System

Abstract

An object of a system according to an embodiment is to efficiently collect and manage information necessary for sales activities and support the sales activities.SOLUTION: A system according to an embodiment includes a database creation unit, an output generation unit, and an information collection unit. The database creation unit creates a database of information useful for business inside and outside the company. The output generation unit generates an output useful for sales activity from the information compiled into the database by the database compiling unit. The information collection unit performs collection, addition, and update of information with respect to the database creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that collecting and managing information necessary for sales activities is time-consuming, making it difficult to carry out efficient sales activities.

[0005] The system according to the embodiment aims to efficiently collect and manage information necessary for sales activities and to support sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a database creation unit, an output generation unit, and an information collection unit. The database creation unit creates a database of information useful for sales both inside and outside the company. The output generation unit generates output useful for sales activities from the information created in the database by the database creation unit. The information collection unit collects, adds, and updates information for the database creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and manage information necessary for sales activities and support sales activities. [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) The proposal generation system according to an embodiment of the present invention is a system in which AI automatically generates appropriate proposals and solutions for various issues faced by corporate customers. This system aims to streamline and enhance sales activities by utilizing internal and external information, and to support anyone in solution sales, from initial proposals to closing deals. As a result, the proposal generation system achieves streamlined and sophisticated sales activities, and allows anyone to support solution sales, from initial proposals to closing deals.

[0029] A proposal generation system according to an embodiment includes a database creation unit, an output generation unit, and an information collection unit. The database creation unit creates a database of information useful for internal and external sales. For example, it registers past success stories, failure stories, customer feedback, and the latest industry news in the database. The database creation unit can also register information useful for sales, such as customer lists, product information, and competitive information, in the database. The output generation unit generates output useful for sales activities from the information databased by the database creation unit. For example, it analyzes which proposals were effective in addressing customer issues and which solutions were successful, and automatically generates new proposals. The output generation unit can also use a generation AI to analyze past success stories and failure stories and identify the factors behind success and the causes of failure. The information collection unit collects, adds, and updates information for the database creation unit. For example, it automatically collects the latest news, industry reports, and customer feedback from the Internet and adds them to the database. The information collection unit can also collect customer feedback in real time and reflect it in the database. As a result, the proposal generation system according to an embodiment improves the efficiency and sophistication of sales activities, enabling anyone to support solution sales from initial proposals to project completion.

[0030] The database creation unit can register past success stories and failure stories, customer feedback, and the latest industry news in the database. The database creation unit, for example, registers past success stories and failure stories in the database. For example, it registers success stories of increased sales and improved customer satisfaction, and the causes of project failures. The database creation unit also registers customer feedback in the database. For example, it collects feedback from surveys, interviews, reviews, etc. and registers it in the database. The database creation unit also registers the latest industry news in the database. For example, it collects the latest news from news sites, industry reports, social media, etc. and registers it in the database. This allows a variety of information to be registered in the database, enabling more accurate proposals.

[0031] The output generation unit can analyze what proposals were effective for solving customer issues and what solutions were successful, and automatically generate new proposals. The output generation unit, for example, analyzes what proposals were effective for solving customer issues. For example, it analyzes the content of past proposals and their results to identify effective proposals. The output generation unit also analyzes what solutions were successful. For example, it analyzes the commonalities and characteristics of successful solutions and reflects these in new proposals. The output generation unit also automatically generates new proposals. For example, it uses generation AI to automatically generate optimal proposals for customer issues. This makes it possible to improve the efficiency of sales activities by automatically generating optimal proposals based on past data.

[0032] The information gathering unit can automatically collect the latest news on the Internet, industry reports, and customer feedback and add them to the database. For example, the information gathering unit automatically collects the latest news on the Internet and adds it to the database. For example, it collects the latest news from news sites, RSS feeds, social media, etc. and adds it to the database. The information gathering unit also automatically collects industry reports and adds them to the database. For example, it collects reports from industry associations, market research reports, analyst reports, etc. and adds them to the database. The information gathering unit also automatically collects feedback from customers and adds it to the database. For example, it collects feedback from questionnaires, interviews, reviews, etc. and adds it to the database. This makes it possible to always make proposals based on the latest information.

[0033] The database creation unit can automatically customize the information in the database according to the industry and size of the customer, and provide the most appropriate information. For example, the database creation unit automatically customizes the information in the database according to the industry and size of the customer, and provides the most appropriate information. For example, information specialized for the manufacturing industry is preferentially displayed to customers in the manufacturing industry. The database creation unit also builds a system that filters the information in the database based on the customer's industry and size, and provides the most appropriate information. For example, information for small and medium-sized enterprises is preferentially displayed to small and medium-sized enterprise customers. The database creation unit also develops an algorithm that customizes the information in the database according to the customer's industry and size, and provides the most appropriate information. For example, information for large enterprises is preferentially displayed to large enterprise customers. This provides the most appropriate information according to the customer's industry and size, and improves the accuracy of proposals.

[0034] The database creation unit can add multimedia information such as images and videos to the database and provide information in a format that is easy to understand visually. The database creation unit, for example, adds multimedia information such as images and videos to the database and provides information in a format that is easy to understand visually. For example, product demonstration videos and presentation materials are registered in the database. The database creation unit also adds multimedia information to the database and builds a system that provides information in a format that is easy to understand visually. For example, product images and videos are registered in the database and provided to customers. The database creation unit also develops an algorithm that adds multimedia information such as images and videos to the database and provides information in a format that is easy to understand visually. For example, a video that explains how to use a product is registered in the database. This provides information in a format that is easy to understand visually, thereby improving the understanding of the proposal.

[0035] The database creation department can integrate databases from different industries and regions to provide information from a global perspective. For example, the database creation department integrates databases from different industries and regions to build a system that provides information from a global perspective. For example, it integrates market data and industry trends from each country and provides them. The database creation department also develops algorithms that integrate databases from different industries and regions to provide information from a global perspective. For example, it integrates competitive information and customer needs from each country and provides them. The database creation department also integrates databases from different industries and regions to develop a system that provides information from a global perspective. For example, it integrates legal regulations and market trends from each country and provides them. This broadens the range of proposals that can be made by providing information from a global perspective.

[0036] The output generation unit is able to take into account the customer's past responses and feedback when generating proposals for solving customer issues and make optimal proposals. For example, the output generation unit builds a system that takes into account the customer's past responses and feedback when generating proposals for customer issues and makes optimal proposals. For example, it adjusts the content of the proposal based on past feedback data. The output generation unit also analyzes the customer's past responses and feedback and develops an algorithm that makes optimal proposals. For example, it analyzes customer satisfaction and dissatisfaction and optimizes the content of the proposal. The output generation unit also develops a system that takes into account the customer's past responses and feedback when generating proposals for customer issues and makes optimal proposals. For example, it compares the customer's responses with past proposals and generates optimal proposals. In this way, more effective proposals can be made by taking into account the customer's past responses and feedback.

[0037] The output generation unit can simulate different proposals in the proposal generation process and select the most effective proposal. The output generation unit, for example, builds a system that simulates different proposals in the proposal generation process and selects the most effective proposal. For example, it simulates multiple proposals and compares their effects. The output generation unit also develops an algorithm that simulates different proposals and selects the most effective proposal. For example, it simulates the effects of the proposals and selects the optimal proposal. The output generation unit also develops a system that simulates different proposals in the proposal generation process and selects the most effective proposal. For example, it selects the optimal proposal based on the results of the proposal simulation. This makes it possible to select the most effective proposal by simulating different proposals.

[0038] The output generation unit can refer to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, the output generation unit builds a system that refers to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, it generates proposals that combine success stories from different industries. The output generation unit also analyzes success stories from different industries and regions to develop an algorithm that generates proposals that promote crossover innovation. For example, it generates new proposals based on success stories from different industries. The output generation unit also develops a system that refers to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, it generates proposals that combine success stories from different regions. This makes it possible to create proposals that promote crossover innovation by referring to success stories from different industries and regions.

[0039] The output generation unit is capable of having the generation AI visualize the proposal in the proposal generation process and provide the proposal in a format that is easy to understand visually. The output generation unit, for example, builds a system in which the generation AI visualizes the proposal in the proposal generation process and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in a graph or chart. The output generation unit also develops an algorithm in which the generation AI visualizes the proposal and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in an infographic. The output generation unit also develops a system in which the generation AI visualizes the proposal in the proposal generation process and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in a presentation format. In this way, by visualizing the proposal, it becomes possible to provide the proposal in a format that is easy to understand visually.

[0040] The information gathering unit can develop an algorithm that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding it to a database. The information gathering unit, for example, develops an algorithm that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding it to a database. For example, the evaluation is based on the source of the information and its reliability score. The information gathering unit also builds a system that scores the reliability of information when automatically collecting the latest news and industry reports and adding them to the database. For example, information with a high reliability score is added preferentially. The information gathering unit also develops a system that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding them to a database. For example, information with a low reliability score is excluded. In this way, by evaluating the reliability of the information, it becomes possible to add highly reliable information to the database.

[0041] The information gathering department collects feedback from customers in real time and reflects it in a database, thereby enabling the provision of always up-to-date information. The information gathering department, for example, builds a system that collects feedback from customers in real time and reflects it in a database. For example, feedback is collected using an online form or a chatbot. The information gathering department also develops an algorithm that analyzes customer feedback in real time and reflects it in a database. For example, the feedback content is automatically analyzed and the database is updated. The information gathering department also develops a system that collects feedback from customers in real time and reflects it in a database. For example, the content of proposals is dynamically updated based on the feedback. This makes it possible to collect feedback from customers in real time and always provide the latest information.

[0042] The information collection unit can integrate data from different sources in the information collection process and automatically eliminate duplication and inconsistency. For example, the information collection unit builds a system that integrates data from different sources in the information collection process and automatically eliminates duplication and inconsistency. For example, duplication is eliminated when the same information is collected from multiple sources. The information collection unit also develops an algorithm that integrates data from different sources and automatically eliminates duplication and inconsistency. For example, the algorithm evaluates the degree of consistency of information and eliminates duplication. The information collection unit also develops a system that integrates data from different sources in the information collection process and automatically eliminates duplication and inconsistency. For example, the algorithm detects contradictory information and prioritizes more reliable information. This makes it possible to provide accurate information by integrating data from different sources and automatically eliminating duplication and inconsistency.

[0043] The information gathering department can collect information from different industries and regions and provide information from a global perspective. For example, the information gathering department collects information from different industries and regions and builds a system that provides information from a global perspective. For example, it integrates market data and industry trends from each country and provides them. The information gathering department also collects information from different industries and regions and develops algorithms that provide information from a global perspective. For example, it integrates competitive information and customer needs from each country and provides them. The information gathering department also collects information from different industries and regions and develops a system that provides information from a global perspective. For example, it integrates and provides information on legal regulations and market trends from each country. This allows the department to collect information from different industries and regions and provide information from a global perspective, thereby broadening the range of proposals.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The proposal generation system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what proposals the user has viewed in the past and what actions the user took in response to each proposal. This allows the user's behavior patterns to be understood and more personalized proposals to be generated. The behavior analysis unit may also identify areas of high interest to the user based on the user's browsing history and click history, and prioritize the generation of proposals related to those areas. Furthermore, the behavior analysis unit may analyze the user's behavior data in real time and dynamically adjust the content of the proposals. This allows the system to provide optimal proposals based on the user's behavior.

[0046] The proposal generation system may further include a schedule adjustment unit that acquires the user's schedule information and adjusts the proposal content. The schedule adjustment unit, for example, acquires the user's calendar information and adjusts the proposal content to match the user's schedule. This makes it possible to provide concise proposals when the user is busy and detailed proposals when the user has more time. The schedule adjustment unit may also optimize the timing of proposals based on the user's schedule. Furthermore, the schedule adjustment unit may acquire the user's schedule information in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals that match the user's schedule.

[0047] The proposal generation system may further include a geographic adjustment unit that acquires the user's geographic information and adjusts the proposal content. The geographic adjustment unit, for example, acquires the user's current location information and makes proposals specific to that area. This makes it possible to suggest tourist information and restaurant information for that area when the user is traveling. The geographic adjustment unit can also analyze past proposal content based on the user's geographic information and generate optimal proposals tailored to the area. Furthermore, the geographic adjustment unit can acquire the user's geographic information in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals tailored to the user's current location.

[0048] The proposal generation system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit, for example, analyzes products and services that the user has purchased in the past and makes related proposals. This makes it possible to propose products and services that the user may be interested in. The purchase analysis unit can also analyze the content of past proposals based on the user's purchasing history and generate optimal proposals based on purchasing trends. Furthermore, the purchase analysis unit can also obtain the user's purchasing data in real time and dynamically adjust the content of proposals. This makes it possible to provide optimal proposals based on the user's purchasing history.

[0049] The proposal generation system may further include a social analysis unit that analyzes the user's social media activity. The social analysis unit, for example, analyzes what kind of posts the user makes on social media and what kind of responses they receive. This allows the system to understand the user's interests and make relevant proposals. The social analysis unit may also analyze past proposal content based on the user's social media activity and generate optimal proposals based on the user's interests. Furthermore, the social analysis unit may acquire the user's social media data in real time and dynamically adjust the proposal content. This allows the system to provide optimal proposals based on the user's social media activity.

[0050] The proposal generation system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit may, for example, analyze what learning content the user has used in the past and what results the user has achieved. This allows the system to understand the user's learning trends and make relevant suggestions. The learning analysis unit may also analyze past proposal content based on the user's learning history and generate optimal suggestions based on the user's learning trends. Furthermore, the learning analysis unit may acquire the user's learning data in real time and dynamically adjust the proposal content. This allows the system to provide optimal suggestions based on the user's learning history.

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

[0052] Step 1: The Database Creation Department creates a database of information useful for internal and external sales, such as past success stories, failure stories, customer feedback, the latest industry news, customer lists, product information, and competitor information. Step 2: The output generation unit generates output useful for sales activities from the information compiled into the database by the database creation unit. For example, it analyzes effective proposals and solutions to customer issues and automatically generates new proposals. It also uses generation AI to analyze past successes and failures and identify the factors behind success and the causes of failure. Step 3: The information gathering section collects, adds, and updates information for the database section. For example, it automatically collects the latest news from the Internet, industry reports, and customer feedback and adds them to the database. It also collects customer feedback in real time and reflects it in the database.

[0053] (Example 2) The proposal generation system according to an embodiment of the present invention is a system in which AI automatically generates appropriate proposals and solutions for various issues faced by corporate customers. This system aims to streamline and enhance sales activities by utilizing internal and external information, and to support anyone in solution sales, from initial proposals to closing deals. As a result, the proposal generation system achieves streamlined and sophisticated sales activities, and allows anyone to support solution sales, from initial proposals to closing deals.

[0054] A proposal generation system according to an embodiment includes a database creation unit, an output generation unit, and an information collection unit. The database creation unit creates a database of information useful for internal and external sales. For example, it registers past success stories, failure stories, customer feedback, and the latest industry news in the database. The database creation unit can also register information useful for sales, such as customer lists, product information, and competitive information, in the database. The output generation unit generates output useful for sales activities from the information databased by the database creation unit. For example, it analyzes which proposals were effective in addressing customer issues and which solutions were successful, and automatically generates new proposals. The output generation unit can also use a generation AI to analyze past success stories and failure stories and identify the factors behind success and the causes of failure. The information collection unit collects, adds, and updates information for the database creation unit. For example, it automatically collects the latest news, industry reports, and customer feedback from the Internet and adds them to the database. The information collection unit can also collect customer feedback in real time and reflect it in the database. As a result, the proposal generation system according to an embodiment improves the efficiency and sophistication of sales activities, enabling anyone to support solution sales from initial proposals to project completion.

[0055] The database creation unit can register past success stories and failure stories, customer feedback, and the latest industry news in the database. The database creation unit, for example, registers past success stories and failure stories in the database. For example, it registers success stories of increased sales and improved customer satisfaction, and the causes of project failures. The database creation unit also registers customer feedback in the database. For example, it collects feedback from surveys, interviews, reviews, etc. and registers it in the database. The database creation unit also registers the latest industry news in the database. For example, it collects the latest news from news sites, industry reports, social media, etc. and registers it in the database. This allows a variety of information to be registered in the database, enabling more accurate proposals.

[0056] The output generation unit can analyze what proposals were effective for solving customer issues and what solutions were successful, and automatically generate new proposals. The output generation unit, for example, analyzes what proposals were effective for solving customer issues. For example, it analyzes the content of past proposals and their results to identify effective proposals. The output generation unit also analyzes what solutions were successful. For example, it analyzes the commonalities and characteristics of successful solutions and reflects these in new proposals. The output generation unit also automatically generates new proposals. For example, it uses generation AI to automatically generate optimal proposals for customer issues. This makes it possible to improve the efficiency of sales activities by automatically generating optimal proposals based on past data.

[0057] The information gathering unit can automatically collect the latest news on the Internet, industry reports, and customer feedback and add them to the database. For example, the information gathering unit automatically collects the latest news on the Internet and adds it to the database. For example, it collects the latest news from news sites, RSS feeds, social media, etc. and adds it to the database. The information gathering unit also automatically collects industry reports and adds them to the database. For example, it collects reports from industry associations, market research reports, analyst reports, etc. and adds them to the database. The information gathering unit also automatically collects feedback from customers and adds it to the database. For example, it collects feedback from questionnaires, interviews, reviews, etc. and adds it to the database. This makes it possible to always make proposals based on the latest information.

[0058] The database creation unit can perform sentiment analysis on the information registered in the database and preferentially display information with a lot of positive feedback. The database creation unit, for example, performs sentiment analysis on each piece of information registered in the database and preferentially displays information with a lot of positive feedback. For example, it analyzes feedback and reviews from customers and displays information with a lot of positive ratings at the top. The database creation unit also uses sentiment analysis to score the information in the database and preferentially displays information with a high positive sentiment score. For example, it preferentially displays proposals and solutions that have a high level of customer satisfaction. The database creation unit also performs sentiment analysis on the information registered in the database and automatically filters and displays information with a lot of positive feedback. For example, it preferentially displays information with a lot of favorable customer comments. This allows for more effective proposals by preferentially displaying information with a lot of positive feedback.

[0059] The database creation unit can automatically customize the information in the database according to the industry and size of the customer, and provide the most appropriate information. For example, the database creation unit automatically customizes the information in the database according to the industry and size of the customer, and provides the most appropriate information. For example, information specialized for the manufacturing industry is preferentially displayed to customers in the manufacturing industry. The database creation unit also builds a system that filters the information in the database based on the customer's industry and size, and provides the most appropriate information. For example, information for small and medium-sized enterprises is preferentially displayed to small and medium-sized enterprise customers. The database creation unit also develops an algorithm that customizes the information in the database according to the customer's industry and size, and provides the most appropriate information. For example, information for large enterprises is preferentially displayed to large enterprise customers. This provides the most appropriate information according to the customer's industry and size, and improves the accuracy of proposals.

[0060] The database creation unit can add multimedia information such as images and videos to the database and provide information in a format that is easy to understand visually. The database creation unit, for example, adds multimedia information such as images and videos to the database and provides information in a format that is easy to understand visually. For example, product demonstration videos and presentation materials are registered in the database. The database creation unit also adds multimedia information to the database and builds a system that provides information in a format that is easy to understand visually. For example, product images and videos are registered in the database and provided to customers. The database creation unit also develops an algorithm that adds multimedia information such as images and videos to the database and provides information in a format that is easy to understand visually. For example, a video that explains how to use a product is registered in the database. This provides information in a format that is easy to understand visually, thereby improving the understanding of the proposal.

[0061] The database creation department can integrate databases from different industries and regions to provide information from a global perspective. For example, the database creation department integrates databases from different industries and regions to build a system that provides information from a global perspective. For example, it integrates market data and industry trends from each country and provides them. The database creation department also develops algorithms that integrate databases from different industries and regions to provide information from a global perspective. For example, it integrates competitive information and customer needs from each country and provides them. The database creation department also integrates databases from different industries and regions to develop a system that provides information from a global perspective. For example, it integrates legal regulations and market trends from each country and provides them. This broadens the range of proposals that can be made by providing information from a global perspective.

[0062] The database creation unit can use the emotion estimation function to collect users' emotional reactions to information registered in the database and preferentially display information with a high number of positive reactions. The database creation unit, for example, uses the emotion estimation function to collect users' emotional reactions to information registered in the database and build a system that preferentially displays information with a high number of positive reactions. For example, the database creation unit analyzes the user's facial expressions and voice and calculates an emotion score. The database creation unit also develops an algorithm that preferentially displays information with a high number of positive reactions based on the user's emotional reaction data. For example, information with a high emotion score is displayed at the top. The database creation unit also uses the emotion estimation function to collect users' emotional reactions to information registered in the database in real time and develops a system that preferentially displays information with a high number of positive reactions. For example, information is dynamically displayed according to changes in the user's emotions. This allows for more effective suggestions to be made by preferentially displaying information with a high number of positive reactions.

[0063] The output generation unit is able to take into account the customer's past responses and feedback when generating proposals for solving customer issues and make optimal proposals. For example, the output generation unit builds a system that takes into account the customer's past responses and feedback when generating proposals for customer issues and makes optimal proposals. For example, it adjusts the content of the proposal based on past feedback data. The output generation unit also analyzes the customer's past responses and feedback and develops an algorithm that makes optimal proposals. For example, it analyzes customer satisfaction and dissatisfaction and optimizes the content of the proposal. The output generation unit also develops a system that takes into account the customer's past responses and feedback when generating proposals for customer issues and makes optimal proposals. For example, it compares the customer's responses with past proposals and generates optimal proposals. In this way, more effective proposals can be made by taking into account the customer's past responses and feedback.

[0064] The output generation unit can simulate different proposals in the proposal generation process and select the most effective proposal. The output generation unit, for example, builds a system that simulates different proposals in the proposal generation process and selects the most effective proposal. For example, it simulates multiple proposals and compares their effects. The output generation unit also develops an algorithm that simulates different proposals and selects the most effective proposal. For example, it simulates the effects of the proposals and selects the optimal proposal. The output generation unit also develops a system that simulates different proposals in the proposal generation process and selects the most effective proposal. For example, it selects the optimal proposal based on the results of the proposal simulation. This makes it possible to select the most effective proposal by simulating different proposals.

[0065] The output generation unit can refer to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, the output generation unit builds a system that refers to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, it generates proposals that combine success stories from different industries. The output generation unit also analyzes success stories from different industries and regions to develop an algorithm that generates proposals that promote crossover innovation. For example, it generates new proposals based on success stories from different industries. The output generation unit also develops a system that refers to success stories from different industries and regions to generate proposals that promote crossover innovation. For example, it generates proposals that combine success stories from different regions. This makes it possible to create proposals that promote crossover innovation by referring to success stories from different industries and regions.

[0066] The output generation unit is capable of having the generation AI visualize the proposal in the proposal generation process and provide the proposal in a format that is easy to understand visually. The output generation unit, for example, builds a system in which the generation AI visualizes the proposal in the proposal generation process and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in a graph or chart. The output generation unit also develops an algorithm in which the generation AI visualizes the proposal and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in an infographic. The output generation unit also develops a system in which the generation AI visualizes the proposal in the proposal generation process and provides the proposal in a format that is easy to understand visually. For example, the content of the proposal is displayed in a presentation format. In this way, by visualizing the proposal, it becomes possible to provide the proposal in a format that is easy to understand visually.

[0067] The output generation unit can use the emotion estimation function to predict the customer's emotional response to the proposal and preferentially generate proposals that will elicit a positive response. The output generation unit, for example, uses the emotion estimation function to predict the customer's emotional response to the proposal and builds a system that preferentially generates proposals that will elicit a positive response. For example, the output generation unit predicts the emotional response based on past customer response data. The output generation unit also develops an algorithm that predicts the customer's emotional response and preferentially generates proposals that will elicit a positive response. For example, the output generation unit analyzes past customer emotion data and generates optimal proposals. The output generation unit also uses the emotion estimation function to predict the customer's emotional response to the proposal in real time and develops a system that preferentially generates proposals that will elicit a positive response. For example, the content of the proposal is dynamically adjusted. This makes it possible to preferentially generate proposals that will elicit a positive response by predicting the customer's emotional response.

[0068] The information gathering unit can develop an algorithm that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding it to a database. The information gathering unit, for example, develops an algorithm that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding it to a database. For example, the evaluation is based on the source of the information and its reliability score. The information gathering unit also builds a system that scores the reliability of information when automatically collecting the latest news and industry reports and adding them to the database. For example, information with a high reliability score is added preferentially. The information gathering unit also develops a system that evaluates the reliability of information when automatically collecting the latest news and industry reports on the Internet and adding them to a database. For example, information with a low reliability score is excluded. In this way, by evaluating the reliability of the information, it becomes possible to add highly reliable information to the database.

[0069] The information gathering department collects feedback from customers in real time and reflects it in a database, thereby enabling the provision of always up-to-date information. The information gathering department, for example, builds a system that collects feedback from customers in real time and reflects it in a database. For example, feedback is collected using an online form or a chatbot. The information gathering department also develops an algorithm that analyzes customer feedback in real time and reflects it in a database. For example, the feedback content is automatically analyzed and the database is updated. The information gathering department also develops a system that collects feedback from customers in real time and reflects it in a database. For example, the content of proposals is dynamically updated based on the feedback. This makes it possible to collect feedback from customers in real time and always provide the latest information.

[0070] The information collection unit can integrate data from different sources in the information collection process and automatically eliminate duplication and inconsistency. For example, the information collection unit builds a system that integrates data from different sources in the information collection process and automatically eliminates duplication and inconsistency. For example, duplication is eliminated when the same information is collected from multiple sources. The information collection unit also develops an algorithm that integrates data from different sources and automatically eliminates duplication and inconsistency. For example, the algorithm evaluates the degree of consistency of information and eliminates duplication. The information collection unit also develops a system that integrates data from different sources in the information collection process and automatically eliminates duplication and inconsistency. For example, the algorithm detects contradictory information and prioritizes more reliable information. This makes it possible to provide accurate information by integrating data from different sources and automatically eliminating duplication and inconsistency.

[0071] The information gathering department can collect information from different industries and regions and provide information from a global perspective. For example, the information gathering department collects information from different industries and regions and builds a system that provides information from a global perspective. For example, it integrates market data and industry trends from each country and provides them. The information gathering department also collects information from different industries and regions and develops algorithms that provide information from a global perspective. For example, it integrates competitive information and customer needs from each country and provides them. The information gathering department also collects information from different industries and regions and develops a system that provides information from a global perspective. For example, it integrates and provides information on legal regulations and market trends from each country. This allows the department to collect information from different industries and regions and provide information from a global perspective, thereby broadening the range of proposals.

[0072] The information collection unit can use the emotion estimation function to analyze the user's emotional response to the collected information and preferentially add information with a high number of positive responses to the database. The information collection unit, for example, uses the emotion estimation function to analyze the user's emotional response to the collected information and builds a system that preferentially adds information with a high number of positive responses to the database. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The information collection unit also develops an algorithm that preferentially adds information with a high number of positive responses to the database based on the user's emotional response data. For example, information with a high emotion score is displayed at the top. The information collection unit also uses the emotion estimation function to analyze the user's emotional response to the collected information in real time and develops a system that preferentially adds information with a high number of positive responses to the database. For example, information is dynamically displayed according to changes in the user's emotions. As a result, using the emotion estimation function makes it possible to preferentially add information with a high number of positive responses to the database.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The proposal generation system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what proposals the user has viewed in the past and what actions the user took in response to each proposal. This allows the user's behavior patterns to be understood and more personalized proposals to be generated. The behavior analysis unit may also identify areas of high interest to the user based on the user's browsing history and click history, and prioritize the generation of proposals related to those areas. Furthermore, the behavior analysis unit may analyze the user's behavior data in real time and dynamically adjust the content of the proposals. This allows the system to provide optimal proposals based on the user's behavior.

[0075] The proposal generation system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of the proposals based on the estimated emotions. The emotion adjustment unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotional state. As a result, if the user is feeling stressed, it can make suggestions that will help them relax. The emotion adjustment unit can also analyze past proposals based on the user's emotional data and prioritize the generation of proposals that have received positive responses. Furthermore, the emotion adjustment unit can monitor the user's emotional changes in real time and dynamically adjust the content of the proposals. This makes it possible to provide optimal suggestions that match the user's emotions.

[0076] The proposal generation system may further include a schedule adjustment unit that acquires the user's schedule information and adjusts the proposal content. The schedule adjustment unit, for example, acquires the user's calendar information and adjusts the proposal content to match the user's schedule. This makes it possible to provide concise proposals when the user is busy and detailed proposals when the user has more time. The schedule adjustment unit may also optimize the timing of proposals based on the user's schedule. Furthermore, the schedule adjustment unit may acquire the user's schedule information in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals that match the user's schedule.

[0077] The proposal generation system may further include a health adjustment unit that acquires the user's health data and adjusts the proposal content. The health adjustment unit acquires, for example, the user's fitness data and sleep data, and adjusts the proposal content to suit the user's health condition. This makes it possible to make relaxing suggestions when the user is tired, and proactive suggestions when the user is in good health. The health adjustment unit can also analyze past proposal content based on the user's health data and generate optimal proposals according to the user's health condition. Furthermore, the health adjustment unit can acquire the user's health data in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals according to the user's health condition.

[0078] The proposal generation system may further include a geographic adjustment unit that acquires the user's geographic information and adjusts the proposal content. The geographic adjustment unit, for example, acquires the user's current location information and makes proposals specific to that area. This makes it possible to suggest tourist information and restaurant information for that area when the user is traveling. The geographic adjustment unit can also analyze past proposal content based on the user's geographic information and generate optimal proposals tailored to the area. Furthermore, the geographic adjustment unit can acquire the user's geographic information in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals tailored to the user's current location.

[0079] The proposal generation system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit, for example, analyzes products and services that the user has purchased in the past and makes related proposals. This makes it possible to propose products and services that the user may be interested in. The purchase analysis unit can also analyze the content of past proposals based on the user's purchasing history and generate optimal proposals based on purchasing trends. Furthermore, the purchase analysis unit can also obtain the user's purchasing data in real time and dynamically adjust the content of proposals. This makes it possible to provide optimal proposals based on the user's purchasing history.

[0080] The proposal generation system may further include a social analysis unit that analyzes the user's social media activity. The social analysis unit, for example, analyzes what kind of posts the user makes on social media and what kind of responses they receive. This allows the system to understand the user's interests and make relevant proposals. The social analysis unit may also analyze past proposal content based on the user's social media activity and generate optimal proposals based on the user's interests. Furthermore, the social analysis unit may acquire the user's social media data in real time and dynamically adjust the proposal content. This allows the system to provide optimal proposals based on the user's social media activity.

[0081] The proposal generation system may further include an emotion feedback unit that estimates the user's emotion and adjusts the content of the proposal based on the estimated emotion. The emotion feedback unit, for example, analyzes the user's facial expression and voice to estimate the user's current emotional state. As a result, if the user is feeling anxious, it is possible to make a proposal that gives the user a sense of security. The emotion feedback unit may also analyze past proposal content based on the user's emotion data and prioritize the generation of proposals that have received positive responses. Furthermore, the emotion feedback unit may monitor changes in the user's emotion in real time and dynamically adjust the content of the proposal. This makes it possible to provide optimal proposals according to the user's emotion.

[0082] The proposal generation system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit may, for example, analyze what learning content the user has used in the past and what results the user has achieved. This allows the system to understand the user's learning trends and make relevant suggestions. The learning analysis unit may also analyze past proposal content based on the user's learning history and generate optimal suggestions based on the user's learning trends. Furthermore, the learning analysis unit may acquire the user's learning data in real time and dynamically adjust the proposal content. This allows the system to provide optimal suggestions based on the user's learning history.

[0083] The proposal generation system may further include an emotion prediction unit that estimates the user's emotion and adjusts the proposal content based on the estimated emotion. The emotion prediction unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotional state. As a result, if the user is happy, it can make suggestions that will further increase the user's joy. The emotion prediction unit can also analyze past proposal content based on the user's emotion data and prioritize the generation of proposals that have received positive responses. Furthermore, the emotion prediction unit can monitor the user's emotional changes in real time and dynamically adjust the proposal content. This makes it possible to provide optimal proposals that match the user's emotions.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The Database Creation Department creates a database of information useful for internal and external sales, such as past success stories, failure stories, customer feedback, the latest industry news, customer lists, product information, and competitor information. Step 2: The output generation unit generates output useful for sales activities from the information compiled into the database by the database creation unit. For example, it analyzes effective proposals and solutions to customer issues and automatically generates new proposals. It also uses generation AI to analyze past successes and failures and identify the factors behind success and the causes of failure. Step 3: The information gathering section collects, adds, and updates information for the database section. For example, it automatically collects the latest news from the Internet, industry reports, and customer feedback and adds them to the database. It also collects customer feedback in real time and reflects it in the database.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the robot 414, 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 robot 414 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 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. The database department creates a database of information useful for internal and external sales, an output generation unit that generates an output useful for sales activities from the information databased by the database creation unit; an information collection unit that collects, adds, and updates information to the database unit; A system characterized by:

2. The database creation unit Create a database of past successes and failures, customer feedback, and the latest industry news.

2. The system of claim 1.

3. The output generation unit Analyze what proposals were effective in solving customer issues and what solutions were successful, and automatically generate new proposals.

2. The system of claim 1.

4. The information collecting unit Automatically gather the latest news, industry reports, and customer feedback from the internet and add them to your database 2. The system of claim 1.

5. The database creation unit Sentiment analysis is performed on the information registered in the database, and the information with the most positive feedback is displayed preferentially.

2. The system of claim 1.

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