System

Abstract

An object of a system according to an embodiment is to obtain a highly accurate search result even if a user does not input an appropriate keyword.SOLUTION: A system includes a keyword input part, a background estimation part, a story generation part, and a WEB search part. The keyword input unit inputs a keyword. The background estimation unit estimates a background from the keyword input by the keyword input unit. The story generation unit generates a plurality of stories on the basis of the background estimated by the background estimation unit and presents the stories to the user. The WEB search unit generates a keyword again on the basis of the story generated by the story generation unit and performs a WEB search.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 had the problem that users could not find the information they wanted unless they entered the appropriate keywords.

[0005] The system according to the embodiment aims to obtain highly accurate search results without the user having to input appropriate keywords. [Means for solving the problem]

[0006] The system according to the embodiment includes a keyword input unit, a background estimation unit, a story generation unit, and a web search unit. The keyword input unit inputs keywords. The background estimation unit estimates a background from the keywords input by the keyword input unit. The story generation unit generates multiple stories based on the background estimated by the background estimation unit and presents them to the user. The web search unit generates keywords again based on the stories generated by the story generation unit and performs a web search. [Effects of the Invention]

[0007] The system according to the embodiment can obtain highly accurate search results without the user having to input appropriate keywords. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 search system according to the embodiment of the present invention infers the background from keywords entered by the user, creates a story, presents multiple options, and generates new keywords based on the selected story to perform a web search. This allows the search system to obtain highly accurate search results even if the user does not have high search literacy.

[0029] A search system according to an embodiment includes a keyword input unit, a background estimation unit, a story generation unit, and a web search unit. The keyword input unit inputs keywords related to the information the user wants to search for. For example, the user inputs "travel." The background estimation unit estimates the background from the keywords input by the keyword input unit. For example, the generation AI estimates information the user may be looking for, such as "travel plans," "recommended travel spots," and "travel costs." The story generation unit generates multiple stories based on the background estimated by the background estimation unit and presents them to the user. For example, specific stories such as "information for planning a trip," "information for finding recommended travel spots," and "ways to reduce travel costs" are presented. The web search unit generates keywords again based on the stories generated by the story generation unit and performs a web search. For example, if the user selects "information for planning a trip," the generation AI generates keywords such as "how to plan a trip," "travel preparation list," and "recommended travel apps" and performs a web search. This allows the search system according to an embodiment to obtain highly accurate search results even if the user does not have high search literacy. For example, even if a user cannot think of specific keywords, the generative AI can present an appropriate story and provide optimal search results. Furthermore, by optimizing search results based on user feedback, it is possible to provide a more satisfying search experience.

[0030] The background estimation unit can perform more accurate background estimation by referring to the user's past search history and browsing history. For example, when the user enters "travel," the background estimation unit extracts related keywords such as "travel plans," "recommended travel spots," and "travel costs" from the past search history and estimates the background. This enables more accurate background estimation based on the user's past behavior.

[0031] The background estimation unit can estimate a region-specific background by utilizing the user's current geographical location information. For example, when the user inputs "restaurant," the background estimation unit preferentially presents nearby restaurant information based on the current geographical location information. For example, if the user is in Tokyo, restaurant information for Tokyo is displayed. This makes it possible to estimate a region-specific background based on the user's current geographical location information.

[0032] The keyword input unit also accepts voice input or image input, and can infer the background from that information. For example, when a user inputs "travel" by voice, the keyword input unit uses voice recognition technology to analyze the keyword and infer the purpose and interests of the trip. For example, "family trip" or "beach resort." The keyword input unit also accepts image input, analyzes the keyword using image recognition technology, and infers the background. This makes it possible to infer the background from a wider variety of information using voice input or image input.

[0033] The keyword input unit can combine multiple keywords selected by the user to infer a complex background. For example, when the user inputs multiple keywords such as "travel," "family," and "beach," the keyword input unit combines them to propose a beach resort travel plan for families. This allows for more complex background inference by combining multiple keywords.

[0034] The story generation unit can refer to the user's past selection history to generate a more personalized story. The story generation unit generates a personalized story based on, for example, travel plans and interests selected by the user in the past. For example, if a user has previously selected a beach resort, stories related to beach resorts are preferentially presented. This makes it possible to generate a more personalized story based on the user's past selection history.

[0035] The story generation unit can incorporate related news articles and trend information to reflect the latest information. For example, when creating a story, the story generation unit incorporates the latest news articles and trend information and presents them to the user. For example, information about recent travel trends and popular tourist spots can be reflected. This allows for more appropriate story generation by reflecting the latest news articles and trend information.

[0036] The story generation unit generates stories from different perspectives and positions, and can provide the user with a wide variety of options. For example, when creating a story, the story generation unit generates stories from different perspectives and positions, and can provide the user with a wide variety of options. For example, stories can be presented from perspectives such as a family trip, a couple's trip, and a solo trip. In this way, by generating stories from different perspectives and positions, the user can be provided with a wide variety of options.

[0037] The story generation unit can simultaneously present related visual content based on a story selected by the user. For example, when creating a story, the story generation unit simultaneously presents related visual content (images and videos) based on the story selected by the user. For example, photos of travel destinations and videos of tourist spots are displayed. This allows the user's understanding to be deepened by simultaneously presenting related visual content.

[0038] The web search unit can automatically add related synonyms and thesauruses to the regenerated keywords, thereby broadening the search range. For example, the web search unit can search for "travel plans" using synonyms such as "travel plan" and "travel schedule." By adding related synonyms and thesauruses, the search range can be broadened.

[0039] The web search unit reflects the user's evaluation of past search results on the regenerated keywords, enabling more accurate searches. The web search unit, for example, reflects the user's evaluation of past search results on the regenerated keywords, enabling more accurate searches. For example, search results that have received high ratings in the past are preferentially displayed. This allows for more accurate searches by reflecting the user's evaluation of past search results.

[0040] The web search unit can automatically translate the regenerated keywords into different languages ​​and perform web searches in multiple languages. For example, the web search unit can automatically translate the regenerated keywords into different languages ​​and perform web searches in multiple languages. For example, "travel plans" can be translated into English and French and searched. This allows for automatic translation into different languages, making web searches in multiple languages ​​possible.

[0041] The web search unit searches for images and videos related to the regenerated keywords and can provide visual information as well. The web search unit searches for images and videos related to the regenerated keywords and can provide visual information as well. For example, the web search unit displays images and videos related to "travel plans." This allows visual information to be provided by providing related images and videos.

[0042] The web search unit can refer to the user's past feedback when presenting search results to present more personalized results. The web search unit, for example, refers to the user's past feedback when presenting search results to present more personalized results. For example, search results that have received high ratings in the past are preferentially displayed. In this way, by referring to the user's past feedback, more personalized search results can be presented.

[0043] The web search unit can incorporate related news articles and trend information to reflect the latest information when presenting search results. For example, the web search unit can incorporate related news articles and trend information to reflect the latest information when presenting search results. For example, it can display information about recent travel trends and popular tourist spots. In this way, by incorporating related news articles and trend information, search results that reflect the latest information can be presented.

[0044] The web search unit can also provide visual information by simultaneously presenting related visual content when presenting search results. For example, the web search unit can also provide visual information by simultaneously presenting related visual content (images and videos) when presenting search results. For example, it can display photos of travel destinations and videos of tourist spots. In this way, visual information can also be provided by simultaneously presenting related visual content.

[0045] The web search unit can automatically suggest related additional information based on the results selected by the user when presenting search results. For example, when presenting search results, the web search unit automatically suggests related additional information based on the results selected by the user. For example, if the user selects "travel planning," the web search unit suggests a travel preparation list and recommended apps. This improves user convenience by automatically suggesting related additional information based on the results selected by the user.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The search system can further include a health condition monitoring unit that monitors the user's health condition. For example, when a user inputs "exercise," the health condition monitoring unit refers to data such as the user's heart rate and number of steps, and suggests an appropriate exercise plan. This makes it possible to provide more personalized search results based on the user's health condition. Furthermore, when a user inputs "diet," the health condition monitoring unit can suggest a meal plan that takes into account the user's calorie consumption and nutritional balance. Furthermore, when a user inputs "sleep," the health condition monitoring unit can analyze the user's sleep patterns and suggest optimal sleeping environments and habits.

[0048] The search system can further include a hobby learning unit that learns the user's hobbies and interests. For example, when a user inputs "movies," the hobby learning unit refers to the user's past viewing history and ratings and suggests movies that match the user's preferences. This makes it possible to provide more appropriate search results based on the user's hobbies and interests. Also, when a user inputs "music," the hobby learning unit can analyze the user's music playback history and suggest artists and songs that match the user's preferences. Furthermore, when a user inputs "reading," the hobby learning unit can refer to the user's reading history and suggest books that interest them.

[0049] The search system can further include a schedule management unit that manages the user's schedule. For example, when a user inputs "meeting," the schedule management unit references the user's calendar and suggests the optimal meeting time and location. This allows for more efficient search results to be provided based on the user's schedule. Also, when a user inputs "travel," the schedule management unit can take the user's plans into consideration and suggest the optimal travel itinerary. Furthermore, when a user inputs "event," the schedule management unit can analyze the user's free time and suggest events that the user can attend.

[0050] The search system may further include a purchase history reference unit that references the user's purchase history. For example, when a user inputs "gifts," the purchase history reference unit references the user's past purchase history and suggests suitable gift ideas. This makes it possible to provide more personalized search results based on the user's purchase history. Also, when a user inputs "fashion," the purchase history reference unit can analyze past purchases and suggest fashion items that match the user's preferences. Furthermore, when a user inputs "home appliances," the purchase history reference unit can reference the user's past purchase history and suggest the home appliances the user needs.

[0051] The search system can further include a learning history reference unit that references the user's learning history. For example, when a user inputs "study," the learning history reference unit references the user's past learning history and suggests appropriate learning resources. This makes it possible to provide more effective search results based on the user's learning history. Also, when a user inputs "language learning," the learning history reference unit can analyze the user's past learning progress and suggest optimal learning methods and learning materials. Furthermore, when a user inputs "qualification exam," the learning history reference unit can reference the user's past learning history and suggest resources that will be useful for exam preparation.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The keyword input section inputs a keyword related to the information the user wants to search for. For example, the user inputs "travel." Step 2: The background prediction unit predicts the background from the keywords entered by the keyword input unit. For example, the generation AI predicts information the user may be looking for, such as "travel plans," "recommended travel spots," and "travel costs." Step 3: The story generation unit generates multiple stories based on the background inferred by the background inference unit and presents them to the user. For example, it presents specific stories such as "information for planning a trip," "information for finding recommended travel spots," and "ways to reduce travel costs." Step 4: The web search unit generates keywords again based on the story generated by the story generation unit and performs a web search. For example, if "information for planning a trip" is selected, the generation AI generates keywords such as "how to plan a trip," "travel preparation list," and "recommended travel apps," and performs a web search.

[0054] (Example 2) The search system according to the embodiment of the present invention infers the background from keywords entered by the user, creates a story, presents multiple options, and generates new keywords based on the selected story to perform a web search. This allows the search system to obtain highly accurate search results even if the user does not have high search literacy.

[0055] A search system according to an embodiment includes a keyword input unit, a background estimation unit, a story generation unit, and a web search unit. The keyword input unit inputs keywords related to the information the user wants to search for. For example, the user inputs "travel." The background estimation unit estimates the background from the keywords input by the keyword input unit. For example, the generation AI estimates information the user may be looking for, such as "travel plans," "recommended travel spots," and "travel costs." The story generation unit generates multiple stories based on the background estimated by the background estimation unit and presents them to the user. For example, specific stories such as "information for planning a trip," "information for finding recommended travel spots," and "ways to reduce travel costs" are presented. The web search unit generates keywords again based on the stories generated by the story generation unit and performs a web search. For example, if the user selects "information for planning a trip," the generation AI generates keywords such as "how to plan a trip," "travel preparation list," and "recommended travel apps" and performs a web search. This allows the search system according to an embodiment to obtain highly accurate search results even if the user does not have high search literacy. For example, even if a user cannot think of specific keywords, the generative AI can present an appropriate story and provide optimal search results. Furthermore, by optimizing search results based on user feedback, it is possible to provide a more satisfying search experience.

[0056] The background estimation unit can perform more accurate background estimation by referring to the user's past search history and browsing history. For example, when the user enters "travel," the background estimation unit extracts related keywords such as "travel plans," "recommended travel spots," and "travel costs" from the past search history and estimates the background. This enables more accurate background estimation based on the user's past behavior.

[0057] The background estimation unit can estimate a region-specific background by utilizing the user's current geographical location information. For example, when the user inputs "restaurant," the background estimation unit preferentially presents nearby restaurant information based on the current geographical location information. For example, if the user is in Tokyo, restaurant information for Tokyo is displayed. This makes it possible to estimate a region-specific background based on the user's current geographical location information.

[0058] The background inference unit uses the emotion inference function to analyze the emotion a user has when entering keywords and infer the background based on that emotion. For example, when a user enters "travel," the background inference unit uses the emotion inference function to analyze the user's emotion, and if the user's emotion is strong, suggests a relaxing travel plan. This enables more appropriate background inference based on the user's emotion.

[0059] The keyword input unit also accepts voice input or image input, and can infer the background from that information. For example, when a user inputs "travel" by voice, the keyword input unit uses voice recognition technology to analyze the keyword and infer the purpose and interests of the trip. For example, "family trip" or "beach resort." The keyword input unit also accepts image input, analyzes the keyword using image recognition technology, and infers the background. This makes it possible to infer the background from a wider variety of information using voice input or image input.

[0060] The keyword input unit can combine multiple keywords selected by the user to infer a complex background. For example, when the user inputs multiple keywords such as "travel," "family," and "beach," the keyword input unit combines them to propose a beach resort travel plan for families. This allows for more complex background inference by combining multiple keywords.

[0061] The background inference unit uses the emotion inference function to analyze the user's emotions in real time when entering keywords and make suggestions that elicit positive emotions. For example, when a user enters "travel," the background inference unit uses the emotion inference function to analyze the user's emotions in real time and proposes a travel plan that elicits positive emotions. For example, it suggests relaxing resorts. This makes it possible to make suggestions that elicit positive emotions based on the user's emotions.

[0062] The story generation unit can refer to the user's past selection history to generate a more personalized story. The story generation unit generates a personalized story based on, for example, travel plans and interests selected by the user in the past. For example, if a user has previously selected a beach resort, stories related to beach resorts are preferentially presented. This makes it possible to generate a more personalized story based on the user's past selection history.

[0063] The story generation unit can incorporate related news articles and trend information to reflect the latest information. For example, when creating a story, the story generation unit incorporates the latest news articles and trend information and presents them to the user. For example, information about recent travel trends and popular tourist spots can be reflected. This allows for more appropriate story generation by reflecting the latest news articles and trend information.

[0064] The story generation unit can use the emotion estimation function to generate and present a story that the user can most easily empathize with. The story generation unit, for example, uses the emotion estimation function to generate a story that the user can most easily empathize with. For example, the story generation unit presents a story that elicits positive emotions based on the user's emotion score. This generates and presents a story that the user can most easily empathize with, thereby improving user satisfaction.

[0065] The story generation unit generates stories from different perspectives and positions, and can provide the user with a wide variety of options. For example, when creating a story, the story generation unit generates stories from different perspectives and positions, and can provide the user with a wide variety of options. For example, stories can be presented from perspectives such as a family trip, a couple's trip, and a solo trip. In this way, by generating stories from different perspectives and positions, the user can be provided with a wide variety of options.

[0066] The story generation unit can simultaneously present related visual content based on a story selected by the user. For example, when creating a story, the story generation unit simultaneously presents related visual content (images and videos) based on the story selected by the user. For example, photos of travel destinations and videos of tourist spots are displayed. This allows the user's understanding to be deepened by simultaneously presenting related visual content.

[0067] The story generation unit can use the emotion estimation function to analyze the emotion of the user when selecting a story in real time and suggest the optimal story. For example, the story generation unit uses the emotion estimation function to analyze the emotion of the user when selecting a story in real time and suggest the optimal story. For example, the story generation unit presents a story that elicits positive emotions based on the user's emotion score. This improves user satisfaction by suggesting the optimal story based on the user's emotion.

[0068] The web search unit can automatically add related synonyms and thesauruses to the regenerated keywords, thereby broadening the search range. For example, the web search unit can search for "travel plans" using synonyms such as "travel plan" and "travel schedule." By adding related synonyms and thesauruses, the search range can be broadened.

[0069] The web search unit reflects the user's evaluation of past search results on the regenerated keywords, enabling more accurate searches. The web search unit, for example, reflects the user's evaluation of past search results on the regenerated keywords, enabling more accurate searches. For example, search results that have received high ratings in the past are preferentially displayed. This allows for more accurate searches by reflecting the user's evaluation of past search results.

[0070] The web search unit can use the emotion estimation function to regenerate keywords that evoke the most positive emotions in the user and perform a search. The web search unit can, for example, use the emotion estimation function to regenerate keywords that evoke the most positive emotions in the user and perform a search. For example, keywords that elicit positive emotions are generated based on the user's emotion score. This allows keywords that elicit positive emotions to be regenerated based on the user's emotions and a search to be performed.

[0071] The web search unit can automatically translate the regenerated keywords into different languages ​​and perform web searches in multiple languages. For example, the web search unit can automatically translate the regenerated keywords into different languages ​​and perform web searches in multiple languages. For example, "travel plans" can be translated into English and French and searched. This allows for automatic translation into different languages, making web searches in multiple languages ​​possible.

[0072] The web search unit searches for images and videos related to the regenerated keywords and can provide visual information as well. The web search unit searches for images and videos related to the regenerated keywords and can provide visual information as well. For example, the web search unit displays images and videos related to "travel plans." This allows visual information to be provided by providing related images and videos.

[0073] The web search unit can use the emotion estimation function to analyze the emotions the user has toward the regenerated keywords in real time and suggest optimal keywords. The web search unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the regenerated keywords in real time and suggest optimal keywords. For example, keywords that elicit positive emotions are suggested based on the user's emotion score. This improves search accuracy by suggesting optimal keywords based on the user's emotions.

[0074] The web search unit can refer to the user's past feedback when presenting search results to present more personalized results. The web search unit, for example, refers to the user's past feedback when presenting search results to present more personalized results. For example, search results that have received high ratings in the past are preferentially displayed. In this way, by referring to the user's past feedback, more personalized search results can be presented.

[0075] The web search unit can incorporate related news articles and trend information to reflect the latest information when presenting search results. For example, the web search unit can incorporate related news articles and trend information to reflect the latest information when presenting search results. For example, it can display information about recent travel trends and popular tourist spots. In this way, by incorporating related news articles and trend information, search results that reflect the latest information can be presented.

[0076] The web search unit can use the emotion estimation function to present search results that will most satisfy the user and collect feedback. The web search unit, for example, uses the emotion estimation function to present search results that will most satisfy the user and collect feedback. For example, search results that elicit positive emotions are displayed based on the user's emotion score. This allows the most satisfying search results to be presented based on the user's emotions and feedback to be collected, thereby improving search accuracy.

[0077] The web search unit can also provide visual information by simultaneously presenting related visual content when presenting search results. For example, the web search unit can also provide visual information by simultaneously presenting related visual content (images and videos) when presenting search results. For example, it can display photos of travel destinations and videos of tourist spots. In this way, visual information can also be provided by simultaneously presenting related visual content.

[0078] The web search unit can automatically suggest related additional information based on the results selected by the user when presenting search results. For example, when presenting search results, the web search unit automatically suggests related additional information based on the results selected by the user. For example, if the user selects "travel planning," the web search unit suggests a travel preparation list and recommended apps. This improves user convenience by automatically suggesting related additional information based on the results selected by the user.

[0079] The web search unit can use the emotion estimation function to analyze the emotions a user has about search results in real time and continuously present optimal results. The web search unit, for example, uses the emotion estimation function to analyze the emotions a user has about search results in real time and continuously present optimal results. For example, search results that elicit positive emotions are displayed based on the user's emotion score. This improves search accuracy by continuously presenting optimal results based on the user's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The search system can further include a health condition monitoring unit that monitors the user's health condition. For example, when a user inputs "exercise," the health condition monitoring unit refers to data such as the user's heart rate and number of steps, and suggests an appropriate exercise plan. This makes it possible to provide more personalized search results based on the user's health condition. Furthermore, when a user inputs "diet," the health condition monitoring unit can suggest a meal plan that takes into account the user's calorie consumption and nutritional balance. Furthermore, when a user inputs "sleep," the health condition monitoring unit can analyze the user's sleep patterns and suggest optimal sleeping environments and habits.

[0082] The search system can further include a hobby learning unit that learns the user's hobbies and interests. For example, when a user inputs "movies," the hobby learning unit refers to the user's past viewing history and ratings and suggests movies that match the user's preferences. This makes it possible to provide more appropriate search results based on the user's hobbies and interests. Also, when a user inputs "music," the hobby learning unit can analyze the user's music playback history and suggest artists and songs that match the user's preferences. Furthermore, when a user inputs "reading," the hobby learning unit can refer to the user's reading history and suggest books that interest them.

[0083] The search system can further include a schedule management unit that manages the user's schedule. For example, when a user inputs "meeting," the schedule management unit references the user's calendar and suggests the optimal meeting time and location. This allows for more efficient search results to be provided based on the user's schedule. Also, when a user inputs "travel," the schedule management unit can take the user's plans into consideration and suggest the optimal travel itinerary. Furthermore, when a user inputs "event," the schedule management unit can analyze the user's free time and suggest events that the user can attend.

[0084] The search system may further include a purchase history reference unit that references the user's purchase history. For example, when a user inputs "gifts," the purchase history reference unit references the user's past purchase history and suggests suitable gift ideas. This makes it possible to provide more personalized search results based on the user's purchase history. Also, when a user inputs "fashion," the purchase history reference unit can analyze past purchases and suggest fashion items that match the user's preferences. Furthermore, when a user inputs "home appliances," the purchase history reference unit can reference the user's past purchase history and suggest the home appliances the user needs.

[0085] The search system can further include a learning history reference unit that references the user's learning history. For example, when a user inputs "study," the learning history reference unit references the user's past learning history and suggests appropriate learning resources. This makes it possible to provide more effective search results based on the user's learning history. Also, when a user inputs "language learning," the learning history reference unit can analyze the user's past learning progress and suggest optimal learning methods and learning materials. Furthermore, when a user inputs "qualification exam," the learning history reference unit can reference the user's past learning history and suggest resources that will be useful for exam preparation.

[0086] The search system can further include a relaxation content providing unit that estimates the user's emotions and provides relaxing content based on the estimated emotions. For example, when a user inputs "stress," the relaxation content providing unit analyzes the user's emotions and suggests relaxing music or videos. This makes it possible to provide relaxing content based on the user's emotions. Also, when a user inputs "tired," the relaxation content providing unit can suggest relaxing massage techniques or meditation guides. Furthermore, when a user inputs "anxiety," the relaxation content providing unit can suggest relaxing breathing techniques or relaxation techniques.

[0087] The search system may further include a positive news provider that estimates the user's emotions and provides positive news based on the estimated emotions. For example, when a user inputs "news," the positive news provider analyzes the user's emotions and suggests positive news articles. This allows positive news to be provided based on the user's emotions. Also, when a user inputs "world affairs," the positive news provider can suggest positive international news. Furthermore, when a user inputs "economy," the positive news provider can suggest bright economic outlooks and success stories.

[0088] The search system may further include an entertainment providing unit that estimates a user's emotion and provides entertainment content based on the estimated emotion. For example, when a user inputs "movie," the entertainment providing unit analyzes the user's emotion and suggests a movie that matches the emotion. This makes it possible to provide entertainment content based on the user's emotion. Also, when a user inputs "music," the entertainment providing unit can suggest music that matches the emotion. Furthermore, when a user inputs "game," the entertainment providing unit can suggest a game that matches the emotion.

[0089] The search system can further include a learning content providing unit that estimates the user's emotions and provides learning content based on the estimated emotions. For example, when a user inputs "study," the learning content providing unit analyzes the user's emotions and suggests learning methods and materials that match the emotions. This makes it possible to provide learning content based on the user's emotions. Also, when a user inputs "language learning," the learning content providing unit can suggest language learning resources that match the emotions. Furthermore, when a user inputs "qualification exam," the learning content providing unit can suggest exam preparation resources that match the emotions.

[0090] The search system may further include a feedback collection unit that estimates a user's emotions and collects feedback based on the estimated emotions. For example, when a user provides feedback on a "search result," the feedback collection unit analyzes the user's emotions and collects feedback based on the emotions. This makes it possible to collect more accurate feedback based on the user's emotions. Furthermore, when a user provides feedback on a "service," the feedback collection unit may also collect feedback based on the emotions. Furthermore, when a user provides feedback on a "product," the feedback collection unit may also collect feedback based on the emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The keyword input section inputs a keyword related to the information the user wants to search for. For example, the user inputs "travel." Step 2: The background prediction unit predicts the background from the keywords entered by the keyword input unit. For example, the generation AI predicts information the user may be looking for, such as "travel plans," "recommended travel spots," and "travel costs." Step 3: The story generation unit generates multiple stories based on the background inferred by the background inference unit and presents them to the user. For example, it presents specific stories such as "information for planning a trip," "information for finding recommended travel spots," and "ways to reduce travel costs." Step 4: The web search unit generates keywords again based on the story generated by the story generation unit and performs a web search. For example, if "information for planning a trip" is selected, the generation AI generates keywords such as "how to plan a trip," "travel preparation list," and "recommended travel apps," and performs a web search.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 keyword input section for inputting a keyword; a background estimation unit that estimates a background from the keyword input by the keyword input unit; a story generation unit that generates a plurality of stories based on the background inferred by the background inferring unit and presents the stories to a user; a web search unit that generates keywords again based on the story generated by the story generation unit and performs a web search. A system characterized by:

2. The keyword input unit It also accepts voice or image input and infers the background from that information.

2. The system of claim 1.

3. The story generation unit Referencing the user's past selection history to generate a more personalized story 2. The system of claim 1.

4. The web search unit Automatically add related synonyms and thesauruses to the regenerated keywords to broaden your search.

2. The system of claim 1.

5. The background estimation unit Using an emotion estimation function, the emotion of the user when entering a keyword is analyzed, and the context is inferred based on that emotion.

2. The system of claim 1.

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