System

Abstract

To provide a system for making it easy to find an appropriate product for a business problem.SOLUTION: A specification processing unit 290 of a data processing device 12 in the system executes assignment input unit processing in which a user inputs an assignment in a sentence, commercial material information input processing in which a commercial material provider inputs commercial material information in a sentence, a unit for matching the assignment input by the assignment input processing with the commercial material information input by the commercial material information input processing, and matching processing.SELECTED DRAWING: Figure 2

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 of making it difficult to find the right product for a business problem.

[0005] The system according to the embodiment aims to make it easier to find suitable commercial materials for business problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a problem input unit, a product information input unit, and a matching unit. The problem input unit allows a user to input a problem in text. The product information input unit allows a product provider to input product information in text. The matching unit matches the problem input by the problem input unit with the product information input by the product information input unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier to find suitable commercial materials for business problems. [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 product matching system according to the embodiment of the present invention is a system in which a user inputs text about a problem, a product provider inputs text about product information, and a generation AI matches the two. This allows the product matching system to efficiently find products appropriate for the user's business problems.

[0029] A product matching system according to an embodiment includes a problem input unit, a product information input unit, and a matching unit. The problem input unit receives input from a user in text. For example, the user may input, "I want to improve the efficiency of inventory management." The problem input unit can also receive input from the user in the form of voice input or image attachment. The product information input unit receives input from a product provider in text. For example, the product provider may input, "Our inventory management system allows us to grasp inventory status in real time and achieve efficient inventory management." The product information input unit can also receive input from the user in the form of voice input or image attachment. The matching unit matches the problem input by the problem input unit with the product information input by the product information input unit. For example, the matching unit uses a generation AI to analyze the user's problem and the product information of the product provider and perform optimal matching. This allows the product matching system according to an embodiment to efficiently find products appropriate for business problems.

[0030] The problem input unit uses a generation AI to analyze the input content in real time and automatically complete it to a more specific problem. For example, if a user inputs "I want to improve the efficiency of inventory management," the generation AI analyzes it in real time and automatically completes it to "I want to improve the efficiency of inventory management. In particular, I want to understand the inventory status in real time." The generation AI uses models such as GPT-3 and BERT to analyze the user's input and automatically complete it to a specific problem. To analyze in real time, the generation AI has high processing capabilities and minimizes latency. To improve the accuracy of the auto-completion, the generation AI learns from large amounts of data and improves the variety and accuracy of the information completed. This allows the user's input to be analyzed in real time and automatically complete it to a specific problem.

[0031] The task input unit can refer to the user's past task input history and automatically suggest similar tasks. For example, if the user inputs "I want to improve the efficiency of inventory management," the task input unit will refer to the past input history and suggest, "Would you like to see other tasks related to improving the efficiency of inventory management?" The past task input history is stored in a database and referenced using a search algorithm. Similar tasks are extracted, for example, based on the similarity calculation method and the algorithm used. This allows the user's past task input history to be referenced and similar tasks to be automatically suggested.

[0032] The task input unit can add a voice input function, allowing the user to input tasks by voice. For example, when the user voice-inputs "I want to improve the efficiency of inventory management," the task input unit converts the voice into text and registers it as a task. The voice input function converts the user's voice into text, for example, using a voice recognition algorithm. The voice is processed based on, for example, the format and preprocessing method of the voice data. This allows the user to input tasks by voice.

[0033] The task input unit adds a function that allows a user to attach images or videos when entering a task, and visual information can also be used for analysis. For example, when a user enters "I want to improve the efficiency of inventory management" and attaches an image showing the current state of inventory management, the task input unit analyzes the image and registers it as a task. The image is processed, for example, based on the image data format and preprocessing method. The video is processed, for example, based on the video data format and preprocessing method. The visual information is analyzed, for example, using an image recognition algorithm or a video analysis algorithm. This allows a user to attach images or videos when entering a task.

[0034] The product information input unit can use generation AI to automatically suggest related implementation cases and success stories. For example, when a product provider inputs "inventory management system," the product information input unit's generation AI automatically suggests "Inventory management system implementation case: Success story at Company A." The generation AI uses models such as GPT-3 or BERT to analyze the information input by the product provider and suggests related implementation cases and success stories. Implementation cases are selected based on, for example, the case selection criteria and the proposal format. Success stories are selected based on, for example, the case selection criteria and the proposal format. This makes it possible to automatically suggest related implementation cases and success stories for the information input by the product provider.

[0035] The product information input unit can compare product information with that of competitors when product information is input, and clarify points of differentiation. For example, when a product provider inputs an "inventory management system," the product information input unit compares the system with that of competitors and presents points of differentiation such as, "Our system excels in that it can grasp inventory status in real time." Competitors are selected, for example, based on competitor selection criteria and comparison measures. Points of differentiation are clarified, for example, based on differentiation criteria and extraction methods. This allows points of differentiation to be clarified when product information is input, by comparing it with that of competitors.

[0036] The product information input unit adds a function that enables images and videos to be attached to the information entered by the product provider, and visual information can also be used for analysis. For example, when the product provider enters an "inventory management system" and attaches an image of the system's operation screen, the product information input unit analyzes the image and registers it as product information. Images are processed, for example, based on the image data format and preprocessing method. Videos are processed, for example, based on the video data format and preprocessing method. Visual information is analyzed, for example, using an image recognition algorithm or a video analysis algorithm. This makes it possible to attach images and videos to the information entered by the product provider.

[0037] The product information input unit can refer to past user feedback when product information is input and automatically suggest areas for improvement. For example, when a product provider inputs an "inventory management system," the product information input unit refers to past user feedback and suggests, "We have improved the operability of the inventory management system based on user requests." Past user feedback is stored in a database and referenced using a search algorithm. Improvements are suggested based on, for example, improvement criteria and suggestion methods. This allows past user feedback to be referenced and improvements to be automatically suggested when product information is input.

[0038] The matching unit takes into account the user's past preferences and behavioral history to make more personalized suggestions. For example, if the user has searched for "inventory management system" in the past, the generation AI will take that history into consideration and preferentially suggest related "inventory management system" searches. Past preferences are stored in a database and referenced using a search algorithm. Behavioral history is stored in a database and referenced using a search algorithm. This allows for more personalized suggestions to be made, taking into account the user's past preferences and behavioral history.

[0039] The matching unit can improve the matching algorithm and consider not only the technical characteristics of the product but also the post-implementation effects and ROI. For example, when the generation AI proposes an "inventory management system," the matching unit considers not only the technical characteristics but also the improvement in inventory management efficiency and ROI after implementation. The matching algorithm is improved, for example, based on the type of algorithm and implementation method. The technical characteristics are evaluated, for example, based on the evaluation criteria and evaluation method for the characteristics. The post-implementation effects are evaluated, for example, based on the effect measurement method and evaluation criteria. The ROI is evaluated, for example, based on the calculation method and evaluation criteria for return on investment. This allows proposals to consider not only the technical characteristics of the product but also the post-implementation effects and ROI.

[0040] When presenting the matching results to the user, the matching unit can simultaneously display a demo video of the product and user reviews to deepen understanding. For example, when the generation AI proposes an "inventory management system," the matching unit can simultaneously display a demo video and user reviews to make it easier for the user to understand the system's functions. The matching results are presented based on, for example, the display format of the results and the timing of presentation. The demo video of the product is presented based on, for example, the selection criteria for the video length and content. The user reviews are presented based on, for example, the selection criteria and display format for the reviews. In this way, when presenting the matching results to the user, the demo video of the product and user reviews can simultaneously be displayed to deepen understanding.

[0041] The matching unit can classify the matching results by different industries or applications, and present them in a format that is likely to interest the user. For example, when the generation AI proposes an "inventory management system," the matching unit classifies them by different industries (e.g., manufacturing, retail) or applications, and presents them in a format that is likely to interest the user. The matching results are presented based on, for example, the display format of the results and the timing of presentation. Different industries are classified based on, for example, industry classification criteria and classification methods. Applications are classified based on, for example, application classification criteria and classification methods. This allows the matching results to be classified by different industries and applications, and presented in a format that is likely to interest the user.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When a user inputs a task, the task input unit can automatically classify the input content using natural language processing technology and assign it to an appropriate category. For example, if a user inputs "I want to improve the efficiency of inventory management," the system analyzes the content and automatically classifies it into the category "inventory management." This ensures that the task entered by the user is classified into the appropriate category, allowing subsequent processing to be carried out efficiently. Furthermore, the task input unit can also suggest related subcategories for the task entered by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest subcategories such as "Introduction of an inventory management system" and "Automation of inventory management." This allows the user to set more specific tasks.

[0044] When a user enters a problem, the problem input unit can automatically check the grammar and spelling of the input and make correction suggestions. For example, if a user enters "I want to improve the efficiency of inventory management," the system will automatically suggest corrections if there are grammatical errors or spelling mistakes. This allows the user to enter an accurate problem. The problem input unit can also automatically extract related keywords for the problem entered by the user, improving search accuracy. For example, if a user enters "I want to improve the efficiency of inventory management," keywords such as "inventory management" and "efficiency" will be extracted, improving the accuracy of search results. This allows the user to find more appropriate products.

[0045] When a user inputs a problem, the problem input unit can automatically suggest related reference materials and guidelines based on the input content. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest related reference materials and guidelines, making it easier for the user to obtain information for solving the problem. This makes it easier for the user to find specific means to solve the problem. The problem input unit can also suggest best practices in related industries for the problem input by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest best practices for inventory management, allowing the user to learn efficient inventory management methods. This allows the user to obtain specific means to improve the efficiency of their work.

[0046] When a user inputs a problem, the problem input unit can automatically translate the input content and display it in multiple languages. For example, if a user inputs "I want to improve the efficiency of inventory management" in Japanese, the system automatically translates it into English, Chinese, etc. and displays it. This makes it easier for users who speak different languages ​​to share the same problem. The problem input unit can also suggest related reference materials and guidelines in multiple languages ​​for the problem input by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest reference materials in English and Chinese, making it easier for the user to obtain information in multiple languages. This makes it easier for users to utilize information in different languages ​​and find ways to solve their problems.

[0047] The product information input unit can automatically suggest related patent information and technical literature for the information entered by the product provider. For example, if a product provider enters "inventory management system," the system will suggest related patent information and technical literature, making it easier for the product provider to obtain technical support. This makes it easier for the product provider to promote the technological superiority of their product. The product information input unit can also suggest related technology trends and market trends for the information entered by the product provider. For example, if "inventory management system" is entered, the system will suggest the latest technology trends and market trends, making it easier for the product provider to provide products that meet market needs. This makes it easier for the product provider to obtain information to provide competitive products.

[0048] The product information input unit can automatically perform compliance checks based on relevant industry regulations and standards for information entered by product providers. For example, when a product provider enters "inventory management system," the system performs a compliance check based on relevant industry regulations and standards and provides information such as, "Our system complies with industry standards." This makes it easier for product providers to promote their products as compliant with regulations and standards. The product information input unit can also automatically evaluate the legal risks associated with the information entered by product providers and make suggestions for risk mitigation. For example, when an "inventory management system" is entered, the system evaluates the legal risks and provides information such as, "Our system complies with data protection laws." This allows product providers to provide products while mitigating legal risks.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The user enters the problem in text form into the problem input section. For example, the user might enter "I want to improve the efficiency of inventory management." The problem input section can also accept input of the problem in the form of voice input or image attachment. Step 2: The product provider enters product information in text form into the product information input section. For example, the product provider might enter, "Our inventory management system allows us to grasp inventory status in real time, enabling efficient inventory management." The product information input section can also accept product information via voice input or image attachments. Step 3: The matching unit matches the problem input by the problem input unit with the product information input by the product information input unit. For example, the matching unit uses a generation AI to analyze the user's problem and the product information of the product provider, and performs optimal matching.

[0051] (Example 2) The product matching system according to the embodiment of the present invention is a system in which a user inputs text about a problem, a product provider inputs text about product information, and a generation AI matches the two. This allows the product matching system to efficiently find products appropriate for the user's business problems.

[0052] A product matching system according to an embodiment includes a problem input unit, a product information input unit, and a matching unit. The problem input unit receives input from a user in text. For example, the user may input, "I want to improve the efficiency of inventory management." The problem input unit can also receive input from the user in the form of voice input or image attachment. The product information input unit receives input from a product provider in text. For example, the product provider may input, "Our inventory management system allows us to grasp inventory status in real time and achieve efficient inventory management." The product information input unit can also receive input from the user in the form of voice input or image attachment. The matching unit matches the problem input by the problem input unit with the product information input by the product information input unit. For example, the matching unit uses a generation AI to analyze the user's problem and the product information of the product provider and perform optimal matching. This allows the product matching system according to an embodiment to efficiently find products appropriate for business problems.

[0053] The problem input unit uses a generation AI to analyze the input content in real time and automatically complete it to a more specific problem. For example, if a user inputs "I want to improve the efficiency of inventory management," the generation AI analyzes it in real time and automatically completes it to "I want to improve the efficiency of inventory management. In particular, I want to understand the inventory status in real time." The generation AI uses models such as GPT-3 and BERT to analyze the user's input and automatically complete it to a specific problem. To analyze in real time, the generation AI has high processing capabilities and minimizes latency. To improve the accuracy of the auto-completion, the generation AI learns from large amounts of data and improves the variety and accuracy of the information completed. This allows the user's input to be analyzed in real time and automatically complete it to a specific problem.

[0054] The task input unit can refer to the user's past task input history and automatically suggest similar tasks. For example, if the user inputs "I want to improve the efficiency of inventory management," the task input unit will refer to the past input history and suggest, "Would you like to see other tasks related to improving the efficiency of inventory management?" The past task input history is stored in a database and referenced using a search algorithm. Similar tasks are extracted, for example, based on the similarity calculation method and the algorithm used. This allows the user's past task input history to be referenced and similar tasks to be automatically suggested.

[0055] The task input unit can use the emotion estimation function to analyze the user's emotion when inputting and provide input support to elicit positive emotions. For example, when the user inputs "I want to improve the efficiency of inventory management," the emotion estimation function analyzes the user's emotion and provides positive completion, such as "I want to improve the efficiency of inventory management. This will help business proceed smoothly." The emotion estimation function can, for example, use an emotion analysis algorithm to analyze the user's emotion when inputting. The emotion when inputting is evaluated based on, for example, emotion classification criteria and analysis accuracy. Positive emotions are defined based on, for example, an emotion scoring method and positive and negative thresholds. Input support is provided based on, for example, an input completion method and a user interface design. This allows the user's emotion when inputting to be analyzed and provides input support to elicit positive emotions.

[0056] The task input unit can add a voice input function, allowing the user to input tasks by voice. For example, when the user voice-inputs "I want to improve the efficiency of inventory management," the task input unit converts the voice into text and registers it as a task. The voice input function converts the user's voice into text, for example, using a voice recognition algorithm. The voice is processed based on, for example, the format and preprocessing method of the voice data. This allows the user to input tasks by voice.

[0057] The task input unit adds a function that allows a user to attach images or videos when entering a task, and visual information can also be used for analysis. For example, when a user enters "I want to improve the efficiency of inventory management" and attaches an image showing the current state of inventory management, the task input unit analyzes the image and registers it as a task. The image is processed, for example, based on the image data format and preprocessing method. The video is processed, for example, based on the video data format and preprocessing method. The visual information is analyzed, for example, using an image recognition algorithm or a video analysis algorithm. This allows a user to attach images or videos when entering a task.

[0058] The task input unit uses the emotion estimation function to provide real-time feedback on the user's emotional response to the task entered by the task input unit, and can adjust the input content. For example, when the user enters "I want to improve the efficiency of inventory management," the emotion estimation function analyzes the user's emotional response in real time and provides feedback such as "I want to improve the efficiency of inventory management. This will help business proceed smoothly." The emotion estimation function analyzes the user's emotional response using, for example, an emotion analysis algorithm. The emotional response is evaluated based on, for example, emotion classification criteria and analysis accuracy. The input content is processed based on, for example, the format and preprocessing method of text data. This allows the user to provide real-time feedback on the user's emotional response to the task entered by the task input unit, and adjust the input content.

[0059] The product information input unit can use generation AI to automatically suggest related implementation cases and success stories. For example, when a product provider inputs "inventory management system," the product information input unit's generation AI automatically suggests "Inventory management system implementation case: Success story at Company A." The generation AI uses models such as GPT-3 or BERT to analyze the information input by the product provider and suggests related implementation cases and success stories. Implementation cases are selected based on, for example, the case selection criteria and the proposal format. Success stories are selected based on, for example, the case selection criteria and the proposal format. This makes it possible to automatically suggest related implementation cases and success stories for the information input by the product provider.

[0060] The product information input unit can compare product information with that of competitors when product information is input, and clarify points of differentiation. For example, when a product provider inputs an "inventory management system," the product information input unit compares the system with that of competitors and presents points of differentiation such as, "Our system excels in that it can grasp inventory status in real time." Competitors are selected, for example, based on competitor selection criteria and comparison measures. Points of differentiation are clarified, for example, based on differentiation criteria and extraction methods. This allows points of differentiation to be clarified when product information is input, by comparing it with that of competitors.

[0061] The product information input unit uses the emotion estimation function to analyze the emotional impact of the product provider's input content on the user and recommend positive expressions. For example, when the product provider inputs "inventory management system," the product information input unit uses the emotion estimation function to analyze the input content and recommend positive expressions such as "Our inventory management system will significantly improve business efficiency." The emotion estimation function uses, for example, an emotion analysis algorithm to analyze the emotional impact of the product provider's input content on the user. The input content is processed, for example, based on the text data format and preprocessing method. The emotional impact is evaluated, for example, based on emotion classification criteria and an impact evaluation method. Positive expressions are recommended, for example, based on an expression scoring method and positive and negative thresholds. This allows the product provider to analyze the emotional impact of the input content on the user and recommend positive expressions.

[0062] The product information input unit adds a function that enables images and videos to be attached to the information entered by the product provider, and visual information can also be used for analysis. For example, when the product provider enters an "inventory management system" and attaches an image of the system's operation screen, the product information input unit analyzes the image and registers it as product information. Images are processed, for example, based on the image data format and preprocessing method. Videos are processed, for example, based on the video data format and preprocessing method. Visual information is analyzed, for example, using an image recognition algorithm or a video analysis algorithm. This makes it possible to attach images and videos to the information entered by the product provider.

[0063] The product information input unit can refer to past user feedback when product information is input and automatically suggest areas for improvement. For example, when a product provider inputs an "inventory management system," the product information input unit refers to past user feedback and suggests, "We have improved the operability of the inventory management system based on user requests." Past user feedback is stored in a database and referenced using a search algorithm. Improvements are suggested based on, for example, improvement criteria and suggestion methods. This allows past user feedback to be referenced and improvements to be automatically suggested when product information is input.

[0064] The product information input unit can use the emotion estimation function to provide real-time feedback on the user's emotional response to the product provider's input content and adjust the input content. For example, when the product provider inputs "inventory management system," the product information input unit uses the emotion estimation function to analyze the user's emotional response in real time and provide feedback such as, "The user's response is positive. It would be a good idea to add more specific implementation examples." The emotion estimation function can, for example, use an emotion analysis algorithm to analyze the user's emotional response. The emotional response is evaluated based on, for example, emotion classification criteria and analysis accuracy. The input content is processed based on, for example, the text data format and preprocessing method. This allows the user's emotional response to the product provider's input content to be provided in real time and adjust the input content.

[0065] The matching unit takes into account the user's past preferences and behavioral history to make more personalized suggestions. For example, if the user has searched for "inventory management system" in the past, the generation AI will take that history into consideration and preferentially suggest related "inventory management system" searches. Past preferences are stored in a database and referenced using a search algorithm. Behavioral history is stored in a database and referenced using a search algorithm. This allows for more personalized suggestions to be made, taking into account the user's past preferences and behavioral history.

[0066] The matching unit can improve the matching algorithm and consider not only the technical characteristics of the product but also the post-implementation effects and ROI. For example, when the generation AI proposes an "inventory management system," the matching unit considers not only the technical characteristics but also the improvement in inventory management efficiency and ROI after implementation. The matching algorithm is improved, for example, based on the type of algorithm and implementation method. The technical characteristics are evaluated, for example, based on the evaluation criteria and evaluation method for the characteristics. The post-implementation effects are evaluated, for example, based on the effect measurement method and evaluation criteria. The ROI is evaluated, for example, based on the calculation method and evaluation criteria for return on investment. This allows proposals to consider not only the technical characteristics of the product but also the post-implementation effects and ROI.

[0067] The matching unit can use the emotion estimation function to analyze the user's emotion when entering a task and the product provider's emotion when entering information, and perform emotionally compatible matching. For example, the matching unit analyzes the user's emotion when entering "I want to improve the efficiency of inventory management" and makes a suggestion if the emotion matches the emotion when entering information into the product provider's "inventory management system." The emotion estimation function can use, for example, an emotion analysis algorithm to analyze the user's emotion when entering a task and the product provider's emotion when entering information. The emotion when entering the task is evaluated, for example, based on emotion classification criteria and analysis accuracy. The product provider's emotion when entering information is evaluated, for example, based on emotion classification criteria and analysis accuracy. Emotionally compatible matching is performed, for example, based on an emotion matching calculation method and a matching algorithm. This allows the user's emotion when entering a task and the product provider's emotion when entering information to be analyzed and perform emotionally compatible matching.

[0068] When presenting the matching results to the user, the matching unit can simultaneously display a demo video of the product and user reviews to deepen understanding. For example, when the generation AI proposes an "inventory management system," the matching unit can simultaneously display a demo video and user reviews to make it easier for the user to understand the system's functions. The matching results are presented based on, for example, the display format of the results and the timing of presentation. The demo video of the product is presented based on, for example, the selection criteria for the video length and content. The user reviews are presented based on, for example, the selection criteria and display format for the reviews. In this way, when presenting the matching results to the user, the demo video of the product and user reviews can simultaneously be displayed to deepen understanding.

[0069] The matching unit can classify the matching results by different industries or applications, and present them in a format that is likely to interest the user. For example, when the generation AI proposes an "inventory management system," the matching unit classifies them by different industries (e.g., manufacturing, retail) or applications, and presents them in a format that is likely to interest the user. The matching results are presented based on, for example, the display format of the results and the timing of presentation. Different industries are classified based on, for example, industry classification criteria and classification methods. Applications are classified based on, for example, application classification criteria and classification methods. This allows the matching results to be classified by different industries and applications, and presented in a format that is likely to interest the user.

[0070] The matching unit uses the emotion estimation function to monitor the user's emotional response to the matching results in real time and continuously suggest optimal products. For example, when the generation AI suggests an "inventory management system," the matching unit monitors the user's emotional response in real time and continuously suggests similar products if there are many positive responses. The emotion estimation function analyzes the user's emotional response using, for example, an emotion analysis algorithm. The matching results are presented based on, for example, the display format and timing of the results. The emotional response is evaluated based on, for example, emotion classification criteria and analysis accuracy. The optimal product is selected based on, for example, the product evaluation criteria and selection method. This allows the system to monitor the user's emotional response to the matching results in real time and continuously suggest optimal products.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When a user inputs a task, the task input unit can automatically classify the input content using natural language processing technology and assign it to an appropriate category. For example, if a user inputs "I want to improve the efficiency of inventory management," the system analyzes the content and automatically classifies it into the category "inventory management." This ensures that the task entered by the user is classified into the appropriate category, allowing subsequent processing to be carried out efficiently. Furthermore, the task input unit can also suggest related subcategories for the task entered by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest subcategories such as "Introduction of an inventory management system" and "Automation of inventory management." This allows the user to set more specific tasks.

[0073] When a user enters a problem, the problem input unit can automatically check the grammar and spelling of the input and make correction suggestions. For example, if a user enters "I want to improve the efficiency of inventory management," the system will automatically suggest corrections if there are grammatical errors or spelling mistakes. This allows the user to enter an accurate problem. The problem input unit can also automatically extract related keywords for the problem entered by the user, improving search accuracy. For example, if a user enters "I want to improve the efficiency of inventory management," keywords such as "inventory management" and "efficiency" will be extracted, improving the accuracy of search results. This allows the user to find more appropriate products.

[0074] When a user inputs a problem, the problem input unit can automatically suggest related reference materials and guidelines based on the input content. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest related reference materials and guidelines, making it easier for the user to obtain information for solving the problem. This makes it easier for the user to find specific means to solve the problem. The problem input unit can also suggest best practices in related industries for the problem input by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest best practices for inventory management, allowing the user to learn efficient inventory management methods. This allows the user to obtain specific means to improve the efficiency of their work.

[0075] The task input unit can use the emotion estimation function to analyze the user's emotions when inputting and suggest relaxation methods to reduce stress. For example, if a user inputs "I want to improve the efficiency of inventory management," and the emotion estimation function detects the user's stress, it will suggest relaxation methods such as "Take a deep breath and relax." This allows the user to input tasks while reducing stress. The emotion estimation function can also change the design and color of the input interface depending on the user's emotional state. For example, if the user is feeling stressed, the color of the interface can be changed to a calming color. This allows the user to input tasks in a relaxed state.

[0076] When a user inputs a problem, the problem input unit can automatically translate the input content and display it in multiple languages. For example, if a user inputs "I want to improve the efficiency of inventory management" in Japanese, the system automatically translates it into English, Chinese, etc. and displays it. This makes it easier for users who speak different languages ​​to share the same problem. The problem input unit can also suggest related reference materials and guidelines in multiple languages ​​for the problem input by the user. For example, if a user inputs "I want to improve the efficiency of inventory management," the system will suggest reference materials in English and Chinese, making it easier for the user to obtain information in multiple languages. This makes it easier for users to utilize information in different languages ​​and find ways to solve their problems.

[0077] The task input unit can use the emotion estimation function to analyze the user's emotions when entering tasks and provide positive feedback to alleviate negative emotions. For example, if a user enters "I want to improve the efficiency of inventory management," and the emotion estimation function detects the user's negative emotions, it will provide positive feedback such as "Your efforts will definitely be rewarded." This allows the user to maintain motivation while entering tasks. The emotion estimation function can also automatically complete the input content and convert it into positive expressions depending on the user's emotional state. For example, if a user enters "I want to improve the efficiency of inventory management," the function will complete it with "I want to improve the efficiency of inventory management. This will help my work go more smoothly." This allows the user to enter tasks with a positive feeling.

[0078] The product information input unit can automatically suggest related patent information and technical literature for the information entered by the product provider. For example, if a product provider enters "inventory management system," the system will suggest related patent information and technical literature, making it easier for the product provider to obtain technical support. This makes it easier for the product provider to promote the technological superiority of their product. The product information input unit can also suggest related technology trends and market trends for the information entered by the product provider. For example, if "inventory management system" is entered, the system will suggest the latest technology trends and market trends, making it easier for the product provider to provide products that meet market needs. This makes it easier for the product provider to obtain information to provide competitive products.

[0079] The product information input unit can use the emotion estimation function to analyze the emotional impact of the product provider's input on the user and recommend expressions to reduce the negative impact. For example, when a product provider inputs "inventory management system," the emotion estimation function analyzes the input and recommends positive expressions such as "Our inventory management system significantly improves business efficiency." This makes it easier for users to have a positive impression of the product. The emotion estimation function can also predict the user's emotional response to the product provider's input and suggest optimal expressions. For example, when "inventory management system" is input, the function predicts the user's emotional response and suggests expressions such as "Our inventory management system is both easy to use and efficient." This makes it easier for product providers to use expressions that leave a positive impression on users.

[0080] The product information input unit can automatically perform compliance checks based on relevant industry regulations and standards for information entered by product providers. For example, when a product provider enters "inventory management system," the system performs a compliance check based on relevant industry regulations and standards and provides information such as, "Our system complies with industry standards." This makes it easier for product providers to promote their products as compliant with regulations and standards. The product information input unit can also automatically evaluate the legal risks associated with the information entered by product providers and make suggestions for risk mitigation. For example, when an "inventory management system" is entered, the system evaluates the legal risks and provides information such as, "Our system complies with data protection laws." This allows product providers to provide products while mitigating legal risks.

[0081] The product information input unit can use the emotion estimation function to provide real-time feedback on the user's emotional response to the product provider's input and adjust the input content. For example, if the product provider inputs "inventory management system," the emotion estimation function analyzes the user's emotional response in real time and provides feedback such as, "The user's response is positive. It would be a good idea to add more specific implementation examples." This allows the product provider to provide optimal information while taking the user's emotional response into consideration. The emotion estimation function can also predict the user's emotional response to the product provider's input and suggest optimal wording. For example, if the product provider inputs "inventory management system," the function predicts the user's emotional response and suggests wording such as, "Our inventory management system is both easy to use and efficient." This makes it easier for the product provider to use wording that leaves a positive impression on the user.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The user enters the problem in text form into the problem input section. For example, the user might enter "I want to improve the efficiency of inventory management." The problem input section can also accept input of the problem in the form of voice input or image attachment. Step 2: The product provider enters product information in text form into the product information input section. For example, the product provider might enter, "Our inventory management system allows us to grasp inventory status in real time, enabling efficient inventory management." The product information input section can also accept product information via voice input or image attachments. Step 3: The matching unit matches the problem input by the problem input unit with the product information input by the product information input unit. For example, the matching unit uses a generation AI to analyze the user's problem and the product information of the product provider, and performs optimal matching.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. an assignment input section in which a user inputs an assignment in text; a product information input unit in which a product provider inputs product information in text; a matching unit that matches the problem input by the problem input unit with the product information input by the product information input unit. A system characterized by:

2. The task input unit Generative AI is used to analyze input content in real time and automatically complete it with more specific issues 2. The system of claim 1.

3. The task input unit Add a voice input function so that the user can input the task by voice.

2. The system of claim 1.

4. The product information input unit Use generative AI to automatically suggest relevant case studies and success stories 2. The system of claim 1.

5. The matching unit Taking into account the user's past preferences and behavioral history, the service makes more personalized suggestions.

2. The system of claim 1.

6. The task input unit Analyze the user's emotions when inputting information and provide input support to elicit positive emotions.

2. The system of claim 1.

7. The product information input unit Analyzes the emotional impact that product providers' input has on users and recommends positive expressions 2. The system of claim 1.

8. The matching unit Analyze the emotions of the user when entering the problem and the emotions of the product provider when entering the information, and perform emotional matching.

2. The system of claim 1.

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