system

Abstract

The system according to this embodiment aims to automate and streamline the work of corporate sales representatives. [Solution] The system according to the embodiment comprises an understanding unit, a reporting unit, and a proposal unit. The understanding unit understands the work of corporate sales representatives. The reporting unit automatically reports today's tasks and to-dos based on the work understood by the understanding unit. The proposal unit plans routine tasks and proposes work based on the tasks and to-dos reported by the reporting unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, many of the tasks of corporate sales representatives are performed manually, leaving room for efficiency improvement.

[0005] The system according to the embodiment aims to automate and improve the efficiency of the tasks of corporate sales representatives.

Means for Solving the Problems

[0006] The system according to the embodiment includes an understanding unit, a reporting unit, and a proposal unit. The understanding unit understands the tasks of corporate sales representatives. The reporting unit automatically reports today's tasks and ToDos based on the tasks understood by the understanding unit. The proposal unit plans routine tasks and makes work proposals based on the tasks and ToDos reported by the reporting unit.

Effects of the Invention

[0007] The system according to this embodiment can automate and streamline the work of corporate sales representatives. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Smart Sales Partner System according to an embodiment of the present invention is a system for streamlining the work of corporate sales representatives. This system fully understands the PC work, smartphone operation, and business system operation of corporate sales representatives, and automatically reports the day's tasks and to-dos upon arrival at the office. Furthermore, it pre-plans the routine tasks that sales representatives perform daily and proposes tasks. Sales representatives can automate 80% of their work by only making final selections and decisions. For example, the Smart Sales Partner System fully understands the PC work, smartphone operation, and business system operation of corporate sales representatives. In this process, the Smart Sales Partner System learns the procedures and operation methods for each task and grasps the optimal operation method. For example, it can automate routine tasks such as data entry and report creation. Next, the Smart Sales Partner System automatically reports the day's tasks and to-dos upon arrival at the office. The Smart Sales Partner System understands the sales representative's schedule and work content and proposes priorities. For example, by prioritizing the reporting of important customer interactions and meeting schedules, sales representatives can proceed with their work efficiently. Furthermore, the Smart Sales Partner System pre-plans the routine tasks that sales representatives perform daily and proposes tasks. The Smart Sales Partner System analyzes sales data and generates and proposes the next actions and customer proposals. For example, based on past sales data, it can automatically generate the next customers to approach and the content of proposals. Sales representatives only need to make final selections and decisions, as 80% of the work can be automated. This allows sales representatives to focus on important customer interactions and creative tasks. For example, they only need to review the proposals and reports generated by the Smart Sales Partner System and make any necessary revisions. This system significantly improves the efficiency of corporate sales operations and reduces the burden on sales representatives. For example, they can concentrate on their core sales activities without spending a lot of time on routine tasks such as administrative work and data entry. They can also work efficiently without getting bogged down in operating business systems and tools. Furthermore, it prevents the cumbersome management of customer information and task organization that can reduce productivity.The Smart Sales Partner System centralizes customer information and suggests task organization and prioritization, enabling sales representatives to work more efficiently. Thus, utilizing the Smart Sales Partner System improves the efficiency of corporate sales operations and reduces the burden on sales representatives. This is expected to improve overall corporate productivity and competitiveness. The Smart Sales Partner System efficiently understands the work of corporate sales representatives, automatically reports tasks and to-dos, and plans and proposes routine tasks.

[0029] The smart sales partner system according to this embodiment comprises an understanding unit, a reporting unit, and a proposal unit. The understanding unit understands the work of corporate sales representatives. The work of corporate sales representatives includes, but is not limited to, customer visits, contract negotiations, and presentations. The understanding unit learns, for example, the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating methods. The reporting unit automatically reports today's tasks and to-dos based on the work understood by the understanding unit. The reporting unit grasps the sales representative's schedule and work content and proposes priorities. For example, the reporting unit prioritizes reporting important customer interactions and meeting schedules. The proposal unit plans routine tasks and proposes work based on the tasks and to-dos reported by the reporting unit. The proposal unit analyzes sales data and generates and proposes the next actions and proposals to customers. For example, the proposal unit automatically generates the next customers to approach and proposal content based on past sales data. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes sales data as input and outputs the next actions and proposal content for the customer. As a result, the smart sales partner system according to the embodiment can efficiently understand the work of corporate sales representatives, automatically report tasks and to-dos, and plan and propose routine tasks.

[0030] The Understanding Unit understands the work of corporate sales representatives. This includes, but is not limited to, customer visits, contract negotiations, and presentations. The Understanding Unit learns about the PC work, smartphone operation, and business system operation of corporate sales representatives, and identifies the optimal operating methods. Specifically, the Understanding Unit learns in detail the operating procedures for software and applications that corporate sales representatives use on a daily basis. For example, it learns how to operate customer relationship management (CRM) systems, presentation software, and email management. This allows the Understanding Unit to grasp the optimal operating procedures for corporate sales representatives to perform their duties efficiently and provide advice as needed. Furthermore, the Understanding Unit analyzes the workflow of corporate sales representatives and identifies bottlenecks and areas for improvement. For example, it can analyze in detail how to manage customer visit schedules, conduct contract negotiations, and prepare presentations, and propose ways to improve work efficiency. In addition, the Understanding Unit collects external data and market information related to the work of corporate sales representatives to deepen its understanding of their operations. For example, it helps corporate sales representatives perform their duties more effectively by understanding industry trends, competitor activities, and customer needs and preferences. This allows the understanding department to comprehensively understand the work of corporate sales representatives and support the streamlining and improvement of those operations.

[0031] The reporting department automatically reports today's tasks and to-dos based on the work understood by the understanding department. The reporting department grasps the sales representative's schedule and work content and proposes priorities. Specifically, the reporting department refers to the corporate sales representative's calendar and task management tools to organize the day's schedule and tasks. For example, it prioritizes reporting important customer interactions and meeting schedules to help sales representatives work efficiently. The reporting department also analyzes the corporate sales representative's past work history and performance data to optimize task priorities. For example, based on past data, it identifies the time of day when negotiations with specific customers are more likely to succeed and the timing of important contract negotiations, and proposes task priorities based on that. Furthermore, the reporting department collects feedback from corporate sales representatives and continuously improves the accuracy and effectiveness of the reports. For example, based on feedback from sales representatives, it reviews task priorities and report content to provide more effective reports. In addition, the reporting department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also email and chat tools to ensure that important information is delivered reliably. This allows the reporting department to quickly and accurately report tasks and to-dos to corporate sales representatives, thereby supporting improved operational efficiency.

[0032] The Proposal Department plans routine tasks and proposes work based on tasks and to-dos reported by the Reporting Department. The Proposal Department analyzes sales data to generate and propose the next actions and customer proposals. Specifically, the Proposal Department analyzes the past sales data and customer information of corporate sales representatives in detail to automatically generate the next customers to approach and the content of proposals. For example, based on past negotiation history and customer purchase history, it generates the optimal proposal for a specific customer and proposes it to the sales representative. The Proposal Department also uses AI to analyze sales data and simulate multiple scenarios to identify the most effective actions. For example, it uses an AI model to simulate proposal content and approach methods for a specific customer and makes the optimal proposal based on the results. Furthermore, the Proposal Department collects feedback from corporate sales representatives and continuously improves the accuracy and effectiveness of proposals. For example, based on feedback from sales representatives, it reviews proposal content and action plans to make more effective proposals. The Proposal Department also utilizes external data and market information to improve the accuracy of proposals. For example, it analyzes industry trends, competitor activities, customer needs and preferences, and optimizes proposal content based on these. This allows the proposal department to provide highly accurate proposals to corporate sales representatives, supporting improved operational efficiency and enhanced results.

[0033] The proposal department can analyze sales data and generate and propose the next actions and customer proposals. For example, the proposal department can analyze sales data and generate and propose the next actions and customer proposals. For example, the proposal department can automatically generate the next customers to approach and the proposal content based on past sales data. For example, the proposal department can analyze sales data such as customer information, sales data, and past proposal content and generate and propose the next actions and customer proposals. This improves the efficiency of sales activities by analyzing sales data and generating and proposing the next actions and customer proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes sales data as input and outputs the next actions and customer proposal content.

[0034] The proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data. For example, the proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data. For example, the proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data such as past sales data and customer responses. This improves the efficiency of sales activities by automatically generating the next customers to approach and the content of proposals based on past sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes past sales data as input and outputs the next customers to approach and the content of proposals.

[0035] The reporting department can understand the schedules and work content of sales representatives and propose priorities. For example, the reporting department can understand the schedules and work content of sales representatives and propose priorities. For example, the reporting department can understand the schedules of sales representatives, such as daily schedules and weekly schedules, and propose priorities. For example, the reporting department can understand the work content, such as customer visits, contract negotiations, and presentations, and propose priorities. This improves work efficiency by understanding the schedules and work content of sales representatives and proposing priorities. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can make proposals using an AI model that takes the schedules and work content of sales representatives as input and outputs priorities.

[0036] The understanding unit can fully understand the PC work, smartphone operation, and business system operation of corporate sales representatives. For example, the understanding unit can fully understand PC work such as sending and receiving emails, creating documents, and entering data. For example, the understanding unit can fully understand smartphone operation such as using apps and sending and receiving messages. For example, the understanding unit can fully understand business system operation such as using CRM systems and operating databases. This improves work efficiency by fully understanding the PC work, smartphone operation, and business system operation of corporate sales representatives. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can perform understanding using an AI model that takes the PC work, smartphone operation, and business system operation of corporate sales representatives as input and outputs understanding.

[0037] The proposal department can automatically generate documents such as sales emails, proposals, and reports using generative AI. For example, the proposal department can automatically generate documents such as sales emails, proposals, and reports using generative AI. For example, the proposal department can automatically generate sales emails using natural language generation technology. For example, the proposal department can automatically generate proposals using machine learning algorithms. For example, the proposal department can automatically generate sales activity reports using generative AI. This improves the work efficiency of sales representatives by automatically generating documents using generative AI. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can automatically generate documents using a generative AI model that takes sales data as input and outputs documents such as sales emails, proposals, and reports.

[0038] The understanding unit can analyze the sales representative's past work history and select the optimal understanding method. For example, the understanding unit can analyze the sales representative's past work history and select the optimal understanding method. For example, the understanding unit can identify tasks that the sales representative has struggled with in the past and provide a detailed explanation for those tasks. For example, the understanding unit can prioritize understanding tasks that the sales representative excels at, enabling them to work more efficiently. For example, the understanding unit can identify frequently performed tasks from the sales representative's past work history and provide an efficient understanding method for those tasks. By analyzing the sales representative's past work history and selecting the optimal understanding method, the efficiency of operations is improved. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can select an understanding method using an AI model that takes the sales representative's past work history as input and outputs the optimal understanding method.

[0039] The understanding unit can filter tasks based on the sales representative's current projects and areas of interest when understanding tasks. For example, the understanding unit prioritizes understanding tasks related to the sales representative's current projects. The understanding unit deepens understanding by filtering relevant tasks based on the sales representative's areas of interest. For example, the understanding unit prioritizes understanding tasks related to the sales representative's current focus customers. This improves the efficiency of tasks by filtering them based on the sales representative's current projects and areas of interest. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can perform filtering using an AI model that takes the sales representative's current projects and areas of interest as input and outputs filtered tasks.

[0040] The understanding unit can prioritize understanding highly relevant tasks by considering the geographical location information of sales representatives when understanding tasks. For example, when understanding tasks, the understanding unit prioritizes understanding highly relevant tasks by considering the geographical location information of sales representatives. For example, if a sales representative is in a specific region, the understanding unit prioritizes understanding tasks related to that region. For example, if a sales representative is on a business trip, the understanding unit prioritizes understanding tasks related to the destination of the business trip. For example, if a sales representative is working from home, the understanding unit prioritizes understanding tasks that can be done from home. This improves the efficiency of operations by prioritizing the understanding of highly relevant tasks by considering the geographical location information of sales representatives. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can understand tasks using an AI model that takes the geographical location information of sales representatives as input and outputs highly relevant tasks.

[0041] The understanding unit can analyze the social media activities of sales representatives to understand related tasks when understanding business operations. For example, the understanding unit can analyze the social media activities of sales representatives to understand related tasks when understanding business operations. For example, the understanding unit can prioritize understanding tasks related to topics that sales representatives have shown interest in on social media. For example, the understanding unit can identify and understand tasks related to important customers or projects from the social media activities of sales representatives. For example, the understanding unit can understand related tasks based on information shared by sales representatives on social media. This improves the efficiency of operations by analyzing the social media activities of sales representatives to understand related tasks. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can understand business operations using an AI model that takes the social media activities of sales representatives as input and outputs related tasks.

[0042] The reporting unit can adjust the level of detail in a report based on the importance of the task when reporting tasks or to-dos. For example, the reporting unit can provide detailed reports for high-importance tasks, and concise reports for low-importance tasks. The reporting unit can also adjust the level of detail in a stepwise manner according to importance. This improves reporting efficiency by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail of the report.

[0043] The reporting department can apply different reporting algorithms depending on the task category when reporting tasks and to-dos. For example, the reporting department might report detailed customer information and progress for sales tasks. For example, it might report concisely on progress and next steps for internal business tasks. For example, it might report urgent tasks quickly to encourage immediate action. By applying different reporting algorithms depending on the task category, the efficiency of reporting is improved. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can perform reporting using an AI model that takes task categories as input and outputs a reporting algorithm.

[0044] The reporting department can prioritize tasks and to-dos based on their submission dates. For example, the reporting department prioritizes reporting tasks with approaching deadlines, while delaying reporting tasks with later deadlines. The reporting department can also adjust the reporting priority in stages according to the submission dates. This improves reporting efficiency by prioritizing reports based on task submission dates. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can use an AI model that takes task submission dates as input and outputs reporting priorities to determine the priority.

[0045] The reporting department can adjust the order of reports based on the relevance of tasks when reporting tasks and to-dos. For example, the reporting department prioritizes reporting tasks related to important customers. For example, the reporting department postpones reporting tasks related to internal operations. For example, the reporting department adjusts the order of reports in stages according to the relevance of tasks. This improves the efficiency of reporting by adjusting the order of reports based on the relevance of tasks. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can adjust the order using an AI model that takes the relevance of tasks as input and outputs the order of reports.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the sales data when proposing tasks. For example, the proposal department can adjust the level of detail in its proposals based on the importance of the sales data when proposing tasks. For example, the proposal department can make detailed proposals for sales data with high importance. For example, the proposal department can make concise proposals for sales data with low importance. For example, the proposal department can adjust the level of detail in its proposals in stages according to importance. This improves the efficiency of proposals by adjusting the level of detail in proposals based on the importance of the sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can adjust the level of detail using an AI model that takes the importance of sales data as input and outputs the level of detail in proposals.

[0047] The proposal department can apply different proposal algorithms depending on the category of sales data when proposing tasks. For example, when proposing tasks, the proposal department can apply different proposal algorithms depending on the category of sales data. For example, for customer support proposals, the proposal department can make proposals based on customer information and past transaction history. For internal operations proposals, the proposal department can make proposals based on the progress of the work and the next steps. For emergency response proposals, the proposal department can make proposals for quick response. By applying different proposal algorithms depending on the category of sales data, the efficiency of proposals is improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes sales data categories as input and outputs a proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission timing of sales data when proposing work. For example, the proposal department will prioritize proposals for sales data with approaching deadlines. For example, the proposal department will postpone proposals for sales data with later submission deadlines. For example, the proposal department will adjust the priority of proposals in stages according to the submission timing. This improves the efficiency of proposals by determining the priority of proposals based on the submission timing of sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can determine the priority using an AI model that takes the submission timing of sales data as input and outputs the priority of proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of sales data when proposing tasks. For example, the proposal department will prioritize proposals related to important customers. For example, it will postpone proposals related to internal operations. The proposal department will adjust the order of proposals in stages according to the relevance of the sales data. This improves the efficiency of proposals by adjusting the order of proposals based on the relevance of sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can adjust the order using an AI model that takes the relevance of sales data as input and outputs the order of proposals.

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

[0051] The Understanding Unit can analyze past success stories of sales representatives and propose the optimal approach for similar situations. For example, the Understanding Unit can identify past successful sales methods and approaches and apply them to the current situation. For example, the Understanding Unit can propose the optimal sales strategy based on data obtained from past success stories. For example, the Understanding Unit can analyze past success stories and propose effective approaches for similar customers. This allows sales representatives to leverage past success stories to conduct sales activities efficiently.

[0052] The Understanding Department can analyze past failures of sales representatives and provide advice to prevent similar mistakes. For example, it can identify sales methods and approaches that have failed in the past and suggest points to avoid in the current situation. For example, it can propose strategies to minimize risk based on data obtained from past failures. For example, it can analyze past failures and suggest points to be careful about with similar customers. This allows sales representatives to conduct sales activities more efficiently by utilizing past failures.

[0053] The understanding unit can assess the skill level of sales representatives and provide methods for understanding tasks that are appropriate to their skill level. For example, if a sales representative is a beginner, the understanding unit will explain basic work procedures in detail. If a sales representative is an intermediate level, the understanding unit will suggest efficient work procedures. If a sales representative is an advanced level, the understanding unit will concisely present the optimal work procedures. By providing methods for understanding tasks according to the skill level of sales representatives, the efficiency of operations is improved.

[0054] The understanding unit can analyze the learning style of sales representatives and provide the optimal learning method. For example, if a sales representative prefers visual learning, the understanding unit will provide explanations that make extensive use of diagrams and graphs. If a sales representative prefers auditory learning, the understanding unit will provide audio guides. If a sales representative prefers experiential learning, the understanding unit will provide practical exercises. By providing the optimal learning method according to the learning style of each sales representative, the efficiency of work is improved.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The Understanding Department will understand the duties of a corporate sales representative. These duties include customer visits, contract negotiations, and presentations. The Understanding Department will learn about the PC work, smartphone operation, and business system operation of corporate sales representatives and grasp the optimal operating methods. Step 2: The reporting department automatically reports today's tasks and to-dos based on the work understood by the understanding department. The reporting department grasps the sales representative's schedule and work content and suggests priorities. For example, it prioritizes reporting important customer interactions and meeting schedules. Step 3: The Proposal Department plans routine tasks and proposes work based on the tasks and to-dos reported by the Reporting Department. The Proposal Department analyzes sales data and generates and proposes the next actions and proposals to customers. For example, it automatically generates the next customers to approach and the content of proposals based on past sales data. Processing in the Proposal Department may or may not be performed using AI.

[0057] (Example of form 2) The Smart Sales Partner System according to an embodiment of the present invention is a system for streamlining the work of corporate sales representatives. This system fully understands the PC work, smartphone operation, and business system operation of corporate sales representatives, and automatically reports the day's tasks and to-dos upon arrival at the office. Furthermore, it pre-plans the routine tasks that sales representatives perform daily and proposes tasks. Sales representatives can automate 80% of their work by only making final selections and decisions. For example, the Smart Sales Partner System fully understands the PC work, smartphone operation, and business system operation of corporate sales representatives. In this process, the Smart Sales Partner System learns the procedures and operation methods for each task and grasps the optimal operation method. For example, it can automate routine tasks such as data entry and report creation. Next, the Smart Sales Partner System automatically reports the day's tasks and to-dos upon arrival at the office. The Smart Sales Partner System understands the sales representative's schedule and work content and proposes priorities. For example, by prioritizing the reporting of important customer interactions and meeting schedules, sales representatives can proceed with their work efficiently. Furthermore, the Smart Sales Partner System pre-plans the routine tasks that sales representatives perform daily and proposes tasks. The Smart Sales Partner System analyzes sales data and generates and proposes the next actions and customer proposals. For example, based on past sales data, it can automatically generate the next customers to approach and the content of proposals. Sales representatives only need to make final selections and decisions, as 80% of the work can be automated. This allows sales representatives to focus on important customer interactions and creative tasks. For example, they only need to review the proposals and reports generated by the Smart Sales Partner System and make any necessary revisions. This system significantly improves the efficiency of corporate sales operations and reduces the burden on sales representatives. For example, they can concentrate on their core sales activities without spending a lot of time on routine tasks such as administrative work and data entry. They can also work efficiently without getting bogged down in operating business systems and tools. Furthermore, it prevents the cumbersome management of customer information and task organization that can reduce productivity.The Smart Sales Partner System centralizes customer information and suggests task organization and prioritization, enabling sales representatives to work more efficiently. Thus, utilizing the Smart Sales Partner System improves the efficiency of corporate sales operations and reduces the burden on sales representatives. This is expected to improve overall corporate productivity and competitiveness. The Smart Sales Partner System efficiently understands the work of corporate sales representatives, automatically reports tasks and to-dos, and plans and proposes routine tasks.

[0058] The smart sales partner system according to this embodiment comprises an understanding unit, a reporting unit, and a proposal unit. The understanding unit understands the work of corporate sales representatives. The work of corporate sales representatives includes, but is not limited to, customer visits, contract negotiations, and presentations. The understanding unit learns, for example, the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating methods. The reporting unit automatically reports today's tasks and to-dos based on the work understood by the understanding unit. The reporting unit grasps the sales representative's schedule and work content and proposes priorities. For example, the reporting unit prioritizes reporting important customer interactions and meeting schedules. The proposal unit plans routine tasks and proposes work based on the tasks and to-dos reported by the reporting unit. The proposal unit analyzes sales data and generates and proposes the next actions and proposals to customers. For example, the proposal unit automatically generates the next customers to approach and proposal content based on past sales data. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes sales data as input and outputs the next actions and proposal content for the customer. As a result, the smart sales partner system according to the embodiment can efficiently understand the work of corporate sales representatives, automatically report tasks and to-dos, and plan and propose routine tasks.

[0059] The Understanding Unit understands the work of corporate sales representatives. This includes, but is not limited to, customer visits, contract negotiations, and presentations. The Understanding Unit learns about the PC work, smartphone operation, and business system operation of corporate sales representatives, and identifies the optimal operating methods. Specifically, the Understanding Unit learns in detail the operating procedures for software and applications that corporate sales representatives use on a daily basis. For example, it learns how to operate customer relationship management (CRM) systems, presentation software, and email management. This allows the Understanding Unit to grasp the optimal operating procedures for corporate sales representatives to perform their duties efficiently and provide advice as needed. Furthermore, the Understanding Unit analyzes the workflow of corporate sales representatives and identifies bottlenecks and areas for improvement. For example, it can analyze in detail how to manage customer visit schedules, conduct contract negotiations, and prepare presentations, and propose ways to improve work efficiency. In addition, the Understanding Unit collects external data and market information related to the work of corporate sales representatives to deepen its understanding of their operations. For example, it helps corporate sales representatives perform their duties more effectively by understanding industry trends, competitor activities, and customer needs and preferences. This allows the understanding department to comprehensively understand the work of corporate sales representatives and support the streamlining and improvement of those operations.

[0060] The reporting department automatically reports today's tasks and to-dos based on the work understood by the understanding department. The reporting department grasps the sales representative's schedule and work content and proposes priorities. Specifically, the reporting department refers to the corporate sales representative's calendar and task management tools to organize the day's schedule and tasks. For example, it prioritizes reporting important customer interactions and meeting schedules to help sales representatives work efficiently. The reporting department also analyzes the corporate sales representative's past work history and performance data to optimize task priorities. For example, based on past data, it identifies the time of day when negotiations with specific customers are more likely to succeed and the timing of important contract negotiations, and proposes task priorities based on that. Furthermore, the reporting department collects feedback from corporate sales representatives and continuously improves the accuracy and effectiveness of the reports. For example, based on feedback from sales representatives, it reviews task priorities and report content to provide more effective reports. In addition, the reporting department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also email and chat tools to ensure that important information is delivered reliably. This allows the reporting department to quickly and accurately report tasks and to-dos to corporate sales representatives, thereby supporting improved operational efficiency.

[0061] The Proposal Department plans routine tasks and proposes work based on tasks and to-dos reported by the Reporting Department. The Proposal Department analyzes sales data to generate and propose the next actions and customer proposals. Specifically, the Proposal Department analyzes the past sales data and customer information of corporate sales representatives in detail to automatically generate the next customers to approach and the content of proposals. For example, based on past negotiation history and customer purchase history, it generates the optimal proposal for a specific customer and proposes it to the sales representative. The Proposal Department also uses AI to analyze sales data and simulate multiple scenarios to identify the most effective actions. For example, it uses an AI model to simulate proposal content and approach methods for a specific customer and makes the optimal proposal based on the results. Furthermore, the Proposal Department collects feedback from corporate sales representatives and continuously improves the accuracy and effectiveness of proposals. For example, based on feedback from sales representatives, it reviews proposal content and action plans to make more effective proposals. The Proposal Department also utilizes external data and market information to improve the accuracy of proposals. For example, it analyzes industry trends, competitor activities, customer needs and preferences, and optimizes proposal content based on these. This allows the proposal department to provide highly accurate proposals to corporate sales representatives, supporting improved operational efficiency and enhanced results.

[0062] The proposal department can analyze sales data and generate and propose the next actions and customer proposals. For example, the proposal department can analyze sales data and generate and propose the next actions and customer proposals. For example, the proposal department can automatically generate the next customers to approach and the proposal content based on past sales data. For example, the proposal department can analyze sales data such as customer information, sales data, and past proposal content and generate and propose the next actions and customer proposals. This improves the efficiency of sales activities by analyzing sales data and generating and proposing the next actions and customer proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes sales data as input and outputs the next actions and customer proposal content.

[0063] The proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data. For example, the proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data. For example, the proposal department can automatically generate the next customers to approach and the content of proposals based on past sales data such as past sales data and customer responses. This improves the efficiency of sales activities by automatically generating the next customers to approach and the content of proposals based on past sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes past sales data as input and outputs the next customers to approach and the content of proposals.

[0064] The reporting department can understand the schedules and work content of sales representatives and propose priorities. For example, the reporting department can understand the schedules and work content of sales representatives and propose priorities. For example, the reporting department can understand the schedules of sales representatives, such as daily schedules and weekly schedules, and propose priorities. For example, the reporting department can understand the work content, such as customer visits, contract negotiations, and presentations, and propose priorities. This improves work efficiency by understanding the schedules and work content of sales representatives and proposing priorities. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can make proposals using an AI model that takes the schedules and work content of sales representatives as input and outputs priorities.

[0065] The understanding unit can fully understand the PC work, smartphone operation, and business system operation of corporate sales representatives. For example, the understanding unit can fully understand PC work such as sending and receiving emails, creating documents, and entering data. For example, the understanding unit can fully understand smartphone operation such as using apps and sending and receiving messages. For example, the understanding unit can fully understand business system operation such as using CRM systems and operating databases. This improves work efficiency by fully understanding the PC work, smartphone operation, and business system operation of corporate sales representatives. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can perform understanding using an AI model that takes the PC work, smartphone operation, and business system operation of corporate sales representatives as input and outputs understanding.

[0066] The proposal department can automatically generate documents such as sales emails, proposals, and reports using generative AI. For example, the proposal department can automatically generate documents such as sales emails, proposals, and reports using generative AI. For example, the proposal department can automatically generate sales emails using natural language generation technology. For example, the proposal department can automatically generate proposals using machine learning algorithms. For example, the proposal department can automatically generate sales activity reports using generative AI. This improves the work efficiency of sales representatives by automatically generating documents using generative AI. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can automatically generate documents using a generative AI model that takes sales data as input and outputs documents such as sales emails, proposals, and reports.

[0067] The understanding unit can estimate the emotions of sales representatives and adjust the level of understanding of tasks based on the estimated emotions. For example, if a sales representative is stressed, the understanding unit simplifies the level of understanding of tasks and emphasizes only the important points. For example, if a sales representative is relaxed, the understanding unit provides detailed work procedures to promote a deeper understanding. For example, if a sales representative is tired, the understanding unit minimizes the level of understanding of tasks and provides only the minimum necessary information. This improves work efficiency by adjusting the level of understanding of tasks according to the emotions of the sales representatives. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can adjust the level of understanding using an AI model that takes sales representative emotion data as input and outputs the level of understanding of tasks.

[0068] The understanding unit can analyze the sales representative's past work history and select the optimal understanding method. For example, the understanding unit can analyze the sales representative's past work history and select the optimal understanding method. For example, the understanding unit can identify tasks that the sales representative has struggled with in the past and provide a detailed explanation for those tasks. For example, the understanding unit can prioritize understanding tasks that the sales representative excels at, enabling them to work more efficiently. For example, the understanding unit can identify frequently performed tasks from the sales representative's past work history and provide an efficient understanding method for those tasks. By analyzing the sales representative's past work history and selecting the optimal understanding method, the efficiency of operations is improved. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can select an understanding method using an AI model that takes the sales representative's past work history as input and outputs the optimal understanding method.

[0069] The understanding unit can filter tasks based on the sales representative's current projects and areas of interest when understanding tasks. For example, the understanding unit prioritizes understanding tasks related to the sales representative's current projects. The understanding unit deepens understanding by filtering relevant tasks based on the sales representative's areas of interest. For example, the understanding unit prioritizes understanding tasks related to the sales representative's current focus customers. This improves the efficiency of tasks by filtering them based on the sales representative's current projects and areas of interest. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can perform filtering using an AI model that takes the sales representative's current projects and areas of interest as input and outputs filtered tasks.

[0070] The understanding unit can estimate the emotions of sales representatives and determine the priority of tasks to be understood based on the estimated emotions. For example, if a sales representative is stressed, the understanding unit will postpone less important tasks and prioritize more important ones. For example, if a sales representative is relaxed, the understanding unit will set the task priorities as usual. For example, if a sales representative is in a hurry, the understanding unit will set the most important tasks as the highest priority. This improves work efficiency by determining task priorities according to the emotions of sales representatives. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can determine priorities using an AI model that takes sales representative emotion data as input and outputs task priorities.

[0071] The understanding unit can prioritize understanding highly relevant tasks by considering the geographical location information of sales representatives when understanding tasks. For example, when understanding tasks, the understanding unit prioritizes understanding highly relevant tasks by considering the geographical location information of sales representatives. For example, if a sales representative is in a specific region, the understanding unit prioritizes understanding tasks related to that region. For example, if a sales representative is on a business trip, the understanding unit prioritizes understanding tasks related to the destination of the business trip. For example, if a sales representative is working from home, the understanding unit prioritizes understanding tasks that can be done from home. This improves the efficiency of operations by prioritizing the understanding of highly relevant tasks by considering the geographical location information of sales representatives. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can understand tasks using an AI model that takes the geographical location information of sales representatives as input and outputs highly relevant tasks.

[0072] The understanding unit can analyze the social media activities of sales representatives to understand related tasks when understanding business operations. For example, the understanding unit can analyze the social media activities of sales representatives to understand related tasks when understanding business operations. For example, the understanding unit can prioritize understanding tasks related to topics that sales representatives have shown interest in on social media. For example, the understanding unit can identify and understand tasks related to important customers or projects from the social media activities of sales representatives. For example, the understanding unit can understand related tasks based on information shared by sales representatives on social media. This improves the efficiency of operations by analyzing the social media activities of sales representatives to understand related tasks. Some or all of the above processing in the understanding unit may be performed using AI, for example, or not using AI. For example, the understanding unit can understand business operations using an AI model that takes the social media activities of sales representatives as input and outputs related tasks.

[0073] The reporting unit can estimate the emotions of sales representatives and adjust the way reports are presented based on the estimated emotions. For example, if a sales representative is stressed, the reporting unit will provide a concise and to-the-point report. If a sales representative is relaxed, the reporting unit will provide a detailed report. If a sales representative is in a hurry, the reporting unit will provide a quick report. This improves the efficiency of reporting by adjusting the presentation of reports according to the emotions of the sales representatives. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can adjust the presentation using an AI model that takes sales representative emotion data as input and outputs a presentation style for reports.

[0074] The reporting unit can adjust the level of detail in a report based on the importance of the task when reporting tasks or to-dos. For example, the reporting unit can provide detailed reports for high-importance tasks, and concise reports for low-importance tasks. The reporting unit can also adjust the level of detail in a stepwise manner according to importance. This improves reporting efficiency by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail of the report.

[0075] The reporting department can apply different reporting algorithms depending on the task category when reporting tasks and to-dos. For example, the reporting department might report detailed customer information and progress for sales tasks. For example, it might report concisely on progress and next steps for internal business tasks. For example, it might report urgent tasks quickly to encourage immediate action. By applying different reporting algorithms depending on the task category, the efficiency of reporting is improved. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can perform reporting using an AI model that takes task categories as input and outputs a reporting algorithm.

[0076] The reporting unit can estimate the emotions of sales representatives and adjust the length of the report based on the estimated emotions. For example, if a sales representative is stressed, the reporting unit will provide a short, concise report. For example, if a sales representative is relaxed, the reporting unit will provide a detailed report. For example, if a sales representative is in a hurry, the reporting unit will provide a quick report. This improves the efficiency of reporting by adjusting the length of the report according to the emotions of the sales representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can adjust the length using an AI model that takes sales representative emotion data as input and outputs the length of the report.

[0077] The reporting department can prioritize tasks and to-dos based on their submission dates. For example, the reporting department prioritizes reporting tasks with approaching deadlines, while delaying reporting tasks with later deadlines. The reporting department can also adjust the reporting priority in stages according to the submission dates. This improves reporting efficiency by prioritizing reports based on task submission dates. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can use an AI model that takes task submission dates as input and outputs reporting priorities to determine the priority.

[0078] The reporting department can adjust the order of reports based on the relevance of tasks when reporting tasks and to-dos. For example, the reporting department prioritizes reporting tasks related to important customers. For example, the reporting department postpones reporting tasks related to internal operations. For example, the reporting department adjusts the order of reports in stages according to the relevance of tasks. This improves the efficiency of reporting by adjusting the order of reports based on the relevance of tasks. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can adjust the order using an AI model that takes the relevance of tasks as input and outputs the order of reports.

[0079] The proposal department can estimate the emotions of sales representatives and adjust the presentation of proposals based on the estimated emotions. For example, if a sales representative is stressed, the proposal department will make a concise and to-the-point proposal. For example, if a sales representative is relaxed, the proposal department will make a detailed proposal. For example, if a sales representative is in a hurry, the proposal department will make a quick proposal. This improves the efficiency of proposals by adjusting the presentation of proposals according to the emotions of the sales representatives. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can adjust the presentation using an AI model that takes sales representative emotion data as input and outputs a presentation of proposals.

[0080] The proposal department can adjust the level of detail in its proposals based on the importance of the sales data when proposing tasks. For example, the proposal department can adjust the level of detail in its proposals based on the importance of the sales data when proposing tasks. For example, the proposal department can make detailed proposals for sales data with high importance. For example, the proposal department can make concise proposals for sales data with low importance. For example, the proposal department can adjust the level of detail in its proposals in stages according to importance. This improves the efficiency of proposals by adjusting the level of detail in proposals based on the importance of the sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can adjust the level of detail using an AI model that takes the importance of sales data as input and outputs the level of detail in proposals.

[0081] The proposal department can apply different proposal algorithms depending on the category of sales data when proposing tasks. For example, when proposing tasks, the proposal department can apply different proposal algorithms depending on the category of sales data. For example, for customer support proposals, the proposal department can make proposals based on customer information and past transaction history. For internal operations proposals, the proposal department can make proposals based on the progress of the work and the next steps. For emergency response proposals, the proposal department can make proposals for quick response. By applying different proposal algorithms depending on the category of sales data, the efficiency of proposals is improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes sales data categories as input and outputs a proposal algorithm.

[0082] The proposal unit can estimate the salesperson's emotions and adjust the length of the proposal based on the estimated emotions. For example, if the salesperson is stressed, the proposal unit will make a short, to-the-point proposal. For example, if the salesperson is relaxed, the proposal unit will make a detailed proposal. For example, if the salesperson is in a hurry, the proposal unit will make a quick proposal. This improves the efficiency of proposals by adjusting the length according to the salesperson's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can adjust the length using an AI model that takes the salesperson's emotion data as input and outputs the length of the proposal.

[0083] The proposal department can determine the priority of proposals based on the submission timing of sales data when proposing work. For example, the proposal department will prioritize proposals for sales data with approaching deadlines. For example, the proposal department will postpone proposals for sales data with later submission deadlines. For example, the proposal department will adjust the priority of proposals in stages according to the submission timing. This improves the efficiency of proposals by determining the priority of proposals based on the submission timing of sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can determine the priority using an AI model that takes the submission timing of sales data as input and outputs the priority of proposals.

[0084] The proposal department can adjust the order of proposals based on the relevance of sales data when proposing tasks. For example, the proposal department will prioritize proposals related to important customers. For example, it will postpone proposals related to internal operations. The proposal department will adjust the order of proposals in stages according to the relevance of the sales data. This improves the efficiency of proposals by adjusting the order of proposals based on the relevance of sales data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can adjust the order using an AI model that takes the relevance of sales data as input and outputs the order of proposals.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The Understanding Unit can analyze past success stories of sales representatives and propose the optimal approach for similar situations. For example, the Understanding Unit can identify past successful sales methods and approaches and apply them to the current situation. For example, the Understanding Unit can propose the optimal sales strategy based on data obtained from past success stories. For example, the Understanding Unit can analyze past success stories and propose effective approaches for similar customers. This allows sales representatives to leverage past success stories to conduct sales activities efficiently.

[0087] The proposal department can estimate the sales representative's emotions and adjust the proposal content based on those estimates. For example, if the sales representative is stressed, the proposal department will make a concise and to-the-point proposal. If the sales representative is relaxed, the proposal department will make a detailed proposal. If the sales representative is in a hurry, the proposal department will make a quick proposal. In this way, the efficiency of proposals is improved by adjusting the content according to the sales representative's emotions.

[0088] The reporting department can estimate the emotions of sales representatives and adjust the timing of reports based on those estimates. For example, if a sales representative is feeling stressed, the reporting department will delay the report. If a sales representative is relaxed, the reporting department will report at the usual time. If a sales representative is in a hurry, the reporting department will report quickly. This improves the efficiency of reporting by adjusting the timing of reports according to the emotions of sales representatives.

[0089] The Understanding Department can analyze past failures of sales representatives and provide advice to prevent similar mistakes. For example, it can identify sales methods and approaches that have failed in the past and suggest points to avoid in the current situation. For example, it can propose strategies to minimize risk based on data obtained from past failures. For example, it can analyze past failures and suggest points to be careful about with similar customers. This allows sales representatives to conduct sales activities more efficiently by utilizing past failures.

[0090] The proposal department can estimate the emotions of sales representatives and prioritize proposals based on those emotions. For example, if a sales representative is stressed, the proposal department will prioritize high-priority proposals. If a sales representative is relaxed, the proposal department will proceed with proposals in the usual order of priority. If a sales representative is in a hurry, the proposal department will prioritize the most important proposals. This improves the efficiency of proposals by prioritizing them according to the emotions of the sales representatives.

[0091] The reporting department can estimate the sales representative's emotions and adjust the report format based on that estimation. For example, if the sales representative is stressed, the reporting department will provide a concise, visual report. If the sales representative is relaxed, for example, the reporting department will provide a detailed, text-based report. If the sales representative is in a hurry, for example, the reporting department will provide a short, to-the-point report. This improves the efficiency of reporting by adjusting the report format according to the sales representative's emotions.

[0092] The understanding unit can assess the skill level of sales representatives and provide methods for understanding tasks that are appropriate to their skill level. For example, if a sales representative is a beginner, the understanding unit will explain basic work procedures in detail. If a sales representative is an intermediate level, the understanding unit will suggest efficient work procedures. If a sales representative is an advanced level, the understanding unit will concisely present the optimal work procedures. By providing methods for understanding tasks according to the skill level of sales representatives, the efficiency of operations is improved.

[0093] The proposal department can estimate the sales representative's emotions and customize the content of the proposal based on those emotions. For example, if the sales representative is stressed, the proposal department will make a concise and actionable proposal. If the sales representative is relaxed, the proposal department will make a detailed proposal. If the sales representative is in a hurry, the proposal department will make a proposal that can be implemented quickly. In this way, the efficiency of proposals is improved by customizing the content of the proposal according to the sales representative's emotions.

[0094] The reporting department can estimate the emotions of sales representatives and adjust the frequency of reports based on those estimates. For example, if a sales representative is stressed, the reporting department will reduce the frequency of reports. If a sales representative is relaxed, the reporting department will report at the normal frequency. If a sales representative is in a hurry, the reporting department will provide only the minimum necessary reports. This improves the efficiency of reporting by adjusting the frequency of reports according to the emotions of the sales representatives.

[0095] The understanding unit can analyze the learning style of sales representatives and provide the optimal learning method. For example, if a sales representative prefers visual learning, the understanding unit will provide explanations that make extensive use of diagrams and graphs. If a sales representative prefers auditory learning, the understanding unit will provide audio guides. If a sales representative prefers experiential learning, the understanding unit will provide practical exercises. By providing the optimal learning method according to the learning style of each sales representative, the efficiency of work is improved.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The Understanding Department will understand the duties of a corporate sales representative. These duties include customer visits, contract negotiations, and presentations. The Understanding Department will learn about the PC work, smartphone operation, and business system operation of corporate sales representatives and grasp the optimal operating methods. Step 2: The reporting department automatically reports today's tasks and to-dos based on the work understood by the understanding department. The reporting department grasps the sales representative's schedule and work content and suggests priorities. For example, it prioritizes reporting important customer interactions and meeting schedules. Step 3: The Proposal Department plans routine tasks and proposes work based on the tasks and to-dos reported by the Reporting Department. The Proposal Department analyzes sales data and generates and proposes the next actions and proposals to customers. For example, it automatically generates the next customers to approach and the content of proposals based on past sales data. Processing in the Proposal Department may or may not be performed using AI.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the understanding unit, reporting unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the smart device 14, which learns the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating method. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the sales representative's schedule and work content and proposes priorities. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes sales data and generates and proposes the next actions and proposals to customers. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the understanding unit, reporting unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the smart glasses 214, which learns the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating method. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the sales representative's schedule and work content and proposes priorities. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes sales data and generates and proposes the next actions and proposals to customers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the understanding unit, reporting unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the headset terminal 314, which learns the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating method. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the sales representative's schedule and work content and proposes priorities. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes sales data and generates and proposes the next actions and proposals to customers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the understanding unit, reporting unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the understanding unit is implemented by the control unit 46A of the robot 414, which learns the PC work, smartphone operation, and business system operation of corporate sales representatives and grasps the optimal operating method. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which grasps the sales representative's schedule and work content and proposes priorities. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes sales data and generates and proposes the next actions and proposals to customers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) The Understanding Department, which understands the work of corporate sales representatives, Based on the tasks understood by the aforementioned understanding unit, the reporting unit automatically reports today's tasks and to-dos, The system includes a proposal unit that plans routine tasks and proposes work based on the tasks and to-dos reported by the reporting unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Analyze sales data to generate and propose the next steps and customer proposals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on past sales data, it automatically generates the next customers to approach and the content of the proposals to be made. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting department, Understand the sales representative's schedule and work content, and propose priorities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned understanding unit is, Completely understand the PC work, smartphone operation, and business system operation of corporate sales representatives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Using generation AI, we automatically generate documents such as sales emails, proposals, and reports. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned understanding unit is, Estimate the emotions of sales representatives and adjust their understanding of the work based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned understanding unit is, Analyze the sales representative's past work history and select the most appropriate method of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned understanding unit is, When understanding the business, we filter based on the sales representative's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned understanding unit is, Estimate the emotions of sales representatives and prioritize tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned understanding unit is, When understanding business operations, prioritize understanding the most relevant tasks by considering the geographical location of the sales representatives. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned understanding unit is, To understand the business, we analyze the social media activities of sales representatives and understand the related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reporting department, Estimate the emotions of sales representatives and adjust the way reports are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reporting department, When reporting tasks or to-dos, adjust the level of detail in the report based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reporting department, When reporting tasks and to-dos, different reporting algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reporting department, Estimate the sales representative's emotions and adjust the length of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reporting department, When reporting tasks and to-dos, prioritize reports based on when the tasks should be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reporting department, When reporting tasks and to-dos, adjust the order of reporting based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, Estimate the emotions of the sales representative and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing work, adjust the level of detail in the proposal based on the importance of the sales data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing tasks, different proposal algorithms are applied depending on the category of sales data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the sales representative's emotions and adjusts the length of the proposal based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing work, we prioritize proposals based on the timing of sales data submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing tasks, adjust the order of proposals based on the relevance of sales data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Understanding Department, which understands the work of corporate sales representatives, Based on the tasks understood by the aforementioned understanding unit, the reporting unit automatically reports today's tasks and to-dos, The system includes a proposal unit that plans routine tasks and proposes work based on the tasks and to-dos reported by the reporting unit. A system characterized by the following features.

2. The aforementioned proposal section is, Analyze sales data to generate and propose the next steps and customer proposals. The system according to feature 1.

3. The aforementioned proposal section is, Based on past sales data, it automatically generates the next customers to approach and the content of the proposals to be made. The system according to feature 1.

4. The aforementioned reporting department, Understand the sales representative's schedule and work content, and propose priorities. The system according to feature 1.

5. The aforementioned understanding unit is, Completely understand the PC work, smartphone operation, and business system operation of corporate sales representatives. The system according to feature 1.

6. The aforementioned proposal section is, Using AI generation, we automatically generate documents such as sales emails, proposals, and reports. The system according to feature 1.

7. The aforementioned understanding unit is, Estimate the emotions of sales representatives and adjust their understanding of the work based on those estimated emotions. The system according to feature 1.

8. The aforementioned understanding unit is, Analyze the sales representative's past work history and select the most appropriate method of understanding. The system according to feature 1.

9. The aforementioned understanding unit is, When understanding the business, we filter based on the sales representative's current projects and areas of interest. The system according to feature 1.

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