System

Abstract

To promptly and appropriately provide responses to personal questions of a user and to suggest relevant products or services.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a response generation unit, a suggestion unit, and a provision unit. The reception unit receives questions from a user. The analysis unit analyzes the questions received by the reception unit. The response generation unit generates appropriate responses on the basis of the questions analyzed by the analysis unit. The suggestion unit suggests products or services on the basis of the responses generated by the response generation unit. The provision unit provides the products or services suggested by the suggestion 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] Conventional technologies have had the problem that it is difficult to provide quick and appropriate answers to users' personal questions, and furthermore, they do not offer suggestions for related products or services.

[0005] The system according to the embodiment aims to provide a user with prompt and appropriate answers to personal questions and to suggest related products and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an answer generation unit, a suggestion unit, and a provision unit. The reception unit receives questions from a user. The analysis unit analyzes the questions received by the reception unit. The answer generation unit generates an appropriate answer based on the questions analyzed by the analysis unit. The suggestion unit suggests products and services based on the answers generated by the answer generation unit. The provision unit provides the products and services suggested by the suggestion unit. Effect of the Invention

[0007] The system according to the embodiment can provide a user with quick and appropriate answers to his / her personal questions and suggest related products and 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) The personalized question answering system according to the embodiment of the present invention is a system in which AI instantly gives the best answer to personal questions that are difficult to ask others. In this system, a user inputs a question, and AI analyzes the input question and generates the best answer. Furthermore, AI can also suggest products and services that are useful for solving the question, leading to monetization. For example, when a user inputs a question such as "I haven't been able to sleep lately, what should I do?", the question is input to AI. Next, AI analyzes the input question. AI uses natural language processing technology to understand the content of the question and searches and generates the best answer from a database. For example, in response to the question "I haven't been able to sleep lately, what should I do?", AI generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Furthermore, AI also suggests products and services that are useful for solving the question. For example, in response to the question "I haven't been able to sleep lately, what should I do?", AI suggests products such as "aroma oils for relaxation" and "pillows that support good sleep." This allows users to know products and services that are useful for solving the question, leading to monetization. With this system, users can ask questions with confidence because the AI ​​instantly gives the best possible answer, even for personal questions that they would find difficult to ask others. In addition, products and services that will help resolve the question are also suggested, so users can obtain information that will help resolve the question. This allows the personalized question answering system to provide quick and accurate answers to users' questions and suggest related products and services.

[0029] The personalized question answering system according to the embodiment includes a reception unit, an analysis unit, an answer generation unit, a suggestion unit, and a provision unit. The reception unit accepts questions from a user. For example, the user can input a question such as "I haven't been able to sleep lately. What should I do?" The analysis unit uses natural language processing technology to analyze the question accepted by the reception unit. For example, the analysis unit understands the content of the question and searches and generates an optimal answer from a database. The answer generation unit generates an optimal answer based on the question analyzed by the analysis unit. For example, the answer generation unit generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." The suggestion unit suggests related products and services based on the answer generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillow that supports good sleep." The provision unit provides the products and services suggested by the suggestion unit. For example, the provision unit provides the suggested products to the user. As a result, the personalized question answering system according to the embodiment can generate an optimal answer to the user's question and suggest and provide related products and services.

[0030] The reception unit accepts questions from users. For example, a user can input a question such as "I can't sleep lately. What should I do?" The reception unit receives the questions input by the user in text format and transfers them to other departments in the system. Specifically, the reception unit accepts questions through a user interface and transmits the input text data to the analysis unit. The user interface is provided on various platforms such as web browsers and mobile applications. In addition, the reception unit can refer to the user's past question history and profile information to provide auxiliary information to understand the context of the question. For example, if a user has asked a similar question in the past, more accurate analysis can be performed based on that history. The reception unit also has a function to automatically correct the user's input errors and ambiguous expressions, supporting the user to input questions smoothly. As a result, the reception unit can efficiently and accurately accept questions from users and improve the performance of the entire system.

[0031] The analysis unit uses natural language processing technology to analyze the questions received by the reception unit. For example, the analysis unit understands the content of the question and searches and generates the optimal answer from the database. Specifically, the analysis unit breaks down the question text through morphological analysis and grammatical analysis to extract keywords and important phrases. Next, based on the extracted keywords, it performs semantic analysis to identify the intention and purpose of the question. The analysis unit uses an AI model to understand the context and nuance of the question and collect information to derive an appropriate answer. For example, if a user asks, "I can't sleep lately. What should I do?", the analysis unit extracts the keyword "can't sleep" and searches the database for information on the cause and countermeasures. In addition, the analysis unit refers to the user's past question history and profile information and utilizes auxiliary information to provide the optimal answer for each individual user. This allows the analysis unit to provide a foundation for accurately understanding the user's question and generating the optimal answer.

[0032] The answer generation unit generates an optimal answer based on the question analyzed by the analysis unit. For example, the answer generation unit generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Specifically, the answer generation unit uses an AI model to generate appropriate answers based on keywords and context information provided by the analysis unit. The AI ​​model is trained with a large amount of data in advance and has the ability to generate appropriate answers to various questions. For example, if a user asks, "I haven't been able to sleep lately. What should I do?", the answer generation unit generates an answer that includes advice on how to relax and how to improve sleep. In addition, the answer generation unit also has a filtering function to ensure that the generated answer is easy for the user to understand and feasible. This allows the answer generation unit to quickly and accurately provide optimal answers to the user's questions.

[0033] The suggestion unit suggests related products and services based on the answers generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillow that supports good sleep". Specifically, the suggestion unit analyzes the content of the answer provided by the answer generation unit and searches the database for related products and services. The suggestion unit refers to the user's profile information and past purchase history to select the most suitable products and services for each user. For example, if a user asks "I haven't been able to sleep lately. What should I do?" and the answer generation unit answers "I recommend taking a warm bath to relax", the suggestion unit suggests products such as "bath salts with a relaxing effect" and "aroma candles". Furthermore, the suggestion unit provides detailed information and reviews of the suggested products and services, and provides information that is useful when the user considers purchasing them. In this way, the suggestion unit can effectively suggest products and services that meet the user's needs and improve the user's satisfaction.

[0034] The providing unit provides the product or service proposed by the suggestion unit. For example, the providing unit provides the proposed product to the user. Specifically, the providing unit performs inventory management and delivery arrangements based on the product or service information provided by the suggestion unit. The providing unit supports the user in the procedure for purchasing the proposed product, providing a smooth purchasing experience. For example, when the user purchases "aroma oil for relaxation," the providing unit checks the inventory status and selects the optimal delivery method. Furthermore, the providing unit provides after-sales service and support after the purchase, and solves problems that the user may encounter when using the product or service. In this way, the providing unit can provide the user with high-quality products and services, and improve the user's satisfaction.

[0035] The analysis unit can analyze the question using a natural language processing technique. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the words of the question using morphological analysis, and analyzes the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the question using semantic analysis. For example, the analysis unit divides the question "I haven't been able to sleep lately, what should I do?" into "I haven't been able to sleep lately, but what should I do?" using morphological analysis. Next, the structure of the sentence is analyzed using grammatical analysis to identify the subject, predicate, object, and the like. Furthermore, the meaning of the question is understood using semantic analysis, and information for generating an optimal answer is extracted. As a result, the content of the question can be accurately analyzed using natural language processing techniques.

[0036] The answer generation unit can search and generate an appropriate answer from a database. The database includes, for example, an FAQ database, a knowledge base, a history of past questions and answers of the user, and the like, but is not limited to such examples. The answer generation unit can search for answers to frequently asked questions, for example, by using the FAQ database. The answer generation unit can also generate answers based on specialized knowledge by using the knowledge base. Furthermore, the answer generation unit can refer to the history of past questions and answers of the user and generate answers to similar questions. For example, in response to the question "I haven't been able to sleep lately, what should I do?", the answer generation unit searches the FAQ database for an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." The answer can also be generated based on specialized knowledge about sleep by using the knowledge base. In this way, the optimal answer can be generated quickly by using the database.

[0037] The suggestion unit can identify and suggest products and services. The suggestion unit can identify related products and services based on, for example, the content of a question from a user. For example, in response to a question such as "I haven't been able to sleep lately, what should I do?", the suggestion unit can identify and suggest products such as "aroma oil for relaxation" and "pillow that supports good sleep". The suggestion unit can also suggest related products and services based on the user's past purchase history and areas of interest. For example, the suggestion unit can refer to the history of products and services purchased by the user in the past and suggest related products. Furthermore, the suggestion unit can also suggest related products and services based on the user's areas of interest. For example, if the user is interested in health, the suggestion unit can suggest products and services related to health. This can help the user solve their problem by suggesting products and services that are useful for solving the question.

[0038] The providing unit can provide the proposed product or service. The providing unit, for example, provides the proposed product to the user. For example, the providing unit enables the proposed product to be purchased at an online store. The providing unit can also provide the proposed service to the user. For example, the providing unit enables the proposed service to be reserved. Furthermore, the providing unit can customize a method of providing the proposed product or service. For example, the providing unit adjusts a method of delivery of the product or a method of providing the service according to the needs of the user. In this way, the convenience of the user can be improved by providing the proposed product or service.

[0039] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit analyzes the patterns of questions frequently asked by the user in the past and automatically accepts similar questions. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can predict questions that will be asked in a specific time period from the user's past question history and adjust the reception method. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0040] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a question. The reception unit filters appropriate questions based on the user's current situation (at work, on a break, etc.), for example. The reception unit can also preferentially receive related questions based on the user's areas of interest (health, work, hobbies, etc.). Furthermore, the reception unit can also filter appropriate questions based on the user's current activity (exercising, reading, etc.). This makes it possible to receive more relevant questions by filtering questions based on the user's current situation and areas of interest.

[0041] When accepting a question, the reception unit can preferentially accept the question by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially accepts questions related to the area. Also, when the user is traveling, the reception unit can preferentially accept questions related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially accept questions related to the home. In this way, by taking into consideration the geographical location information of the user, highly relevant questions can be preferentially accepted.

[0042] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. For example, the reception unit preferentially receives related questions based on the content frequently posted by the user on social media. The reception unit can also receive related questions based on the content of accounts the user follows on social media. Furthermore, the reception unit can also receive related questions based on the content of groups the user participates in on social media. In this way, by analyzing the user's social media activity, related questions can be preferentially received.

[0043] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. Also, the analysis unit can perform a concise analysis on a question with a low level of importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, it is possible to provide a more appropriate analysis result.

[0044] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply an analysis algorithm using a medical database to a question about health. The analysis unit can also apply an analysis algorithm using a business database to a question about work. Furthermore, the analysis unit can also apply an analysis algorithm using an entertainment database to a question about hobbies. In this way, by applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0045] When analyzing a question, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit can adjust the analysis priority based on, for example, the time of day when the question was submitted. The analysis unit can also adjust the analysis priority based on the date when the question was submitted. Furthermore, the analysis unit can also adjust the analysis priority based on the day of the week when the question was submitted. In this way, by determining the analysis priority based on the time when the question was submitted, more appropriate analysis results can be provided.

[0046] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. For example, when the relevance of a question is high, the analysis unit performs the analysis as a priority. Also, when the relevance of a question is low, the analysis unit can postpone the analysis. Furthermore, the analysis unit can also adjust the order of analysis depending on the relevance of the question. In this way, by adjusting the order of analysis based on the relevance of the question, more appropriate analysis results can be provided.

[0047] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit provides a detailed answer to a question of high importance. Also, the answer generation unit can provide a concise answer to a question of low importance. Furthermore, the answer generation unit can adjust the priority of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided.

[0048] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies an answer generation algorithm using a medical database to a question about health. The answer generation unit can also apply an answer generation algorithm using a business database to a question about work. Furthermore, the answer generation unit can also apply an answer generation algorithm using an entertainment database to a question about hobbies. In this way, by applying different answer generation algorithms depending on the category of the question, a more appropriate answer can be provided.

[0049] The answer generation unit can determine the priority of the answers based on the time when the question was submitted when generating the answer. The answer generation unit adjusts the priority of the answers based on, for example, the time of day when the question was submitted. The answer generation unit can also adjust the priority of the answers based on the date when the question was submitted. Furthermore, the answer generation unit can also adjust the priority of the answers based on the day of the week when the question was submitted. In this way, by determining the priority of the answers based on the time when the question was submitted, a more appropriate answer can be provided.

[0050] The answer generation unit can adjust the order of answers based on the relevance of the question when generating an answer. For example, when the relevance of a question is high, the answer generation unit generates an answer preferentially. Also, when the relevance of a question is low, the answer generation unit can generate an answer later. Furthermore, the answer generation unit can also adjust the order of answers depending on the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, a more appropriate answer can be provided.

[0051] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit provides a detailed suggestion for a product with a high level of importance. Also, the suggestion unit can provide a concise suggestion for a product with a low level of importance. Furthermore, the suggestion unit can adjust the priority of the suggestion according to the importance of the product. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, a more appropriate suggestion can be provided.

[0052] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the product. For example, the suggestion unit can apply a suggestion algorithm using a medical database to a health-related product. The suggestion unit can also apply a suggestion algorithm using a business database to a work-related product. Furthermore, the suggestion unit can also apply a suggestion algorithm using an entertainment database to a hobby-related product. In this way, by applying different suggestion algorithms depending on the category of the product, more appropriate suggestions can be provided.

[0053] The suggestion unit can determine the priority of the suggestion based on the submission time of the product at the time of suggestion. The suggestion unit can adjust the priority of the suggestion based on, for example, the time of day when the product was submitted. The suggestion unit can also adjust the priority of the suggestion based on the date when the product was submitted. Furthermore, the suggestion unit can also adjust the priority of the suggestion based on the day of the week when the product was submitted. In this way, by determining the priority of the suggestion based on the submission time of the product, more appropriate suggestions can be provided.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, when the relevance of the products is high, the suggestion unit makes the suggestions preferentially. Also, when the relevance of the products is low, the suggestion unit can make the suggestions later. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the products. In this way, by adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions can be provided.

[0055] The provision unit can select an appropriate provision method by analyzing the user's past purchase history when providing a product or service. For example, the provision unit prioritizes the provision of related products based on products previously purchased by the user. The provision unit can also prioritize the provision of products of a specific brand based on the user's past purchase history. Furthermore, the provision unit can also analyze the user's past purchase history and select the most efficient provision method. In this way, the optimal provision method can be selected by analyzing the user's past purchase history.

[0056] The providing unit can customize the means of delivery based on the user's current living situation when providing a product or service. For example, when the user is busy, the providing unit can provide a quick delivery method. Also, when the user is relaxed, the providing unit can provide a detailed product description. Furthermore, when the user is traveling, the providing unit can also provide a method of receiving the product at the travel destination. In this way, by customizing the means of delivery based on the user's current living situation, a more appropriate delivery method can be selected.

[0057] The providing unit can select the optimal delivery method in consideration of the user's geographical location information when providing a product or service. For example, when the user is in a specific area, the providing unit prioritizes the provision of products related to that area. In addition, when the user is traveling, the providing unit can provide a method for receiving the product at the travel destination. Furthermore, when the user is at home, the providing unit can also provide a quick delivery method. In this way, the optimal delivery method can be selected by considering the user's geographical location information.

[0058] The providing unit can analyze the user's social media activity and suggest a means of providing the product or service when providing the product or service. For example, the providing unit prioritizes the provision of related products based on the content frequently posted by the user on social media. The providing unit can also prioritize the provision of related products based on the content of accounts the user follows on social media. Furthermore, the providing unit can also prioritize the provision of related products based on the content of groups the user participates in on social media. In this way, a more appropriate means of providing the product can be suggested by analyzing the user's social media activity.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting a question from a user, the reception unit refers to the user's past question history, and if a similar question already exists, the reception unit can immediately provide an answer to the question. For example, if a user has previously asked the question "I haven't been able to sleep lately. What should I do?", the reception unit can immediately provide the past answer when a similar question is input again. The reception unit can also analyze the user's past question history and prepare answers in advance for frequently asked questions. Furthermore, the reception unit can find specific patterns from the user's past question history and take measures in advance against predicted questions. In this way, by utilizing the user's past question history, quick and efficient question responses are possible.

[0061] When analyzing a question, the analysis unit can adjust the accuracy of the analysis based on the user's current situation and environment. For example, when the user is in a noisy environment, the analysis unit can improve the accuracy of the voice input using noise canceling technology. Also, when the user is in a quiet environment, a detailed analysis can be performed. Furthermore, when the user is moving, the analysis unit can also perform the analysis taking into account the location information. This allows the analysis accuracy to be adjusted according to the user's current situation and environment, thereby providing more accurate analysis results.

[0062] The answer generation unit can generate an individualized answer by referring to the user's past behavior history. For example, if the user has preferred to accept a particular piece of advice in the past, similar advice is preferentially provided. The answer generation unit can also find a particular pattern from the user's past behavior history and generate an answer based on the predicted behavior. Furthermore, the answer generation unit can analyze the user's past behavior history and generate an answer to encourage a particular behavior. In this way, by utilizing the user's past behavior history, a more individualized answer can be provided.

[0063] The suggestion unit can monitor the user's current health condition and suggest appropriate products and services. For example, when the user uses a health management app, the suggestion unit refers to the data and suggests products and services according to the health condition. The suggestion unit can also suggest preventive products and services based on the user's health condition. Furthermore, the suggestion unit can also monitor the user's health condition and, if an abnormality is detected, suggest products and services for a quick response. This makes it possible to make suggestions that take the user's health condition into consideration.

[0064] The providing unit can analyze the user's purchase history and provide related products and services. For example, related products are suggested based on products purchased by the user in the past. The providing unit can also provide products of a specific brand or category preferentially based on the user's purchase history. Furthermore, the providing unit can analyze the user's purchase history and provide products related to a specific season or event. In this way, more relevant products and services can be provided by utilizing the user's purchase history.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives a question from a user. For example, the user can input a question such as "I haven't been able to sleep lately. What should I do?" Step 2: The analysis unit uses natural language processing technology to analyze the question received by the reception unit. For example, the analysis unit understands the content of the question and searches and generates the optimal answer from a database. Step 3: The answer generator generates an optimal answer based on the question analyzed by the analyzer. For example, the answer generator generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Step 4: The suggestion unit suggests related products and services based on the answers generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillows that support good sleep." Step 5: The providing unit provides the product or service proposed by the suggesting unit. For example, the providing unit provides the proposed product to the user.

[0067] (Example 2) The personalized question answering system according to the embodiment of the present invention is a system in which AI instantly gives the best answer to personal questions that are difficult to ask others. In this system, a user inputs a question, and AI analyzes the input question and generates the best answer. Furthermore, AI can also suggest products and services that are useful for solving the question, leading to monetization. For example, when a user inputs a question such as "I haven't been able to sleep lately, what should I do?", the question is input to AI. Next, AI analyzes the input question. AI uses natural language processing technology to understand the content of the question and searches and generates the best answer from a database. For example, in response to the question "I haven't been able to sleep lately, what should I do?", AI generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Furthermore, AI also suggests products and services that are useful for solving the question. For example, in response to the question "I haven't been able to sleep lately, what should I do?", AI suggests products such as "aroma oils for relaxation" and "pillows that support good sleep." This allows users to know products and services that are useful for solving the question, leading to monetization. With this system, users can ask questions with confidence because the AI ​​instantly gives the best possible answer, even for personal questions that they would find difficult to ask others. In addition, products and services that will help resolve the question are also suggested, so users can obtain information that will help resolve the question. This allows the personalized question answering system to provide quick and accurate answers to users' questions and suggest related products and services.

[0068] The personalized question answering system according to the embodiment includes a reception unit, an analysis unit, an answer generation unit, a suggestion unit, and a provision unit. The reception unit accepts questions from a user. For example, the user can input a question such as "I haven't been able to sleep lately. What should I do?" The analysis unit uses natural language processing technology to analyze the question accepted by the reception unit. For example, the analysis unit understands the content of the question and searches and generates an optimal answer from a database. The answer generation unit generates an optimal answer based on the question analyzed by the analysis unit. For example, the answer generation unit generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." The suggestion unit suggests related products and services based on the answer generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillow that supports good sleep." The provision unit provides the products and services suggested by the suggestion unit. For example, the provision unit provides the suggested products to the user. As a result, the personalized question answering system according to the embodiment can generate an optimal answer to the user's question and suggest and provide related products and services.

[0069] The reception unit accepts questions from users. For example, a user can input a question such as "I can't sleep lately. What should I do?" The reception unit receives the questions input by the user in text format and transfers them to other departments in the system. Specifically, the reception unit accepts questions through a user interface and transmits the input text data to the analysis unit. The user interface is provided on various platforms such as web browsers and mobile applications. In addition, the reception unit can refer to the user's past question history and profile information to provide auxiliary information to understand the context of the question. For example, if a user has asked a similar question in the past, more accurate analysis can be performed based on that history. The reception unit also has a function to automatically correct the user's input errors and ambiguous expressions, supporting the user to input questions smoothly. As a result, the reception unit can efficiently and accurately accept questions from users and improve the performance of the entire system.

[0070] The analysis unit uses natural language processing technology to analyze the questions received by the reception unit. For example, the analysis unit understands the content of the question and searches and generates the optimal answer from the database. Specifically, the analysis unit breaks down the question text through morphological analysis and grammatical analysis to extract keywords and important phrases. Next, based on the extracted keywords, it performs semantic analysis to identify the intention and purpose of the question. The analysis unit uses an AI model to understand the context and nuance of the question and collect information to derive an appropriate answer. For example, if a user asks, "I can't sleep lately. What should I do?", the analysis unit extracts the keyword "can't sleep" and searches the database for information on the cause and countermeasures. In addition, the analysis unit refers to the user's past question history and profile information and utilizes auxiliary information to provide the optimal answer for each individual user. This allows the analysis unit to provide a foundation for accurately understanding the user's question and generating the optimal answer.

[0071] The answer generation unit generates an optimal answer based on the question analyzed by the analysis unit. For example, the answer generation unit generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Specifically, the answer generation unit uses an AI model to generate appropriate answers based on keywords and context information provided by the analysis unit. The AI ​​model is trained with a large amount of data in advance and has the ability to generate appropriate answers to various questions. For example, if a user asks, "I haven't been able to sleep lately. What should I do?", the answer generation unit generates an answer that includes advice on how to relax and how to improve sleep. In addition, the answer generation unit also has a filtering function to ensure that the generated answer is easy for the user to understand and feasible. This allows the answer generation unit to quickly and accurately provide optimal answers to the user's questions.

[0072] The suggestion unit suggests related products and services based on the answers generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillow that supports good sleep". Specifically, the suggestion unit analyzes the content of the answer provided by the answer generation unit and searches the database for related products and services. The suggestion unit refers to the user's profile information and past purchase history to select the most suitable products and services for each user. For example, if a user asks "I haven't been able to sleep lately. What should I do?" and the answer generation unit answers "I recommend taking a warm bath to relax", the suggestion unit suggests products such as "bath salts with a relaxing effect" and "aroma candles". Furthermore, the suggestion unit provides detailed information and reviews of the suggested products and services, and provides information that is useful when the user considers purchasing them. In this way, the suggestion unit can effectively suggest products and services that meet the user's needs and improve the user's satisfaction.

[0073] The providing unit provides the product or service proposed by the suggestion unit. For example, the providing unit provides the proposed product to the user. Specifically, the providing unit performs inventory management and delivery arrangements based on the product or service information provided by the suggestion unit. The providing unit supports the user in the procedure for purchasing the proposed product, providing a smooth purchasing experience. For example, when the user purchases "aroma oil for relaxation," the providing unit checks the inventory status and selects the optimal delivery method. Furthermore, the providing unit provides after-sales service and support after the purchase, and solves problems that the user may encounter when using the product or service. In this way, the providing unit can provide the user with high-quality products and services, and improve the user's satisfaction.

[0074] The analysis unit can analyze the question using a natural language processing technique. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides the words of the question using morphological analysis, and analyzes the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the question using semantic analysis. For example, the analysis unit divides the question "I haven't been able to sleep lately, what should I do?" into "I haven't been able to sleep lately, but what should I do?" using morphological analysis. Next, the structure of the sentence is analyzed using grammatical analysis to identify the subject, predicate, object, and the like. Furthermore, the meaning of the question is understood using semantic analysis, and information for generating an optimal answer is extracted. As a result, the content of the question can be accurately analyzed using natural language processing techniques.

[0075] The answer generation unit can search and generate an appropriate answer from a database. The database includes, for example, an FAQ database, a knowledge base, a history of past questions and answers of the user, and the like, but is not limited to such examples. The answer generation unit can search for answers to frequently asked questions, for example, by using the FAQ database. The answer generation unit can also generate answers based on specialized knowledge by using the knowledge base. Furthermore, the answer generation unit can refer to the history of past questions and answers of the user and generate answers to similar questions. For example, in response to the question "I haven't been able to sleep lately, what should I do?", the answer generation unit searches the FAQ database for an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." The answer can also be generated based on specialized knowledge about sleep by using the knowledge base. In this way, the optimal answer can be generated quickly by using the database.

[0076] The suggestion unit can identify and suggest products and services. The suggestion unit can identify related products and services based on, for example, the content of a question from a user. For example, in response to a question such as "I haven't been able to sleep lately, what should I do?", the suggestion unit can identify and suggest products such as "aroma oil for relaxation" and "pillow that supports good sleep". The suggestion unit can also suggest related products and services based on the user's past purchase history and areas of interest. For example, the suggestion unit can refer to the history of products and services purchased by the user in the past and suggest related products. Furthermore, the suggestion unit can also suggest related products and services based on the user's areas of interest. For example, if the user is interested in health, the suggestion unit can suggest products and services related to health. This can help the user solve their problem by suggesting products and services that are useful for solving the question.

[0077] The providing unit can provide the proposed product or service. The providing unit, for example, provides the proposed product to the user. For example, the providing unit enables the proposed product to be purchased at an online store. The providing unit can also provide the proposed service to the user. For example, the providing unit enables the proposed service to be reserved. Furthermore, the providing unit can customize a method of providing the proposed product or service. For example, the providing unit adjusts a method of delivery of the product or a method of providing the service according to the needs of the user. In this way, the convenience of the user can be improved by providing the proposed product or service.

[0078] The reception unit can estimate the user's emotion and adjust the timing of question reception based on the estimated user's emotion. For example, when the user is stressed, the reception unit temporarily delays the reception of the question until the user is relaxed. Also, when the user is relaxed, the reception unit can immediately receive the question and respond quickly. Furthermore, when the user is in a hurry, the reception unit can immediately receive the question and process it preferentially. In this way, by adjusting the timing of question reception according to the user's emotion, the question can be received at a more appropriate timing. 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.

[0079] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit analyzes the patterns of questions frequently asked by the user in the past and automatically accepts similar questions. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can predict questions that will be asked in a specific time period from the user's past question history and adjust the reception method. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0080] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a question. The reception unit filters appropriate questions based on the user's current situation (at work, on a break, etc.), for example. The reception unit can also preferentially receive related questions based on the user's areas of interest (health, work, hobbies, etc.). Furthermore, the reception unit can also filter appropriate questions based on the user's current activity (exercising, reading, etc.). This makes it possible to receive more relevant questions by filtering questions based on the user's current situation and areas of interest.

[0081] The reception unit can estimate the user's emotion and determine the priority of questions to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives questions related to stress reduction. Also, when the user is relaxed, the reception unit can preferentially receive general questions. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive questions with high urgency. In this way, by determining the priority of questions according to the user's emotion, more appropriate questions can be preferentially received. 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.

[0082] When accepting a question, the reception unit can preferentially accept the question by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the reception unit preferentially accepts questions related to the area. Also, when the user is traveling, the reception unit can preferentially accept questions related to the travel destination. Furthermore, when the user is at home, the reception unit can also preferentially accept questions related to the home. In this way, by taking into consideration the geographical location information of the user, highly relevant questions can be preferentially accepted.

[0083] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. For example, the reception unit preferentially receives related questions based on the content frequently posted by the user on social media. The reception unit can also receive related questions based on the content of accounts the user follows on social media. Furthermore, the reception unit can also receive related questions based on the content of groups the user participates in on social media. In this way, by analyzing the user's social media activity, related questions can be preferentially received.

[0084] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that focuses on the main points. This makes it possible to provide a more appropriate analysis result by adjusting the expression method of the analysis according to 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.

[0085] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. Also, the analysis unit can perform a concise analysis on a question with a low level of importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, it is possible to provide a more appropriate analysis result.

[0086] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit can apply an analysis algorithm using a medical database to a question about health. The analysis unit can also apply an analysis algorithm using a business database to a question about work. Furthermore, the analysis unit can also apply an analysis algorithm using an entertainment database to a question about hobbies. In this way, by applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. In addition, the analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows for a more appropriate analysis result to be provided by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When analyzing a question, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit can adjust the analysis priority based on, for example, the time of day when the question was submitted. The analysis unit can also adjust the analysis priority based on the date when the question was submitted. Furthermore, the analysis unit can also adjust the analysis priority based on the day of the week when the question was submitted. In this way, by determining the analysis priority based on the time when the question was submitted, more appropriate analysis results can be provided.

[0089] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. For example, when the relevance of a question is high, the analysis unit performs the analysis as a priority. Also, when the relevance of a question is low, the analysis unit can postpone the analysis. Furthermore, the analysis unit can also adjust the order of analysis depending on the relevance of the question. In this way, by adjusting the order of analysis based on the relevance of the question, more appropriate analysis results can be provided.

[0090] The answer generation unit can estimate the user's emotion and adjust the way of expressing the answer based on the estimated emotion of the user. For example, when the user is nervous, the answer generation unit provides a simple and highly visible answer. Also, when the user is relaxed, the answer generation unit can provide a detailed answer. Furthermore, when the user is in a hurry, the answer generation unit can also provide an answer that focuses on the main points. In this way, by adjusting the way of expressing the answer according to the user's emotion, a more appropriate answer can be provided. 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.

[0091] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit provides a detailed answer to a question of high importance. Also, the answer generation unit can provide a concise answer to a question of low importance. Furthermore, the answer generation unit can adjust the priority of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided.

[0092] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies an answer generation algorithm using a medical database to a question about health. The answer generation unit can also apply an answer generation algorithm using a business database to a question about work. Furthermore, the answer generation unit can also apply an answer generation algorithm using an entertainment database to a question about hobbies. In this way, by applying different answer generation algorithms depending on the category of the question, a more appropriate answer can be provided.

[0093] The answer generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. For example, when the user is in a hurry, the answer generation unit provides a short and to the point answer. Also, when the user is relaxed, the answer generation unit can provide a detailed answer. Furthermore, when the user is excited, the answer generation unit can also provide a visually stimulating answer. This allows a more appropriate answer to be provided by adjusting the length of the answer according to 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 can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0094] The answer generation unit can determine the priority of the answers based on the time when the question was submitted when generating the answer. The answer generation unit adjusts the priority of the answers based on, for example, the time of day when the question was submitted. The answer generation unit can also adjust the priority of the answers based on the date when the question was submitted. Furthermore, the answer generation unit can also adjust the priority of the answers based on the day of the week when the question was submitted. In this way, by determining the priority of the answers based on the time when the question was submitted, a more appropriate answer can be provided.

[0095] The answer generation unit can adjust the order of answers based on the relevance of the question when generating an answer. For example, when the relevance of a question is high, the answer generation unit generates an answer preferentially. Also, when the relevance of a question is low, the answer generation unit can generate an answer later. Furthermore, the answer generation unit can also adjust the order of answers depending on the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, a more appropriate answer can be provided.

[0096] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated user's emotion. For example, when the user is nervous, the suggestion unit provides a simple and highly visible suggestion. Moreover, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. In this way, by adjusting the way of expressing the suggestion according to the user's emotion, more appropriate suggestions can be 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. For example, the suggestion unit provides a detailed suggestion for a product with a high level of importance. Also, the suggestion unit can provide a concise suggestion for a product with a low level of importance. Furthermore, the suggestion unit can adjust the priority of the suggestion according to the importance of the product. In this way, by adjusting the level of detail of the suggestion based on the importance of the product, a more appropriate suggestion can be provided.

[0098] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the product. For example, the suggestion unit can apply a suggestion algorithm using a medical database to a health-related product. The suggestion unit can also apply a suggestion algorithm using a business database to a work-related product. Furthermore, the suggestion unit can also apply a suggestion algorithm using an entertainment database to a hobby-related product. In this way, by applying different suggestion algorithms depending on the category of the product, more appropriate suggestions can be provided.

[0099] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Also, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is excited, the suggestion unit can provide a visually stimulating suggestion. This allows for more appropriate suggestions to be provided by adjusting the length of the suggestion according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The suggestion unit can determine the priority of the suggestion based on the submission time of the product at the time of suggestion. The suggestion unit can adjust the priority of the suggestion based on, for example, the time of day when the product was submitted. The suggestion unit can also adjust the priority of the suggestion based on the date when the product was submitted. Furthermore, the suggestion unit can also adjust the priority of the suggestion based on the day of the week when the product was submitted. In this way, by determining the priority of the suggestion based on the submission time of the product, more appropriate suggestions can be provided.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, when the relevance of the products is high, the suggestion unit makes the suggestions preferentially. Also, when the relevance of the products is low, the suggestion unit can make the suggestions later. Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the products. In this way, by adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions can be provided.

[0102] The providing unit can estimate the user's emotion and determine the priority order of products to be provided based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit prioritizes providing products with a relaxing effect. Also, when the user is relaxed, the providing unit can prioritize providing general products. Furthermore, when the user is in a hurry, the providing unit can also prioritize providing products with immediate effects. In this way, by determining the priority order of products to be provided according to the user's emotion, more appropriate products can be provided. 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.

[0103] The provision unit can select an appropriate provision method by analyzing the user's past purchase history when providing a product or service. For example, the provision unit prioritizes the provision of related products based on products previously purchased by the user. The provision unit can also prioritize the provision of products of a specific brand based on the user's past purchase history. Furthermore, the provision unit can also analyze the user's past purchase history and select the most efficient provision method. In this way, the optimal provision method can be selected by analyzing the user's past purchase history.

[0104] The providing unit can customize the means of delivery based on the user's current living situation when providing a product or service. For example, when the user is busy, the providing unit can provide a quick delivery method. Also, when the user is relaxed, the providing unit can provide a detailed product description. Furthermore, when the user is traveling, the providing unit can also provide a method of receiving the product at the travel destination. In this way, by customizing the means of delivery based on the user's current living situation, a more appropriate delivery method can be selected.

[0105] The providing unit can estimate the user's emotion and adjust the display method of the product to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. Also, when the user is relaxed, the providing unit can provide a detailed product explanation. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the product to be provided according to the user's emotion, it is possible to provide a more appropriate product. 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.

[0106] The providing unit can select the optimal delivery method in consideration of the user's geographical location information when providing a product or service. For example, when the user is in a specific area, the providing unit prioritizes the provision of products related to that area. In addition, when the user is traveling, the providing unit can provide a method for receiving the product at the travel destination. Furthermore, when the user is at home, the providing unit can also provide a quick delivery method. In this way, the optimal delivery method can be selected by considering the user's geographical location information.

[0107] The providing unit can analyze the user's social media activity and suggest a means of providing the product or service when providing the product or service. For example, the providing unit prioritizes the provision of related products based on the content frequently posted by the user on social media. The providing unit can also prioritize the provision of related products based on the content of accounts the user follows on social media. Furthermore, the providing unit can also prioritize the provision of related products based on the content of groups the user participates in on social media. In this way, a more appropriate means of providing the product can be suggested by analyzing the user's social media activity.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When accepting a question from a user, the reception unit refers to the user's past question history, and if a similar question already exists, the reception unit can immediately provide an answer to the question. For example, if a user has previously asked the question "I haven't been able to sleep lately. What should I do?", the reception unit can immediately provide the past answer when a similar question is input again. The reception unit can also analyze the user's past question history and prepare answers in advance for frequently asked questions. Furthermore, the reception unit can find specific patterns from the user's past question history and take measures in advance against predicted questions. In this way, by utilizing the user's past question history, quick and efficient question responses are possible.

[0110] When analyzing a question, the analysis unit can adjust the accuracy of the analysis based on the user's current situation and environment. For example, when the user is in a noisy environment, the analysis unit can improve the accuracy of the voice input using noise canceling technology. Also, when the user is in a quiet environment, a detailed analysis can be performed. Furthermore, when the user is moving, the analysis unit can also perform the analysis taking into account the location information. This allows the analysis accuracy to be adjusted according to the user's current situation and environment, thereby providing more accurate analysis results.

[0111] The answer generation unit can generate an individualized answer by referring to the user's past behavior history. For example, if the user has preferred to accept a particular piece of advice in the past, similar advice is preferentially provided. The answer generation unit can also find a particular pattern from the user's past behavior history and generate an answer based on the predicted behavior. Furthermore, the answer generation unit can analyze the user's past behavior history and generate an answer to encourage a particular behavior. In this way, by utilizing the user's past behavior history, a more individualized answer can be provided.

[0112] The suggestion unit can monitor the user's current health condition and suggest appropriate products and services. For example, when the user uses a health management app, the suggestion unit refers to the data and suggests products and services according to the health condition. The suggestion unit can also suggest preventive products and services based on the user's health condition. Furthermore, the suggestion unit can also monitor the user's health condition and, if an abnormality is detected, suggest products and services for a quick response. This makes it possible to make suggestions that take the user's health condition into consideration.

[0113] The providing unit can analyze the user's purchase history and provide related products and services. For example, related products are suggested based on products purchased by the user in the past. The providing unit can also provide products of a specific brand or category preferentially based on the user's purchase history. Furthermore, the providing unit can analyze the user's purchase history and provide products related to a specific season or event. In this way, more relevant products and services can be provided by utilizing the user's purchase history.

[0114] The reception unit can estimate the user's emotions and adjust the way in which questions are received based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can receive questions in a gentle tone. If the user is relaxed, the reception unit can receive questions in a casual tone. Furthermore, if the user is in a hurry, a simple interface can be provided to quickly receive questions. This allows for more appropriate responses by adjusting the way questions are received according to the user's emotions.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis approach based on the estimated user's emotions. For example, if the user feels anxious, the analysis unit can provide reassuring analysis results. If the user feels excited, the analysis unit can provide detailed and interesting analysis results. Furthermore, if the user feels tired, the analysis unit can provide concise and to the point analysis results. In this way, by adjusting the analysis approach according to the user's emotions, more appropriate analysis results can be provided.

[0116] The answer generation unit can estimate the user's emotion and adjust the tone of the answer based on the estimated user's emotion. For example, if the user is sad, the answer generation unit can provide an answer including encouraging words. Also, if the user is happy, the answer generation unit can provide an answer that shows empathy. Furthermore, if the user is angry, the answer generation unit can provide an answer with a calm and collected tone. In this way, by adjusting the tone of the answer according to the user's emotion, a more appropriate answer can be provided.

[0117] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, products and services that have a relaxing effect can be suggested. Also, if the user is relaxed, products and services that increase enjoyment can be suggested. Furthermore, if the user is excited, products and services related to active activities can be suggested. In this way, more appropriate suggestions can be provided by adjusting the content of the suggestions according to the user's emotions.

[0118] The providing unit can estimate the user's emotions and adjust the packaging of the product to be provided based on the estimated user's emotions. For example, if the user is nervous, a package with a simple and calm design can be provided. Also, if the user is relaxed, a package with a colorful and fun design can be provided. Furthermore, if the user is in a hurry, a package that is easy to open can also be provided. In this way, by adjusting the packaging of the product to be provided according to the user's emotions, a more appropriate product can be provided.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit receives a question from a user. For example, the user can input a question such as "I haven't been able to sleep lately. What should I do?" Step 2: The analysis unit uses natural language processing technology to analyze the question received by the reception unit. For example, the analysis unit understands the content of the question and searches and generates the optimal answer from a database. Step 3: The answer generator generates an optimal answer based on the question analyzed by the analyzer. For example, the answer generator generates an answer such as "I recommend taking a warm bath to relax. Also, try not to use your smartphone before going to bed." Step 4: The suggestion unit suggests related products and services based on the answers generated by the answer generation unit. For example, the suggestion unit suggests products such as "aroma oil for relaxation" and "pillows that support good sleep." Step 5: The providing unit provides the product or service proposed by the suggesting unit. For example, the providing unit provides the proposed product to the user.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements including the above-mentioned reception unit, analysis unit, answer generation unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal answer based on the analyzed question. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests related products and services based on the generated answer. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the proposed products and services to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, answer generation unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal answer based on the analyzed question. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests related products and services based on the generated answer. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the proposed products and services to the user. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, answer generation unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and accepts a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal answer based on the analyzed question. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests related products and services based on the generated answer. The provision unit is realized, for example, by the speaker 240 of the headset type terminal 314 and provides the proposed products and services to the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements including the above-mentioned reception unit, analysis unit, answer generation unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts a question from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal answer based on the analyzed question. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests related products and services based on the generated answer. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the proposed products and services to the user. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Appendix 1) A reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; an answer generation unit that generates an appropriate answer based on the question analyzed by the analysis unit; a suggestion unit that proposes products and services based on the answer generated by the answer generation unit; a providing unit that provides the product or service proposed by the suggestion unit; A system characterized by: (Appendix 2) The analysis unit is Analyze questions using natural language processing techniques 2. The system of claim 1, (Appendix 3) The answer generation unit: Search and generate appropriate answers from a database 2. The system of claim 1, (Appendix 4) The suggestion unit, Identify and recommend products and services 2. The system of claim 1, (Appendix 5) The providing unit is Offering proposed products and services 2. The system of claim 1, (Appendix 6) The reception unit is The system estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1, (Appendix 8) The reception unit is Filter questions based on the user's current situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When accepting questions, the system takes into account the user's geographic location information to give priority to the questions. 2. The system of claim 1, (Appendix 11) The reception unit is When a question is submitted, we analyze the user's social media activity and submit related questions. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is When parsing questions, adjust the level of detail based on the question 2. The system of claim 1, (Appendix 14) The analysis unit is When parsing questions, apply different parsing algorithms depending on the question category 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is When parsing questions, prioritize parsing based on when the question was submitted 2. The system of claim 1, (Appendix 17) The analysis unit is When parsing questions, adjust the order of parsing based on question relevance 2. The system of claim 1, (Appendix 18) The answer generation unit: Infer user sentiment and adjust answer expression based on the inferred sentiment 2. The system of claim 1, (Appendix 19) The answer generation unit: When generating answers, adjust the detail of the answer based on the question 2. The system of claim 1, (Appendix 20) The answer generation unit: When generating answers, different answer generation algorithms are applied depending on the question category. 2. The system of claim 1, (Appendix 21) The answer generation unit: Infer user sentiment and adjust the length of answers based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The answer generation unit: When generating answers, prioritize answers based on when the question was submitted 2. The system of claim 1, (Appendix 23) The answer generation unit: During answer generation, adjust the order of answers based on the relevance of the question 2. The system of claim 1, (Appendix 24) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The suggestion unit, When making a proposal, adjust the proposal detail based on the product 2. The system of claim 1, (Appendix 26) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the product category 2. The system of claim 1, (Appendix 27) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The suggestion unit, At the time of proposal, prioritize proposals based on when the product was submitted 2. The system of claim 1, (Appendix 29) The suggestion unit, When making suggestions, adjust the order of suggestions based on product relevance 2. The system of claim 1, (Appendix 30) The providing unit is Estimate user emotions and prioritize products to provide based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing products or services, analyze the user's past purchase history to select the appropriate delivery method 2. The system of claim 1, (Appendix 32) The providing unit is Customize delivery of products and services based on the user's current life situation 2. The system of claim 1, (Appendix 33) The providing unit is Infer user emotions and adjust the display method of offered products based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The providing unit is When providing products or services, select the most appropriate method of delivery by taking into account the user's geographic location information 2. The system of claim 1, (Appendix 35) The providing unit is When offering products or services, analyze your social media activity to suggest ways to offer them to you 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; an answer generation unit that generates an appropriate answer based on the question analyzed by the analysis unit; a suggestion unit that proposes products and services based on the answer generated by the answer generation unit; a providing unit that provides the product or service proposed by the suggestion unit; A system characterized by:

2. The analysis unit is Analyze questions using natural language processing techniques 2. The system of claim 1 .

3. The answer generation unit: Search and generate appropriate answers from a database 2. The system of claim 1 .

4. The suggestion unit, Identify and recommend products and services 2. The system of claim 1 .

5. The reception unit is The system estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1 .

7. The reception unit is Filter questions based on the user's current situation and interests 2. The system of claim 1 .

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