System

Abstract

To provide a system that automatically inputs schedules utilizing talk contents of a messenger application and proposes appropriate services.SOLUTION: A system according to an embodiment includes an analysis unit, an input unit, and a proposal unit. The analysis unit analyzes talk contents. The input unit inputs a schedule based on the contents analyzed by the analysis unit. The proposal unit proposes a service based on the contents analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was an issue that the content of conversations in messenger apps was not fully utilized to automatically input schedules or suggest appropriate services.

[0005] The system according to the embodiment aims to automatically input schedules using chat content in a messenger app and to suggest appropriate services. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an input unit, and a proposal unit. The analysis unit analyzes talk content. The input unit inputs a schedule based on the content analyzed by the analysis unit. The proposal unit proposes a service based on the content analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can automatically input schedules using chat content from messenger apps and suggest appropriate services. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A messenger app analysis system according to an embodiment of the present invention is a system that analyzes the chat contents of a messenger app and improves the convenience of the user. This system provides a function in which an AI analyzes the chat contents and automatically inputs a schedule based on the analysis results, and a function in which a help button is created on the chat screen and appropriate services are suggested according to the contents of the conversation. For example, if a user says, "I have a meeting next Monday," the AI ​​extracts this information and automatically inputs it into the schedule. Also, if the user talks about travel, the AI ​​suggests travel-related services. Specifically, it provides information on accommodation reservations and tourist spots. Furthermore, if the topic is fashion, it suggests fashion-related services. For example, it provides links to online shopping sites. This mechanism allows users to easily manage their schedules and use services through messenger apps. For example, if a user says, "I have a meeting next Monday," the AI ​​automatically inputs it into the schedule, allowing the user to manage their schedule without any hassle. Also, if the topic is travel, the AI ​​suggests travel-related services, allowing the user to easily book accommodation and get information on tourist spots. In this way, a system in which AI analyzes chat content on messenger apps and improves user convenience not only makes schedule management and service use easier, but also has the effect of increasing user satisfaction. As a result, the messenger app analysis system improves user convenience and makes schedule management and service use easier.

[0029] The messenger application analysis system according to the embodiment includes an analysis unit, an input unit, and a suggestion unit. The analysis unit analyzes the talk content. The talk content includes, for example, voice talk, text talk, video talk, and the like, but is not limited to such examples. The analysis unit analyzes the talk content using, for example, natural language processing technology. Natural language processing technology is a technology that analyzes text data and understands the meaning. For example, the analysis unit extracts information related to a schedule from the talk content. The input unit automatically inputs a schedule based on the content analyzed by the analysis unit. The schedule input method includes, for example, input to a calendar app, reminder setting, and the like, but is not limited to such examples. For example, the input unit automatically inputs the schedule information extracted by the analysis unit into a calendar app. The input unit can also set a reminder. The suggestion unit suggests an appropriate service based on the content analyzed by the analysis unit. The proposed services include, for example, travel services, fashion services, e-commerce services, and the like, but are not limited to such examples. For example, the suggestion unit suggests a travel-related service. Specifically, the suggestion unit provides information on accommodation reservations and tourist spots. The suggestion unit also suggests fashion-related services. Specifically, the suggestion unit provides links to online shopping sites. The suggestion unit also suggests e-commerce-related services. Specifically, the suggestion unit performs product recommendations and price comparisons. This enables the messenger app analysis system according to the embodiment to analyze the contents of the chat, automatically input schedules, and suggest appropriate services.

[0030] The analysis unit analyzes the talk contents. The talk contents include, for example, voice talk, text talk, video talk, etc., but are not limited to such examples. The analysis unit analyzes the talk contents using, for example, natural language processing technology. Natural language processing technology is a technology that analyzes text data and understands the meaning. Specifically, the analysis unit converts the voice talk into text using voice recognition technology, and then applies natural language processing technology to analyze the contents. In the case of video talk, audio is extracted from the video and similarly converted into text using voice recognition technology. The analysis unit analyzes these text data and extracts the user's intentions, emotions, and important information. For example, when extracting information related to a schedule from the talk contents, keywords such as date and time, location, and event name are identified and organized as schedule information. In addition, the analysis unit can also perform emotion analysis from the user's talk contents to grasp the user's emotional state. This allows the analysis unit to analyze the user's talk contents from various angles and accurately extract the necessary information. Furthermore, the analysis unit can learn the patterns of the talk contents based on past talk contents and the user's behavior history, and perform more accurate analysis. For example, the system learns the phrases frequently used by users and the contents of conversations on specific topics, and improves the accuracy of analysis based on that information. This allows the analysis unit to analyze the contents of users' conversations efficiently and accurately, improving the performance of the entire system.

[0031] The input unit automatically inputs the schedule based on the contents analyzed by the analysis unit. The method of inputting the schedule includes, for example, inputting to a calendar app, setting a reminder, and the like, but is not limited to such examples. Specifically, the input unit automatically inputs the schedule information extracted by the analysis unit to a calendar app. For example, if a user mentions "a meeting tomorrow at 3 p.m." in the talk content, the input unit reflects the information in the calendar app and automatically adds the meeting schedule. The input unit can also set a reminder. For example, if a user mentions "remind me tomorrow at 9 a.m.", the input unit sets a reminder based on the information and notifies at the specified time. Furthermore, the input unit can link with multiple schedule management apps and devices. For example, if a user manages a schedule on both a smartphone and a PC, the input unit synchronizes the schedule information to each device. The input unit can also customize the display format and notification method of the schedule according to the user's preferences. For example, if a user prefers visual notifications, a pop-up notification is displayed, and if a user prefers audio notifications, an audio alert is set. This allows the input unit to realize flexible schedule management according to the user's needs, thereby improving user convenience.

[0032] The suggestion unit suggests an appropriate service based on the content analyzed by the analysis unit. The suggested services include, but are not limited to, travel services, fashion services, and e-commerce services. Specifically, the suggestion unit suggests a travel-related service. For example, if the user mentions "I want to go on a trip next weekend" in the talk content, the suggestion unit provides accommodation reservations and tourist destination information. The suggestion unit also suggests a fashion-related service. For example, if the user mentions "I want new clothes," the suggestion unit provides a link to an online shopping site and recommends products that match the user's preferences. The suggestion unit also suggests an e-commerce-related service. For example, if the user mentions "I want to buy a new smartphone," the suggestion unit makes product recommendations and price comparisons to provide optimal purchasing options. The suggestion unit can learn the user's preferences and needs based on the user's past talk content and behavior history, and make more personalized suggestions. For example, based on services and products that the user has frequently used in the past and products that he or she has purchased, the suggestion unit suggests new related services and products. The suggestion unit can also suggest the latest services and products based on information updated in real time. This enables the suggestion unit to always provide optimal services and products to the user, thereby improving user satisfaction.

[0033] The suggestion unit can suggest travel-related services. The suggestion unit, for example, suggests travel-related services. Travel-related services include, for example, hotel reservations, flight reservations, and tourist destination information, but are not limited to such examples. For example, when a user is talking about travel, the suggestion unit provides a link to a hotel reservation site. The suggestion unit can also provide a link to a flight reservation site. Furthermore, the suggestion unit can provide information on tourist destinations. For example, the suggestion unit provides a link to a review site for the tourist destination. This improves the user's convenience by suggesting travel-related services.

[0034] The suggestion unit can suggest a fashion-related service. The suggestion unit, for example, suggests a fashion-related service. The fashion-related service includes, for example, styling suggestions, clothing coordination, purchasing of fashion items, and the like, but is not limited to such examples. For example, when the user is talking about fashion, the suggestion unit provides a link to a styling suggestion site. The suggestion unit can also provide a link to a clothing coordination site. Furthermore, the suggestion unit can provide a link to a site for purchasing fashion items. For example, the suggestion unit provides a link to an online shopping site. This improves the convenience of the user by suggesting fashion-related services.

[0035] The suggestion unit can suggest an e-commerce related service. The suggestion unit, for example, suggests an e-commerce related service. The e-commerce related service includes, for example, product recommendation, price comparison, online shopping, and the like, but is not limited to such examples. For example, when a user is talking about a product, the suggestion unit provides a link to a product recommendation site. The suggestion unit can also provide a link to a price comparison site. Furthermore, the suggestion unit can also provide a link to an online shopping site. For example, when a user is talking about a specific product, the suggestion unit provides a purchase link for the product. This improves the user's convenience by suggesting an e-commerce related service.

[0036] The analysis unit can adjust the analysis algorithm by referring to the user's past talk history during analysis. The analysis unit, for example, adjusts the analysis algorithm by referring to the user's past talk history during analysis. The method of adjusting the analysis algorithm includes, for example, parameter optimization, introduction of a feedback loop, and the like, but is not limited to such examples. For example, the analysis unit extracts frequently used keywords from the user's past talk history and reflects them in the analysis algorithm. The analysis unit can also extract specific patterns from the user's past talk history to optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past talk history and prioritize analysis of important information. As a result, the analysis algorithm is optimized by referring to the user's past talk history, and the analysis accuracy is improved.

[0037] The input unit can automatically input the schedule based on the analysis result. The input unit, for example, automatically inputs the schedule based on the analysis result. Methods for automatically inputting the schedule include, for example, input by voice command, predictive input by AI, and the like, but are not limited to such examples. For example, the input unit automatically inputs the schedule information extracted by the analysis unit into a calendar app. The input unit can also set a reminder. Furthermore, the input unit can also select an optimal input method by referring to the user's past schedule history. For example, the input unit preferentially suggests a schedule input method that the user has frequently used in the past. As a result, the user's schedule management is made more efficient by automatically inputting the schedule based on the analysis result.

[0038] The analysis unit can optimize the analysis algorithm by referring to the user's past talk history during analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past talk history during analysis, for example. Methods for optimizing the analysis algorithm include, but are not limited to, tuning a machine learning model and expanding a data set. For example, the analysis unit optimizes the analysis algorithm based on keywords frequently used by the user in the past. The analysis unit can also extract specific patterns from the user's past talk history and reflect them in the analysis algorithm. Furthermore, the analysis unit can analyze the user's past talk history and prioritize analysis of important information. As a result, the analysis algorithm is optimized by referring to the user's past talk history, and the analysis accuracy is improved.

[0039] The analysis unit can apply different analysis methods depending on the category of the talk content during analysis. For example, the analysis unit applies different analysis methods depending on the category of the talk content during analysis. The categories of the talk content include, for example, business, entertainment, education, etc., but are not limited to such examples. For example, the analysis unit applies an analysis method specialized for travel information to travel-related talk content. In addition, the analysis unit can also apply an analysis method specialized for fashion information to fashion-related talk content. Furthermore, the analysis unit can apply an analysis method specialized for EC information to e-commerce-related talk content. Thereby, by applying an analysis method depending on the category of the talk content, the analysis accuracy is improved.

[0040] The analysis unit can customize the analysis result by considering the geographical location information of the user during the analysis. For example, the analysis unit customizes the analysis result by considering the geographical location information of the user during the analysis. Methods for acquiring the geographical location information include, but are not limited to, GPS data, Wi-Fi location information, beacon data, and the like. For example, when the user is in a specific area, the analysis unit preferentially analyzes information related to the area. In addition, the analysis unit can also suggest services specific to the area based on the geographical location information of the user. Furthermore, when the user is traveling, the analysis unit can also preferentially analyze information about the travel destination. As a result, by considering the geographical location information of the user, a more relevant analysis result can be obtained.

[0041] The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. The analysis unit, for example, analyzes the user's social media activity during the analysis and reflects related information in the analysis. Methods of analyzing social media activity include, but are not limited to, analysis of posted content, analysis of followers, and evaluation of engagement. For example, the analysis unit customizes the analysis results based on information shared by the user on social media. The analysis unit can also extract topics of interest from the user's social media activity and reflect them in the analysis. Furthermore, the analysis unit can analyze related information taking into account the user's friendships on social media. As a result, more relevant analysis results can be obtained by analyzing the user's social media activity.

[0042] The input unit can select the optimal input method by referring to the user's past schedule history when inputting a schedule. For example, the input unit selects the optimal input method by referring to the user's past schedule history when inputting a schedule. Criteria for selecting the optimal input method include, but are not limited to, for example, voice input, manual input, and template input. For example, the input unit preferentially suggests a schedule input method that the user has frequently used in the past. In addition, the input unit can also extract a specific pattern from the user's past schedule history and select the optimal input method. Furthermore, the input unit can analyze the user's past schedule history and input important information preferentially. As a result, the optimal input method is selected by referring to the user's past schedule history, and schedule management becomes more efficient.

[0043] The input unit can adjust the level of detail of the input based on the importance of the talk content when inputting the schedule. The input unit adjusts the level of detail of the input based on the importance of the talk content when inputting the schedule, for example. The adjustment method of the level of detail of the input includes, for example, a detailed schedule, a simplified schedule, only a reminder, and the like, but is not limited to such examples. For example, the input unit performs detailed schedule input in the case of an important meeting or event. In addition, the input unit can also perform simplified schedule input in the case of daily tasks. Furthermore, the input unit can also perform schedule input with the main points in mind when the user is in a hurry. As a result, important information is preferentially input by adjusting the level of detail of the input based on the importance of the talk content.

[0044] The input unit can preferentially input a highly relevant schedule in consideration of the geographical location information of the user when inputting a schedule. For example, the input unit preferentially inputs a highly relevant schedule in consideration of the geographical location information of the user when inputting a schedule. Selection criteria for highly relevant schedules include, for example, geographical location information, past schedules, and user interests, but are not limited to such examples. For example, when the user is in a specific area, the input unit preferentially inputs a schedule related to the area. In addition, the input unit can also input events specific to the area into the schedule based on the geographical location information of the user. Furthermore, when the user is traveling, the input unit can also preferentially input a schedule for the travel destination. In this way, highly relevant schedules are preferentially input by considering the geographical location information of the user.

[0045] The input unit can analyze the user's social media activity and input the related schedule when inputting a schedule. The input unit can analyze the user's social media activity and input the related schedule when inputting a schedule, for example. Selection criteria for the related schedule include, but are not limited to, for example, social media activity, friends' schedules, and event information. For example, the input unit inputs events shared by the user on social media into the schedule. The input unit can also extract events of interest from the user's social media activity and input them into the schedule. Furthermore, the input unit can input the related schedule in consideration of the user's friendships on social media. In this way, the related schedule is input by analyzing the user's social media activity.

[0046] The suggestion unit can select the optimal suggestion method by referring to the user's past usage history at the time of suggestion. For example, the suggestion unit selects the optimal suggestion method by referring to the user's past usage history at the time of suggestion. Selection criteria for the optimal suggestion method include, for example, past usage history, user preferences, and the effect of the suggestion, but are not limited to such examples. For example, the suggestion unit selects the optimal suggestion method based on a service used by the user in the past. In addition, the suggestion unit can extract a specific pattern from the user's past usage history and reflect it in the suggestion method. Furthermore, the suggestion unit can analyze the user's past usage history and preferentially suggest important information. As a result, the optimal suggestion method is selected by referring to the user's past usage history, and the accuracy of the suggestion is improved.

[0047] The suggestion unit can apply different proposal algorithms depending on the category of the talk content at the time of proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the talk content at the time of proposal. The proposal algorithms include, but are not limited to, collaborative filtering, content-based filtering, hybrid filtering, and the like. For example, the suggestion unit applies a proposal algorithm specialized for travel information to travel-related talk content. In addition, the suggestion unit can also apply a proposal algorithm specialized for fashion information to fashion-related talk content. Furthermore, the suggestion unit can also apply a proposal algorithm specialized for EC information to e-commerce-related talk content. Thereby, by applying the proposal algorithm depending on the category of the talk content, the accuracy of the proposal is improved.

[0048] The suggestion unit can preferentially suggest highly relevant services in consideration of the geographical location information of the user at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant services in consideration of the geographical location information of the user at the time of suggestion. Selection criteria for highly relevant services include, but are not limited to, for example, geographical location information, user interests, and past usage history. For example, when the user is in a specific area, the suggestion unit preferentially suggests services related to that area. In addition, the suggestion unit can also suggest services specific to the area based on the geographical location information of the user. Furthermore, when the user is traveling, the suggestion unit can also preferentially suggest services at the travel destination. As a result, highly relevant services are preferentially suggested by considering the geographical location information of the user.

[0049] The suggestion unit can analyze the user's social media activity at the time of suggestion and suggest related services. For example, the suggestion unit can analyze the user's social media activity at the time of suggestion and suggest related services. Selection criteria for related services include, but are not limited to, for example, social media activity, friend usage history, trend information, and the like. For example, the suggestion unit can suggest related services based on information shared by the user on social media. In addition, the suggestion unit can extract and suggest services of interest from the user's social media activity. Furthermore, the suggestion unit can suggest related services taking into account the user's friendships on social media. In this way, related services are suggested by analyzing the user's social media activity.

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

[0051] The suggestion unit can also suggest entertainment-related services based on the user's hobbies and interests. For example, if the user is talking about movies, the suggestion unit can provide movie screening information and streaming service links. If the user is talking about music, the suggestion unit can provide concert information and music streaming service links. Furthermore, if the user is talking about reading, the suggestion unit can provide book recommendations and e-book service links. This allows entertainment-related services according to the user's hobbies and interests to be provided, improving the user's satisfaction.

[0052] The suggestion unit can also suggest communities related to the user's hobbies and interests based on the user's talk content. For example, if the user talks about a particular sport, the suggestion unit can provide links to online communities and forums related to that sport. If the user talks about cooking, the suggestion unit can provide information on recipe sharing sites and cooking classes. Furthermore, if the user talks about arts and crafts, the suggestion unit can provide information on artwork exhibitions and craft workshops. This allows communities based on the user's hobbies and interests to be suggested, expanding the range of interactions between the user.

[0053] The suggestion unit can also suggest events related to the user's hobbies and interests based on the user's talk content. For example, if the user is talking about music, the suggestion unit can provide information about concerts and live events. If the user is talking about sports, the suggestion unit can provide information about sporting events and matches. Furthermore, if the user is talking about arts and crafts, the suggestion unit can provide information about art exhibitions and craft fairs. This allows events based on the user's hobbies and interests to be suggested, making the user's life more fulfilling.

[0054] The suggestion unit can also suggest products related to the user's hobbies and interests based on the user's talk content. For example, if the user is talking about cooking, the suggestion unit can provide purchase links for cooking utensils and ingredients. If the user is talking about gardening, the suggestion unit can also provide purchase links for gardening supplies and plants. Furthermore, if the user is talking about DIY, the suggestion unit can also provide purchase links for DIY kits and tools. This allows products according to the user's hobbies and interests to be suggested, improving the user's purchasing experience.

[0055] The suggestion unit can also suggest learning resources related to the user's hobbies and interests based on the user's talk content. For example, if the user talks about programming, the suggestion unit can provide links to online programming courses and materials. If the user talks about photography, the suggestion unit can provide links to photography techniques and online workshops. Furthermore, if the user talks about cooking, the suggestion unit can provide links to cooking recipes and cooking classes. This suggests learning resources according to the user's hobbies and interests, supporting the user's skill improvement.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The analysis unit analyzes the talk content. The talk content includes voice talk, text talk, video talk, etc. The analysis unit analyzes the talk content using natural language processing technology and extracts information related to a schedule, for example. Step 2: The input unit automatically inputs the schedule based on the contents analyzed by the analysis unit. Methods for inputting the schedule include inputting the schedule into a calendar app, setting a reminder, etc. For example, the input unit can automatically input the schedule information extracted by the analysis unit into a calendar app and set a reminder. Step 3: The suggestion unit suggests appropriate services based on the content analyzed by the analysis unit. The suggested services include travel services, fashion services, e-commerce services, etc. For example, the suggestion unit may provide accommodation reservations and tourist spot information as travel-related services, provide links to online shopping sites as fashion-related services, and recommend products and make price comparisons as e-commerce-related services.

[0058] (Example 2) A messenger app analysis system according to an embodiment of the present invention is a system that analyzes the chat contents of a messenger app and improves the convenience of the user. This system provides a function in which an AI analyzes the chat contents and automatically inputs a schedule based on the analysis results, and a function in which a help button is created on the chat screen and appropriate services are suggested according to the contents of the conversation. For example, if a user says, "I have a meeting next Monday," the AI ​​extracts this information and automatically inputs it into the schedule. Also, if the user talks about travel, the AI ​​suggests travel-related services. Specifically, it provides information on accommodation reservations and tourist spots. Furthermore, if the topic is fashion, it suggests fashion-related services. For example, it provides links to online shopping sites. This mechanism allows users to easily manage their schedules and use services through messenger apps. For example, if a user says, "I have a meeting next Monday," the AI ​​automatically inputs it into the schedule, allowing the user to manage their schedule without any hassle. Also, if the topic is travel, the AI ​​suggests travel-related services, allowing the user to easily book accommodation and get information on tourist spots. In this way, a system in which AI analyzes chat content on messenger apps and improves user convenience not only makes schedule management and service use easier, but also has the effect of increasing user satisfaction. As a result, the messenger app analysis system improves user convenience and makes schedule management and service use easier.

[0059] The messenger application analysis system according to the embodiment includes an analysis unit, an input unit, and a suggestion unit. The analysis unit analyzes the talk content. The talk content includes, for example, voice talk, text talk, video talk, and the like, but is not limited to such examples. The analysis unit analyzes the talk content using, for example, natural language processing technology. Natural language processing technology is a technology that analyzes text data and understands the meaning. For example, the analysis unit extracts information related to a schedule from the talk content. The input unit automatically inputs a schedule based on the content analyzed by the analysis unit. The schedule input method includes, for example, input to a calendar app, reminder setting, and the like, but is not limited to such examples. For example, the input unit automatically inputs the schedule information extracted by the analysis unit into a calendar app. The input unit can also set a reminder. The suggestion unit suggests an appropriate service based on the content analyzed by the analysis unit. The proposed services include, for example, travel services, fashion services, e-commerce services, and the like, but are not limited to such examples. For example, the suggestion unit suggests a travel-related service. Specifically, the suggestion unit provides information on accommodation reservations and tourist spots. The suggestion unit also suggests fashion-related services. Specifically, the suggestion unit provides links to online shopping sites. The suggestion unit also suggests e-commerce-related services. Specifically, the suggestion unit performs product recommendations and price comparisons. This enables the messenger app analysis system according to the embodiment to analyze the contents of the chat, automatically input schedules, and suggest appropriate services.

[0060] The analysis unit analyzes the talk contents. The talk contents include, for example, voice talk, text talk, video talk, etc., but are not limited to such examples. The analysis unit analyzes the talk contents using, for example, natural language processing technology. Natural language processing technology is a technology that analyzes text data and understands the meaning. Specifically, the analysis unit converts the voice talk into text using voice recognition technology, and then applies natural language processing technology to analyze the contents. In the case of video talk, audio is extracted from the video and similarly converted into text using voice recognition technology. The analysis unit analyzes these text data and extracts the user's intentions, emotions, and important information. For example, when extracting information related to a schedule from the talk contents, keywords such as date and time, location, and event name are identified and organized as schedule information. In addition, the analysis unit can also perform emotion analysis from the user's talk contents to grasp the user's emotional state. This allows the analysis unit to analyze the user's talk contents from various angles and accurately extract the necessary information. Furthermore, the analysis unit can learn the patterns of the talk contents based on past talk contents and the user's behavior history, and perform more accurate analysis. For example, the system learns the phrases frequently used by users and the contents of conversations on specific topics, and improves the accuracy of analysis based on that information. This allows the analysis unit to analyze the contents of users' conversations efficiently and accurately, improving the performance of the entire system.

[0061] The input unit automatically inputs the schedule based on the contents analyzed by the analysis unit. The method of inputting the schedule includes, for example, inputting to a calendar app, setting a reminder, and the like, but is not limited to such examples. Specifically, the input unit automatically inputs the schedule information extracted by the analysis unit to a calendar app. For example, if a user mentions "a meeting tomorrow at 3 p.m." in the talk content, the input unit reflects the information in the calendar app and automatically adds the meeting schedule. The input unit can also set a reminder. For example, if a user mentions "remind me tomorrow at 9 a.m.", the input unit sets a reminder based on the information and notifies at the specified time. Furthermore, the input unit can link with multiple schedule management apps and devices. For example, if a user manages a schedule on both a smartphone and a PC, the input unit synchronizes the schedule information to each device. The input unit can also customize the display format and notification method of the schedule according to the user's preferences. For example, if a user prefers visual notifications, a pop-up notification is displayed, and if a user prefers audio notifications, an audio alert is set. This allows the input unit to realize flexible schedule management according to the user's needs, thereby improving user convenience.

[0062] The suggestion unit suggests an appropriate service based on the content analyzed by the analysis unit. The suggested services include, but are not limited to, travel services, fashion services, and e-commerce services. Specifically, the suggestion unit suggests a travel-related service. For example, if the user mentions "I want to go on a trip next weekend" in the talk content, the suggestion unit provides accommodation reservations and tourist destination information. The suggestion unit also suggests a fashion-related service. For example, if the user mentions "I want new clothes," the suggestion unit provides a link to an online shopping site and recommends products that match the user's preferences. The suggestion unit also suggests an e-commerce-related service. For example, if the user mentions "I want to buy a new smartphone," the suggestion unit makes product recommendations and price comparisons to provide optimal purchasing options. The suggestion unit can learn the user's preferences and needs based on the user's past talk content and behavior history, and make more personalized suggestions. For example, based on services and products that the user has frequently used in the past and products that he or she has purchased, the suggestion unit suggests new related services and products. The suggestion unit can also suggest the latest services and products based on information updated in real time. This enables the suggestion unit to always provide optimal services and products to the user, thereby improving user satisfaction.

[0063] The suggestion unit can suggest travel-related services. The suggestion unit, for example, suggests travel-related services. Travel-related services include, for example, hotel reservations, flight reservations, and tourist destination information, but are not limited to such examples. For example, when a user is talking about travel, the suggestion unit provides a link to a hotel reservation site. The suggestion unit can also provide a link to a flight reservation site. Furthermore, the suggestion unit can provide information on tourist destinations. For example, the suggestion unit provides a link to a review site for the tourist destination. This improves the user's convenience by suggesting travel-related services.

[0064] The suggestion unit can suggest a fashion-related service. The suggestion unit, for example, suggests a fashion-related service. The fashion-related service includes, for example, styling suggestions, clothing coordination, purchasing of fashion items, and the like, but is not limited to such examples. For example, when the user is talking about fashion, the suggestion unit provides a link to a styling suggestion site. The suggestion unit can also provide a link to a clothing coordination site. Furthermore, the suggestion unit can provide a link to a site for purchasing fashion items. For example, the suggestion unit provides a link to an online shopping site. This improves the convenience of the user by suggesting fashion-related services.

[0065] The suggestion unit can suggest an e-commerce related service. The suggestion unit, for example, suggests an e-commerce related service. The e-commerce related service includes, for example, product recommendation, price comparison, online shopping, and the like, but is not limited to such examples. For example, when a user is talking about a product, the suggestion unit provides a link to a product recommendation site. The suggestion unit can also provide a link to a price comparison site. Furthermore, the suggestion unit can also provide a link to an online shopping site. For example, when a user is talking about a specific product, the suggestion unit provides a purchase link for the product. This improves the user's convenience by suggesting an e-commerce related service.

[0066] The analysis unit can adjust the analysis algorithm by referring to the user's past talk history during analysis. The analysis unit, for example, adjusts the analysis algorithm by referring to the user's past talk history during analysis. The method of adjusting the analysis algorithm includes, for example, parameter optimization, introduction of a feedback loop, and the like, but is not limited to such examples. For example, the analysis unit extracts frequently used keywords from the user's past talk history and reflects them in the analysis algorithm. The analysis unit can also extract specific patterns from the user's past talk history to optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past talk history and prioritize analysis of important information. As a result, the analysis algorithm is optimized by referring to the user's past talk history, and the analysis accuracy is improved.

[0067] The input unit can automatically input the schedule based on the analysis result. The input unit, for example, automatically inputs the schedule based on the analysis result. Methods for automatically inputting the schedule include, for example, input by voice command, predictive input by AI, and the like, but are not limited to such examples. For example, the input unit automatically inputs the schedule information extracted by the analysis unit into a calendar app. The input unit can also set a reminder. Furthermore, the input unit can also select an optimal input method by referring to the user's past schedule history. For example, the input unit preferentially suggests a schedule input method that the user has frequently used in the past. As a result, the user's schedule management is made more efficient by automatically inputting the schedule based on the analysis result.

[0068] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the accuracy of the analysis based on the estimated user's emotion. Methods for estimating the user's emotion include, for example, voice tone analysis, facial expression recognition, text analysis, and the like, but are not limited to such examples. For example, when the user is stressed, the analysis unit increases the accuracy of the analysis so as not to miss important information. In addition, when the user is relaxed, the analysis unit can also adjust the accuracy of the analysis to extract more information. Furthermore, when the user is in a hurry, the analysis unit can also optimize the accuracy of the analysis to provide results quickly. As a result, by adjusting the accuracy of the analysis based on the user's emotion, a more appropriate analysis result can be obtained. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0069] The analysis unit can optimize the analysis algorithm by referring to the user's past talk history during analysis. The analysis unit optimizes the analysis algorithm by referring to the user's past talk history during analysis, for example. Methods for optimizing the analysis algorithm include, but are not limited to, tuning a machine learning model and expanding a data set. For example, the analysis unit optimizes the analysis algorithm based on keywords frequently used by the user in the past. The analysis unit can also extract specific patterns from the user's past talk history and reflect them in the analysis algorithm. Furthermore, the analysis unit can analyze the user's past talk history and prioritize analysis of important information. As a result, the analysis algorithm is optimized by referring to the user's past talk history, and the analysis accuracy is improved.

[0070] The analysis unit can apply different analysis methods depending on the category of the talk content during analysis. For example, the analysis unit applies different analysis methods depending on the category of the talk content during analysis. The categories of the talk content include, for example, business, entertainment, education, etc., but are not limited to such examples. For example, the analysis unit applies an analysis method specialized for travel information to travel-related talk content. In addition, the analysis unit can also apply an analysis method specialized for fashion information to fashion-related talk content. Furthermore, the analysis unit can apply an analysis method specialized for EC information to e-commerce-related talk content. Thereby, by applying an analysis method depending on the category of the talk content, the analysis accuracy is improved.

[0071] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. The display method of the analysis result includes, for example, graph display, text display, voice feedback, etc., but is not limited to such examples. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. In this way, by adjusting the display method of the analysis result based on the user's emotion, a display that is easy for the user to see is provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] The analysis unit can customize the analysis result by considering the geographical location information of the user during the analysis. For example, the analysis unit customizes the analysis result by considering the geographical location information of the user during the analysis. Methods for acquiring the geographical location information include, but are not limited to, GPS data, Wi-Fi location information, beacon data, and the like. For example, when the user is in a specific area, the analysis unit preferentially analyzes information related to the area. In addition, the analysis unit can also suggest services specific to the area based on the geographical location information of the user. Furthermore, when the user is traveling, the analysis unit can also preferentially analyze information about the travel destination. As a result, by considering the geographical location information of the user, a more relevant analysis result can be obtained.

[0073] The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. The analysis unit, for example, analyzes the user's social media activity during the analysis and reflects related information in the analysis. Methods of analyzing social media activity include, but are not limited to, analysis of posted content, analysis of followers, and evaluation of engagement. For example, the analysis unit customizes the analysis results based on information shared by the user on social media. The analysis unit can also extract topics of interest from the user's social media activity and reflect them in the analysis. Furthermore, the analysis unit can analyze related information taking into account the user's friendships on social media. As a result, more relevant analysis results can be obtained by analyzing the user's social media activity.

[0074] The input unit can estimate the user's emotion and adjust the schedule input timing based on the estimated user's emotion. The input unit, for example, estimates the user's emotion and adjusts the schedule input timing based on the estimated user's emotion. The method of adjusting the schedule input timing includes, for example, the user's activity pattern, before and after an important event, and the like, but is not limited to such examples. For example, the input unit delays the schedule input timing when the user is stressed. Also, the input unit can advance the schedule input timing when the user is relaxed. Furthermore, the input unit can optimize the schedule input timing when the user is in a hurry. As a result, the schedule is input at a more appropriate timing by adjusting the schedule input timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The input unit can select the optimal input method by referring to the user's past schedule history when inputting a schedule. For example, the input unit selects the optimal input method by referring to the user's past schedule history when inputting a schedule. Criteria for selecting the optimal input method include, but are not limited to, for example, voice input, manual input, and template input. For example, the input unit preferentially suggests a schedule input method that the user has frequently used in the past. In addition, the input unit can also extract a specific pattern from the user's past schedule history and select the optimal input method. Furthermore, the input unit can analyze the user's past schedule history and input important information preferentially. As a result, the optimal input method is selected by referring to the user's past schedule history, and schedule management becomes more efficient.

[0076] The input unit can adjust the level of detail of the input based on the importance of the talk content when inputting the schedule. The input unit adjusts the level of detail of the input based on the importance of the talk content when inputting the schedule, for example. The adjustment method of the level of detail of the input includes, for example, a detailed schedule, a simplified schedule, only a reminder, and the like, but is not limited to such examples. For example, the input unit performs detailed schedule input in the case of an important meeting or event. In addition, the input unit can also perform simplified schedule input in the case of daily tasks. Furthermore, the input unit can also perform schedule input with the main points in mind when the user is in a hurry. As a result, important information is preferentially input by adjusting the level of detail of the input based on the importance of the talk content.

[0077] The input unit can estimate the user's emotion and determine the priority of the schedule based on the estimated user's emotion. The input unit, for example, estimates the user's emotion and determines the priority of the schedule based on the estimated user's emotion. The method of setting the priority of the schedule includes, for example, importance, urgency, user's preference, etc., but is not limited to such examples. For example, when the user is feeling stressed, the input unit inputs important schedules with priority. Also, when the user is relaxed, the input unit can input a detailed schedule. Furthermore, when the user is in a hurry, the input unit can input a schedule with the main points. In this way, by determining the priority of the schedule based on the user's emotion, important schedules are input with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The input unit can preferentially input a highly relevant schedule in consideration of the geographical location information of the user when inputting a schedule. For example, the input unit preferentially inputs a highly relevant schedule in consideration of the geographical location information of the user when inputting a schedule. Selection criteria for highly relevant schedules include, for example, geographical location information, past schedules, and user interests, but are not limited to such examples. For example, when the user is in a specific area, the input unit preferentially inputs a schedule related to the area. In addition, the input unit can also input events specific to the area into the schedule based on the geographical location information of the user. Furthermore, when the user is traveling, the input unit can also preferentially input a schedule for the travel destination. In this way, highly relevant schedules are preferentially input by considering the geographical location information of the user.

[0079] The input unit can analyze the user's social media activity and input the related schedule when inputting a schedule. The input unit can analyze the user's social media activity and input the related schedule when inputting a schedule, for example. Selection criteria for the related schedule include, but are not limited to, for example, social media activity, friends' schedules, and event information. For example, the input unit inputs events shared by the user on social media into the schedule. The input unit can also extract events of interest from the user's social media activity and input them into the schedule. Furthermore, the input unit can input the related schedule in consideration of the user's friendships on social media. In this way, the related schedule is input by analyzing the user's social media activity.

[0080] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the proposal based on the estimated user's emotion. The way of expressing the proposal includes, for example, text suggestion, audio suggestion, visual suggestion, and the like, but is not limited to such examples. For example, when the user is nervous, the suggestion unit provides a suggestion method that is simple and highly visible. In addition, when the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. As a result, by adjusting the way of expressing the proposal based on the user's emotion, a more appropriate suggestion is made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The suggestion unit can select the optimal suggestion method by referring to the user's past usage history at the time of suggestion. For example, the suggestion unit selects the optimal suggestion method by referring to the user's past usage history at the time of suggestion. Selection criteria for the optimal suggestion method include, for example, past usage history, user preferences, and the effect of the suggestion, but are not limited to such examples. For example, the suggestion unit selects the optimal suggestion method based on a service used by the user in the past. In addition, the suggestion unit can extract a specific pattern from the user's past usage history and reflect it in the suggestion method. Furthermore, the suggestion unit can analyze the user's past usage history and preferentially suggest important information. As a result, the optimal suggestion method is selected by referring to the user's past usage history, and the accuracy of the suggestion is improved.

[0082] The suggestion unit can apply different proposal algorithms depending on the category of the talk content at the time of proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the talk content at the time of proposal. The proposal algorithms include, but are not limited to, collaborative filtering, content-based filtering, hybrid filtering, and the like. For example, the suggestion unit applies a proposal algorithm specialized for travel information to travel-related talk content. In addition, the suggestion unit can also apply a proposal algorithm specialized for fashion information to fashion-related talk content. Furthermore, the suggestion unit can also apply a proposal algorithm specialized for EC information to e-commerce-related talk content. Thereby, by applying the proposal algorithm depending on the category of the talk content, the accuracy of the proposal is improved.

[0083] The suggestion unit can estimate the user's emotion and determine the priority of the suggestions based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and determines the priority of the suggestions based on the estimated user's emotion. The method of setting the priority of the suggestions includes, for example, the user's emotion, the importance of the suggestions, and the urgency of the suggestions, but is not limited to such examples. For example, when the user is stressed, the suggestion unit prioritizes important suggestions. In addition, when the user is relaxed, the suggestion unit can also make detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. In this way, by determining the priority of the suggestions based on the user's emotion, important suggestions are prioritized. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The suggestion unit can preferentially suggest highly relevant services in consideration of the geographical location information of the user at the time of suggestion. For example, the suggestion unit preferentially suggests highly relevant services in consideration of the geographical location information of the user at the time of suggestion. Selection criteria for highly relevant services include, but are not limited to, for example, geographical location information, user interests, and past usage history. For example, when the user is in a specific area, the suggestion unit preferentially suggests services related to that area. In addition, the suggestion unit can also suggest services specific to the area based on the geographical location information of the user. Furthermore, when the user is traveling, the suggestion unit can also preferentially suggest services at the travel destination. As a result, highly relevant services are preferentially suggested by considering the geographical location information of the user.

[0085] The suggestion unit can analyze the user's social media activity at the time of suggestion and suggest related services. For example, the suggestion unit can analyze the user's social media activity at the time of suggestion and suggest related services. Selection criteria for related services include, but are not limited to, for example, social media activity, friend usage history, trend information, and the like. For example, the suggestion unit can suggest related services based on information shared by the user on social media. In addition, the suggestion unit can extract and suggest services of interest from the user's social media activity. Furthermore, the suggestion unit can suggest related services taking into account the user's friendships on social media. In this way, related services are suggested by analyzing the user's social media activity.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The analysis unit can also estimate the user's health condition based on the content of the user's conversation and suggest health-related services. For example, if the user talks about feeling tired, the analysis unit can suggest relaxation services and health foods. If the user talks about exercise, the analysis unit can suggest fitness gyms and exercise equipment. Furthermore, if the user complains of feeling unwell, the analysis unit can suggest information about medical institutions and online medical consultation services. This provides appropriate services according to the user's health condition and supports the user's health management.

[0088] The suggestion unit can also suggest entertainment-related services based on the user's hobbies and interests. For example, if the user is talking about movies, the suggestion unit can provide movie screening information and streaming service links. If the user is talking about music, the suggestion unit can provide concert information and music streaming service links. Furthermore, if the user is talking about reading, the suggestion unit can provide book recommendations and e-book service links. This allows entertainment-related services according to the user's hobbies and interests to be provided, improving the user's satisfaction.

[0089] The analysis unit can also estimate the user's learning needs based on the user's chat content and suggest education-related services. For example, if the user says that he or she wants to learn a specific skill, the analysis unit can provide links to online courses and learning platforms. If the user is preparing for an exam, the analysis unit can provide links to exam preparation materials and mock exams. Furthermore, if the user says that he or she wants to learn a new language, the analysis unit can provide links to language learning apps and online lessons. This provides education-related services according to the user's learning needs, improving the user's learning efficiency.

[0090] The suggestion unit can also suggest communities related to the user's hobbies and interests based on the user's talk content. For example, if the user talks about a particular sport, the suggestion unit can provide links to online communities and forums related to that sport. If the user talks about cooking, the suggestion unit can provide information on recipe sharing sites and cooking classes. Furthermore, if the user talks about arts and crafts, the suggestion unit can provide information on artwork exhibitions and craft workshops. This allows communities based on the user's hobbies and interests to be suggested, expanding the range of interactions between the user.

[0091] The analysis unit can also estimate the user's emotions based on the content of the user's conversation and suggest relaxation methods according to the emotions. For example, if the user is feeling stressed, the analysis unit can provide relaxation music or a link to a meditation app. If the user is sad, the analysis unit can provide positive content or encouraging messages to lift the user's mood. Furthermore, if the user is tired, the analysis unit can provide information on relaxation massages and aromatherapy. This allows relaxation methods to be suggested according to the user's emotions, supporting the user's physical and mental health.

[0092] The suggestion unit can also suggest events related to the user's hobbies and interests based on the user's talk content. For example, if the user is talking about music, the suggestion unit can provide information about concerts and live events. If the user is talking about sports, the suggestion unit can provide information about sporting events and matches. Furthermore, if the user is talking about arts and crafts, the suggestion unit can provide information about art exhibitions and craft fairs. This allows events based on the user's hobbies and interests to be suggested, making the user's life more fulfilling.

[0093] The analysis unit can also estimate the user's emotions based on the content of the user's chat and provide feedback according to the emotions. For example, if the user is happy, the analysis unit can provide a positive message to share the user's joy. If the user is angry, the analysis unit can provide advice to stay calm or relaxation methods. Furthermore, if the user is feeling anxious, the analysis unit can provide an encouraging message to give a sense of security or relaxation methods. This provides feedback according to the user's emotions and supports the user's emotional management.

[0094] The suggestion unit can also suggest products related to the user's hobbies and interests based on the user's talk content. For example, if the user is talking about cooking, the suggestion unit can provide purchase links for cooking utensils and ingredients. If the user is talking about gardening, the suggestion unit can also provide purchase links for gardening supplies and plants. Furthermore, if the user is talking about DIY, the suggestion unit can also provide purchase links for DIY kits and tools. This allows products according to the user's hobbies and interests to be suggested, improving the user's purchasing experience.

[0095] The analysis unit can also estimate the user's emotions based on the content of the user's conversation and suggest music that corresponds to the emotions. For example, if the user says that he or she wants to relax, the analysis unit can provide links to relaxation music or healing music. If the user says that he or she wants to cheer up, the analysis unit can provide up-tempo music or songs with positive lyrics. Furthermore, if the user says that he or she wants to concentrate, the analysis unit can provide music or environmental sounds to improve concentration. This allows music that corresponds to the user's emotions to be suggested, improving the user's mood and concentration.

[0096] The suggestion unit can also suggest learning resources related to the user's hobbies and interests based on the user's talk content. For example, if the user talks about programming, the suggestion unit can provide links to online programming courses and materials. If the user talks about photography, the suggestion unit can provide links to photography techniques and online workshops. Furthermore, if the user talks about cooking, the suggestion unit can provide links to cooking recipes and cooking classes. This suggests learning resources according to the user's hobbies and interests, supporting the user's skill improvement.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The analysis unit analyzes the talk content. The talk content includes voice talk, text talk, video talk, etc. The analysis unit analyzes the talk content using natural language processing technology and extracts information related to a schedule, for example. Step 2: The input unit automatically inputs the schedule based on the contents analyzed by the analysis unit. Methods for inputting the schedule include inputting the schedule into a calendar app, setting a reminder, etc. For example, the input unit can automatically input the schedule information extracted by the analysis unit into a calendar app and set a reminder. Step 3: The suggestion unit suggests appropriate services based on the content analyzed by the analysis unit. The suggested services include travel services, fashion services, e-commerce services, etc. For example, the suggestion unit may provide accommodation reservations and tourist spot information as travel-related services, provide links to online shopping sites as fashion-related services, and recommend products and make price comparisons as e-commerce-related services.

[0099] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0100] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned analysis unit, input unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the talk content. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically inputs a schedule based on the analyzed content. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests an appropriate service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0106] 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.

[0107] 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 the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] 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 performed in a secure state.

[0110] Fig. 4 shows an example of 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned analysis unit, input unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the talk content. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically inputs a schedule based on the analyzed content. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests an appropriate service. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 voice indicating a user input for 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned analysis unit, input unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the talk content. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically inputs a schedule based on the analyzed content. The suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314 and suggests appropriate services. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] 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 performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0151] Each of the multiple elements including the above-mentioned analysis unit, input unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the talk content. The input unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically inputs a schedule based on the analyzed content. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests appropriate services. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] 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 Universal Serial Bus (USB) 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.

[0161] In addition, 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 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) an analysis unit that analyzes the contents of the talk; an input unit for inputting a schedule based on the contents analyzed by the analysis unit; a proposal unit that proposes a service based on the content analyzed by the analysis unit; Equipped A system characterized by: (Appendix 2) The suggestion unit, Offer travel-related services 2. The system of claim 1, (Appendix 3) The suggestion unit, Offer fashion-related services 2. The system of claim 1, (Appendix 4) The suggestion unit, Propose e-commerce related services 2. The system of claim 1, (Appendix 5) The analysis unit is When analyzing, the analysis algorithm is adjusted by referring to the user's past chat history. 2. The system of claim 1, (Appendix 6) The input unit includes: Automatically input schedule based on analysis results 2. The system of claim 1, (Appendix 7) The analysis unit is Estimate user emotions and change analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The analysis unit is When analyzing, the analysis algorithm is adjusted by referring to the user's past chat history. 2. The system of claim 1, (Appendix 9) The analysis unit is During analysis, different analysis methods are applied depending on the category of the talk content. 2. The system of claim 1, (Appendix 10) The analysis unit is Infer user emotions and change the way analysis results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, take into account the user's geographic location to customize analysis results 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, we analyze users' social media activities and incorporate relevant information into the analysis. 2. The system of claim 1, (Appendix 13) The input unit includes: To estimate a user's emotion and change the timing of schedule input based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The input unit includes: When entering schedule information, select the appropriate input method by referring to the user's past schedule history. 2. The system of claim 1, (Appendix 15) The input unit includes: When entering schedule information, adjust the level of detail based on the importance of the content 2. The system of claim 1, (Appendix 16) The input unit includes: Infer user emotions and set schedule priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The input unit includes: When entering schedule information, the system takes into account the user's geographic location information and prioritizes the entry of schedules that are highly relevant. 2. The system of claim 1, (Appendix 18) The input unit includes: When entering schedule information, analyze the user's social media activity and enter relevant schedule information. 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user sentiment and change the way suggestions are presented based on the inferred sentiment 2. The system of claim 1, (Appendix 20) The suggestion unit, When making a proposal, the system selects the appropriate proposal method by referring to the user's past usage history. 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the talk content. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and prioritize suggestions based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, the system takes into account the user's geographic location information to prioritize relevant services. 2. The system of claim 1, (Appendix 24) The suggestion unit, When making a recommendation, we analyze your social media activity to suggest relevant services to you. 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the contents of the talk; an input unit for inputting a schedule based on the contents analyzed by the analysis unit; a proposal unit that proposes a service based on the content analyzed by the analysis unit; Equipped A system characterized by:

2. The suggestion unit, Offer travel-related services 2. The system of claim 1 .

3. The suggestion unit, Offer fashion-related services 2. The system of claim 1 .

4. The suggestion unit, Propose e-commerce related services 2. The system of claim 1 .

5. The analysis unit is When analyzing, the analysis algorithm is adjusted by referring to the user's past chat history.

2. The system of claim 1 .

6. The input unit includes: Automatically input schedule based on analysis results 2. The system of claim 1 .

7. The analysis unit is Estimate user emotions and change analysis accuracy based on the estimated user emotions 2. The system of claim 1 .

8. The analysis unit is When analyzing, the analysis algorithm is adjusted by referring to the user's past chat history.

2. The system of claim 1 .

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