user is in a hurry, it can store immediately required merchandise information with higher priority. Thus, by determining the priority of merchandise information based on the user's emotions, it becomes possible to manage information according to the user's needs.

[0095] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. For example, if the user is nervous, an interface with subdued colors can be provided. If the user is having fun, an interface with bright colors can be provided. Furthermore, if the user is tired, an interface with simple and high visibility can be provided. In this way, by adjusting the display method of the input interface based on the user's emotion, an interface that is easy for the user to use can be provided.

[0096] The selection unit can estimate the user's emotions and adjust the product selection criteria based on the estimated user emotions. For example, if the user is feeling stressed, simple selection criteria can be used. On the other hand, if the user is relaxed, detailed selection criteria can be used. Furthermore, if the user is in a hurry, products that are needed immediately can be prioritized for selection. In this way, by adjusting the product selection criteria based on the user's emotions, it becomes possible to select products that meet the user's needs.

[0097] The generation unit can estimate the user's emotions and adjust the display method of the estimate based on the estimated user's emotions. For example, if the user is nervous, 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, by adjusting the display method of the estimate based on the user's emotions, it is possible to provide an estimate that is easy for the user to read.

[0098] The generation unit can estimate the user's emotions and determine the priority of estimates based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize generating estimates with high importance. Also, if the user is relaxed, it can prioritize generating detailed estimates. Furthermore, if the user is in a hurry, it can prioritize generating estimates that are needed immediately. In this way, by prioritizing estimates based on the user's emotions, it is possible to provide estimates that meet the user's needs.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The storage unit stores information about the product. The product information includes product information, service information, price information, terms and conditions information, cost (unit price), etc. For example, the terms and conditions information and cost (unit price) of product A are stored in the database. Step 2: The reception unit receives input from the user. User input includes text input, selection input, numerical input, etc. For example, the size of the company, number of employees, annual sales, quantity of required products, etc. are received as input. Step 3: The selection unit uses AI to select the optimal product combination based on the information received by the reception unit. AI uses technologies such as machine learning, deep learning, and natural language processing. For example, if the company is large, it will select products that meet its requirements and optimize the product combination based on specific conditions. Step 4: The generation unit generates an estimate based on the products selected by the selection unit. The estimate includes the price calculation method and the estimate format. For example, an estimate is generated that includes the total price of the selected products and detailed information about each product.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[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] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 storage unit that stores information about products; a reception unit that receives input from a user; a selection unit that selects a commercial product using AI based on the information received by the reception unit; a generating unit that generates an estimate based on the product selected by the selecting unit; Equipped with A system characterized by:

2. The storage unit Store product terms and conditions and costs 2. The system of claim 1.

3. The reception unit Accepts company size and specific criteria as input 2. The system of claim 1.

4. The selection unit Using AI, an optimal combination of products is selected based on the information received by the reception unit.

2. The system of claim 1.

5. The generation unit A composite estimate is generated based on the products selected by the selection unit.

2. The system of claim 1.

6. The storage unit Estimate the user's emotions and determine the priority of merchandise information to be stored based on the estimated user emotions.

2. The system of claim 1.

7. The storage unit In addition to product terms and conditions and costs, product supplier and delivery date information is also stored.

2. The system of claim 1.

8. The storage unit Store past transaction history of products and use it for future quotes 2. The system of claim 1.

Images