system

Abstract

The system according to the embodiment aims to automate the handover of sales information from a predecessor to a successor in an efficient and accurate manner. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a creation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The creation unit creates handover materials based on the data analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] In conventional technology, the handover of sales information from predecessor to successor is done manually, which is inefficient and can lead to information omissions or errors.

[0005] The system according to the embodiment aims to automate the handover of sales information from a predecessor to a successor in an efficient and accurate manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a creation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The creation unit creates handover materials based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the transfer of sales information from a predecessor to a successor, and can do so efficiently and accurately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sales information handover system according to an embodiment of the present invention automatically prepares sales information handover documents from a predecessor to a successor. This sales information handover system collects and analyzes data such as past transaction information, internal communication information, project information, and business card information, and automatically prepares handover documents. For example, the sales information handover system collects transaction details and customer information from past transaction information and internal communication history from internal communication information. It ascertains the status of ongoing projects from project information and collects business card information from business card information. The sales information handover system then analyzes the collected data. It uses AI for the analysis to integrate information obtained from each data source and identify correlations. For example, it compares transaction information with project information to identify which customers are involved in which projects. It also analyzes internal communication information to extract important communication history. Based on the analysis results, the sales information handover system automatically prepares handover documents. The handover documents include customer information, transaction history, ongoing project information, and important communication history. This allows the successor to grasp the necessary information at a glance and begin work quickly. For example, when a sales representative leaves the company, this sales information handover system is used to create handover materials. The sales information handover system collects and analyzes past transaction information, internal communication information, project information, and business card information. Based on the analysis results, it automatically creates handover materials that include customer information, transaction history, ongoing project information, and important communication history. The successor can start work quickly based on these materials. In this way, the sales information handover system streamlines the handover process and enables the successor to start work quickly.

[0029] A sales information handover system according to an embodiment includes a collection unit, an analysis unit, and a creation unit. The collection unit collects data such as past transaction information, internal communication information, project information, and business card information. For example, the collection unit can collect transaction details and customer information from the past transaction information. The collection unit can also collect internal communication history from internal communication information. Furthermore, the collection unit can grasp the status of ongoing projects from project information and collect business card information from business card information. The analysis unit analyzes the data collected by the collection unit. The analysis uses AI to integrate information obtained from each data source and find correlations. For example, the analysis unit can cross-reference transaction information with project information and identify which customers are involved in which projects. The analysis unit can also analyze internal communication information and extract important communication history. The creation unit creates handover materials based on the data analyzed by the analysis unit. The handover materials include customer information, transaction history, ongoing project information, and important communication history. For example, the creation unit organizes customer information, summarizes transaction history, records information on ongoing projects, extracts important communication history, and creates handover materials based on the analysis results. As a result, the sales information handover system according to the embodiment can improve the efficiency of handover work and enable successors to start work quickly.

[0030] The collection unit can collect at least one of past transaction information, internal communication information, project information, and business card information. The collection unit, for example, collects transaction details and customer information from past transaction information. For example, the collection unit can collect information such as transaction date and time, transaction amount, and transaction partner. The collection unit can also collect internal communication history from internal communication information. For example, the collection unit can collect information such as emails, chat logs, and meeting records. Furthermore, the collection unit can grasp the status of ongoing projects from project information and collect business card information from business card information. For example, the collection unit can collect information such as the progress of the project, the person in charge, and the deadline. The collection unit can also collect information such as name, company name, position, and contact information from business card information. In this way, the collection unit can comprehensively collect necessary information and enrich the content of handover materials.

[0031] The analysis unit can integrate the collected data and apply an algorithm to find associations. The analysis unit can, for example, apply a clustering algorithm to integrate the collected data and find associations. For example, the analysis unit can cluster the collected data and group highly related data. The analysis unit can also apply regression analysis to integrate the collected data and find associations. For example, the analysis unit can perform regression analysis on the collected data to identify relationships between the data. The analysis unit can also apply a machine learning algorithm to integrate the collected data and find associations. For example, the analysis unit can input the collected data into a machine learning algorithm to learn patterns between the data and find associations. In this way, the analysis unit can improve the accuracy of handover materials by finding associations in the data.

[0032] The analysis unit can set criteria for comparing transaction information with project information and identifying which customers are involved in which projects. The analysis unit can, for example, set criteria for comparing transaction information with project information and identifying which customers are involved in which projects. For example, the analysis unit can set the degree of customer involvement as a criterion and compare the transaction information with the project information. The analysis unit can also set transaction frequency as a criterion and compare the transaction information with the project information. Furthermore, the analysis unit can identify which customers are involved in which projects based on the degree of customer involvement and transaction frequency. In this way, the analysis unit can specify the content of handover materials by identifying the involvement relationship between customers and projects.

[0033] The analysis unit can set criteria for analyzing internal communication information and extracting important communication histories. The analysis unit can, for example, analyze internal communication information and set criteria for extracting important communication histories. For example, the analysis unit can set the frequency of appearance of specific keywords as a criterion and extract important communication histories. The analysis unit can also set important decisions as a criterion and extract important communication histories. Furthermore, the analysis unit can extract important communication histories based on the frequency of appearance of specific keywords and important decisions. In this way, the analysis unit can enrich the content of handover materials by extracting important communication histories.

[0034] The creation unit can set criteria for creating handover materials including customer information, transaction history, ongoing project information, and important communication history based on the analysis results. The creation unit can set criteria for creating handover materials including customer information, transaction history, ongoing project information, and important communication history based on, for example, the analysis results. For example, the creation unit can set the importance of customer information as a criterion and create handover materials. The creation unit can also set the level of detail of transaction history as a criterion and create handover materials. Furthermore, the creation unit can create handover materials based on the importance of customer information and the level of detail of transaction history. In this way, the creation unit can create comprehensive handover materials based on the analysis results, allowing a successor to start work quickly.

[0035] The collection unit can analyze past data collection history and select an efficient collection method. For example, the collection unit can identify the most efficient collection method from the past data collection history and use that method preferentially. The collection unit can also analyze the past data collection history to identify data sources that take a long time to collect and improve the collection method. Furthermore, the collection unit can optimize the collection frequency based on the past data collection history and efficiently collect necessary data. In this way, the collection unit can collect data efficiently by analyzing the past data collection history.

[0036] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can prioritize collection of data related to the work the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the collection unit can exclude unnecessary data and collect only necessary data according to the user's work situation. This enables the collection unit to collect data according to the user's work situation and areas of interest.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also prioritize collecting nearby customer information based on the user's current location. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. This enables the collection unit to collect data based on the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related customer information. The collection unit can also analyze the user's social media followers and friendships and collect related business information. Furthermore, the collection unit can analyze the user's social media activity time and determine the optimal timing for data collection. This enables the collection unit to collect data based on the user's social media activities.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance and provide deep insight. The analysis unit can also perform a concise analysis on data of low importance and provide the minimum necessary information. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data and perform analysis efficiently. This enables the analysis unit to perform analysis according to the importance of the data.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm that analyzes transaction patterns to transaction information. The analysis unit can also apply an algorithm that analyzes communication patterns to internal communication information. Furthermore, the analysis unit can apply an algorithm that analyzes the progress of a case to case information. This allows the analysis unit to perform analysis according to the data category.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit can prioritize analysis of the latest data and provide results quickly. The analysis unit can also prioritize analysis of data whose submission deadline is approaching and provide results within the deadline. Furthermore, the analysis unit can optimize the analysis schedule based on the submission time and perform analysis efficiently. This allows the analysis unit to perform analysis based on the time of data submission.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data and provide integrated results. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data and perform analysis efficiently. This enables the analysis unit to perform analysis based on the relevance of the data.

[0043] The creation unit can adjust the level of detail of the materials based on the importance of the analysis results when creating them. For example, the creation unit can create materials that include detailed explanations for analysis results with high importance. The creation unit can also create materials that include concise explanations for analysis results with low importance. Furthermore, the creation unit can optimize the number of pages and content of the materials based on the importance of the analysis results. This allows the creation unit to create materials based on the importance of the analysis results.

[0044] The creation unit can apply different creation algorithms depending on the category of the document when creating it. For example, for customer information, the creation unit can create a document including a detailed customer profile. For transaction history, the creation unit can also create a document including a detailed transaction history. Furthermore, for case information, the creation unit can create a document that details the progress of the case. This allows the creation unit to create the optimal document depending on the category of the document.

[0045] The creation department can determine the priority of documents based on the submission timing of analysis results when creating them. For example, the creation department can prioritize the creation of documents based on analysis results whose submission deadline is approaching. The creation department can also optimize the document creation schedule based on the submission timing. Furthermore, the creation department can adjust the level of detail of the documents depending on the submission timing and create them efficiently. This allows the creation department to create documents based on the submission timing.

[0046] The creation unit can adjust the order of the materials based on the relevance of the analysis results when creating the materials. For example, the creation unit can prioritize the description of highly relevant analysis results to create integrated materials. The creation unit can also postpone less relevant analysis results and prioritize the description of important information. Furthermore, the creation unit can optimize the chapter structure and order of the materials based on the relevance of the analysis results. This enables the creation unit to create optimal materials based on relevance.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can analyze the user's past behavioral history and predict the optimal timing for data collection. For example, the collection unit can analyze the time periods and days of the week when the user collected data in the past and identify the most efficient collection timing. The collection unit can also predict the optimal collection timing in the future by learning the user's behavioral patterns and building a prediction model. Furthermore, the collection unit can improve the user's work efficiency by referring to the user's calendar information and collecting data before and after important meetings and events. This enables the collection unit to efficiently collect data based on the user's behavioral history.

[0049] The analysis unit can evaluate the reliability of collected data and filter out unreliable data. For example, the analysis unit can evaluate the source of the data and the collection method, and filter out unreliable data. The analysis unit can also check the consistency and integrity of the data and filter out inconsistent data. Furthermore, the analysis unit can calculate a reliability score for the data and use only reliable data for analysis. This allows the analysis unit to provide accurate analysis results based on reliable data.

[0050] The production department can add interactive elements to the handover materials to enable successors to quickly search for the information they need. For example, the production department can add a search function to the handover materials to enable successors to quickly find relevant information by entering specific keywords or phrases. The production department can also embed hyperlinks in the handover materials to provide direct access to related information or external resources. Furthermore, the production department can add interactive graphs and charts to the handover materials to facilitate visual understanding of the data. This allows the production department to enable successors to efficiently retrieve information and start work quickly.

[0051] The collection unit can determine the priority of data collection taking into account the user's geographical location information. For example, if the user is in a particular area, the collection unit can prioritize collecting data related to that area. The collection unit can also prioritize collecting nearby customer information based on the user's current location. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. This enables the collection unit to collect data based on the user's geographical location information.

[0052] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm that analyzes transaction patterns to transaction information. The analysis unit can also apply an algorithm that analyzes communication patterns to internal communication information. Furthermore, the analysis unit can apply an algorithm that analyzes the progress of a project to project information. This allows the analysis unit to perform analysis according to the data category.

[0053] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related customer information. The collection unit can also analyze the user's social media followers and friendships and collect related business information. Furthermore, the collection unit can analyze the user's social media activity time and determine the optimal timing for data collection. This enables the collection unit to collect data based on the user's social media activities.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection department collects data such as past transaction information, internal communication information, project information, and business card information. For example, the collection department collects transaction details and customer information from past transaction information, and collects internal communication history from internal communication information. Furthermore, the department grasps the status of ongoing projects from project information and collects business card information from business card information. Step 2: The analysis department analyzes the data collected by the collection department. AI is used for the analysis, integrating information obtained from each data source and identifying correlations. For example, transaction information and project information are collated to identify which customers are involved in which projects. Internal communication information is also analyzed to extract important communication histories. Step 3: The creation department creates handover materials based on the data analyzed by the analysis department. The handover materials include customer information, transaction history, ongoing project information, and important communication history. For example, based on the analysis results, the creation of handover materials involves organizing customer information, summarizing transaction history, recording ongoing project information, and extracting important communication history.

[0056] (Example 2) A sales information handover system according to an embodiment of the present invention automatically prepares sales information handover documents from a predecessor to a successor. This sales information handover system collects and analyzes data such as past transaction information, internal communication information, project information, and business card information, and automatically prepares handover documents. For example, the sales information handover system collects transaction details and customer information from past transaction information and internal communication history from internal communication information. It ascertains the status of ongoing projects from project information and collects business card information from business card information. The sales information handover system then analyzes the collected data. It uses AI for the analysis to integrate information obtained from each data source and identify correlations. For example, it compares transaction information with project information to identify which customers are involved in which projects. It also analyzes internal communication information to extract important communication history. Based on the analysis results, the sales information handover system automatically prepares handover documents. The handover documents include customer information, transaction history, ongoing project information, and important communication history. This allows the successor to grasp the necessary information at a glance and begin work quickly. For example, when a sales representative leaves the company, this sales information handover system is used to create handover materials. The sales information handover system collects and analyzes past transaction information, internal communication information, project information, and business card information. Based on the analysis results, it automatically creates handover materials that include customer information, transaction history, ongoing project information, and important communication history. The successor can start work quickly based on these materials. In this way, the sales information handover system streamlines the handover process and enables the successor to start work quickly.

[0057] A sales information handover system according to an embodiment includes a collection unit, an analysis unit, and a creation unit. The collection unit collects data such as past transaction information, internal communication information, project information, and business card information. For example, the collection unit can collect transaction details and customer information from the past transaction information. The collection unit can also collect internal communication history from internal communication information. Furthermore, the collection unit can grasp the status of ongoing projects from project information and collect business card information from business card information. The analysis unit analyzes the data collected by the collection unit. The analysis uses AI to integrate information obtained from each data source and find correlations. For example, the analysis unit can cross-reference transaction information with project information and identify which customers are involved in which projects. The analysis unit can also analyze internal communication information and extract important communication history. The creation unit creates handover materials based on the data analyzed by the analysis unit. The handover materials include customer information, transaction history, ongoing project information, and important communication history. For example, the creation unit organizes customer information, summarizes transaction history, records information on ongoing projects, extracts important communication history, and creates handover materials based on the analysis results. As a result, the sales information handover system according to the embodiment can improve the efficiency of handover work and enable successors to start work quickly.

[0058] The collection unit can collect at least one of past transaction information, internal communication information, project information, and business card information. The collection unit, for example, collects transaction details and customer information from past transaction information. For example, the collection unit can collect information such as transaction date and time, transaction amount, and transaction partner. The collection unit can also collect internal communication history from internal communication information. For example, the collection unit can collect information such as emails, chat logs, and meeting records. Furthermore, the collection unit can grasp the status of ongoing projects from project information and collect business card information from business card information. For example, the collection unit can collect information such as the progress of the project, the person in charge, and the deadline. The collection unit can also collect information such as name, company name, position, and contact information from business card information. In this way, the collection unit can comprehensively collect necessary information and enrich the content of handover materials.

[0059] The analysis unit can integrate the collected data and apply an algorithm to find associations. The analysis unit can, for example, apply a clustering algorithm to integrate the collected data and find associations. For example, the analysis unit can cluster the collected data and group highly related data. The analysis unit can also apply regression analysis to integrate the collected data and find associations. For example, the analysis unit can perform regression analysis on the collected data to identify relationships between the data. The analysis unit can also apply a machine learning algorithm to integrate the collected data and find associations. For example, the analysis unit can input the collected data into a machine learning algorithm to learn patterns between the data and find associations. In this way, the analysis unit can improve the accuracy of handover materials by finding associations in the data.

[0060] The analysis unit can set criteria for comparing transaction information with project information and identifying which customers are involved in which projects. The analysis unit can, for example, set criteria for comparing transaction information with project information and identifying which customers are involved in which projects. For example, the analysis unit can set the degree of customer involvement as a criterion and compare the transaction information with the project information. The analysis unit can also set transaction frequency as a criterion and compare the transaction information with the project information. Furthermore, the analysis unit can identify which customers are involved in which projects based on the degree of customer involvement and transaction frequency. In this way, the analysis unit can specify the content of handover materials by identifying the involvement relationship between customers and projects.

[0061] The analysis unit can set criteria for analyzing internal communication information and extracting important communication histories. The analysis unit can, for example, analyze internal communication information and set criteria for extracting important communication histories. For example, the analysis unit can set the frequency of appearance of specific keywords as a criterion and extract important communication histories. The analysis unit can also set important decisions as a criterion and extract important communication histories. Furthermore, the analysis unit can extract important communication histories based on the frequency of appearance of specific keywords and important decisions. In this way, the analysis unit can enrich the content of handover materials by extracting important communication histories.

[0062] The creation unit can set criteria for creating handover materials including customer information, transaction history, ongoing project information, and important communication history based on the analysis results. The creation unit can set criteria for creating handover materials including customer information, transaction history, ongoing project information, and important communication history based on, for example, the analysis results. For example, the creation unit can set the importance of customer information as a criterion and create handover materials. The creation unit can also set the level of detail of transaction history as a criterion and create handover materials. Furthermore, the creation unit can create handover materials based on the importance of customer information and the level of detail of transaction history. In this way, the creation unit can create comprehensive handover materials based on the analysis results, allowing a successor to start work quickly.

[0063] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay data collection and start collection when the user is relaxed. The collection unit can also immediately start data collection when the user is concentrating, thereby efficiently collecting information. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The collection unit can analyze past data collection history and select an efficient collection method. For example, the collection unit can identify the most efficient collection method from the past data collection history and use that method preferentially. The collection unit can also analyze the past data collection history to identify data sources that take a long time to collect and improve the collection method. Furthermore, the collection unit can optimize the collection frequency based on the past data collection history and efficiently collect necessary data. In this way, the collection unit can collect data efficiently by analyzing the past data collection history.

[0065] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can prioritize collection of data related to the work the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the collection unit can exclude unnecessary data and collect only necessary data according to the user's work situation. This enables the collection unit to collect data according to the user's work situation and areas of interest.

[0066] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can postpone collecting less important data and prioritize collecting more important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data to facilitate later analysis. Furthermore, when the user is in a hurry, the collection unit can quickly collect the most important data and collect additional detailed data later. This allows the collection unit to prioritize data according to the user's emotions, thereby enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also prioritize collecting nearby customer information based on the user's current location. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. This enables the collection unit to collect data based on the user's geographical location information.

[0068] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related customer information. The collection unit can also analyze the user's social media followers and friendships and collect related business information. Furthermore, the collection unit can analyze the user's social media activity time and determine the optimal timing for data collection. This enables the collection unit to collect data based on the user's social media activities.

[0069] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results to enable deeper insights. Furthermore, if the user is in a hurry, the analysis unit can quickly perform analysis and extract only the important points. This enables the analysis unit to analyze data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance and provide deep insight. The analysis unit can also perform a concise analysis on data of low importance and provide the minimum necessary information. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data and perform analysis efficiently. This enables the analysis unit to perform analysis according to the importance of the data.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm that analyzes transaction patterns to transaction information. The analysis unit can also apply an algorithm that analyzes communication patterns to internal communication information. Furthermore, the analysis unit can apply an algorithm that analyzes the progress of a case to case information. This allows the analysis unit to perform analysis according to the data category.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This enables the analysis unit to display the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit can prioritize analysis of the latest data and provide results quickly. The analysis unit can also prioritize analysis of data whose submission deadline is approaching and provide results within the deadline. Furthermore, the analysis unit can optimize the analysis schedule based on the submission time and perform analysis efficiently. This allows the analysis unit to perform analysis based on the time of data submission.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data and provide integrated results. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can optimize the order of analysis based on the relevance of the data and perform analysis efficiently. This enables the analysis unit to perform analysis based on the relevance of the data.

[0075] The creation unit can estimate the user's emotions and adjust the creation method of handover materials based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit can create handover materials that are concise and to the point. Furthermore, if the user is relaxed, the creation unit can create handover materials that include detailed information. Furthermore, if the user is in a hurry, the creation unit can create handover materials that include only the important points so that they can be created quickly. This enables the creation unit to create handover materials that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The creation unit can adjust the level of detail of the materials based on the importance of the analysis results when creating them. For example, the creation unit can create materials that include detailed explanations for analysis results with high importance. The creation unit can also create materials that include concise explanations for analysis results with low importance. Furthermore, the creation unit can optimize the number of pages and content of the materials based on the importance of the analysis results. This allows the creation unit to create materials based on the importance of the analysis results.

[0077] The creation unit can apply different creation algorithms depending on the category of the document when creating it. For example, for customer information, the creation unit can create a document including a detailed customer profile. For transaction history, the creation unit can also create a document including a detailed transaction history. Furthermore, for case information, the creation unit can create a document that details the progress of the case. This allows the creation unit to create the optimal document depending on the category of the document.

[0078] The creation unit can estimate the user's emotions and adjust the display method of the handover materials based on the estimated user emotions. For example, if the user is nervous, the creation unit can provide a simple, highly visible display method. If the user is relaxed, the creation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the creation unit can also provide a display method that focuses on the main points. This enables the creation unit to display handover materials according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The creation department can determine the priority of documents based on the submission timing of analysis results when creating them. For example, the creation department can prioritize the creation of documents based on analysis results whose submission deadline is approaching. The creation department can also optimize the document creation schedule based on the submission timing. Furthermore, the creation department can adjust the level of detail of the documents depending on the submission timing and create them efficiently. This allows the creation department to create documents based on the submission timing.

[0080] The creation unit can adjust the order of the materials based on the relevance of the analysis results when creating the materials. For example, the creation unit can prioritize the description of highly relevant analysis results to create integrated materials. The creation unit can also postpone less relevant analysis results and prioritize the description of important information. Furthermore, the creation unit can optimize the chapter structure and order of the materials based on the relevance of the analysis results. This enables the creation unit to create optimal materials based on relevance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and creation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects past transaction information and business card information using the camera 42 and communication I / F 44 of the smart device 14, and collects internal communication information and project information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to find correlations. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates handover materials based on the analysis results. Some or all of the collection unit, analysis unit, and creation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and creation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects past transaction information and business card information using the camera 42 and communication I / F 44 of the smart glasses 214, and collects internal communication information and project information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to find correlations. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates handover materials based on the analysis results. Some or all of the collection unit, analysis unit, and creation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and creation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects past transaction information and business card information using the camera 42 and communication I / F 44 of the headset terminal 314, and collects internal communication information and project information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to find correlations. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates handover materials based on the analysis results. Some or all of the collection unit, analysis unit, and creation unit may be realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects past transaction information and business card information using the camera 42 and communication I / F 44 of the robot 414, and collects internal communication information and project information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to find correlations. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates handover materials based on the analysis results. Some or all of the collection unit, analysis unit, and creation unit may be realized, for example, by the control unit 46A of the robot 414.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can analyze the user's past behavioral history and predict the optimal timing for data collection. For example, the collection unit can analyze the time periods and days of the week when the user collected data in the past and identify the most efficient collection timing. The collection unit can also predict the optimal collection timing in the future by learning the user's behavioral patterns and building a prediction model. Furthermore, the collection unit can improve the user's work efficiency by referring to the user's calendar information and collecting data before and after important meetings and events. This enables the collection unit to efficiently collect data based on the user's behavioral history.

[0083] The analysis unit can evaluate the reliability of collected data and filter out unreliable data. For example, the analysis unit can evaluate the source of the data and the collection method, and filter out unreliable data. The analysis unit can also check the consistency and integrity of the data and filter out inconsistent data. Furthermore, the analysis unit can calculate a reliability score for the data and use only reliable data for analysis. This allows the analysis unit to provide accurate analysis results based on reliable data.

[0084] The production department can add interactive elements to the handover materials to enable successors to quickly search for the information they need. For example, the production department can add a search function to the handover materials to enable successors to quickly find relevant information by entering specific keywords or phrases. The production department can also embed hyperlinks in the handover materials to provide direct access to related information or external resources. Furthermore, the production department can add interactive graphs and charts to the handover materials to facilitate visual understanding of the data. This allows the production department to enable successors to efficiently retrieve information and start work quickly.

[0085] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed and increase the frequency of data collection when the user is relaxed. The collection unit can also increase the frequency of data collection when the user is concentrating and decrease the frequency of data collection when the user is tired. Furthermore, the collection unit can adjust the timing of data collection according to the user's emotions to reduce the burden on the user. This enables the collection unit to flexibly collect data based on the user's emotions.

[0086] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, the analysis unit can send a concise and to-the-point notification when the user is feeling stressed, and send a notification including detailed analysis results when the user is relaxed. The analysis unit can also send a quick notification when the user is in a hurry, and adjust the timing of the notification when the user has time to spare. Furthermore, the analysis unit can customize the format and content of the notification according to the user's emotions, reducing the burden on the user. This allows the analysis unit to provide a flexible notification method based on the user's emotions.

[0087] The creation unit can add a feedback function to reflect the user's emotions in the handover materials. For example, the creation unit can provide an interface that can provide emotional feedback when a successor views the handover materials. The creation unit can also collect feedback from the successor and reflect it when creating the next handover materials. Furthermore, the creation unit can improve the content and format of the handover materials based on the successor's emotional feedback. This allows the creation unit to provide handover materials that take the successor's emotions into consideration.

[0088] The collection unit can determine the priority of data collection taking into account the user's geographical location information. For example, if the user is in a particular area, the collection unit can prioritize collecting data related to that area. The collection unit can also prioritize collecting nearby customer information based on the user's current location. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. This enables the collection unit to collect data based on the user's geographical location information.

[0089] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm that analyzes transaction patterns to transaction information. The analysis unit can also apply an algorithm that analyzes communication patterns to internal communication information. Furthermore, the analysis unit can apply an algorithm that analyzes the progress of a project to project information. This allows the analysis unit to perform analysis according to the data category.

[0090] The creation unit can estimate the user's emotions from the handover materials and adjust the display method of the materials based on the estimated user's emotions. For example, if the user is nervous, the creation unit can provide a simple, highly visible display method. If the user is relaxed, the creation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the creation unit can also provide a display method that focuses on the main points. This enables the creation unit to display handover materials according to the user's emotions.

[0091] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related customer information. The collection unit can also analyze the user's social media followers and friendships and collect related business information. Furthermore, the collection unit can analyze the user's social media activity time and determine the optimal timing for data collection. This enables the collection unit to collect data based on the user's social media activities.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection department collects data such as past transaction information, internal communication information, project information, and business card information. For example, the collection department collects transaction details and customer information from past transaction information, and collects internal communication history from internal communication information. Furthermore, the department grasps the status of ongoing projects from project information and collects business card information from business card information. Step 2: The analysis department analyzes the data collected by the collection department. AI is used for the analysis, integrating information obtained from each data source and identifying correlations. For example, transaction information and project information are collated to identify which customers are involved in which projects. Internal communication information is also analyzed to extract important communication histories. Step 3: The creation department creates handover materials based on the data analyzed by the analysis department. The handover materials include customer information, transaction history, ongoing project information, and important communication history. For example, based on the analysis results, the creation of handover materials involves organizing customer information, summarizing transaction history, recording ongoing project information, and extracting important communication history.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a creation unit that creates handover materials based on the data analyzed by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect at least one of the following information: past transaction information, internal communication information, project information, and business card information 2. The system of claim 1.

3. The analysis unit Synthesize the collected data and apply algorithms to find connections 2. The system of claim 1.

4. The analysis unit Matching transaction information with deal information and establishing criteria to identify which customers are involved in which deals 2. The system of claim 1.

5. The analysis unit Analyze internal communication information and set criteria to extract important communication history 2. The system of claim 1.

6. The creation unit Based on the analysis, establish criteria for creating handover documents that include customer information, transaction history, ongoing case information, and key communication history.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past data collection history and select efficient collection methods 2. The system of claim 1.

Images