system

Abstract

An object of a system according to an embodiment is to efficiently perform brush-up of a material before a business talk.SOLUTION: A system includes a material analysis part, a successful case reflection part, an SFA data reflection part, and a market information reflection part. The material analysis part analyzes a material before business negotiation. A successful case reflection part specifies and improves the point of concern of the material analyzed by the material analysis part. An SFA data reflection part optimizes materials on the basis of the successful cases of the other person in charge of sales reflected by the successful case reflection part. The market-information-reflecting unit performs brush-up of materials based on the SFA-data reflected by the SFA-data-reflecting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process for effectively polishing documents before a business meeting is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently polish up materials before business negotiations. [Means for solving the problem]

[0006] The system according to the embodiment comprises a document analysis unit, a success story reflection unit, an SFA data reflection unit, and a market information reflection unit. The document analysis unit analyzes documents before negotiations. The success story reflection unit identifies and improves concerns about the documents analyzed by the document analysis unit. The SFA data reflection unit optimizes the documents based on success stories of other sales representatives reflected by the success story reflection unit. The market information reflection unit refines the documents based on the SFA data reflected by the SFA data reflection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently polish up materials before business negotiations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The document brush-up system according to an embodiment of the present invention is a system that improves pre-negotiation documents by showing them to a generation AI, taking into consideration concerns about the documents, success stories of other sales representatives, SFA data, and client market information. As a result, the document brush-up system can brush up pre-negotiation documents to make them more effective and persuasive.

[0029] A document brush-up system according to an embodiment includes a document analysis unit, a success story reflection unit, an SFA data reflection unit, and a market information reflection unit. The document analysis unit analyzes documents before a sales meeting. For example, the document analysis unit allows the generation AI to analyze documents before a sales meeting and identify deficiencies or concerns in the content. The document analysis unit also allows the generation AI to identify unclear parts or potentially misleading expressions in the documents and make suggestions for improving them. The success story reflection unit identifies and improves concerns in the documents analyzed by the document analysis unit. For example, the success story reflection unit allows the generation AI to analyze past success stories of other sales representatives and reflects them in the documents before a sales meeting. The success story reflection unit also incorporates approaches and presentation techniques that have been successful for the generation AI with clients in the same industry or similar clients. The SFA data reflection unit optimizes the documents based on success stories of other sales representatives reflected by the success story reflection unit. For example, the SFA data reflection unit allows the generation AI to analyze data obtained from an SFA system and reflects it in the documents before a sales meeting. In addition, the SFA data reflection unit allows the generation AI to optimize the content of the materials based on past sales negotiation history, client purchasing history, sales activity results, etc. The market information reflection unit refines the materials based on the SFA data reflected by the SFA data reflection unit. For example, the market information reflection unit allows the generation AI to analyze the client's market and issues in the news and reflect them in the pre-negotiation materials. In addition, the market information reflection unit allows the generation AI to incorporate current hot topics and issues in the client's industry into the materials, making the content more relevant to the client. As a result, the material refinement system according to the embodiment can refine pre-negotiation materials to be more effective and persuasive. For example, by improving deficiencies in the materials and incorporating success stories from other sales representatives, the success rate of sales negotiations can be increased. In addition, by reflecting SFA data and client market information, it is possible to provide materials that are more relevant to the client.

[0030] The document analysis section can refer to examples of failed sales negotiations and make proposals to prevent similar concerns from occurring in the future. For example, the generative AI extracts past failed sales negotiations from a database and compares them with the documents prepared before the negotiation. For example, it can identify expressions or unclear parts that clients have misunderstood in the past and check whether similar issues are included in the current documents. This makes it possible to prevent concerns from occurring in the future based on past failure cases.

[0031] The document analysis unit uses natural language processing technology to deeply understand the context and detect potentially misleading expressions. For example, the generative AI uses natural language processing technology to analyze the context of the document and identify expressions that may be misleading. For example, it detects ambiguous phrases and ambiguous terms and suggests replacing them with clearer expressions. This allows for a deep understanding of the context of the document and the detection of misleading expressions.

[0032] The success story reflection unit can refer to and incorporate success stories from different industries. For example, the generation AI can extract success stories from a database of different industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be enhanced by incorporating success stories from other industries.

[0033] The success story reflection unit can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generation AI analyzes images and graphs in a document and makes suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding captions to images, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0034] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI analyzes the success stories of other sales representatives and identifies the market conditions and client characteristics behind the success. For example, it analyzes specific market needs and client purchasing behavior and reflects them in the materials. In this way, by analyzing the factors behind success stories in detail and reflecting them in the materials, more effective materials can be created.

[0035] The success story reflection unit can extract particularly effective presentation slides and materials from past success stories and incorporate them into pre-negotiation materials. For example, the success story reflection unit uses a generation AI to analyze presentation slides and materials from past success stories and extract particularly effective parts. For example, slides that attracted the client's attention and persuasive data can be incorporated into pre-negotiation materials. In this way, by extracting effective parts from past success stories and incorporating them into materials, the success rate of business negotiations can be increased.

[0036] The success story reflection section also takes into account success stories from different regions and cultural spheres, making it possible to improve materials from a global perspective. For example, the generation AI extracts success stories from a database of different regions and cultural spheres and applies them to materials before negotiations. For example, it incorporates marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0037] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips into the materials. For example, the success story reflection unit uses a generation AI to analyze video and audio data from past success stories and extract presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0038] When analyzing SFA data, the SFA data reflection unit can perform a detailed analysis of the factors that led to the success and failure of past sales negotiations and reflect this in the materials. For example, the SFA data reflection unit uses a generation AI to analyze SFA data and identify the factors that led to the success and failure of past sales negotiations. For example, it analyzes the commonalities between successful sales negotiations and the causes of unsuccessful sales negotiations and reflects this in the materials. In this way, by performing a detailed analysis of the factors that led to the success and failure of past sales negotiations and reflecting this in the materials, it is possible to increase the success rate of sales negotiations.

[0039] The SFA data reflection unit can also reference SFA data from other industries and reflect success factors in other industries in the materials. For example, the SFA data reflection unit uses a generation AI to extract SFA data from a database from other industries and apply it to materials before negotiations. For example, it can incorporate sales techniques and customer service methods that have been successful in other industries. In this way, by reflecting success factors from other industries in the materials, it is possible to increase the success rate of negotiations.

[0040] The SFA data reflection unit can also analyze clients' social media activity and online behavior data and reflect it in materials. For example, the SFA data reflection unit uses generative AI to analyze clients' social media activity and online behavior data and reflect it in materials before negotiations. For example, materials can be optimized based on topics the client has shown interest in and content they have shared. In this way, analyzing clients' social media activity and online behavior data and reflecting it in materials can increase the success rate of negotiations.

[0041] When analyzing a client's market information, the Market Information Reflection Department can perform a detailed analysis of the latest market trends and competitors' activities and reflect them in the materials. For example, the Market Information Reflection Department uses a generation AI to analyze the client's market information and identify the latest market trends and competitors' activities. For example, the Market Information Reflection Department reflects these in the materials based on industry reports and news articles. This allows the latest market trends and competitors' activities to be reflected in the materials, thereby increasing the success rate of business negotiations.

[0042] The market information reflection unit can refer to industry reports and expert opinions to strengthen the content of the materials. For example, the generation AI in the market information reflection unit can refer to industry reports and expert opinions to strengthen the content of materials before negotiations. For example, the latest industry trends and expert opinions can be reflected in the materials. In this way, by referring to industry reports and expert opinions, the content of the materials can be strengthened and the success rate of negotiations can be increased.

[0043] The market information reflection unit takes into account market information from different regions and cultural spheres, enabling materials to be improved from a global perspective. For example, the generation AI extracts market information from a database for different regions and cultural spheres and applies it to materials before negotiations. For example, it incorporates topics and issues that are gaining attention in a particular region. This allows materials to be improved from a global perspective by taking into account market information from different regions and cultural spheres.

[0044] The market information reflection unit can analyze not only news articles but also social media trends and user opinions and reflect them in materials. For example, the market information reflection unit's generation AI can analyze not only news articles but also social media trends and user opinions and reflect them in materials before business negotiations. For example, it can incorporate popular topics in the client's industry and user opinions. This can increase the success rate of business negotiations by reflecting not only news articles but also social media trends and user opinions in materials.

[0045] The document analysis section can refer to examples of failed sales negotiations and make proposals to prevent similar concerns from occurring in the future. For example, the generative AI extracts past failed sales negotiations from a database and compares them with the documents prepared before the negotiation. For example, it can identify expressions or unclear parts that clients have misunderstood in the past and check whether similar issues are included in the current documents. This makes it possible to prevent concerns from occurring in the future based on past failure cases.

[0046] The document analysis unit uses natural language processing technology to deeply understand the context and detect potentially misleading expressions. For example, the generative AI uses natural language processing technology to analyze the context of the document and identify expressions that may be misleading. For example, it detects ambiguous phrases and ambiguous terms and suggests replacing them with clearer expressions. This allows for a deep understanding of the context of the document and the detection of misleading expressions.

[0047] The success story reflection unit can refer to and incorporate success stories from different industries. For example, the generation AI can extract success stories from a database of different industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be enhanced by incorporating success stories from other industries.

[0048] The success story reflection unit can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generation AI analyzes images and graphs in a document and makes suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding captions to images, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0049] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI analyzes the success stories of other sales representatives and identifies the market conditions and client characteristics behind the success. For example, it analyzes specific market needs and client purchasing behavior and reflects them in the materials. In this way, by analyzing the factors behind success stories in detail and reflecting them in the materials, more effective materials can be created.

[0050] The success story reflection section also takes into account success stories from different regions and cultural spheres, making it possible to improve materials from a global perspective. For example, the generation AI extracts success stories from a database of different regions and cultural spheres and applies them to materials before negotiations. For example, it incorporates marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0051] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips into the materials. For example, the success story reflection unit uses a generation AI to analyze video and audio data from past success stories and extract presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The document analysis department can extract elements that were particularly effective in documents used in past negotiations and reflect them in the current documents. For example, it can identify slides and data that attracted the client's attention in past negotiations and incorporate them into the current documents. It can also incorporate presentation structures and storytelling techniques that were particularly effective in past negotiations. This makes it possible to improve documents based on past success stories.

[0054] The success story reflection section can refer to and incorporate success stories from other industries. For example, the generation AI can extract success stories from a database of other industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be improved by incorporating success stories from other industries.

[0055] The Success Case Reflection Department can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generative AI can analyze images and graphs in a document and make suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding image captions, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0056] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI can analyze the success stories of other sales representatives and identify the market conditions and client characteristics behind the success. For example, it can analyze specific market needs and client purchasing behavior and reflect them in the materials. This allows for a detailed analysis of the factors behind success stories and reflects them in the materials, making it possible to create more effective materials.

[0057] The success story reflection section can extract particularly effective presentation slides and materials from past success stories and incorporate them into pre-negotiation materials. For example, the generation AI can analyze the presentation slides and materials of past success stories and extract the particularly effective parts. For example, slides that attracted the client's attention and persuasive data can be incorporated into pre-negotiation materials. In this way, by extracting the effective parts of past success stories and incorporating them into materials, the success rate of sales negotiations can be increased.

[0058] The success story reflection section can also take into account success stories from different regions and cultural spheres, improving materials from a global perspective. For example, the generation AI can extract success stories from different regions and cultural spheres from a database and apply them to pre-negotiation materials. For example, it can incorporate marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0059] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips in the materials. For example, the generation AI analyzes video and audio data from past success stories and extracts presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The document analysis unit analyzes documents prepared before a sales meeting. For example, the generation AI analyzes documents prepared before a sales meeting and identifies deficiencies or concerns in the content. The document analysis unit also allows the generation AI to identify unclear parts or expressions that may be misleading in the documents and makes suggestions for improving them. Step 2: The Success Case Reflection Department identifies and improves the issues with the materials analyzed by the Document Analysis Department. For example, the Generation AI analyzes past success stories of other sales representatives and reflects them in the materials before sales negotiations. The Generation AI also incorporates approaches and presentation techniques that have been successful with clients in the same industry or similar situations. Step 3: The SFA Data Reflection Department optimizes the materials based on the success stories of other sales representatives reflected by the Success Story Reflection Department. For example, the Generation AI analyzes data obtained from the SFA system and reflects it in the materials before sales negotiations. The Generation AI also optimizes the content of the materials based on past sales negotiation history, client purchasing history, sales activity results, etc. Step 4: The Market Information Reflection Department refines the materials based on the SFA data reflected by the SFA Data Reflection Department. For example, the Generation AI analyzes the client's market and issues in the news, and reflects this in the materials before the sales meeting. The Generation AI also incorporates current hot topics and issues in the client's industry into the materials, making them more relevant to the client.

[0062] (Example 2) The document brush-up system according to an embodiment of the present invention is a system that improves pre-negotiation documents by showing them to a generation AI, taking into consideration concerns about the documents, success stories of other sales representatives, SFA data, and client market information. As a result, the document brush-up system can brush up pre-negotiation documents to make them more effective and persuasive.

[0063] A document brush-up system according to an embodiment includes a document analysis unit, a success story reflection unit, an SFA data reflection unit, and a market information reflection unit. The document analysis unit analyzes documents before a sales meeting. For example, the document analysis unit allows the generation AI to analyze documents before a sales meeting and identify deficiencies or concerns in the content. The document analysis unit also allows the generation AI to identify unclear parts or potentially misleading expressions in the documents and make suggestions for improving them. The success story reflection unit identifies and improves concerns in the documents analyzed by the document analysis unit. For example, the success story reflection unit allows the generation AI to analyze past success stories of other sales representatives and reflects them in the documents before a sales meeting. The success story reflection unit also incorporates approaches and presentation techniques that have been successful for the generation AI with clients in the same industry or similar clients. The SFA data reflection unit optimizes the documents based on success stories of other sales representatives reflected by the success story reflection unit. For example, the SFA data reflection unit allows the generation AI to analyze data obtained from an SFA system and reflects it in the documents before a sales meeting. In addition, the SFA data reflection unit allows the generation AI to optimize the content of the materials based on past sales negotiation history, client purchasing history, sales activity results, etc. The market information reflection unit refines the materials based on the SFA data reflected by the SFA data reflection unit. For example, the market information reflection unit allows the generation AI to analyze the client's market and issues in the news and reflect them in the pre-negotiation materials. In addition, the market information reflection unit allows the generation AI to incorporate current hot topics and issues in the client's industry into the materials, making the content more relevant to the client. As a result, the material refinement system according to the embodiment can refine pre-negotiation materials to be more effective and persuasive. For example, by improving deficiencies in the materials and incorporating success stories from other sales representatives, the success rate of sales negotiations can be increased. In addition, by reflecting SFA data and client market information, it is possible to provide materials that are more relevant to the client.

[0064] The document analysis section can refer to examples of failed sales negotiations and make proposals to prevent similar concerns from occurring in the future. For example, the generative AI extracts past failed sales negotiations from a database and compares them with the documents prepared before the negotiation. For example, it can identify expressions or unclear parts that clients have misunderstood in the past and check whether similar issues are included in the current documents. This makes it possible to prevent concerns from occurring in the future based on past failure cases.

[0065] The document analysis unit uses natural language processing technology to deeply understand the context and detect potentially misleading expressions. For example, the generative AI uses natural language processing technology to analyze the context of the document and identify expressions that may be misleading. For example, it detects ambiguous phrases and ambiguous terms and suggests replacing them with clearer expressions. This allows for a deep understanding of the context of the document and the detection of misleading expressions.

[0066] The document analysis unit uses the emotion estimation function to predict the user's emotional reaction and can improve parts that cause negative reactions. For example, the generative AI uses the emotion estimation function to predict the user's emotional reaction to each section of the document. For example, it identifies expressions and data that are likely to cause negative emotions and makes suggestions for improvement. This makes it possible to predict the user's emotional reaction and improve parts that cause negative reactions.

[0067] The success story reflection unit can refer to and incorporate success stories from different industries. For example, the generation AI can extract success stories from a database of different industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be enhanced by incorporating success stories from other industries.

[0068] The success story reflection unit can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generation AI analyzes images and graphs in a document and makes suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding captions to images, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0069] The success story reflection unit can use the emotion estimation function to monitor the user's emotions in real time and make suggestions to elicit positive emotions. For example, the success story reflection unit uses the emotion estimation function to monitor the emotions of users reading materials in real time. For example, if negative emotions are detected, it will make suggestions for improvements to elicit positive emotions. In this way, the effectiveness of the materials can be increased by monitoring the user's emotions in real time and making suggestions to elicit positive emotions.

[0070] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI analyzes the success stories of other sales representatives and identifies the market conditions and client characteristics behind the success. For example, it analyzes specific market needs and client purchasing behavior and reflects them in the materials. In this way, by analyzing the factors behind success stories in detail and reflecting them in the materials, more effective materials can be created.

[0071] The success story reflection unit can extract particularly effective presentation slides and materials from past success stories and incorporate them into pre-negotiation materials. For example, the success story reflection unit uses a generation AI to analyze presentation slides and materials from past success stories and extract particularly effective parts. For example, slides that attracted the client's attention and persuasive data can be incorporated into pre-negotiation materials. In this way, by extracting effective parts from past success stories and incorporating them into materials, the success rate of business negotiations can be increased.

[0072] The success story reflection unit can use the emotion estimation function to identify the elements in a success story to which the client responded most positively and reflect these in the materials. For example, the generation AI can use the emotion estimation function to identify the elements in a success story to which the client responded most positively. For example, the success story reflection unit can analyze the emotion scores for specific slides or data and reflect them in the materials. This can increase the success rate of business negotiations by reflecting the elements to which the client responded positively in the materials.

[0073] The success story reflection section also takes into account success stories from different regions and cultural spheres, making it possible to improve materials from a global perspective. For example, the generation AI extracts success stories from a database of different regions and cultural spheres and applies them to materials before negotiations. For example, it incorporates marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0074] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips into the materials. For example, the success story reflection unit uses a generation AI to analyze video and audio data from past success stories and extract presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0075] The success story reflection unit can use the emotion estimation function to identify the parts of a success story that resonated most emotionally with the client and reflect that in the materials. For example, the generation AI can use the emotion estimation function to identify the parts of a success story that resonated most emotionally with the client. For example, the success story reflection unit can analyze the emotion scores for specific stories or episodes and reflect them in the materials. This can increase the success rate of business negotiations by reflecting the parts that resonated most emotionally with the client in the materials.

[0076] When analyzing SFA data, the SFA data reflection unit can perform a detailed analysis of the factors that led to the success and failure of past sales negotiations and reflect this in the materials. For example, the SFA data reflection unit uses a generation AI to analyze SFA data and identify the factors that led to the success and failure of past sales negotiations. For example, it analyzes the commonalities between successful sales negotiations and the causes of unsuccessful sales negotiations and reflects this in the materials. In this way, by performing a detailed analysis of the factors that led to the success and failure of past sales negotiations and reflecting this in the materials, it is possible to increase the success rate of sales negotiations.

[0077] The SFA data reflection unit can use the emotion estimation function to identify the elements of sales negotiations to which clients responded most positively from the SFA data and reflect these in the materials. For example, the SFA data reflection unit uses the emotion estimation function of the generation AI to identify the elements of sales negotiations to which clients responded most positively from the SFA data. For example, it can analyze the emotion scores for specific presentations or proposals and reflect these in the materials. This allows the success rate of sales negotiations to be increased by reflecting the elements of sales negotiations to which clients responded most positively in the materials.

[0078] The SFA data reflection unit can also reference SFA data from other industries and reflect success factors in other industries in the materials. For example, the SFA data reflection unit uses a generation AI to extract SFA data from a database from other industries and apply it to materials before negotiations. For example, it can incorporate sales techniques and customer service methods that have been successful in other industries. In this way, by reflecting success factors from other industries in the materials, it is possible to increase the success rate of negotiations.

[0079] The SFA data reflection unit can also analyze clients' social media activity and online behavior data and reflect it in materials. For example, the SFA data reflection unit uses generative AI to analyze clients' social media activity and online behavior data and reflect it in materials before negotiations. For example, materials can be optimized based on topics the client has shown interest in and content they have shared. In this way, analyzing clients' social media activity and online behavior data and reflecting it in materials can increase the success rate of negotiations.

[0080] The SFA data reflection unit uses the emotion estimation function to identify from the SFA data the elements of the business negotiations that the client most emotionally identified with and can reflect these in the materials. For example, the SFA data reflection unit uses the emotion estimation function of the generation AI to identify from the SFA data the elements of the business negotiations that the client most emotionally identified with. For example, it analyzes the emotion scores for specific stories or episodes and reflects them in the materials. This allows the success rate of business negotiations to be increased by reflecting the elements of the business negotiations that the client most emotionally identified with in the materials.

[0081] When analyzing a client's market information, the Market Information Reflection Department can perform a detailed analysis of the latest market trends and competitors' activities and reflect them in the materials. For example, the Market Information Reflection Department uses a generation AI to analyze the client's market information and identify the latest market trends and competitors' activities. For example, the Market Information Reflection Department reflects these in the materials based on industry reports and news articles. This allows the latest market trends and competitors' activities to be reflected in the materials, thereby increasing the success rate of business negotiations.

[0082] The market information reflection unit can refer to industry reports and expert opinions to strengthen the content of the materials. For example, the generation AI in the market information reflection unit can refer to industry reports and expert opinions to strengthen the content of materials before negotiations. For example, the latest industry trends and expert opinions can be reflected in the materials. In this way, by referring to industry reports and expert opinions, the content of the materials can be strengthened and the success rate of negotiations can be increased.

[0083] The market information reflection unit can use the emotion estimation function to identify the issues that the client is most interested in from the client's market information and reflect that in the materials. For example, the market information reflection unit uses the emotion estimation function of the generation AI to identify the issues that the client is most interested in from the client's market information. For example, it analyzes the emotion scores for specific topics or issues and reflects them in the materials. This allows the issues that the client is most interested in to be reflected in the materials, thereby increasing the success rate of business negotiations.

[0084] The market information reflection unit takes into account market information from different regions and cultural spheres, enabling materials to be improved from a global perspective. For example, the generation AI extracts market information from a database for different regions and cultural spheres and applies it to materials before negotiations. For example, it incorporates topics and issues that are gaining attention in a particular region. This allows materials to be improved from a global perspective by taking into account market information from different regions and cultural spheres.

[0085] The market information reflection unit can analyze not only news articles but also social media trends and user opinions and reflect them in materials. For example, the market information reflection unit's generation AI can analyze not only news articles but also social media trends and user opinions and reflect them in materials before business negotiations. For example, it can incorporate popular topics in the client's industry and user opinions. This can increase the success rate of business negotiations by reflecting not only news articles but also social media trends and user opinions in materials.

[0086] The market information reflection unit can use the emotion estimation function to identify the issues that the client most emotionally identifies with from the client's market information and reflect that in the materials. For example, the market information reflection unit uses the emotion estimation function of the generation AI to identify the issues that the client most emotionally identifies with from the client's market information. For example, the market information reflection unit analyzes the emotion scores for specific topics or issues and reflects them in the materials. This allows the issues that the client most emotionally identifies with to be reflected in the materials, thereby increasing the success rate of business negotiations.

[0087] The document analysis section can refer to examples of failed sales negotiations and make proposals to prevent similar concerns from occurring in the future. For example, the generative AI extracts past failed sales negotiations from a database and compares them with the documents prepared before the negotiation. For example, it can identify expressions or unclear parts that clients have misunderstood in the past and check whether similar issues are included in the current documents. This makes it possible to prevent concerns from occurring in the future based on past failure cases.

[0088] The document analysis unit uses natural language processing technology to deeply understand the context and detect potentially misleading expressions. For example, the generative AI uses natural language processing technology to analyze the context of the document and identify expressions that may be misleading. For example, it detects ambiguous phrases and ambiguous terms and suggests replacing them with clearer expressions. This allows for a deep understanding of the context of the document and the detection of misleading expressions.

[0089] The document analysis unit uses the emotion estimation function to predict the user's emotional reaction and can improve parts that cause negative reactions. For example, the generative AI uses the emotion estimation function to predict the user's emotional reaction to each section of the document. For example, it identifies expressions and data that are likely to cause negative emotions and makes suggestions for improvement. This makes it possible to predict the user's emotional reaction and improve parts that cause negative reactions.

[0090] The success story reflection unit can refer to and incorporate success stories from different industries. For example, the generation AI can extract success stories from a database of different industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be enhanced by incorporating success stories from other industries.

[0091] The success story reflection unit can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generation AI analyzes images and graphs in a document and makes suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding captions to images, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0092] The success story reflection unit can use the emotion estimation function to monitor the user's emotions in real time and make suggestions to elicit positive emotions. For example, the success story reflection unit uses the emotion estimation function to monitor the emotions of users reading materials in real time. For example, if negative emotions are detected, it will make suggestions for improvements to elicit positive emotions. In this way, the effectiveness of the materials can be increased by monitoring the user's emotions in real time and making suggestions to elicit positive emotions.

[0093] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI analyzes the success stories of other sales representatives and identifies the market conditions and client characteristics behind the success. For example, it analyzes specific market needs and client purchasing behavior and reflects them in the materials. In this way, by analyzing the factors behind success stories in detail and reflecting them in the materials, more effective materials can be created.

[0094] The success story reflection unit can use the emotion estimation function to identify the elements in a success story to which the client responded most positively and reflect these in the materials. For example, the generation AI can use the emotion estimation function to identify the elements in a success story to which the client responded most positively. For example, the success story reflection unit can analyze the emotion scores for specific slides or data and reflect them in the materials. This can increase the success rate of business negotiations by reflecting the elements to which the client responded positively in the materials.

[0095] The success story reflection section also takes into account success stories from different regions and cultural spheres, making it possible to improve materials from a global perspective. For example, the generation AI extracts success stories from a database of different regions and cultural spheres and applies them to materials before negotiations. For example, it incorporates marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0096] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips into the materials. For example, the success story reflection unit uses a generation AI to analyze video and audio data from past success stories and extract presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0097] The success story reflection unit can use the emotion estimation function to identify the parts of a success story that resonated most emotionally with the client and reflect that in the materials. For example, the generation AI can use the emotion estimation function to identify the parts of a success story that resonated most emotionally with the client. For example, the success story reflection unit can analyze the emotion scores for specific stories or episodes and reflect them in the materials. This can increase the success rate of business negotiations by reflecting the parts that resonated most emotionally with the client in the materials.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The document analysis department can extract elements that were particularly effective in documents used in past negotiations and reflect them in the current documents. For example, it can identify slides and data that attracted the client's attention in past negotiations and incorporate them into the current documents. It can also incorporate presentation structures and storytelling techniques that were particularly effective in past negotiations. This makes it possible to improve documents based on past success stories.

[0100] The success story reflection section can refer to and incorporate success stories from other industries. For example, the generation AI can extract success stories from a database of other industries and apply them to pre-negotiation materials. For example, it can incorporate presentation techniques and data presentation methods that have been effective in other industries. In this way, the effectiveness of the materials can be improved by incorporating success stories from other industries.

[0101] The document analysis unit uses the emotion estimation function to predict the user's emotional response and improve the parts that cause negative reactions. For example, the generative AI uses the emotion estimation function to predict the user's emotional response to each section of the document. For example, it identifies expressions and data that may cause negative emotions and makes suggestions for improvement. This makes it possible to predict the user's emotional response and improve the parts that cause negative reactions.

[0102] The Success Case Reflection Department can analyze visual elements and make suggestions to prevent visual misunderstandings. For example, the generative AI can analyze images and graphs in a document and make suggestions for improvements to prevent visual misunderstandings. For example, it can suggest clarifying graph axis labels and legends, adding image captions, etc. This can prevent misunderstandings about visual elements and enhance the visual impact of the document.

[0103] The success story reflection unit can use the emotion estimation function to monitor the user's emotions in real time and make suggestions to elicit positive emotions. For example, the generation AI uses the emotion estimation function to monitor the emotions of the user reading the materials in real time. For example, if negative emotions are detected, it will make suggestions for improvements to elicit positive emotions. This makes it possible to monitor the user's emotions in real time and make suggestions to elicit positive emotions, thereby increasing the effectiveness of the materials.

[0104] The success story reflection section can perform a detailed analysis of the factors behind success stories and reflect them in the materials. For example, the generation AI can analyze the success stories of other sales representatives and identify the market conditions and client characteristics behind the success. For example, it can analyze specific market needs and client purchasing behavior and reflect them in the materials. This allows for a detailed analysis of the factors behind success stories and reflects them in the materials, making it possible to create more effective materials.

[0105] The success story reflection section can extract particularly effective presentation slides and materials from past success stories and incorporate them into pre-negotiation materials. For example, the generation AI can analyze the presentation slides and materials of past success stories and extract the particularly effective parts. For example, slides that attracted the client's attention and persuasive data can be incorporated into pre-negotiation materials. In this way, by extracting the effective parts of past success stories and incorporating them into materials, the success rate of sales negotiations can be increased.

[0106] The success story reflection unit uses the emotion estimation function to identify the elements in a success story to which the client responded most positively, and can reflect these in the materials. For example, the generation AI uses the emotion estimation function to identify the elements in a success story to which the client responded most positively. For example, it can analyze the emotion scores for specific slides or data and reflect them in the materials. This allows the success rate of sales negotiations to be increased by reflecting the elements to which the client responded positively in the materials.

[0107] The success story reflection section can also take into account success stories from different regions and cultural spheres, improving materials from a global perspective. For example, the generation AI can extract success stories from different regions and cultural spheres from a database and apply them to pre-negotiation materials. For example, it can incorporate marketing techniques and customer service methods that were effective in a particular region. This allows materials to be improved from a global perspective by taking into account success stories from different regions and cultural spheres.

[0108] The success story reflection unit can also analyze video and audio data and reflect presentation tone and speaking tips in the materials. For example, the generation AI analyzes video and audio data from past success stories and extracts presentation tone and speaking tips. For example, it can reflect effective intonation and pauses in the materials. This allows the effectiveness of business negotiations to be improved by analyzing video and audio data and reflecting presentation tone and speaking tips in the materials.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The document analysis unit analyzes documents prepared before a sales meeting. For example, the generation AI analyzes documents prepared before a sales meeting and identifies deficiencies or concerns in the content. The document analysis unit also allows the generation AI to identify unclear parts or expressions that may be misleading in the documents and makes suggestions for improving them. Step 2: The Success Case Reflection Department identifies and improves the issues with the materials analyzed by the Document Analysis Department. For example, the Generation AI analyzes past success stories of other sales representatives and reflects them in the materials before sales negotiations. The Generation AI also incorporates approaches and presentation techniques that have been successful with clients in the same industry or similar situations. Step 3: The SFA Data Reflection Department optimizes the materials based on the success stories of other sales representatives reflected by the Success Story Reflection Department. For example, the Generation AI analyzes data obtained from the SFA system and reflects it in the materials before sales negotiations. The Generation AI also optimizes the content of the materials based on past sales negotiation history, client purchasing history, sales activity results, etc. Step 4: The Market Information Reflection Department refines the materials based on the SFA data reflected by the SFA Data Reflection Department. For example, the Generation AI analyzes the client's market and issues in the news, and reflects this in the materials before the sales meeting. The Generation AI also incorporates current hot topics and issues in the client's industry into the materials, making them more relevant to the client.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 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 document analysis department that analyzes documents before business negotiations; a success case reflection unit that identifies and improves concerns about the data analyzed by the data analysis unit; an SFA data reflection department that optimizes materials based on success stories of other sales representatives reflected by the success story reflection department; and a market information reflection unit that refines materials based on the SFA data reflected by the SFA data reflection unit. A system characterized by:

2. The data analysis unit Uses natural language processing technology to deeply understand context and detect potentially misleading expressions 2. The system of claim 1.

3. The success case reflection unit Refer to and incorporate success stories from different industries 2. The system of claim 1.

4. The SFA data reflection unit Personalize content of materials based on client purchasing history and product information 2. The system of claim 1.

5. The market information reflection unit When analyzing market information for clients, we analyze the latest market trends and competitors' activities in detail and reflect them in our materials.

2. The system of claim 1.

6. The data analysis unit Uses emotion estimation to predict users' emotional reactions and improve areas that cause negative reactions 2. The system of claim 1.

7. The success case reflection unit Emotion estimation function monitors user emotions in real time and makes suggestions that elicit positive emotions 2. The system of claim 1.

8. The SFA data reflection unit Using the sentiment estimation function, identify the elements of the sales negotiations that the client responded most positively to from the SFA data and reflect them in the materials.

2. The system of claim 1.

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