System

Abstract

An object of the system according to the embodiment is to efficiently search for a specific passage of a book or a paper read in the past and acquire necessary information.SOLUTION: A system includes an information retrieval part, a retrieval result presentation part, and a purchase support part. The information retrieval part performs interactive information retrieval using the generated AI. The retrieval result presentation unit presents a result retrieved by the information retrieval unit. The purchase support unit supports a purchase procedure of the information presented by the search result presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to recall specific passages from books or papers that have been read in the past, making it difficult to efficiently obtain the necessary information.

[0005] The system according to the embodiment aims to efficiently search for a specific passage in a book or paper that has been read in the past and to acquire the necessary information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information search unit, a search result presentation unit, and a purchase support unit. The information search unit performs an interactive information search using a generation AI. The search result presentation unit presents the results searched by the information search unit. The purchase support unit supports the purchase procedure for the information presented by the search result presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for specific passages in books or papers that have been read in the past and obtain the necessary information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The information retrieval system according to an embodiment of the present invention uses a generative AI to interactively search for passages in books or papers that a user cannot remember, and enables the user to reliably acquire (purchase) the necessary information. This allows the information retrieval system to recapture the information that the user cannot remember, and correctly delivers the value of the information.

[0029] An information acquisition system according to an embodiment includes an interactive information search unit, a search result presentation unit, and a purchase support unit. The interactive information search unit interacts with the generation AI to provide information about a book or paper passage the user cannot remember. For example, if the user inputs a vague memory, such as "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI uses that information to search for related books or papers. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates search results based on the prompt. The search result presentation unit presents the search results of the generation AI to the user, allowing the user to further narrow down the search results. For example, the generation AI may present multiple candidates, such as "The following books or papers may be relevant," and the user can provide more detailed information to narrow down the search results. The generation AI then conducts a second search based on the user's additional information to identify the most suitable book or paper. The purchase support unit allows the generation AI to obtain information about the book or paper identified by the user and support the purchase process. For example, when a user gives an instruction such as "I want to buy this book," the generation AI provides a link to purchase the book, allowing the user to smoothly complete the purchase procedure. Similarly, when subscribing to a paid service, the generation AI also supports the procedure. In this way, the information acquisition system according to the embodiment allows the user to regain information that the user cannot remember, and can correctly deliver the value of the information. For example, when a user recalls a passage from a book they read a long time ago and purchases that book, fair compensation is paid to the author and publisher. In addition, when a user subscribes to a paid service, appropriate compensation is also paid to the information provider. This is expected to reevaluate the value of information and deliver it correctly.

[0030] The information retrieval unit can automatically generate related keywords or phrases based on the user's vague memories and use them to improve search accuracy. For example, if a user inputs, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI will automatically generate related keywords and phrases based on that information. For example, it can generate keywords such as "scientific advances," "technological innovation," and "future predictions," and use them to perform searches. This allows the system to automatically generate related keywords and phrases based on the user's vague memories, improving search accuracy.

[0031] The information search unit can refer to the user's past search history or reading history and prioritize searching for more relevant books or papers. For example, if a user inputs, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI will refer to the user's past search history and reading history and prioritize searching for related books and papers. For example, if the user has read many science-related books in the past, books in that field will be presented first. This allows the information search unit to refer to the user's past search history and reading history and prioritize searching for more relevant books and papers.

[0032] The information search unit uses voice input to analyze what the user says in real time and search for related books or papers. For example, if the user says, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI analyzes the voice in real time and searches for related books and papers. For example, it searches based on keywords such as "scientific advances," "technological innovation," and "future predictions." This allows the system to analyze what the user says in real time and search for related books and papers.

[0033] The information search unit can analyze images or handwritten notes provided by the user and search for related information based on that. For example, if a user provides an image of a part of a book they read a long time ago, the generation AI will analyze the image and search for related books and papers. For example, it can extract text from the image and perform a search based on keywords such as "scientific advances" and "technological innovation." This allows the system to analyze images or handwritten notes provided by the user and search for related information based on that.

[0034] The search result presentation unit can automatically generate a summary of each candidate when presenting search results, allowing the user to make a decision quickly. For example, when the generation AI presents search results, the search result presentation unit can automatically generate a summary of each candidate, allowing the user to make a decision quickly. For example, a summary such as "This book describes in detail the progress of science and introduces the history of technological innovation" is presented. This allows the search results to be presented automatically, allowing the user to make a decision quickly.

[0035] The search result presentation unit can reflect user feedback in real time and dynamically update the search results. For example, after the generation AI presents the search results, the search result presentation unit reflects user feedback in real time and dynamically updates the search results. For example, if the user provides feedback such as "this book is not right," the generation AI will search again based on that information and present more suitable books. This allows user feedback to be reflected in real time and search results to be dynamically updated.

[0036] The search result presentation unit can generate interactive graphs or charts to visually display the search results and allow the user to intuitively understand them. The search result presentation unit, for example, generates interactive graphs or charts so that the generation AI can visually display the search results and allow the user to intuitively understand them. For example, related books and papers can be displayed in a graph and the user can click to access detailed information. This makes it possible to generate interactive graphs or charts to visually display the search results and allow the user to intuitively understand them.

[0037] The search result presentation unit can provide a function for sharing search results with other users and for collaboratively narrowing down the search results. For example, after the generation AI presents search results, the search result presentation unit provides a function for a user to share the results with other users and for collaboratively narrowing down the search results. For example, the search result presentation unit generates a shared link so that other users can click on the link to access the search results. This makes it possible to provide a function for sharing search results with other users and for collaboratively narrowing down the search results.

[0038] The purchase support section allows the generation AI to automatically suggest the optimal purchase option when going through the purchase process. For example, when a user instructs the purchase support section that "I would like to purchase this book," the generation AI automatically suggests the optimal purchase option. For example, options such as e-book, hardcover, rental, etc. are presented and the user can select. This allows the generation AI to automatically suggest the optimal purchase option when going through the purchase process.

[0039] The purchasing support department can recommend related books or papers based on the user's purchasing history or preferences. For example, when a user indicates, "I want to buy this book," the generating AI will recommend related books based on the user's purchasing history and preferences. For example, it will suggest books on a similar topic to books previously purchased. This makes it possible to recommend related books and papers based on the user's purchasing history and preferences.

[0040] The purchase support unit allows the generation AI to automatically apply discount coupons or promotional codes when carrying out the purchase process. For example, when a user instructs the purchase support unit to "purchase this book," the generation AI automatically applies discount coupons or promotional codes. For example, the purchase support unit displays "A 10% off coupon has been applied" during the purchase process. This allows the generation AI to automatically apply discount coupons or promotional codes when carrying out the purchase process.

[0041] The purchase support department allows the generation AI to provide related reading guides or reviews as a follow-up after a purchase. For example, after a user purchases a book, the generation AI provides related reading guides or reviews. For example, a link such as "Here is the reading guide for this book" is provided. This allows the generation AI to provide related reading guides or reviews as a follow-up after a purchase.

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

[0043] The information acquisition system can further include an interest analysis unit that analyzes the user's interests. The interest analysis unit analyzes the user's interests based on keywords the user has searched for in the past and the genres of books the user has purchased. For example, if the user has searched for many science fiction novels in the past, the interest analysis unit can determine that the user is interested in science fiction and prioritize the presentation of related books and papers. Furthermore, if the user shows interest in a specific topic, the system can provide information on new releases and reviews related to that topic. This makes it possible to provide more personalized information based on the user's interests.

[0044] The information retrieval system may further include a learning unit that learns the user's search behavior. The learning unit learns the user's search behavior based on the user's past searches and selected results. For example, if the user frequently uses a particular keyword, the learning unit can adjust the system to prioritize the use of that keyword. Also, if the user prefers information in a particular format, the learning unit can prioritize the presentation of information in that format. This allows the system to learn the user's search behavior and provide more efficient and effective information.

[0045] The information retrieval system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user-provided feedback and improves the accuracy and usability of the system based on the feedback. For example, if a user provides feedback such as "this search result was not useful," the feedback collection unit can improve the search algorithm based on that information. Also, if a user provides feedback such as "this book was very useful," the feedback collection unit can recommend similar books to other users. In this way, user feedback can be collected and the accuracy and usability of the system can be improved.

[0046] The information acquisition system can further include an interest analysis unit that analyzes the user's interests. The interest analysis unit analyzes the user's interests based on keywords the user has searched for in the past and the genres of books the user has purchased. For example, if the user has searched for many science fiction novels in the past, the interest analysis unit can determine that the user is interested in science fiction and prioritize the presentation of related books and papers. Furthermore, if the user shows interest in a specific topic, the system can provide information on new releases and reviews related to that topic. This makes it possible to provide more personalized information based on the user's interests.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The interactive information retrieval unit interactively provides information to the generation AI about passages from books or papers that the user cannot remember. When the user inputs a vague memory, the generation AI searches for related books or papers based on that information. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates search results based on that prompt. Step 2: The search result presentation section presents the search results of the generation AI to the user, who then narrows down the results further. The generation AI presents multiple candidates, and the user narrows down the results by providing more detailed information from among them. The generation AI then conducts another search based on the user's additional information, identifying the most suitable book or paper. Step 3: In the purchasing support department, the generation AI retrieves information about the books and papers specified by the user and supports the purchase process. When the user specifies the book they want to purchase, the generation AI provides a purchase link for that book, allowing the user to complete the purchase process smoothly. The generation AI also supports the process when subscribing to paid services.

[0049] (Example 2) The information retrieval system according to an embodiment of the present invention uses a generative AI to interactively search for passages in books or papers that a user cannot remember, and enables the user to reliably acquire (purchase) the necessary information. This allows the information retrieval system to recapture the information that the user cannot remember, and correctly delivers the value of the information.

[0050] An information acquisition system according to an embodiment includes an interactive information search unit, a search result presentation unit, and a purchase support unit. The interactive information search unit interacts with the generation AI to provide information about a book or paper passage the user cannot remember. For example, if the user inputs a vague memory, such as "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI uses that information to search for related books or papers. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates search results based on the prompt. The search result presentation unit presents the search results of the generation AI to the user, allowing the user to further narrow down the search results. For example, the generation AI may present multiple candidates, such as "The following books or papers may be relevant," and the user can provide more detailed information to narrow down the search results. The generation AI then conducts a second search based on the user's additional information to identify the most suitable book or paper. The purchase support unit allows the generation AI to obtain information about the book or paper identified by the user and support the purchase process. For example, when a user gives an instruction such as "I want to buy this book," the generation AI provides a link to purchase the book, allowing the user to smoothly complete the purchase procedure. Similarly, when subscribing to a paid service, the generation AI also supports the procedure. In this way, the information acquisition system according to the embodiment allows the user to regain information that the user cannot remember, and can correctly deliver the value of the information. For example, when a user recalls a passage from a book they read a long time ago and purchases that book, fair compensation is paid to the author and publisher. In addition, when a user subscribes to a paid service, appropriate compensation is also paid to the information provider. This is expected to reevaluate the value of information and deliver it correctly.

[0051] The information retrieval unit can automatically generate related keywords or phrases based on the user's vague memories and use them to improve search accuracy. For example, if a user inputs, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI will automatically generate related keywords and phrases based on that information. For example, it can generate keywords such as "scientific advances," "technological innovation," and "future predictions," and use them to perform searches. This allows the system to automatically generate related keywords and phrases based on the user's vague memories, improving search accuracy.

[0052] The information search unit can refer to the user's past search history or reading history and prioritize searching for more relevant books or papers. For example, if a user inputs, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI will refer to the user's past search history and reading history and prioritize searching for related books and papers. For example, if the user has read many science-related books in the past, books in that field will be presented first. This allows the information search unit to refer to the user's past search history and reading history and prioritize searching for more relevant books and papers.

[0053] The information search unit uses the emotion estimation function to analyze the emotion the user feels about the passage they are trying to remember, and can search for related information based on that emotion. For example, if a user inputs, "I can't remember a passage that moved me in a book I read a long time ago," the generation AI will use the emotion estimation function to analyze the user's emotion and search for books and papers related to that emotion. For example, it will perform a search based on keywords such as "moving," "tearful," and "emotional." This allows the information search unit to analyze the emotion the user feels about the passage they are trying to remember, and search for related information based on that emotion.

[0054] The information search unit uses voice input to analyze what the user says in real time and search for related books or papers. For example, if the user says, "I can't remember a passage about scientific advances in a book I read a long time ago," the generation AI analyzes the voice in real time and searches for related books and papers. For example, it searches based on keywords such as "scientific advances," "technological innovation," and "future predictions." This allows the system to analyze what the user says in real time and search for related books and papers.

[0055] The information search unit can analyze images or handwritten notes provided by the user and search for related information based on that. For example, if a user provides an image of a part of a book they read a long time ago, the generation AI will analyze the image and search for related books and papers. For example, it can extract text from the image and perform a search based on keywords such as "scientific advances" and "technological innovation." This allows the system to analyze images or handwritten notes provided by the user and search for related information based on that.

[0056] The information search unit uses the emotion estimation function to analyze the emotions of the user when they speak in real time, and can engage in dialogue that elicits positive emotions. For example, when a user says, "I can't remember a passage from a book I read a long time ago that really moved me," the information search unit analyzes the emotions in real time, and the generation AI engages in dialogue that elicits positive emotions. For example, the dialogue might be something like, "That book was really moving. I'd like to experience that emotion again." This allows the information search unit to analyze the emotions of the user when they speak in real time, and can engage in dialogue that elicits positive emotions.

[0057] The search result presentation unit can automatically generate a summary of each candidate when presenting search results, allowing the user to make a decision quickly. For example, when the generation AI presents search results, the search result presentation unit can automatically generate a summary of each candidate, allowing the user to make a decision quickly. For example, a summary such as "This book describes in detail the progress of science and introduces the history of technological innovation" is presented. This allows the search results to be presented automatically, allowing the user to make a decision quickly.

[0058] The search result presentation unit can reflect user feedback in real time and dynamically update the search results. For example, after the generation AI presents the search results, the search result presentation unit reflects user feedback in real time and dynamically updates the search results. For example, if the user provides feedback such as "this book is not right," the generation AI will search again based on that information and present more suitable books. This allows user feedback to be reflected in real time and search results to be dynamically updated.

[0059] The search result presentation unit can use the emotion estimation function to analyze the emotions the user feels toward the presented search results and prioritize presenting candidates with positive emotions. For example, after the generation AI presents search results, the search result presentation unit can use the emotion estimation function to analyze the emotions the user feels toward the presented search results and prioritize presenting candidates with positive emotions. For example, if the user feels that "this book looks good," that book will be presented preferentially. This makes it possible to analyze the emotions the user feels toward the presented search results and prioritize presenting candidates with positive emotions.

[0060] The search result presentation unit can generate interactive graphs or charts to visually display the search results and allow the user to intuitively understand them. The search result presentation unit, for example, generates interactive graphs or charts so that the generation AI can visually display the search results and allow the user to intuitively understand them. For example, related books and papers can be displayed in a graph and the user can click to access detailed information. This makes it possible to generate interactive graphs or charts to visually display the search results and allow the user to intuitively understand them.

[0061] The search result presentation unit can provide a function for sharing search results with other users and for collaboratively narrowing down the search results. For example, after the generation AI presents search results, the search result presentation unit provides a function for a user to share the results with other users and for collaboratively narrowing down the search results. For example, the search result presentation unit generates a shared link so that other users can click on the link to access the search results. This makes it possible to provide a function for sharing search results with other users and for collaboratively narrowing down the search results.

[0062] The search result presentation unit uses the emotion estimation function to monitor in real time the emotions the user feels about the presented search results and continuously present the optimal results. For example, after the generation AI presents search results, the search result presentation unit uses the emotion estimation function to monitor in real time the emotions the user feels about the presented search results and continuously present the optimal results. For example, if the user feels that "this book looks good," that book will be presented preferentially. This makes it possible to monitor in real time the emotions the user feels about the presented search results and continuously present the optimal results.

[0063] The purchase support section allows the generation AI to automatically suggest the optimal purchase option when going through the purchase process. For example, when a user instructs the purchase support section that "I would like to purchase this book," the generation AI automatically suggests the optimal purchase option. For example, options such as e-book, hardcover, rental, etc. are presented and the user can select. This allows the generation AI to automatically suggest the optimal purchase option when going through the purchase process.

[0064] The purchasing support department can recommend related books or papers based on the user's purchasing history or preferences. For example, when a user indicates, "I want to buy this book," the generating AI will recommend related books based on the user's purchasing history and preferences. For example, it will suggest books on a similar topic to books previously purchased. This makes it possible to recommend related books and papers based on the user's purchasing history and preferences.

[0065] The purchase support unit uses the emotion estimation function to analyze the user's emotions when going through the purchasing process and make suggestions to provide a positive purchasing experience. For example, when a user indicates, "I would like to purchase this book," the generation AI uses the emotion estimation function to analyze the user's emotions and make suggestions to provide a positive purchasing experience. For example, it displays a message such as, "This book is a great choice for you." This allows the purchase support unit to analyze the user's emotions when going through the purchasing process and make suggestions to provide a positive purchasing experience.

[0066] The purchase support unit allows the generation AI to automatically apply discount coupons or promotional codes when carrying out the purchase process. For example, when a user instructs the purchase support unit to "purchase this book," the generation AI automatically applies discount coupons or promotional codes. For example, the purchase support unit displays "A 10% off coupon has been applied" during the purchase process. This allows the generation AI to automatically apply discount coupons or promotional codes when carrying out the purchase process.

[0067] The purchase support department allows the generation AI to provide related reading guides or reviews as a follow-up after a purchase. For example, after a user purchases a book, the generation AI provides related reading guides or reviews. For example, a link such as "Here is the reading guide for this book" is provided. This allows the generation AI to provide related reading guides or reviews as a follow-up after a purchase.

[0068] The purchase support unit uses the emotion estimation function to monitor the user's emotions in real time as they go through the purchase process, and is able to provide the optimal purchase experience. For example, when a user purchases a book, the generation AI uses the emotion estimation function to monitor the user's emotions in real time and is able to provide the optimal purchase experience. For example, if the user is feeling anxious, a message that gives a sense of security is displayed. This allows the purchase support unit to monitor the user's emotions in real time as they go through the purchase process, and is able to provide the optimal purchase experience.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The information acquisition system can further include an interest analysis unit that analyzes the user's interests. The interest analysis unit analyzes the user's interests based on keywords the user has searched for in the past and the genres of books the user has purchased. For example, if the user has searched for many science fiction novels in the past, the interest analysis unit can determine that the user is interested in science fiction and prioritize the presentation of related books and papers. Furthermore, if the user shows interest in a specific topic, the system can provide information on new releases and reviews related to that topic. This makes it possible to provide more personalized information based on the user's interests.

[0071] The information retrieval system may further include a learning unit that learns the user's search behavior. The learning unit learns the user's search behavior based on the user's past searches and selected results. For example, if the user frequently uses a particular keyword, the learning unit can adjust the system to prioritize the use of that keyword. Also, if the user prefers information in a particular format, the learning unit can prioritize the presentation of information in that format. This allows the system to learn the user's search behavior and provide more efficient and effective information.

[0072] The information retrieval system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user-provided feedback and improves the accuracy and usability of the system based on the feedback. For example, if a user provides feedback such as "this search result was not useful," the feedback collection unit can improve the search algorithm based on that information. Also, if a user provides feedback such as "this book was very useful," the feedback collection unit can recommend similar books to other users. In this way, user feedback can be collected and the accuracy and usability of the system can be improved.

[0073] The information retrieval system can further include a filtering unit that estimates the user's emotions and filters search results based on the estimated emotions. The filtering unit analyzes the emotions the user feels about the search results and preferentially presents results that have a positive emotion. For example, if the user feels that "this book looks interesting," the filtering unit will preferentially present that book. Also, if the user feels that "this paper looks difficult," the filtering unit can exclude that paper. This allows the system to filter search results based on the user's emotions and provide more appropriate information.

[0074] The information acquisition system may further include a dialogue adjustment unit that estimates the user's emotions and adjusts the tone of the dialogue based on the estimated emotions. The dialogue adjustment unit analyzes the emotions shown by the user during the dialogue and adjusts the tone of the dialogue according to the emotions. For example, if the user is feeling stressed, the dialogue adjustment unit will proceed with the dialogue in a relaxed tone. Also, if the user is excited, the dialogue adjustment unit will proceed with the dialogue in a tone that shares the user's excitement. This allows the dialogue to be tailored to the user's emotions, providing a better user experience.

[0075] The information retrieval system can further include a ranking unit that estimates the user's emotions and ranks search results based on the estimated emotions. The ranking unit analyzes the emotions the user feels about the search results and displays results with positive emotions at the top. For example, if the user feels that "this book is interesting," the book will be displayed at the top of the search results. Also, if the user feels that "this paper seems boring," the paper can be displayed at the bottom. This allows the search results to be ranked based on the user's emotions, making it possible to provide more appropriate information.

[0076] The information retrieval system can further include a customization unit that estimates the user's emotions and customizes search results based on the estimated emotions. The customization unit analyzes the emotions the user feels about the search results and customizes the search results according to those emotions. For example, if the user feels that "this book looks interesting," additional information related to the book can be provided. Also, if the user feels that "this paper looks difficult," a summary that makes the paper easy to understand can be provided. In this way, search results can be customized based on the user's emotions, and more appropriate information can be provided.

[0077] The information retrieval system can further include a filtering unit that estimates the user's emotions and filters search results based on the estimated emotions. The filtering unit analyzes the emotions the user feels about the search results and preferentially presents results that have a positive emotion. For example, if the user feels that "this book looks interesting," the filtering unit will preferentially present that book. Also, if the user feels that "this paper looks difficult," the filtering unit can exclude that paper. This allows the system to filter search results based on the user's emotions and provide more appropriate information.

[0078] The information acquisition system may further include a dialogue adjustment unit that estimates the user's emotions and adjusts the tone of the dialogue based on the estimated emotions. The dialogue adjustment unit analyzes the emotions shown by the user during the dialogue and adjusts the tone of the dialogue according to the emotions. For example, if the user is feeling stressed, the dialogue adjustment unit will proceed with the dialogue in a relaxed tone. Also, if the user is excited, the dialogue adjustment unit will proceed with the dialogue in a tone that shares the user's excitement. This allows the dialogue to be tailored to the user's emotions, providing a better user experience.

[0079] The information acquisition system can further include an interest analysis unit that analyzes the user's interests. The interest analysis unit analyzes the user's interests based on keywords the user has searched for in the past and the genres of books the user has purchased. For example, if the user has searched for many science fiction novels in the past, the interest analysis unit can determine that the user is interested in science fiction and prioritize the presentation of related books and papers. Furthermore, if the user shows interest in a specific topic, the system can provide information on new releases and reviews related to that topic. This makes it possible to provide more personalized information based on the user's interests.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The interactive information retrieval unit interactively provides information to the generation AI about passages from books or papers that the user cannot remember. When the user inputs a vague memory, the generation AI searches for related books or papers based on that information. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates search results based on that prompt. Step 2: The search result presentation section presents the search results of the generation AI to the user, who then narrows down the results further. The generation AI presents multiple candidates, and the user narrows down the results by providing more detailed information from among them. The generation AI then conducts another search based on the user's additional information, identifying the most suitable book or paper. Step 3: In the purchasing support department, the generation AI retrieves information about the books and papers specified by the user and supports the purchase process. When the user specifies the book they want to purchase, the generation AI provides a purchase link for that book, allowing the user to complete the purchase process smoothly. The generation AI also supports the process when subscribing to paid services.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] 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.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An interactive information search section using generative AI, a search result presentation unit that presents the results searched by the information search unit; a purchase support unit that supports a purchase procedure for the information presented by the search result presentation unit. A system characterized by:

2. The information search unit Automatically generate relevant keywords or phrases based on the user's vague memory and use them to improve search accuracy 2. The system of claim 1.

3. The information search unit Using voice input, what the user says is analyzed in real time to search for related books or papers.

2. The system of claim 1.

4. The search result presentation unit When presenting search results, a summary of each candidate is automatically generated, allowing users to make a decision quickly.

2. The system of claim 1.

5. The purchasing support department When checking out, the generative AI automatically suggests the best purchase options.

2. The system of claim 1.

6. The information search unit Analyzes the user's sentiment towards the passage they are trying to recall and searches for related information based on that sentiment 2. The system of claim 1.

7. The search result presentation unit Analyzes the user's feelings toward the search results presented and prioritizes candidates with positive feelings 2. The system of claim 1.

8. The purchasing support department Analyze users' emotions as they check out and make suggestions to provide a positive purchasing experience 2. The system of claim 1.

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