system

Abstract

The system according to the embodiment aims to automatically generate a description based on keywords. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input of keywords. The analyzing unit analyzes the keywords received by the receiving unit. The generating unit generates an explanatory text based on the keywords analyzed by the analyzing unit. The providing unit provides the explanatory text generated by the generating unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide functionality for automatically generating descriptions based on keywords, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate a description based on keywords. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input of keywords. The analyzing unit analyzes the keywords received by the receiving unit. The generating unit generates an explanatory text based on the keywords analyzed by the analyzing unit. The providing unit provides the explanatory text generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a description based on keywords. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A coupon description generation system according to an embodiment of the present invention accepts input keywords, uses a generation AI to analyze the keywords, and generates and provides an appropriate description. The coupon description generation system automatically generates a description simply by allowing a user to input a coupon keyword. For example, when a user inputs keywords such as "10% off" or "first-time only," a corresponding description is automatically generated. This mechanism allows users to easily create coupon descriptions, saving time and effort. For example, in a coupon description generation system, a user inputs coupon keywords, such as "10% off" or "first-time only." This information is input into the generation AI. The coupon description generation system then uses the generation AI to analyze the input keywords. The generation AI understands the meaning of the keywords and generates an appropriate description based on that. For example, for the keyword "10% off," the system generates a description such as "Use this coupon to receive 10% off all products." The generated description clearly conveys the coupon's content and how to use it. For example, for the keyword "first-time only," the system generates a description such as "This coupon is valid only for first-time users." This allows the coupon description generation system to easily and efficiently generate descriptions when creating coupons. This allows users to easily create coupon descriptions, saving time and effort. The generated descriptions are consistent and accurately convey the content of the coupon.

[0029] A coupon description generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives coupon keywords input by a user. For example, the user can input keywords such as "10% off" or "first-time only." The analysis unit uses a generation AI to analyze the keywords received by the reception unit. The generation AI understands the meaning of the keywords and generates an appropriate description based on the keywords. For example, the generation AI generates a description such as "Use this coupon to get 10% off all products" for the keyword "10% off." The generation unit uses the generation AI to generate a description based on the keywords analyzed by the analysis unit. For example, the generation AI generates a description such as "This coupon is only available to first-time users" for the keyword "first-time only." The provision unit provides the description generated by the generation unit. For example, the generated description can be displayed in real time. This allows the coupon description generation system according to an embodiment to efficiently automate and perform processes from keyword input to description generation and provision.

[0030] The analysis unit can refer to past coupon data or related context. The analysis unit, for example, refers to past coupon data. Past coupon data includes coupon type, issue date, usage history, etc. The analysis unit can also refer to related context. Related context includes past purchase history, user behavior patterns, etc. By referring to past data and context, more appropriate analysis is possible.

[0031] The generation unit can be equipped with a mechanism for assuring the quality of the generated descriptions. The generation unit, for example, is equipped with a review system to assure the quality of the generated descriptions. The review system evaluates the generated descriptions and assures their quality. The generation unit can also set quality evaluation standards. The quality evaluation standards are standards for evaluating the accuracy and consistency of the descriptions. This ensures the quality of the generated descriptions, making it possible to provide highly reliable descriptions.

[0032] The providing unit can display the generated explanatory text in real time. For example, the providing unit displays the generated explanatory text in real time. Real time means that the explanatory text is displayed immediately with almost no delay time. For example, the generated explanatory text is displayed immediately on the user's screen. In this way, by displaying the generated explanatory text in real time, the user can immediately check it.

[0033] The providing unit may include an interface that allows the user to edit the generated description. The providing unit may include, for example, an interface that allows the user to edit the generated description. The editable interface may include a text editor, a drag-and-drop function, or the like. For example, the user may edit the generated description using a text editor. The user may also move part of the description by dragging and dropping. This allows the user to edit the generated description, thereby enabling flexible response.

[0034] The reception unit can analyze the user's past keyword input history and select the optimal input method. The reception unit, for example, analyzes the user's past keyword input history. The past keyword input history includes the input date and time, input content, input frequency, etc. For example, keywords that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest keywords that will be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method.

[0035] The reception unit can filter keywords based on the user's current promotions and campaigns when the keyword is input. For example, the reception unit filters keywords based on the user's current promotions and campaigns when the keyword is input. Current promotions and campaigns include discount information, special offers, limited-time offers, etc. For example, keywords related to currently running promotions can be preferentially displayed. Related keywords can also be suggested based on campaigns in which the user is participating. Furthermore, appropriate keywords can be filtered by referring to promotions the user has participated in in the past. In this way, highly relevant keywords can be provided by filtering keywords based on current promotions and campaigns.

[0036] The reception unit can select an appropriate input means according to the user's input method when inputting a keyword. For example, when inputting a keyword, the reception unit selects an appropriate input means according to the user's input method (voice, text, image, etc.). Input methods include voice input, text input, image input, etc. For example, if the user selects voice input, keywords can be input using voice recognition technology. Also, if the user selects text input, keyboard input can be supported. Furthermore, if the user selects image input, keywords can also be extracted using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0037] The reception unit can prioritize inputting highly relevant keywords in consideration of the user's geographical location information when inputting keywords. For example, the reception unit prioritizes inputting highly relevant keywords in consideration of the user's geographical location information when inputting keywords. Geographical location information includes GPS data, IP address, location information services, etc. For example, if the user is in a specific area, keywords related to that area can be prioritized. Also, if the user is traveling, keywords related to the travel destination can be prioritized. Furthermore, if the user is at home, keywords related to the area around the user's home can be prioritized. In this way, more relevant keywords can be provided by taking geographical location information into consideration.

[0038] The reception unit can analyze the user's social media activity and input related keywords when a keyword is input. For example, the reception unit analyzes the user's social media activity and inputs related keywords when a keyword is input. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, keywords related to places where the user has checked in on social media can be displayed. The content of the user's social media posts can also be analyzed to suggest related keywords. Furthermore, related keywords can be suggested based on the activity of the user's friends on social media. In this way, highly relevant keywords can be provided by analyzing social media activity.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when entering a keyword. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering a keyword. Past feedback includes user reviews, survey results, support history, etc. For example, the optimal input method can be suggested based on feedback provided by the user in the past. Also, it can preferentially display specific input methods based on the user's past feedback. Furthermore, it can analyze the user's feedback and continuously improve the input method. In this way, it is possible to provide the optimal input method to the user by reflecting past feedback.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the keyword during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the keyword during analysis. The importance of the keyword is evaluated using frequency analysis, influence score, etc. For example, a detailed analysis can be performed on important keywords. Also, a simple analysis can be performed on general keywords. Furthermore, a special analysis can be performed on keywords related to a specific campaign. In this way, by adjusting the level of detail of the analysis based on the importance of the keyword, efficient analysis is possible.

[0041] The analysis unit can apply different analysis algorithms depending on the keyword category during analysis. For example, the analysis unit applies different analysis algorithms depending on the keyword category during analysis. Keyword categories are classified using topic classification, semantic clustering, or the like. For example, a specific analysis algorithm can be applied to keywords related to discounts. Also, a different analysis algorithm can be applied to keywords related to limitations. Furthermore, yet another analysis algorithm can be applied to keywords related to promotions. In this way, applying different analysis algorithms depending on the keyword category enables more appropriate analysis.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include analysis reports, database records, etc. For example, the accuracy of the current analysis can be improved based on the user's past analysis results. The analysis algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past analysis history can be analyzed and the optimal analysis method can be proposed. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0043] The analysis unit can determine the analysis priority based on the time of submission of the keywords during analysis. For example, the analysis unit determines the analysis priority based on the time of submission of the keywords during analysis. The submission time includes the submission date and time, the submission frequency, etc. For example, the most recent keywords can be analyzed preferentially. Keywords submitted during a specific campaign period can also be analyzed preferentially. Furthermore, the analysis priority can be determined based on a deadline specified by the user. In this way, efficient analysis is possible by determining the analysis priority based on the time of submission.

[0044] The analysis unit can adjust the analysis order based on the relevance of keywords during analysis. For example, the analysis unit adjusts the analysis order based on the relevance of keywords during analysis. Keyword relevance is evaluated using co-occurrence frequency, semantic similarity, etc. For example, highly relevant keywords can be analyzed preferentially. Also, less relevant keywords can be postponed. Furthermore, the analysis order can be dynamically adjusted based on the relevance of keywords. In this way, adjusting the analysis order based on relevance enables efficient analysis.

[0045] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is evaluated using qualification information, past work experience, etc. For example, technical terms can be used extensively for users with high levels of expertise. Furthermore, analysis results can be provided in simple language for users with low levels of expertise. Furthermore, the way in which the analysis results are expressed can be adjusted according to the user's level of expertise. In this way, analysis results that are easy to understand can be provided by providing analysis results according to the user's level of expertise.

[0046] The generation unit can adjust the level of detail of the description based on the importance of the keyword when generating the description. For example, the generation unit adjusts the level of detail of the description based on the importance of the keyword when generating the description. The importance of the keyword is evaluated using frequency analysis, an influence score, or the like. For example, a detailed description can be generated for an important keyword. Also, a simplified description can be generated for a general keyword. Furthermore, a special description can be generated for a keyword related to a specific campaign. In this way, by adjusting the level of detail of the description based on the importance of the keyword, efficient description generation is possible.

[0047] The generation unit can apply different generation algorithms depending on the keyword category when generating a description. For example, the generation unit applies different generation algorithms depending on the keyword category when generating a description. Keyword categories are classified using topic classification, semantic clustering, or the like. For example, a specific generation algorithm can be applied to keywords related to discounts. Also, a different generation algorithm can be applied to keywords related to limitations. Furthermore, yet another generation algorithm can be applied to keywords related to promotions. In this way, by applying different generation algorithms depending on the keyword category, more appropriate description generation is possible.

[0048] The generation unit can improve the accuracy of a description by referring to the user's past generation results when generating the description. For example, the generation unit can improve the accuracy of a description by referring to the user's past generation results when generating the description. Past generation results include generation reports, database records, etc. For example, the accuracy of a current description can be improved based on the user's past generation results. The generation algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past generation history can be analyzed to suggest an optimal generation method. In this way, the accuracy of a description can be improved by referring to past generation results.

[0049] The generation unit can determine the priority of descriptions based on the submission time of keywords when generating descriptions. For example, the generation unit determines the priority of descriptions based on the submission time of keywords when generating descriptions. The submission time includes the submission date and time, the submission frequency, etc. For example, descriptions can be generated preferentially based on the most recent keywords. Descriptions can also be generated preferentially based on keywords submitted during a specific campaign period. Furthermore, the generation priority of descriptions can be determined based on a deadline specified by the user. This enables efficient description generation by determining the priority of descriptions based on the submission time.

[0050] The generation unit can adjust the order of the descriptions based on the relevance of the keywords when generating the descriptions. For example, the generation unit adjusts the order of the descriptions based on the relevance of the keywords when generating the descriptions. The relevance of the keywords is evaluated using co-occurrence frequency, semantic similarity, or the like. For example, descriptions can be generated preferentially based on highly relevant keywords. Descriptions can also be generated later based on less relevant keywords. Furthermore, the generation order of the descriptions can be dynamically adjusted based on the relevance of the keywords. This allows for efficient description generation by adjusting the order of the descriptions based on the relevance.

[0051] The generation unit can adjust the use of technical terms in the description in accordance with the user's level of expertise when generating the description. For example, the generation unit adjusts the use of technical terms in the description in accordance with the user's level of expertise when generating the description. The level of expertise is evaluated using qualification information, past work experience, etc. For example, a description that uses a lot of technical terms can be generated for a user with high expertise. Also, a description that uses simple language can be generated for a user with low expertise. Furthermore, the expression method of the description can be adjusted in accordance with the user's level of expertise. In this way, by providing a description in accordance with the user's level of expertise, it is possible to provide a description that is easy to understand.

[0052] The providing unit can select the optimal display method by referring to the user's past operation history when providing the explanation. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the explanation. The past operation history includes a click history, a browsing history, an operation log, etc. For example, it is possible to provide preferentially a display method that the user has used preferably in the past. It is also possible to suggest the optimal display method based on the user's past operation history. Furthermore, it is also possible to analyze the user's past operation history and continuously improve the display method. In this way, it is possible to provide the optimal display method to the user by referring to the past operation history.

[0053] The providing unit can customize the display content according to the user's current task when providing the description. For example, the providing unit customizes the display content according to the user's current task when providing the description. The current task includes a project being worked on, current work content, etc. For example, if the user is creating a coupon, related descriptions can be displayed with priority. Also, if the user is planning a promotion, related descriptions can be displayed with priority. Furthermore, the optimal display content can be suggested based on the user's current task. In this way, more appropriate information can be provided by customizing the display content according to the current task.

[0054] The providing unit can improve the display method by reflecting user feedback when providing an explanatory text. For example, the providing unit improves the display method by reflecting user feedback when providing an explanatory text. Feedback includes user reviews, survey results, support history, etc. For example, the display method can be adjusted based on feedback provided by the user. In addition, the display method can be continuously improved by analyzing user feedback. Furthermore, the optimal display method can be proposed by referring to user feedback. In this way, the display method can be continuously improved by reflecting feedback.

[0055] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the description. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the description. The device information includes the device type, screen size, OS version, etc. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. In this way, the optimal display method can be provided by taking into consideration the device information.

[0056] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the description. For example, the providing unit makes the display content multilingual in accordance with the user's language setting when providing the description. Language settings include browser language settings and language information in a user profile. For example, the language of the description can be automatically set based on the language setting of the user's device. In addition, a language switching function can be provided when a user uses multiple languages. Furthermore, if a user selects a specific language, the description can be provided in that language. This allows for multilingual support according to language settings, making it possible to accommodate a larger number of users.

[0057] The providing unit can prioritize displaying highly relevant descriptions in consideration of the user's geographical location information when providing descriptions. For example, the providing unit prioritizes displaying highly relevant descriptions in consideration of the user's geographical location information when providing descriptions. Geographical location information includes GPS data, IP address, location information services, etc. For example, if the user is in a specific area, descriptions related to that area can be prioritized. Also, if the user is traveling, descriptions related to the travel destination can be prioritized. Furthermore, if the user is at home, descriptions related to the area around the user's home can be prioritized. In this way, by taking geographical location information into consideration, more relevant descriptions can be provided.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can monitor the user's input speed in real time and dynamically display keyword candidates according to the input speed. For example, if the user is typing quickly, candidate keywords can be displayed immediately to support input. If the user is typing slowly, candidate keywords can be displayed with a slight delay to give the user time to think. Furthermore, if the user pauses input, related keywords can be suggested to encourage input. This allows for flexible response according to the user's input speed.

[0060] The analysis unit analyzes the user's past purchase history and can improve the accuracy of keyword analysis based on the purchase history. For example, it prioritizes analysis of keywords related to products the user has purchased in the past. It can also analyze related keywords based on product categories that the user frequently purchases. It can also extract specific trends from the user's purchase history and analyze keywords based on those trends. This makes it possible to utilize the user's purchase history to perform more accurate analysis.

[0061] The generation unit can collect user feedback on the generated descriptions and improve the description generation algorithm based on that feedback. For example, the generation unit can provide an interface for users to rate the generated descriptions and collect the evaluation results. It can also provide a comment field so that users can provide specific feedback. It can also analyze the collected feedback, identify areas for improvement in the generation algorithm, and reflect those areas in the next description generation. This makes it possible to continuously improve the quality of descriptions by utilizing user feedback.

[0062] The providing unit can display the generated description in a format optimized for the user's device. For example, a simple display tailored to the screen size can be provided to a user using a smartphone. A display including detailed information can be provided to a user using a tablet. Furthermore, multiple descriptions can be displayed simultaneously to a user using a desktop computer. This can improve the user experience by providing an optimal display format according to the user's device.

[0063] The providing unit can display the generated description in multiple languages. For example, the description can be automatically translated and displayed based on the language setting of the user's browser. In addition, if the user uses multiple languages, a language switching function can be provided. Furthermore, if the user selects a specific language, the description can be provided in that language. This allows for multilingual support, making it possible to accommodate a larger number of users.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives a coupon keyword input by the user. For example, the user can input keywords such as "10% off" or "first-time only." Step 2: The analysis unit uses the generation AI to analyze the keywords received by the reception unit. The generation AI understands the meaning of the keywords and generates an appropriate explanation based on that. Step 3: The generation unit uses the generation AI to generate a description based on the keywords analyzed by the analysis unit. For example, for the keyword "10% off," the generation AI generates the description "Use this coupon to get 10% off all products." Step 4: The providing unit provides the explanatory text generated by the generating unit. For example, the generated explanatory text can be displayed in real time.

[0066] (Example 2) A coupon description generation system according to an embodiment of the present invention accepts input keywords, uses a generation AI to analyze the keywords, and generates and provides an appropriate description. The coupon description generation system automatically generates a description simply by allowing a user to input a coupon keyword. For example, when a user inputs keywords such as "10% off" or "first-time only," a corresponding description is automatically generated. This mechanism allows users to easily create coupon descriptions, saving time and effort. For example, in a coupon description generation system, a user inputs coupon keywords, such as "10% off" or "first-time only." This information is input into the generation AI. The coupon description generation system then uses the generation AI to analyze the input keywords. The generation AI understands the meaning of the keywords and generates an appropriate description based on that. For example, for the keyword "10% off," the system generates a description such as "Use this coupon to receive 10% off all products." The generated description clearly conveys the coupon's content and how to use it. For example, for the keyword "first-time only," the system generates a description such as "This coupon is valid only for first-time users." This allows the coupon description generation system to easily and efficiently generate descriptions when creating coupons. This allows users to easily create coupon descriptions, saving time and effort. The generated descriptions are consistent and accurately convey the content of the coupon.

[0067] A coupon description generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives coupon keywords input by a user. For example, the user can input keywords such as "10% off" or "first-time only." The analysis unit uses a generation AI to analyze the keywords received by the reception unit. The generation AI understands the meaning of the keywords and generates an appropriate description based on the keywords. For example, the generation AI generates a description such as "Use this coupon to get 10% off all products" for the keyword "10% off." The generation unit uses the generation AI to generate a description based on the keywords analyzed by the analysis unit. For example, the generation AI generates a description such as "This coupon is only available to first-time users" for the keyword "first-time only." The provision unit provides the description generated by the generation unit. For example, the generated description can be displayed in real time. This allows the coupon description generation system according to an embodiment to efficiently automate and perform processes from keyword input to description generation and provision.

[0068] The analysis unit can refer to past coupon data or related context. The analysis unit, for example, refers to past coupon data. Past coupon data includes coupon type, issue date, usage history, etc. The analysis unit can also refer to related context. Related context includes past purchase history, user behavior patterns, etc. By referring to past data and context, more appropriate analysis is possible.

[0069] The generation unit can be equipped with a mechanism for assuring the quality of the generated descriptions. The generation unit, for example, is equipped with a review system to assure the quality of the generated descriptions. The review system evaluates the generated descriptions and assures their quality. The generation unit can also set quality evaluation standards. The quality evaluation standards are standards for evaluating the accuracy and consistency of the descriptions. This ensures the quality of the generated descriptions, making it possible to provide highly reliable descriptions.

[0070] The providing unit can display the generated explanatory text in real time. For example, the providing unit displays the generated explanatory text in real time. Real time means that the explanatory text is displayed immediately with almost no delay time. For example, the generated explanatory text is displayed immediately on the user's screen. In this way, by displaying the generated explanatory text in real time, the user can immediately check it.

[0071] The providing unit may include an interface that allows the user to edit the generated description. The providing unit may include, for example, an interface that allows the user to edit the generated description. The editable interface may include a text editor, a drag-and-drop function, or the like. For example, the user may edit the generated description using a text editor. The user may also move part of the description by dragging and dropping. This allows the user to edit the generated description, thereby enabling flexible response.

[0072] The reception unit includes a specific method for estimating a user's emotion and adjusting the timing of keyword input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the reception unit adjusts the timing of keyword input based on the estimated user's emotion. For example, if the user is feeling stressed, the input timing can be delayed to relax the user. If the user is relaxed, the input timing can be accelerated to encourage efficient input. Furthermore, if the user is in a hurry, the input timing can be optimized to quickly complete input. This allows the input timing to be adjusted according to the user's emotion, providing a more comfortable input experience.

[0073] The reception unit can analyze the user's past keyword input history and select the optimal input method. The reception unit, for example, analyzes the user's past keyword input history. The past keyword input history includes the input date and time, input content, input frequency, etc. For example, keywords that the user has frequently used in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest keywords that will be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method.

[0074] The reception unit can filter keywords based on the user's current promotions and campaigns when the keyword is input. For example, the reception unit filters keywords based on the user's current promotions and campaigns when the keyword is input. Current promotions and campaigns include discount information, special offers, limited-time offers, etc. For example, keywords related to currently running promotions can be preferentially displayed. Related keywords can also be suggested based on campaigns in which the user is participating. Furthermore, appropriate keywords can be filtered by referring to promotions the user has participated in in the past. In this way, highly relevant keywords can be provided by filtering keywords based on current promotions and campaigns.

[0075] The reception unit can select an appropriate input means according to the user's input method when inputting a keyword. For example, when inputting a keyword, the reception unit selects an appropriate input means according to the user's input method (voice, text, image, etc.). Input methods include voice input, text input, image input, etc. For example, if the user selects voice input, keywords can be input using voice recognition technology. Also, if the user selects text input, keyboard input can be supported. Furthermore, if the user selects image input, keywords can also be extracted using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0076] The reception unit includes a specific method for estimating a user's emotion and determining a priority order of keywords to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the reception unit determines a priority order of keywords to be input based on the estimated user's emotion. For example, if the user is feeling stressed, simple and intuitive keywords can be displayed preferentially. If the user is relaxed, detailed keywords can be displayed preferentially. Furthermore, if the user is in a hurry, keywords that can be input quickly can be displayed preferentially. In this way, by determining the priority order of keywords according to the user's emotion, more appropriate keywords can be provided.

[0077] The reception unit can prioritize inputting highly relevant keywords in consideration of the user's geographical location information when inputting keywords. For example, the reception unit prioritizes inputting highly relevant keywords in consideration of the user's geographical location information when inputting keywords. Geographical location information includes GPS data, IP address, location information services, etc. For example, if the user is in a specific area, keywords related to that area can be prioritized. Also, if the user is traveling, keywords related to the travel destination can be prioritized. Furthermore, if the user is at home, keywords related to the area around the user's home can be prioritized. In this way, more relevant keywords can be provided by taking geographical location information into consideration.

[0078] The reception unit can analyze the user's social media activity and input related keywords when a keyword is input. For example, the reception unit analyzes the user's social media activity and inputs related keywords when a keyword is input. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, keywords related to places where the user has checked in on social media can be displayed. The content of the user's social media posts can also be analyzed to suggest related keywords. Furthermore, related keywords can be suggested based on the activity of the user's friends on social media. In this way, highly relevant keywords can be provided by analyzing social media activity.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when entering a keyword. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering a keyword. Past feedback includes user reviews, survey results, support history, etc. For example, the optimal input method can be suggested based on feedback provided by the user in the past. Also, it can preferentially display specific input methods based on the user's past feedback. Furthermore, it can analyze the user's feedback and continuously improve the input method. In this way, it is possible to provide the optimal input method to the user by reflecting past feedback.

[0080] The analysis unit includes a specific method for estimating a user's emotion and adjusting a keyword analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the analysis unit adjusts the keyword analysis method based on the estimated user's emotion. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a quick analysis can be performed. Furthermore, if the user is stressed, a simple and intuitive analysis can be performed. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotion.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the keyword during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the keyword during analysis. The importance of the keyword is evaluated using frequency analysis, influence score, etc. For example, a detailed analysis can be performed on important keywords. Also, a simple analysis can be performed on general keywords. Furthermore, a special analysis can be performed on keywords related to a specific campaign. In this way, by adjusting the level of detail of the analysis based on the importance of the keyword, efficient analysis is possible.

[0082] The analysis unit can apply different analysis algorithms depending on the keyword category during analysis. For example, the analysis unit applies different analysis algorithms depending on the keyword category during analysis. Keyword categories are classified using topic classification, semantic clustering, or the like. For example, a specific analysis algorithm can be applied to keywords related to discounts. Also, a different analysis algorithm can be applied to keywords related to limitations. Furthermore, yet another analysis algorithm can be applied to keywords related to promotions. In this way, applying different analysis algorithms depending on the keyword category enables more appropriate analysis.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include analysis reports, database records, etc. For example, the accuracy of the current analysis can be improved based on the user's past analysis results. The analysis algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past analysis history can be analyzed and the optimal analysis method can be proposed. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0084] The analysis unit includes a specific method for estimating a user's emotion and determining an analysis priority based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the analysis unit determines an analysis priority based on the estimated user's emotion. For example, if the user is in a hurry, analysis of important keywords can be prioritized. If the user is relaxed, detailed analysis can be prioritized. Furthermore, if the user is stressed, simple analysis can be prioritized. This enables efficient analysis by determining the analysis priority according to the user's emotion.

[0085] The analysis unit can determine the analysis priority based on the time of submission of the keywords during analysis. For example, the analysis unit determines the analysis priority based on the time of submission of the keywords during analysis. The submission time includes the submission date and time, the submission frequency, etc. For example, the most recent keywords can be analyzed preferentially. Keywords submitted during a specific campaign period can also be analyzed preferentially. Furthermore, the analysis priority can be determined based on a deadline specified by the user. In this way, efficient analysis is possible by determining the analysis priority based on the time of submission.

[0086] The analysis unit can adjust the analysis order based on the relevance of keywords during analysis. For example, the analysis unit adjusts the analysis order based on the relevance of keywords during analysis. Keyword relevance is evaluated using co-occurrence frequency, semantic similarity, etc. For example, highly relevant keywords can be analyzed preferentially. Also, less relevant keywords can be postponed. Furthermore, the analysis order can be dynamically adjusted based on the relevance of keywords. In this way, adjusting the analysis order based on relevance enables efficient analysis.

[0087] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise is evaluated using qualification information, past work experience, etc. For example, technical terms can be used extensively for users with high levels of expertise. Furthermore, analysis results can be provided in simple language for users with low levels of expertise. Furthermore, the way in which the analysis results are expressed can be adjusted according to the user's level of expertise. In this way, analysis results that are easy to understand can be provided by providing analysis results according to the user's level of expertise.

[0088] The generation unit includes a specific method for estimating a user's emotion and adjusting the expression style of the explanatory text based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the emotion from the user's facial expression. Voice analysis technology can also be used to estimate the emotion from the tone and speed of the user's voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the generation unit adjusts the expression style of the explanatory text based on the estimated user's emotion. For example, if the user is relaxed, friendly expressions can be used. If the user is in a hurry, concise and to-the-point expressions can be used. Furthermore, if the user is stressed, gentle expressions can be used. In this way, by adjusting the expression style of the explanatory text according to the user's emotion, more appropriate explanatory text can be provided.

[0089] The generation unit can adjust the level of detail of the description based on the importance of the keyword when generating the description. For example, the generation unit adjusts the level of detail of the description based on the importance of the keyword when generating the description. The importance of the keyword is evaluated using frequency analysis, an influence score, or the like. For example, a detailed description can be generated for an important keyword. Also, a simplified description can be generated for a general keyword. Furthermore, a special description can be generated for a keyword related to a specific campaign. In this way, by adjusting the level of detail of the description based on the importance of the keyword, efficient description generation is possible.

[0090] The generation unit can apply different generation algorithms depending on the keyword category when generating a description. For example, the generation unit applies different generation algorithms depending on the keyword category when generating a description. Keyword categories are classified using topic classification, semantic clustering, or the like. For example, a specific generation algorithm can be applied to keywords related to discounts. Also, a different generation algorithm can be applied to keywords related to limitations. Furthermore, yet another generation algorithm can be applied to keywords related to promotions. In this way, by applying different generation algorithms depending on the keyword category, more appropriate description generation is possible.

[0091] The generation unit can improve the accuracy of a description by referring to the user's past generation results when generating the description. For example, the generation unit can improve the accuracy of a description by referring to the user's past generation results when generating the description. Past generation results include generation reports, database records, etc. For example, the accuracy of a current description can be improved based on the user's past generation results. The generation algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past generation history can be analyzed to suggest an optimal generation method. In this way, the accuracy of a description can be improved by referring to past generation results.

[0092] The generation unit includes a specific method for estimating a user's emotion and adjusting the length of the explanation based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the generation unit adjusts the length of the explanation based on the estimated user's emotion. For example, if the user is in a hurry, a short and to-the-point explanation can be generated. If the user is relaxed, a longer explanation including detailed explanations can be generated. Furthermore, if the user is stressed, a simple and intuitive explanation can be generated. As a result, by adjusting the length of the explanation according to the user's emotion, more appropriate explanations can be provided.

[0093] The generation unit can determine the priority of descriptions based on the submission time of keywords when generating descriptions. For example, the generation unit determines the priority of descriptions based on the submission time of keywords when generating descriptions. The submission time includes the submission date and time, the submission frequency, etc. For example, descriptions can be generated preferentially based on the most recent keywords. Descriptions can also be generated preferentially based on keywords submitted during a specific campaign period. Furthermore, the generation priority of descriptions can be determined based on a deadline specified by the user. This enables efficient description generation by determining the priority of descriptions based on the submission time.

[0094] The generation unit can adjust the order of the descriptions based on the relevance of the keywords when generating the descriptions. For example, the generation unit adjusts the order of the descriptions based on the relevance of the keywords when generating the descriptions. The relevance of the keywords is evaluated using co-occurrence frequency, semantic similarity, or the like. For example, descriptions can be generated preferentially based on highly relevant keywords. Descriptions can also be generated later based on less relevant keywords. Furthermore, the generation order of the descriptions can be dynamically adjusted based on the relevance of the keywords. This allows for efficient description generation by adjusting the order of the descriptions based on the relevance.

[0095] The generation unit can adjust the use of technical terms in the description in accordance with the user's level of expertise when generating the description. For example, the generation unit adjusts the use of technical terms in the description in accordance with the user's level of expertise when generating the description. The level of expertise is evaluated using qualification information, past work experience, etc. For example, a description that uses a lot of technical terms can be generated for a user with high expertise. Also, a description that uses simple language can be generated for a user with low expertise. Furthermore, the expression method of the description can be adjusted in accordance with the user's level of expertise. In this way, by providing a description in accordance with the user's level of expertise, it is possible to provide a description that is easy to understand.

[0096] The providing unit includes a specific method for estimating a user's emotion and adjusting the display method of the explanation based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from their facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of their voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the providing unit adjusts the display method of the explanation based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method to be adjusted according to the user's emotion, enabling more appropriate display.

[0097] The providing unit can select the optimal display method by referring to the user's past operation history when providing the explanation. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the explanation. The past operation history includes a click history, a browsing history, an operation log, etc. For example, it is possible to provide preferentially a display method that the user has used preferably in the past. It is also possible to suggest the optimal display method based on the user's past operation history. Furthermore, it is also possible to analyze the user's past operation history and continuously improve the display method. In this way, it is possible to provide the optimal display method to the user by referring to the past operation history.

[0098] The providing unit can customize the display content according to the user's current task when providing the description. For example, the providing unit customizes the display content according to the user's current task when providing the description. The current task includes a project being worked on, current work content, etc. For example, if the user is creating a coupon, related descriptions can be displayed with priority. Also, if the user is planning a promotion, related descriptions can be displayed with priority. Furthermore, the optimal display content can be suggested based on the user's current task. In this way, more appropriate information can be provided by customizing the display content according to the current task.

[0099] The providing unit can improve the display method by reflecting user feedback when providing an explanatory text. For example, the providing unit improves the display method by reflecting user feedback when providing an explanatory text. Feedback includes user reviews, survey results, support history, etc. For example, the display method can be adjusted based on feedback provided by the user. In addition, the display method can be continuously improved by analyzing user feedback. Furthermore, the optimal display method can be proposed by referring to user feedback. In this way, the display method can be continuously improved by reflecting feedback.

[0100] The providing unit includes a specific method for estimating a user's emotion and adjusting the display order of explanations based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, survey results, etc. For example, facial expression recognition technology can be used to estimate the user's emotion from the user's facial expression. Voice analysis technology can also be used to estimate the user's emotion from the tone and speed of the user's voice. Furthermore, the user's emotion can be estimated based on survey results. Next, the providing unit adjusts the display order of explanations based on the estimated user's emotion. For example, if the user is nervous, important information can be displayed first. If the user is relaxed, detailed information can be displayed first. Furthermore, if the user is in a hurry, information with the main points can be displayed first. In this way, by adjusting the display order according to the user's emotion, more appropriate information can be provided.

[0101] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the description. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the description. The device information includes the device type, screen size, OS version, etc. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a desktop, a display method that includes detailed information can be provided. In this way, the optimal display method can be provided by taking into consideration the device information.

[0102] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the description. For example, the providing unit makes the display content multilingual in accordance with the user's language setting when providing the description. Language settings include browser language settings and language information in a user profile. For example, the language of the description can be automatically set based on the language setting of the user's device. In addition, a language switching function can be provided when a user uses multiple languages. Furthermore, if a user selects a specific language, the description can be provided in that language. This allows for multilingual support according to language settings, making it possible to accommodate a larger number of users.

[0103] The providing unit can prioritize displaying highly relevant descriptions in consideration of the user's geographical location information when providing descriptions. For example, the providing unit prioritizes displaying highly relevant descriptions in consideration of the user's geographical location information when providing descriptions. Geographical location information includes GPS data, IP address, location information services, etc. For example, if the user is in a specific area, descriptions related to that area can be prioritized. Also, if the user is traveling, descriptions related to the travel destination can be prioritized. Furthermore, if the user is at home, descriptions related to the area around the user's home can be prioritized. In this way, by taking geographical location information into consideration, more relevant descriptions can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives coupon keywords input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an explanation based on the analyzed keywords. The provision unit is realized by the control unit 46A of the smart device 14 and displays the generated explanation in real time. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a coupon keyword input by a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the keyword using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an explanatory text based on the analyzed keyword. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays the generated explanatory text in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives coupon keywords input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an explanation based on the analyzed keywords. The provision unit is realized by the control unit 46A of the headset type terminal 314 and displays the generated explanation in real time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives coupon keywords input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an explanatory text based on the analyzed keywords. The provision unit is realized by the control unit 46A of the robot 414 and displays the generated explanatory text in real time.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can monitor the user's input speed in real time and dynamically display keyword candidates according to the input speed. For example, if the user is typing quickly, candidate keywords can be displayed immediately to support input. If the user is typing slowly, candidate keywords can be displayed with a slight delay to give the user time to think. Furthermore, if the user pauses input, related keywords can be suggested to encourage input. This allows for flexible response according to the user's input speed.

[0106] The analysis unit analyzes the user's past purchase history and can improve the accuracy of keyword analysis based on the purchase history. For example, it prioritizes analysis of keywords related to products the user has purchased in the past. It can also analyze related keywords based on product categories that the user frequently purchases. It can also extract specific trends from the user's purchase history and analyze keywords based on those trends. This makes it possible to utilize the user's purchase history to perform more accurate analysis.

[0107] The generation unit can collect user feedback on the generated descriptions and improve the description generation algorithm based on that feedback. For example, the generation unit can provide an interface for users to rate the generated descriptions and collect the evaluation results. It can also provide a comment field so that users can provide specific feedback. It can also analyze the collected feedback, identify areas for improvement in the generation algorithm, and reflect those areas in the next description generation. This makes it possible to continuously improve the quality of descriptions by utilizing user feedback.

[0108] The providing unit can display the generated description in a format optimized for the user's device. For example, a simple display tailored to the screen size can be provided to a user using a smartphone. A display including detailed information can be provided to a user using a tablet. Furthermore, multiple descriptions can be displayed simultaneously to a user using a desktop computer. This can improve the user experience by providing an optimal display format according to the user's device.

[0109] The providing unit can display the generated description in multiple languages. For example, the description can be automatically translated and displayed based on the language setting of the user's browser. In addition, if the user uses multiple languages, a language switching function can be provided. Furthermore, if the user selects a specific language, the description can be provided in that language. This allows for multilingual support, making it possible to accommodate a larger number of users.

[0110] The reception unit can estimate the user's emotions and provide keyword input assistance based on the estimated user emotions. For example, if the user is feeling stressed, simple and intuitive keywords can be displayed preferentially. If the user is relaxed, detailed keywords can be suggested. Furthermore, if the user is in a hurry, keywords that can be entered quickly can be displayed preferentially. This allows input assistance to be provided according to the user's emotions, providing a more comfortable input experience.

[0111] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is in a hurry, the analysis of important keywords can be prioritized. If the user is relaxed, detailed analysis can be prioritized. Furthermore, if the user is stressed, simple analysis can be prioritized. In this way, efficient analysis can be achieved by determining the analysis priority according to the user's emotions.

[0112] The generation unit can estimate the user's emotions and adjust the way in which the explanatory text is expressed based on the estimated user's emotions. For example, if the user is relaxed, friendly expressions can be used. If the user is in a hurry, concise and to the point expressions can be used. Furthermore, if the user is stressed, gentle expressions can be used. In this way, by adjusting the way in which the explanatory text is expressed according to the user's emotions, more appropriate explanatory text can be provided.

[0113] The providing unit can estimate the user's emotions and adjust the display method of the explanation based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method according to the user's emotions.

[0114] The providing unit can estimate the user's emotions and adjust the display order of the explanations based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed first. If the user is relaxed, detailed information can be displayed first. Furthermore, if the user is in a hurry, information that covers the main points can be displayed first. In this way, by adjusting the display order according to the user's emotions, more appropriate information can be provided.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives a coupon keyword input by the user. For example, the user can input keywords such as "10% off" or "first-time only." Step 2: The analysis unit uses the generation AI to analyze the keywords received by the reception unit. The generation AI understands the meaning of the keywords and generates an appropriate explanation based on that. Step 3: The generation unit uses the generation AI to generate a description based on the keywords analyzed by the analysis unit. For example, for the keyword "10% off," the generation AI generates the description "Use this coupon to get 10% off all products." Step 4: The providing unit provides the explanatory text generated by the generating unit. For example, the generated explanatory text can be displayed in real time.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a keyword; an analysis unit that analyzes the keywords received by the reception unit; a generation unit that generates an explanatory sentence based on the keywords analyzed by the analysis unit; a providing unit that provides the description generated by the generating unit; Equipped with A system characterized by:

2. The analysis unit See historical coupon data or relevant context 2. The system of claim 1.

3. The generation unit Equipped with a mechanism to guarantee the quality of generated descriptions 2. The system of claim 1.

4. The providing unit Display the generated description in real time 2. The system of claim 1.

5. The providing unit Provides an interface that allows users to edit the generated descriptions 2. The system of claim 1.

6. The reception unit This includes a specific method for estimating a user's emotions and adjusting the timing of keyword input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past keyword input history and select the appropriate input method 2. The system of claim 1.

8. The reception unit Filter based on your current promotions and offers as you type in keywords 2. The system of claim 1.

Images