system

Abstract

We provide the system. [Solution] A means for entering the address of the planned business trip location, A method for searching for nearby accommodations based on the entered address information, Means for collecting detailed information, ratings, and reviews of nearby accommodations, A means of generating visual images of accommodations using generation AI, A means of suggesting the most suitable accommodation to users based on collected information and visual images, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to solve the problem that it takes time and effort to find an optimal accommodation place during a business trip. It is required to efficiently obtain information regarding evaluations and characteristics when a user selects an accommodation place at a business trip destination and to enable visual confirmation at a glance. Also, it is required to aggregate reviews across the entire network to provide a highly reliable evaluation and to assist the user in selecting an optimal accommodation place.

Means for Solving the Problems

[0005] This invention provides a system that includes means for inputting the address of a planned business trip destination, means for searching for nearby accommodations based on the input address information, means for collecting detailed information, ratings, and reviews of nearby accommodations, means for generating visual images of accommodations using generative AI, and means for suggesting the most suitable accommodation to the user based on the collected information and visual images. Furthermore, by including means for obtaining feedback from the user and customizing the selection of accommodations based on additional conditions, it is possible to efficiently suggest the most suitable accommodation to each individual user. In addition, by using generative AI that analyzes reviews from across the internet to calculate the overall rating of each accommodation, a highly reliable rating can be achieved.

[0006] "Address of planned business trip location" refers to address information indicating the geographical location planned for the business trip.

[0007] "Means of input" refers to interfaces or devices that enable users to input information into a system.

[0008] "Nearby accommodations" refers to hotels and other lodgings located within a certain range of the specified address.

[0009] "Detailed information" refers to specific information about the accommodation, such as facilities, rates, and services offered.

[0010] "Rating" refers to an overall assessment of an accommodation facility, expressed using numerical values ​​or star ratings.

[0011] A "review" is an individual evaluation comment posted on a website or application by people who have used an accommodation facility.

[0012] "Generative AI" refers to systems or algorithms that use artificial intelligence technology to generate or process specific data.

[0013] "Visual images" refer to visual images and videos of the rooms and facilities of accommodations, allowing users to grasp the interior and atmosphere through visual information.

[0014] "Feedback" refers to the opinions and requests that users provide to the system, and is used for improving and customizing the system.

[0015] "Overall rating" refers to the overall evaluation based on the analysis of each review, and is often expressed as a numerical value or a number of stars. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The embodiments for carrying out the present invention will be described in detail below.

[0038] Program processing overview:

[0039] The system of this invention allows users to input the address of their planned business trip destination and then search for, rate, review, and provide visual images of nearby accommodations to suggest the most suitable place to stay.

[0040] Program details:

[0041] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[0042] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[0043] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[0044] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[0045] The server then uses the collected ratings, features, and visual images to suggest the most suitable accommodations to the user. These suggestions are optimized based on the user's conditions and requirements. For example, facilities with high ratings, within the user's budget, and judged to be clean based on their visual images might be selected.

[0046] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[0047] Specific example:

[0048] Example 1: In the case of a business trip to Tokyo

[0049] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0050] 2. The device sends that information to the server.

[0051] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0052] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0053] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0054] 6. The server then uses a generation AI to generate visual images of each facility.

[0055] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[0056] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[0057] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[0058] 10. The server sends reservation completion information to the terminal and notifies the user.

[0059] The present invention, in the form described above, is a system that significantly reduces the time and effort required to select accommodation during business trips and efficiently provides users with the most suitable accommodation.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[0063] Step 2:

[0064] The terminal sends the entered address information to the server. The terminal then sends this information to the server as a request.

[0065] Step 3:

[0066] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[0067] Step 4:

[0068] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[0069] Step 5:

[0070] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[0071] Step 6:

[0072] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[0073] Step 7:

[0074] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[0075] Step 8:

[0076] The server determines the best accommodation for the user based on collected ratings, features, and visual images. For example, it selects the best accommodation based on ratings, price, and user preferences.

[0077] Step 9:

[0078] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[0079] Step 10:

[0080] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[0081] Step 11:

[0082] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[0083] Step 12:

[0084] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[0085] Step 13:

[0086] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[0087] Step 14:

[0088] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[0089] (Example 1)

[0090] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In modern business and travel planning, choosing the right accommodation is crucial. However, finding the optimal accommodation from the vast amount of information available online is time-consuming and laborious. Furthermore, the scattered nature of reviews and ratings makes it difficult to gather reliable information in one place. Additionally, the lack of visual information makes it difficult to grasp the actual condition of the facilities.

[0092] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0093] In this invention, the server includes means for inputting address information, means for searching for nearby facilities based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby facilities, means for generating visual information of facilities using a generative AI model, and means for presenting appropriate facilities to the user based on the acquired information and visual information. As a result, users can easily select accommodations based on reliable and detailed information and can also confirm visual images, thereby improving the accuracy and efficiency of their selection.

[0094] "Address information" refers to location information that users enter as their destination for business trips or travel.

[0095] "Nearby facilities" refers to accommodations and other related facilities located within a specific area, which are searched based on the entered address information.

[0096] "Detailed information" refers to information about the specific characteristics and services offered by surrounding facilities, such as equipment, fees, and services.

[0097] "Ratings" refer to scores or ranks assigned to surrounding facilities by users or third parties, indicating the quality of the facilities and the level of satisfaction with the services provided.

[0098] A "review" refers to a written account of user or third-party experiences and feedback regarding nearby facilities.

[0099] A "generative AI model" refers to a model that analyzes and generates data using artificial intelligence technology, and in this invention, it is particularly used for generating visual information of accommodation facilities and for analyzing evaluations and reviews.

[0100] "Visual information" refers to high-resolution images and visual representations of facilities generated using generative AI models.

[0101] "Means of presenting appropriate facilities to users" refers to a function that selects and suggests the most suitable accommodation for the user based on acquired detailed information, ratings, reviews, and visual information.

[0102] The embodiments for carrying out the present invention will be described in detail below.

[0103] The system of this invention allows the user to input the address of their planned business trip destination, search for, evaluate, and collect reviews of accommodations in the surrounding area, generate visual images of the accommodations using a generative AI model, and suggest the most suitable place to stay.

[0104] First, the user logs into the terminal and enters the specific address information of their planned business trip location. For example, the user can enter a specific address such as "Chiyoda-ku, Tokyo." At this point, the address information is entered into the terminal.

[0105] Next, the terminal sends the entered address information to the server. The server receives this address information and uses it to search for nearby facilities. For the search, it uses existing search services such as the Google Maps API or the APIs of accommodation booking sites to list accommodations within a specific range (e.g., within 10km).

[0106] The server uses the API again to collect detailed information about each accommodation based on the acquired list of accommodations. This information includes facilities, rates, and services offered. Next, a generative AI model is used to collect reviews and ratings from across the internet and analyze and calculate an overall rating for each accommodation. Data acquisition from review sites and social media is considered for review collection.

[0107] Furthermore, the server uses a generative AI model to generate visual images of the accommodations. Based on the collected image data, it generates high-resolution visual images and links them to detailed information about each accommodation.

[0108] The server suggests the most suitable accommodation to the user based on collected ratings, features, and visual images. These suggestions are optimized based on the user's conditions and requirements (e.g., budget, cleanliness, high ratings, etc.). For example, facilities with high ratings, within budget, and high cleanliness based on visual images will be selected.

[0109] Finally, the terminal receives suggested information from the server and displays it to the user. The user can review detailed information and visual images of the suggested accommodations and further specify conditions (e.g., room type, price range, etc.). When the user finally selects an accommodation and decides to make a reservation, that information is sent via the terminal to the server, which then works with the accommodation's reservation system to confirm the final reservation. After the reservation is complete, the server sends a reservation confirmation notification to the terminal, notifying the user.

[0110] Specific example

[0111] Example 1: In the case of a business trip to Tokyo

[0112] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0113] 2. The device sends that information to the server.

[0114] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0115] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0116] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0117] 6. The server then uses a generation AI to generate visual images of each facility.

[0118] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[0119] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[0120] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[0121] 10. The server sends reservation completion information to the terminal and notifies the user.

[0122] An example of a prompt is a sentence that you input to the generating AI depending on a specific destination or condition, and is shown as follows:

[0123] "Please recommend highly-rated accommodations in and around Chiyoda Ward, Tokyo, that are within my budget."

[0124] The above describes the embodiments for carrying out the present invention, which significantly reduces the time and effort required to select accommodations during business trips and makes it possible to efficiently provide users with the most suitable accommodations.

[0125] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0126] Step 1:

[0127] The user logs into the device and enters the address of their planned business trip location.

[0128] Specifically, the user enters their username and password on the terminal's login screen to access the system. Next, they enter the address of their planned business trip location (e.g., "Chiyoda-ku, Tokyo") into the input field.

[0129] Input: User information (username, password), address of the planned business trip location.

[0130] Output: Login authentication result, address information.

[0131] Step 2:

[0132] The terminal sends the entered address information to the server.

[0133] Specifically, the terminal converts the entered address information into an appropriate format (e.g., JSON format) and sends a request to the server.

[0134] Input: Address information.

[0135] Output: Request containing address information.

[0136] Step 3:

[0137] The server searches for accommodations near the planned business trip location.

[0138] Specifically, the server uses the Google Maps API and the APIs of accommodation booking websites to search for accommodations within a specified range (e.g., within 10km) based on the entered address. The search results are then stored in a list format.

[0139] Input: Request including address information.

[0140] Output: List of accommodations.

[0141] Step 4:

[0142] The server collects detailed information about accommodations and analyzes ratings and reviews.

[0143] Specifically, the server uses the acquired list of accommodations to collect detailed information about each facility (such as facilities, prices, and services) via an API. Next, it uses a generative AI model to collect and analyze reviews and ratings from across the internet to calculate an overall rating.

[0144] Input: List of accommodations.

[0145] Output: Detailed information on accommodations, and analysis results of ratings and reviews for each property.

[0146] Step 5:

[0147] The server generates a visual image of the accommodation.

[0148] Specifically, the server uses a generative AI model to generate high-resolution visual images based on the collected image data, and links these images to detailed information about each accommodation.

[0149] Input: Accommodation details and image data.

[0150] Output: Detailed information about the accommodation, including visual images.

[0151] Step 6:

[0152] The server selects and suggests the most suitable accommodation.

[0153] In terms of specific operations, the server considers the user's conditions (budget, rating, cleanliness, etc.) and selects the most suitable accommodation based on collected ratings, features, and visual images. It then generates a recommendation and sends it to the terminal.

[0154] Input: User criteria, detailed information about the accommodation including visual images.

[0155] Output: Suggestions for the most suitable accommodation.

[0156] Step 7:

[0157] The device displays suggestions to the user.

[0158] Specifically, the terminal displays the suggested information received from the server on the user interface, allowing the user to view the details.

[0159] Input: Proposal information.

[0160] Output: Suggestion information displayed in the user interface.

[0161] Step 8:

[0162] The user selects an accommodation and makes a reservation.

[0163] In terms of specific actions, the user reviews the details of the suggested accommodations, considers conditions such as room type and price range, and makes a selection. After making a selection, they confirm the final reservation details on the reservation confirmation screen.

[0164] Input: Suggested information, user selection.

[0165] Output: Final reservation confirmed.

[0166] Step 9:

[0167] The device sends the reservation information to the server, completing the reservation.

[0168] Specifically, the terminal converts the user's reservation confirmation information into the appropriate format and sends it to the server. The server then integrates the received reservation information with the accommodation's reservation system and confirms the reservation.

[0169] Input: Final reservation confirmation information.

[0170] Output: Reservation confirmation information.

[0171] Step 10:

[0172] The server sends a reservation completion notification to the terminal, informing the user.

[0173] Specifically, the server generates reservation completion information and sends it to the terminal. The terminal then displays a reservation completion notification to the user.

[0174] Input: Reservation confirmation information.

[0175] Output: Reservation confirmation.

[0176] (Application Example 1)

[0177] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Currently, finding dining options while on a business trip or staying at a hotel is time-consuming and laborious. Furthermore, the need to check reviews and ratings individually makes it difficult to make the best choice. Choosing dining options in an unfamiliar place is particularly stressful, highlighting the need for a system that efficiently suggests optimal choices.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0180] In this invention, the server includes means for inputting the address of the planned business trip destination or place of stay; means for searching for nearby dining options based on the input address information; means for collecting detailed information, ratings, and reviews of nearby dining options; means for generating visual images of dining options using a generative AI model; and means for suggesting the most suitable dining option to the user based on the collected information and visual images. This enables the user to efficiently find a highly-rated and appropriate dining option, saving time and effort.

[0181] "Planned business trip location" refers to a place where the user will temporarily stay for business purposes.

[0182] "Place of stay" refers to the address or location where a user will temporarily stay or reside.

[0183] "Address information" refers to information that indicates a specific geographical location, including the city name and street number.

[0184] "Food and beverage options" refer to restaurants and delivery services offered within a specific area.

[0185] "Detailed information" refers to information about food and beverage options, such as facilities, menus, and services.

[0186] "Rating" refers to the overall value or score that users or customers give to food and beverage options.

[0187] A "review" is a written record and publication of a user's experience and opinion regarding a food and beverage option.

[0188] A "generative AI model" is an artificial intelligence algorithm that generates information from text and images.

[0189] "Visual image" refers to the appearance of food and beverage options, as well as visual images and videos of the meals being served.

[0190] "Proposal" refers to the act of presenting suitable options to users based on collected information and AI analysis results.

[0191] "Feedback" refers to responses and answers such as opinions, evaluations, and requests received from users.

[0192] "Additional conditions" refer to specific requests or requirements from the user, which may result in adjustments to the services provided.

[0193] "Overall rating" refers to a general evaluation score calculated based on multiple reviews and evaluation data.

[0194] The embodiments for carrying out the present invention will be described in detail below.

[0195] System Configuration

[0196] The system of the present invention includes the following components:

[0197] 1. User terminal: A device such as a smartphone or tablet.

[0198] 2. Server: A system for cloud-based data management and processing.

[0199] 3. APIs: Google Maps API and APIs for restaurant delivery services

[0200] 4. Generative AI Models: AI algorithms that generate information from text and images.

[0201] Program Processing Overview

[0202] Address input via user terminal

[0203] First, the user logs into their terminal and enters the address of their planned business trip destination or place of stay. The entered address information is then sent from the user's terminal to the server.

[0204] Information retrieval and collection by server

[0205] The server uses the Google Maps API to convert address information into latitude and longitude. Then, it utilizes a restaurant delivery service API to search for dining options around the specified latitude and longitude. The search results include detailed information about the dining options (menu, prices, services, etc.).

[0206] Server-based evaluation and review collection and analysis

[0207] The server uses a generative AI model to collect and analyze reviews from across the internet based on the detailed information of the food and beverage options it has gathered. The generative AI model uses prompts like the following to calculate an overall rating for each food and beverage option.

[0208] Example of a prompt

[0209] Please calculate the overall rating of the restaurant based on the following reviews:

[0210] "The food was delicious, and the service was excellent."

[0211] "Considering the high price, the taste was disappointing."

[0212] "The staff were friendly, and the waiting time was short."

[0213] "The delivery was fast, and the food was still warm."

[0214] Server-based generation of visual images

[0215] Furthermore, the server uses a generative AI model to generate high-resolution visual images based on the collected image data of food and beverage options. These visual images are linked as detailed information.

[0216] Suggestions and feedback for users

[0217] The server suggests optimal food and beverage options to the user based on the collected information and generated visual images. The suggestions are displayed on the user's terminal, allowing the user to review the suggestions, specify further conditions, and provide feedback.

[0218] Hardware and software to be used

[0219] User devices: Smartphones, tablets, etc.

[0220] Server: Cloud-based database system

[0221] APIs: Google Maps API, Restaurant Delivery Service API

[0222] Generative AI models: Natural language processing and image generation models such as the OpenAI API.

[0223] Specific example

[0224] For example, if a user enters "Chiyoda-ku, Tokyo," the process would be as follows:

[0225] 1. Address information is sent from the user's terminal to the server.

[0226] 2. The server uses the Google Maps API to convert the address to latitude and longitude.

[0227] 3. Use a restaurant delivery service API to search for food and beverage options within a specified range (e.g., within 5km).

[0228] 4. The server collects and analyzes reviews from across the internet using a generation AI model and calculates an overall rating.

[0229] 5. Generate visual images using a generative AI model.

[0230] 6. Based on this information, suggest the most suitable dining options to the user.

[0231] Thus, the system of the present invention enables users to efficiently find highly-rated and appropriate dining options.

[0232] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0233] Step 1:

[0234] The user logs into the terminal and enters the address of their planned business trip destination or place of stay. This information includes the city name and street number. For example, they might enter "Chiyoda-ku, Tokyo".

[0235] Step 2:

[0236] The terminal sends the entered address information to the server. The transmitted address information is then prepared for processing on the server.

[0237] Step 3:

[0238] The server uses the Google Maps API to convert address information into latitude and longitude. This process converts the entered text-based address information into numerical latitude and longitude coordinates. The output latitude and longitude coordinates are then used in the next step.

[0239] Step 4:

[0240] The server uses the acquired latitude and longitude information to search for food and beverage options within a specified range (e.g., within 5km) using a restaurant delivery service API. The input is latitude and longitude information, and the output is a list of food and beverage options as search results.

[0241] Step 5:

[0242] The server then uses the API to collect detailed information (menu, prices, services, etc.) for each food and beverage option from the search results. In this collection process, the list of search results is input into the API, and a list of detailed information is output.

[0243] Step 6:

[0244] The server uses a generative AI model to collect reviews from across the internet, analyzes them, and calculates an overall rating for each dining option. Specifically, the review content is input to the generative AI model as prompts, and the rating is obtained as output. An example of a prompt used is as follows:

[0245] Please calculate the overall rating of the restaurant based on the following reviews:

[0246] "The food was delicious, and the service was excellent."

[0247] "Considering the high price, the taste was disappointing."

[0248] "The staff were friendly, and the waiting time was short."

[0249] "The delivery was fast, and the food was still warm."

[0250] Step 7:

[0251] The server uses a generative AI model to generate high-resolution visual images based on collected image data. These generated visual images are linked to detailed information about food and beverage options. The input is image data, and the output is the generated high-resolution visual image.

[0252] Step 8:

[0253] The server suggests the most suitable dining options to the user based on collected ratings, characteristics, and visual images. These suggestions are optimized based on the user's conditions and requirements. The input is the collected ratings, characteristics, and visual images, and the output is the suggested optimal dining options.

[0254] Step 9:

[0255] The terminal receives suggestion information from the server and displays it to the user. The user reviews the detailed information and visual images of the suggested food and beverage options and selects their desired menu and order. The input is the suggestion information, and the output is the selected menu and order information.

[0256] Step 10:

[0257] The terminal sends the user's selected order information to the server, which then confirms the order in conjunction with the delivery service system. The server then sends order completion information to the terminal, notifying the user. The input is the order information, and the output is the order completion notification.

[0258] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0259] The embodiments for carrying out the present invention will be described in detail below.

[0260] Overview of program processing

[0261] The system of this invention suggests the most suitable accommodation for the user by having them input the address of their planned business trip destination and then searching for, rating, reviewing, and providing visual images of nearby accommodations. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[0262] Program details

[0263] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[0264] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[0265] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[0266] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[0267] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0268] The server then works with an emotion engine to suggest the best accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[0269] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[0270] Specific example

[0271] Example 1: In the case of a business trip to Tokyo

[0272] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0273] 2. The device sends that information to the server.

[0274] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0275] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0276] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0277] 6. The server then uses a generation AI to generate visual images of each facility.

[0278] 7. The server uses an emotion engine to recognize the user's emotions. For example, it analyzes comments the user enters into the system or actions such as repeatedly clicking on facilities that the user is interested in.

[0279] 8. The server synthesizes this information and proposes "Hotel A" to the user. For example, it is a facility that has been judged to have an evaluation of 4.5, a price within the budget, and a sense of cleanliness from the visual image, and further, it is judged that the user is seeking relaxation.

[0280] 9. The terminal displays the proposal to the user, and the user selects the room type, price, etc. and determines the reservation.

[0281] 10. The terminal sends the information to the server, and the server completes the reservation in cooperation with the hotel reservation system.

[0282] 11. The server sends the reservation completion information to the terminal and notifies the user.

[0283] The present invention aims to make an optimal proposal considering the user's feelings in selecting accommodation when on a business trip in the above form, greatly reducing time and labor, and providing a more comfortable experience for the user.

[0284] The processing flow will be described below.

[0285] Step 1:

[0286] The user enters the address of the business trip destination into the terminal. As an example, "Chiyoda-ku, Tokyo" is entered.

[0287] Step 2:

[0288] The terminal sends the entered address information to the server. <000091​​​​​​​​​​​

[0292] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[0293] Step 5:

[0294] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[0295] Step 6:

[0296] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[0297] Step 7:

[0298] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[0299] Step 8:

[0300] The server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0301] Step 9:

[0302] The server works in conjunction with an emotion engine to suggest the most suitable accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[0303] Step 10:

[0304] The server sends the information of the selected accommodation location to the terminal. The information includes details of the accommodation facility, visual images, prices, evaluations, etc.

[0305] Step 11:

[0306] The terminal displays the information of the proposed accommodation location to the user. The user can check the detailed information and visual images, and further specify the room type and additional conditions.

[0307] Step 12:

[0308] The user selects the final accommodation location and determines the reservation. The user checks the selected accommodation location, room type, price, etc., and confirms the reservation.

[0309] Step 13:

[0310] The terminal sends the user's final selection to the server. A reservation request is sent to the server.

[0311] Step 14:

[0312] The server coordinates with the hotel reservation system to finalize the reservation. When the reservation is completed, the server sends reservation completion information to the terminal.

[0313] Step 15:

[0314] The terminal notifies the user of the reservation completion information. The user checks that the reservation has been confirmed and recognizes that the accommodation for the business trip has been secured.

[0315] Step 16:

[0316] The server evaluates the user's satisfaction after the reservation using an emotion engine. For example, after the reservation, the system is made to input feedback, and emotions are analyzed from the comments and operation history.

[0317] Step 17:

[0318] The server saves the user's emotional state and feedback in the system so that it can be incorporated into future suggestions. This will enable more personalized suggestions tailored to the user's preferences and satisfaction level.

[0319] (Example 2)

[0320] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0321] When selecting appropriate accommodation for business trips or vacations, users face the challenge of having to gather and compare a large amount of information, which is time-consuming and laborious. Furthermore, the information provided often does not adequately address the individual feelings and needs of users, making it difficult to choose the best accommodation.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes means for inputting the address of the planned business trip destination, means for searching for nearby accommodations based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby accommodations, means for creating a visual image of the accommodation using generative AI, means for recognizing the user's emotions, and means for suggesting the most suitable place to stay based on the collected information and visual image, as well as the user's emotions. This makes it possible for the user to easily find the most suitable accommodation that suits their emotions and needs.

[0324] "Method for entering the address of a planned business trip location" refers to an interface that allows users to enter the address of a place they plan to visit during a business trip or vacation in text format.

[0325] "Methods for searching for nearby accommodations" refers to systems or algorithms that search for accommodations in the vicinity of a specified address.

[0326] "Means of obtaining detailed information, ratings, and reviews" refers to the function of collecting information such as accommodation overviews, user ratings, and user review comments from the internet.

[0327] "Generative AI" is artificial intelligence that uses machine learning algorithms based on large amounts of data to automatically perform specific tasks.

[0328] "Means for creating visual images of accommodation facilities" refers to systems or programs that generate high-resolution visual images using collected image data.

[0329] "Means of recognizing user emotions" refers to systems and algorithms that analyze and determine a user's emotional state at a given time based on their input data and operation history.

[0330] "A means of suggesting the optimal accommodation" refers to a system that selects and displays the most suitable accommodation for the user based on collected information and the user's sentiments.

[0331] "A means of analyzing reviews across the entire internet using generative AI" refers to a function that uses generative AI to analyze review information existing on the internet and calculate an overall rating.

[0332] Modes for carrying out the invention

[0333] This invention is a system that, by entering the address of a planned business trip destination, searches for, rates, reviews, and provides visual images of nearby accommodations, and suggests the most suitable place to stay. Furthermore, by using an emotion engine that recognizes the user's emotions, it achieves more personalized suggestions.

[0334] The overall system

[0335] This system allows users to enter an address, which is then used to search for nearby accommodations, collecting detailed information, ratings, and reviews for each property. It then uses a generative AI to create visual images of the accommodations and, based on this data, suggests the most suitable place for the user. Furthermore, it uses an emotion engine to analyze the user's emotions and uses that information to provide even more optimal suggestions.

[0336] Hardware and software configuration

[0337] hardware

[0338] User devices: Laptops, desktop PCs, smartphones, tablets, etc.

[0339] Server: High-performance cloud server (e.g., AWS, Google Cloud)

[0340] software

[0341] API services: Google Maps API, hotel booking site APIs (e.g., Booking.com API)

[0342] Generative AI: OpenAI's GPT-3 or a similar generative AI model.

[0343] Emotion engine: Affectiva's Emotion AI, etc.

[0344] System operation

[0345] 1. The user enters the address of their planned business trip location into the terminal interface. For example, they might enter an address such as "Chiyoda-ku, Tokyo".

[0346] 2. The device sends the entered address information to the server as an HTTP request.

[0347] 3. The server uses the entered address information to call the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[0348] 4. The server then uses an API to collect additional detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[0349] 5. The server uses a generative AI (e.g., GPT-3) to collect reviews and ratings from across the network and calculate an overall rating for each facility.

[0350] 6. The server uses a generation AI to generate visual images of the accommodations. Specifically, it generates high-resolution visual images based on the collected image data and links them to information about each facility.

[0351] 7. The server recognizes the user's emotions using an emotion engine (e.g., Affectiva's Emotion AI). It analyzes comments entered by the user into the system, the speed and frequency of their actions, etc., to evaluate the user's emotions.

[0352] 8. The server suggests the most suitable accommodation based on the collected data and visual images, as well as the user's sentiment analysis results. For example, if the user is feeling stressed, it will suggest relaxing facilities or highly-rated accommodations.

[0353] 9. The terminal displays suggested information received from the server to the user. Based on this information, the user can select their final accommodation and further specify conditions (e.g., room type, budget, etc.).

[0354] 10. The user selects the most suitable accommodation and confirms the reservation from their device.

[0355] 11. The device sends the reservation information to the server, and the server works with the hotel's reservation system (e.g., Booking.com API) to finalize the reservation.

[0356] 12. The server sends a reservation completion notification to the terminal and notifies the user.

[0357] Specific example

[0358] Let's take the example of a user entering "Chiyoda-ku, Tokyo". The following are possible prompt messages.

[0359] Example of a prompt:

[0360] "Please conduct an emotionally-based evaluation of accommodations and suggest relaxing hotels near Chiyoda Ward, Tokyo."

[0361] By using the system of this invention, the process of selecting accommodations for business trips and vacations is significantly simplified. Furthermore, by incorporating the user's emotions, it becomes possible to suggest accommodations that better suit individual needs, thereby improving the user experience.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user enters the address of their planned business trip destination into the terminal's interface.

[0365] Specific operation: The user uses the keyboard to enter a specific address, such as "Chiyoda-ku, Tokyo," and clicks the "Search" button on the interface.

[0366] Input: Address of the planned business trip location.

[0367] Output: Data including address information.

[0368] Step 2:

[0369] The terminal sends the entered address information to the server in the form of an HTTP request.

[0370] Specific operation: The device converts the address information into JSON format and sends it to the server's API endpoint as an HTTP POST request.

[0371] Input: Address information in JSON format.

[0372] Output: Request data received by the server.

[0373] Step 3:

[0374] The server uses the received address information to send requests to the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[0375] Specific operation: The server sends requests containing address information to the Google Maps API and hotel booking site APIs and receives search results.

[0376] Input: Address information.

[0377] Output: List of accommodations.

[0378] Step 4:

[0379] The server uses an API to collect detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[0380] Specific operation: The server calls the API for each accommodation facility, retrieves detailed information, and saves it to the database.

[0381] Input: List of accommodations.

[0382] Output: Detailed information on each facility.

[0383] Step 5:

[0384] The server uses AI generation to collect reviews and ratings from across the internet and calculates an overall rating for each facility.

[0385] Specific operation: The server collects review information using web scraping tools and APIs, and analyzes it using a generative AI model.

[0386] Input: URLs and review site information for each facility.

[0387] Output: Overall evaluation data for each facility.

[0388] Step 6:

[0389] The server uses a generation AI to create visual images of the accommodation facilities.

[0390] Specific operation: The server uses collected image data to generate high-resolution visual images using AI and saves them to a database.

[0391] Input: Image data for each facility.

[0392] Output: High-resolution visual image.

[0393] Step 7:

[0394] The server uses an emotion engine to recognize the user's emotions.

[0395] Specific operation: The server inputs the user's past activity history and comments into the sentiment engine and receives the analysis results.

[0396] Input: User's operation history and comment data.

[0397] Output: User sentiment data.

[0398] Step 8:

[0399] The server suggests the most suitable accommodation based on the collected data, visual images, and user sentiment analysis results.

[0400] Specific operation: The server aggregates all data, uses an algorithm to select the most suitable accommodation, and creates a recommendation list.

[0401] Input: Accommodation details, overall rating data, visual images, and user sentiment data.

[0402] Output: Recommended accommodation list.

[0403] Step 9:

[0404] The terminal displays the suggested information received from the server to the user.

[0405] Specific operation: The terminal displays a recommendation list in a GUI, allowing the user to specify additional conditions.

[0406] Input: List of recommended accommodations.

[0407] Output: Data display that the user can view.

[0408] Step 10:

[0409] Users select the most suitable accommodation and confirm their reservation from their device.

[0410] Specific operation: The user selects an accommodation from the recommendation list, sets the booking details (room type, budget, etc.), and clicks the "Book Now" button.

[0411] Input: User-selected accommodation information and reservation details.

[0412] Output: Booking confirmation request data.

[0413] Step 11:

[0414] The terminal sends the reservation information to the server, which then works with the hotel's reservation system to finalize the reservation.

[0415] Specific operation: The device sends the reservation information to the server in JSON format, and the server calls the hotel reservation system's API to confirm the reservation.

[0416] Input: Booking confirmation request data.

[0417] Output: Confirmed reservation information sent to the hotel reservation system.

[0418] Step 12:

[0419] The server sends a reservation completion notification to the terminal, informing the user.

[0420] Specific operation: The server sends a reservation completion notification to the terminal as an HTTP response, and the terminal notifies the user via a pop-up or email.

[0421] Input: Confirmed reservation information.

[0422] Output: Reservation complete notification.

[0423] (Application Example 2)

[0424] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0425] Conventional work support systems struggle to quickly and efficiently optimize equipment placement within a specific work area. Furthermore, they often fail to consider user emotions, potentially leading to user stress and decreased work efficiency. Additionally, the lack of visual confirmation of the work environment makes it time-consuming and cumbersome to grasp the actual situation.

[0426] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting the address of the planned business trip location, means for searching for nearby accommodations based on the input address information, means for collecting detailed information, ratings, and reviews of nearby accommodations, means for generating a visual image of the accommodation using a generation AI, means for suggesting the most suitable accommodation to the user based on the collected information and visual image, means for inputting the planned work location, means for searching for work support equipment within a specific range based on the input information, means for collecting detailed information, ratings, and reviews of work support equipment within a specific range, and means for recognizing the user's emotions using an emotion engine and suggesting the most suitable work support equipment based on that. As a result, optimal equipment placement within the work area can be performed quickly and efficiently, work support that takes into account the user's emotions can be provided, and work efficiency can be improved and stress can be reduced.

[0427] "Means for entering the address of a planned business trip location" refers to an interface for users to input the address of a specific planned business trip location into the system.

[0428] "A means of searching for nearby accommodations based on entered address information" refers to a function that searches for accommodations located in the vicinity of an entered address.

[0429] "Means for collecting detailed information, ratings, and reviews of nearby accommodations" refers to the function of collecting information on facilities, rates, services, and user ratings and reviews related to the searched accommodations.

[0430] "Method for generating visual images of accommodations using generative AI" refers to a function that utilizes generative AI to generate high-resolution visual images of accommodations.

[0431] "A means of suggesting the most suitable accommodation to users based on collected information and visual images" refers to a function that suggests the most suitable accommodation to users based on collected information and generated visual images.

[0432] "Means for inputting work locations" refers to an interface for users to input information about specific work locations.

[0433] "A means of searching for work support equipment within a specific range based on input information" refers to a function that searches for work support equipment located in the vicinity of a planned work location based on the input information about that location.

[0434] "Means for collecting detailed information, evaluations, and reviews of work assistance equipment within a specific range" refers to a function for collecting detailed information, performance evaluations, and reviews of searched work assistance equipment.

[0435] "A means of recognizing the user's emotions using an emotion engine and proposing the most suitable work support equipment based on those emotions" refers to a function that uses an emotion engine to analyze the user's emotions and then proposes the most suitable work support equipment based on the results.

[0436] Specific embodiments for carrying out the present invention are described in detail below. This system allows users to input the address of their planned business trip destination, search for, evaluate, and review nearby accommodations, and provides visual images to suggest the most suitable place to stay. The same method can also be applied to work arrangements within a factory.

[0437] Overview of program processing

[0438] This system takes the address of a planned business trip location or planned work location within a factory as input, and based on that, it searches, evaluates, and reviews nearby accommodations and work support equipment, and provides visual images to offer the most suitable suggestions to the user. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[0439] Hardware and software to be used

[0440] Hardware: Autonomous mobile robots, control terminals in the factory (PCs, tablets), user terminals (smartphones)

[0441] Software: Google Maps API, hotel booking site API, factory equipment management system API, generative AI (e.g., OpenAI GPT-4), emotion engine (Emotion AI)

[0442] System Processing Procedure

[0443] The user logs into the system and enters their planned business trip location or planned work location within the factory. The entered information is sent to the server, which uses this information to search for accommodations and work support equipment within the specified area.

[0444] Search and evaluation

[0445] The server uses an API to collect detailed information about accommodations and work assistance equipment within a specified range. This includes facilities, rates, services offered, operational status, performance, and maintenance status. The server also uses generative AI to collect and analyze reviews and ratings of accommodations and work assistance equipment to calculate an overall rating.

[0446] Visual image generation

[0447] Next, the server uses a generation AI to create visual images of each accommodation and work support equipment, linking them to detailed information. In this process, high-resolution visual previews are created based on the collected image data.

[0448] Using an Emotion Engine

[0449] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0450] Specific usage examples

[0451] Example 1: Optimizing work layout within a factory

[0452] When the operator inputs "Section A of the third manufacturing line" into the robot, the robot sends that information to the server. The server uses the factory equipment management system API to list support robots and equipment within 50 meters. The server collects detailed information on each piece of equipment and uses generative AI to calculate an evaluation of each piece of equipment. Furthermore, it uses generative AI to generate a visual image of the site, and after recognizing the user's emotions using an emotion engine, it suggests the most suitable support equipment.

[0453] Example of a prompt:

[0454] "Here, we need to provide information to optimally position work support robots and equipment operating within a designated factory area. Users will be assisted in selecting the most suitable equipment based on comments and operation history entered using an emotion engine."

[0455] In summary, the present invention aims to significantly improve work efficiency and provide a more comfortable experience for users by making optimal suggestions that take into account the user's feelings when selecting accommodation during business trips and arranging work support equipment within factories.

[0456] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0457] Step 1:

[0458] The user logs into the system and enters their planned business trip location or planned work location within the factory. The address and work location information is entered from the user's terminal and sent to the server.

[0459] Input: Address of the planned business trip location or information of the planned work site.

[0460] Output: Input information is sent to the server.

[0461] Step 2:

[0462] The server searches for accommodations or work support equipment within a specified range based on the entered information. This search is performed using the Google Maps API, hotel booking site APIs, and factory equipment management system APIs.

[0463] Input: Address or work location information

[0464] Output: List of accommodations and work support equipment within the specified range.

[0465] Step 3:

[0466] Based on the acquired list, the server collects detailed information about each accommodation and work support equipment. This detailed information includes facilities, rates, services offered, operational status, performance, and maintenance status.

[0467] Input: List of accommodations and work support equipment

[0468] Output: Detailed information on each facility and piece of equipment.

[0469] Step 4:

[0470] The server uses generation AI to collect and analyze internet-wide reviews of each accommodation and work support equipment. This allows it to calculate ratings for each facility and piece of equipment.

[0471] Input: Detailed information and reviews

[0472] Output: Overall evaluation of each facility and piece of equipment.

[0473] Step 5:

[0474] The server uses a generation AI to generate visual images of each accommodation facility and work support equipment. It processes the collected image data to generate high-resolution images and links them to detailed information.

[0475] Input: Image data

[0476] Output: Visual image

[0477] Step 6:

[0478] The server uses an emotion engine to recognize emotions from comments and activity history entered by the user. This allows the server to evaluate what emotions the user is feeling.

[0479] Input: Comments and operation history

[0480] Output: User sentiment rating

[0481] Step 7:

[0482] The server synthesizes this information and suggests the most suitable accommodation and work support equipment to the user. The suggestions also take into account the user's emotional evaluation.

[0483] Input: Detailed information, overall rating, visual image, sentiment rating

[0484] Output: Suggestions for optimal accommodation and work support equipment.

[0485] Step 8:

[0486] The user terminal receives suggested information from the server and displays it to the user. The user reviews detailed information and visual images of the suggested accommodations and work support equipment and makes a final selection.

[0487] Input: Suggestion information from the server

[0488] Output: Information displayed to the user

[0489] Step 9:

[0490] Once the user makes their final selection and decides on a reservation or work order, that information is sent to the server via the user's terminal. The server then works in conjunction with the hotel reservation system or work management system to confirm the final reservation or order.

[0491] Input: User's last selection

[0492] Output: Confirmation of reservation or work order

[0493] Step 10:

[0494] The server sends information about the completion of a reservation or work order to the user's terminal and notifies the user.

[0495] Input: Completion information for reservations or work orders

[0496] Output: User notification

[0497] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0498] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0499] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0500] [Second Embodiment]

[0501] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0502] As shown in Figure 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.

[0503] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0504] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0505] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0506] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0507] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0508] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0509] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0510] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0511] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0512] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0513] The embodiments for carrying out the present invention will be described in detail below.

[0514] Program processing overview:

[0515] The system of this invention allows users to input the address of their planned business trip destination and then search for, rate, review, and provide visual images of nearby accommodations to suggest the most suitable place to stay.

[0516] Program details:

[0517] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[0518] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[0519] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[0520] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[0521] The server then uses the collected ratings, features, and visual images to suggest the most suitable accommodations to the user. These suggestions are optimized based on the user's conditions and requirements. For example, facilities with high ratings, within the user's budget, and judged to be clean based on their visual images might be selected.

[0522] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[0523] Specific example:

[0524] Example 1: In the case of a business trip to Tokyo

[0525] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0526] 2. The device sends that information to the server.

[0527] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0528] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0529] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0530] 6. The server then uses a generation AI to generate visual images of each facility.

[0531] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[0532] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[0533] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[0534] 10. The server sends reservation completion information to the terminal and notifies the user.

[0535] The present invention, in the form described above, is a system that significantly reduces the time and effort required to select accommodation during business trips and efficiently provides users with the most suitable accommodation.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[0539] Step 2:

[0540] The terminal sends the entered address information to the server. The terminal then sends this information to the server as a request.

[0541] Step 3:

[0542] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[0543] Step 4:

[0544] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[0545] Step 5:

[0546] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[0547] Step 6:

[0548] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[0549] Step 7:

[0550] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[0551] Step 8:

[0552] The server determines the best accommodation for the user based on collected ratings, features, and visual images. For example, it selects the best accommodation based on ratings, price, and user preferences.

[0553] Step 9:

[0554] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[0555] Step 10:

[0556] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[0557] Step 11:

[0558] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[0559] Step 12:

[0560] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[0561] Step 13:

[0562] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[0563] Step 14:

[0564] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[0565] (Example 1)

[0566] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0567] In modern business and travel planning, choosing the right accommodation is crucial. However, finding the optimal accommodation from the vast amount of information available online is time-consuming and laborious. Furthermore, the scattered nature of reviews and ratings makes it difficult to gather reliable information in one place. Additionally, the lack of visual information makes it difficult to grasp the actual condition of the facilities.

[0568] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0569] In this invention, the server includes means for inputting address information, means for searching for nearby facilities based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby facilities, means for generating visual information of facilities using a generative AI model, and means for presenting appropriate facilities to the user based on the acquired information and visual information. As a result, users can easily select accommodations based on reliable and detailed information and can also confirm visual images, thereby improving the accuracy and efficiency of their selection.

[0570] "Address information" refers to location information that users enter as their destination for business trips or travel.

[0571] "Nearby facilities" refers to accommodations and other related facilities located within a specific area, which are searched based on the entered address information.

[0572] "Detailed information" refers to information about the specific characteristics and services offered by surrounding facilities, such as equipment, fees, and services.

[0573] "Ratings" refer to scores or ranks assigned to surrounding facilities by users or third parties, indicating the quality of the facilities and the level of satisfaction with the services provided.

[0574] A "review" refers to a written account of user or third-party experiences and feedback regarding nearby facilities.

[0575] A "generative AI model" refers to a model that analyzes and generates data using artificial intelligence technology, and in this invention, it is particularly used for generating visual information of accommodation facilities and for analyzing evaluations and reviews.

[0576] "Visual information" refers to high-resolution images and visual representations of facilities generated using generative AI models.

[0577] "Means of presenting appropriate facilities to users" refers to a function that selects and suggests the most suitable accommodation for the user based on acquired detailed information, ratings, reviews, and visual information.

[0578] The embodiments for carrying out the present invention will be described in detail below.

[0579] The system of this invention allows the user to input the address of their planned business trip destination, search for, evaluate, and collect reviews of accommodations in the surrounding area, generate visual images of the accommodations using a generative AI model, and suggest the most suitable place to stay.

[0580] First, the user logs into the terminal and enters the specific address information of their planned business trip location. For example, the user can enter a specific address such as "Chiyoda-ku, Tokyo." At this point, the address information is entered into the terminal.

[0581] Next, the terminal sends the entered address information to the server. The server receives this address information and uses it to search for nearby facilities. For the search, it uses existing search services such as the Google Maps API or the APIs of accommodation booking sites to list accommodations within a specific range (e.g., within 10km).

[0582] The server uses the API again to collect detailed information about each accommodation based on the acquired list of accommodations. This information includes facilities, rates, and services offered. Next, a generative AI model is used to collect reviews and ratings from across the internet and analyze and calculate an overall rating for each accommodation. Data acquisition from review sites and social media is considered for review collection.

[0583] Furthermore, the server uses a generative AI model to generate visual images of the accommodations. Based on the collected image data, it generates high-resolution visual images and links them to detailed information about each accommodation.

[0584] The server suggests the most suitable accommodation to the user based on collected ratings, features, and visual images. These suggestions are optimized based on the user's conditions and requirements (e.g., budget, cleanliness, high ratings, etc.). For example, facilities with high ratings, within budget, and high cleanliness based on visual images will be selected.

[0585] Finally, the terminal receives suggested information from the server and displays it to the user. The user can review detailed information and visual images of the suggested accommodations and further specify conditions (e.g., room type, price range, etc.). When the user finally selects an accommodation and decides to make a reservation, that information is sent via the terminal to the server, which then works with the accommodation's reservation system to confirm the final reservation. After the reservation is complete, the server sends a reservation confirmation notification to the terminal, notifying the user.

[0586] Specific example

[0587] Example 1: In the case of a business trip to Tokyo

[0588] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0589] 2. The device sends that information to the server.

[0590] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0591] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0592] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0593] 6. The server then uses a generation AI to generate visual images of each facility.

[0594] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[0595] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[0596] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[0597] 10. The server sends reservation completion information to the terminal and notifies the user.

[0598] An example of a prompt is a sentence that you input to the generating AI depending on a specific destination or condition, and is shown as follows:

[0599] "Please recommend highly-rated accommodations in and around Chiyoda Ward, Tokyo, that are within my budget."

[0600] The above describes the embodiments for carrying out the present invention, which significantly reduces the time and effort required to select accommodations during business trips and makes it possible to efficiently provide users with the most suitable accommodations.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] The user logs into the device and enters the address of their planned business trip location.

[0604] Specifically, the user enters their username and password on the terminal's login screen to access the system. Next, they enter the address of their planned business trip location (e.g., "Chiyoda-ku, Tokyo") into the input field.

[0605] Input: User information (username, password), address of the planned business trip location.

[0606] Output: Login authentication result, address information.

[0607] Step 2:

[0608] The terminal sends the entered address information to the server.

[0609] Specifically, the terminal converts the entered address information into an appropriate format (e.g., JSON format) and sends a request to the server.

[0610] Input: Address information.

[0611] Output: Request containing address information.

[0612] Step 3:

[0613] The server searches for accommodations near the planned business trip location.

[0614] Specifically, the server uses the Google Maps API and the APIs of accommodation booking websites to search for accommodations within a specified range (e.g., within 10km) based on the entered address. The search results are then stored in a list format.

[0615] Input: Request including address information.

[0616] Output: List of accommodations.

[0617] Step 4:

[0618] The server collects detailed information about accommodations and analyzes ratings and reviews.

[0619] Specifically, the server uses the acquired list of accommodations to collect detailed information about each facility (such as facilities, prices, and services) via an API. Next, it uses a generative AI model to collect and analyze reviews and ratings from across the internet to calculate an overall rating.

[0620] Input: List of accommodations.

[0621] Output: Detailed information on accommodations, and analysis results of ratings and reviews for each property.

[0622] Step 5:

[0623] The server generates a visual image of the accommodation.

[0624] Specifically, the server uses a generative AI model to generate high-resolution visual images based on the collected image data, and links these images to detailed information about each accommodation.

[0625] Input: Accommodation details and image data.

[0626] Output: Detailed information about the accommodation, including visual images.

[0627] Step 6:

[0628] The server selects and suggests the most suitable accommodation.

[0629] In terms of specific operations, the server considers the user's conditions (budget, rating, cleanliness, etc.) and selects the most suitable accommodation based on collected ratings, features, and visual images. It then generates a recommendation and sends it to the terminal.

[0630] Input: User criteria, detailed information about the accommodation including visual images.

[0631] Output: Suggestions for the most suitable accommodation.

[0632] Step 7:

[0633] The device displays suggestions to the user.

[0634] Specifically, the terminal displays the suggested information received from the server on the user interface, allowing the user to view the details.

[0635] Input: Proposal information.

[0636] Output: Suggestion information displayed in the user interface.

[0637] Step 8:

[0638] The user selects an accommodation and makes a reservation.

[0639] In terms of specific actions, the user reviews the details of the suggested accommodations, considers conditions such as room type and price range, and makes a selection. After making a selection, they confirm the final reservation details on the reservation confirmation screen.

[0640] Input: Suggested information, user selection.

[0641] Output: Final reservation confirmed.

[0642] Step 9:

[0643] The device sends the reservation information to the server, completing the reservation.

[0644] Specifically, the terminal converts the user's reservation confirmation information into the appropriate format and sends it to the server. The server then integrates the received reservation information with the accommodation's reservation system and confirms the reservation.

[0645] Input: Final reservation confirmation information.

[0646] Output: Reservation confirmation information.

[0647] Step 10:

[0648] The server sends a reservation completion notification to the terminal, informing the user.

[0649] Specifically, the server generates reservation completion information and sends it to the terminal. The terminal then displays a reservation completion notification to the user.

[0650] Input: Reservation confirmation information.

[0651] Output: Reservation confirmation.

[0652] (Application Example 1)

[0653] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0654] Currently, finding dining options while on a business trip or staying at a hotel is time-consuming and laborious. Furthermore, the need to check reviews and ratings individually makes it difficult to make the best choice. Choosing dining options in an unfamiliar place is particularly stressful, highlighting the need for a system that efficiently suggests optimal choices.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0656] In this invention, the server includes means for inputting the address of the planned business trip destination or place of stay; means for searching for nearby dining options based on the input address information; means for collecting detailed information, ratings, and reviews of nearby dining options; means for generating visual images of dining options using a generative AI model; and means for suggesting the most suitable dining option to the user based on the collected information and visual images. This enables the user to efficiently find a highly-rated and appropriate dining option, saving time and effort.

[0657] "Planned business trip location" refers to a place where the user will temporarily stay for business purposes.

[0658] "Place of stay" refers to the address or location where a user will temporarily stay or reside.

[0659] "Address information" refers to information that indicates a specific geographical location, including the city name and street number.

[0660] "Food and beverage options" refer to restaurants and delivery services offered within a specific area.

[0661] "Detailed information" refers to information about food and beverage options, such as facilities, menus, and services.

[0662] "Rating" refers to the overall value or score that users or customers give to food and beverage options.

[0663] A "review" is a written record and publication of a user's experience and opinion regarding a food and beverage option.

[0664] A "generative AI model" is an artificial intelligence algorithm that generates information from text and images.

[0665] "Visual image" refers to the appearance of food and beverage options, as well as visual images and videos of the meals being served.

[0666] "Proposal" refers to the act of presenting suitable options to users based on collected information and AI analysis results.

[0667] "Feedback" refers to responses and answers such as opinions, evaluations, and requests received from users.

[0668] "Additional conditions" refer to specific requests or requirements from the user, which may result in adjustments to the services provided.

[0669] "Overall rating" refers to a general evaluation score calculated based on multiple reviews and evaluation data.

[0670] The embodiments for carrying out the present invention will be described in detail below.

[0671] System Configuration

[0672] The system of the present invention includes the following components:

[0673] 1. User terminal: A device such as a smartphone or tablet.

[0674] 2. Server: A system for cloud-based data management and processing.

[0675] 3. APIs: Google Maps API and APIs for restaurant delivery services

[0676] 4. Generative AI Models: AI algorithms that generate information from text and images.

[0677] Program Processing Overview

[0678] Address input via user terminal

[0679] First, the user logs into their terminal and enters the address of their planned business trip destination or place of stay. The entered address information is then sent from the user's terminal to the server.

[0680] Information retrieval and collection by server

[0681] The server uses the Google Maps API to convert address information into latitude and longitude. Then, it utilizes a restaurant delivery service API to search for dining options around the specified latitude and longitude. The search results include detailed information about the dining options (menu, prices, services, etc.).

[0682] Server-based evaluation and review collection and analysis

[0683] The server uses a generative AI model to collect and analyze reviews from across the internet based on the detailed information of the food and beverage options it has gathered. The generative AI model uses prompts like the following to calculate an overall rating for each food and beverage option.

[0684] Example of a prompt

[0685] Please calculate the overall rating of the restaurant based on the following reviews:

[0686] "The food was delicious, and the service was excellent."

[0687] "Considering the high price, the taste was disappointing."

[0688] "The staff were friendly, and the waiting time was short."

[0689] "The delivery was fast, and the food was still warm."

[0690] Server-based generation of visual images

[0691] Furthermore, the server uses a generative AI model to generate high-resolution visual images based on the collected image data of food and beverage options. These visual images are linked as detailed information.

[0692] Suggestions and feedback for users

[0693] The server suggests optimal food and beverage options to the user based on the collected information and generated visual images. The suggestions are displayed on the user's terminal, allowing the user to review the suggestions, specify further conditions, and provide feedback.

[0694] Hardware and software to be used

[0695] User devices: Smartphones, tablets, etc.

[0696] Server: Cloud-based database system

[0697] APIs: Google Maps API, Restaurant Delivery Service API

[0698] Generative AI models: Natural language processing and image generation models such as the OpenAI API.

[0699] Specific example

[0700] For example, if a user enters "Chiyoda-ku, Tokyo," the process would be as follows:

[0701] 1. Address information is sent from the user's terminal to the server.

[0702] 2. The server uses the Google Maps API to convert the address to latitude and longitude.

[0703] 3. Use a restaurant delivery service API to search for food and beverage options within a specified range (e.g., within 5km).

[0704] 4. The server collects and analyzes reviews from across the internet using a generation AI model and calculates an overall rating.

[0705] 5. Generate visual images using a generative AI model.

[0706] 6. Based on this information, suggest the most suitable dining options to the user.

[0707] Thus, the system of the present invention enables users to efficiently find highly-rated and appropriate dining options.

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The user logs into the terminal and enters the address of their planned business trip destination or place of stay. This information includes the city name and street number. For example, they might enter "Chiyoda-ku, Tokyo".

[0711] Step 2:

[0712] The terminal sends the entered address information to the server. The transmitted address information is then prepared for processing on the server.

[0713] Step 3:

[0714] The server uses the Google Maps API to convert address information into latitude and longitude. This process converts the entered text-based address information into numerical latitude and longitude coordinates. The output latitude and longitude coordinates are then used in the next step.

[0715] Step 4:

[0716] The server uses the acquired latitude and longitude information to search for food and beverage options within a specified range (e.g., within 5km) using a restaurant delivery service API. The input is latitude and longitude information, and the output is a list of food and beverage options as search results.

[0717] Step 5:

[0718] The server then uses the API to collect detailed information (menu, prices, services, etc.) for each food and beverage option from the search results. In this collection process, the list of search results is input into the API, and a list of detailed information is output.

[0719] Step 6:

[0720] The server uses a generative AI model to collect reviews from across the internet, analyzes them, and calculates an overall rating for each dining option. Specifically, the review content is input to the generative AI model as prompts, and the rating is obtained as output. An example of a prompt used is as follows:

[0721] Please calculate the overall rating of the restaurant based on the following reviews:

[0722] "The food was delicious, and the service was excellent."

[0723] "Considering the high price, the taste was disappointing."

[0724] "The staff were friendly, and the waiting time was short."

[0725] "The delivery was fast, and the food was still warm."

[0726] Step 7:

[0727] The server uses a generative AI model to generate high-resolution visual images based on collected image data. These generated visual images are linked to detailed information about food and beverage options. The input is image data, and the output is the generated high-resolution visual image.

[0728] Step 8:

[0729] The server suggests the most suitable dining options to the user based on collected ratings, characteristics, and visual images. These suggestions are optimized based on the user's conditions and requirements. The input is the collected ratings, characteristics, and visual images, and the output is the suggested optimal dining options.

[0730] Step 9:

[0731] The terminal receives suggestion information from the server and displays it to the user. The user reviews the detailed information and visual images of the suggested food and beverage options and selects their desired menu and order. The input is the suggestion information, and the output is the selected menu and order information.

[0732] Step 10:

[0733] The terminal sends the user's selected order information to the server, which then confirms the order in conjunction with the delivery service system. The server then sends order completion information to the terminal, notifying the user. The input is the order information, and the output is the order completion notification.

[0734] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0735] The embodiments for carrying out the present invention will be described in detail below.

[0736] Overview of program processing

[0737] The system of this invention suggests the most suitable accommodation for the user by having them input the address of their planned business trip destination and then searching for, rating, reviewing, and providing visual images of nearby accommodations. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[0738] Program details

[0739] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[0740] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[0741] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[0742] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[0743] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0744] The server then works with an emotion engine to suggest the best accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[0745] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[0746] Specific example

[0747] Example 1: In the case of a business trip to Tokyo

[0748] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[0749] 2. The device sends that information to the server.

[0750] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[0751] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[0752] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[0753] 6. The server then uses a generation AI to generate visual images of each facility.

[0754] 7. The server uses an emotion engine to recognize the user's emotions. For example, it analyzes comments the user enters into the system or actions such as repeatedly clicking on facilities that the user is interested in.

[0755] 8. The server combines this information and suggests "Hotel A" to the user. For example, if the hotel has a rating of 4.5, is within the budget, appears clean based on its visual image, and the user is determined to be seeking relaxation.

[0756] 9. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[0757] 10. The device sends that information to the server, which then works with the hotel reservation system to complete the reservation.

[0758] 11. The server sends reservation completion information to the terminal and notifies the user.

[0759] The present invention aims to provide users with a more comfortable experience by offering optimal suggestions for selecting accommodation during business trips, taking into account the user's feelings, thereby significantly reducing time and effort.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[0763] Step 2:

[0764] The terminal sends the entered address information to the server.

[0765] Step 3:

[0766] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[0767] Step 4:

[0768] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[0769] Step 5:

[0770] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[0771] Step 6:

[0772] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[0773] Step 7:

[0774] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[0775] Step 8:

[0776] The server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0777] Step 9:

[0778] The server works in conjunction with an emotion engine to suggest the most suitable accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[0779] Step 10:

[0780] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[0781] Step 11:

[0782] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[0783] Step 12:

[0784] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[0785] Step 13:

[0786] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[0787] Step 14:

[0788] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[0789] Step 15:

[0790] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[0791] Step 16:

[0792] The server uses an emotion engine to evaluate user satisfaction after a reservation. For example, after a reservation, the system prompts the user to input feedback, and then analyzes their emotions based on those comments and their operation history.

[0793] Step 17:

[0794] The server saves the user's emotional state and feedback in the system so that it can be incorporated into future suggestions. This will enable more personalized suggestions tailored to the user's preferences and satisfaction level.

[0795] (Example 2)

[0796] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0797] When selecting appropriate accommodation for business trips or vacations, users face the challenge of having to gather and compare a large amount of information, which is time-consuming and laborious. Furthermore, the information provided often does not adequately address the individual feelings and needs of users, making it difficult to choose the best accommodation.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0799] In this invention, the server includes means for inputting the address of the planned business trip destination, means for searching for nearby accommodations based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby accommodations, means for creating a visual image of the accommodation using generative AI, means for recognizing the user's emotions, and means for suggesting the most suitable place to stay based on the collected information and visual image, as well as the user's emotions. This makes it possible for the user to easily find the most suitable accommodation that suits their emotions and needs.

[0800] "Method for entering the address of a planned business trip location" refers to an interface that allows users to enter the address of a place they plan to visit during a business trip or vacation in text format.

[0801] "Methods for searching for nearby accommodations" refers to systems or algorithms that search for accommodations in the vicinity of a specified address.

[0802] "Means of obtaining detailed information, ratings, and reviews" refers to the function of collecting information such as accommodation overviews, user ratings, and user review comments from the internet.

[0803] "Generative AI" is artificial intelligence that uses machine learning algorithms based on large amounts of data to automatically perform specific tasks.

[0804] "Means for creating visual images of accommodation facilities" refers to systems or programs that generate high-resolution visual images using collected image data.

[0805] "Means of recognizing user emotions" refers to systems and algorithms that analyze and determine a user's emotional state at a given time based on their input data and operation history.

[0806] "A means of suggesting the optimal accommodation" refers to a system that selects and displays the most suitable accommodation for the user based on collected information and the user's sentiments.

[0807] "A means of analyzing reviews across the entire internet using generative AI" refers to a function that uses generative AI to analyze review information existing on the internet and calculate an overall rating.

[0808] Modes for carrying out the invention

[0809] This invention is a system that, by entering the address of a planned business trip destination, searches for, rates, reviews, and provides visual images of nearby accommodations, and suggests the most suitable place to stay. Furthermore, by using an emotion engine that recognizes the user's emotions, it achieves more personalized suggestions.

[0810] The overall system

[0811] This system allows users to enter an address, which is then used to search for nearby accommodations, collecting detailed information, ratings, and reviews for each property. It then uses a generative AI to create visual images of the accommodations and, based on this data, suggests the most suitable place for the user. Furthermore, it uses an emotion engine to analyze the user's emotions and uses that information to provide even more optimal suggestions.

[0812] Hardware and software configuration

[0813] hardware

[0814] User devices: Laptops, desktop PCs, smartphones, tablets, etc.

[0815] Server: High-performance cloud server (e.g., AWS, Google Cloud)

[0816] software

[0817] API services: Google Maps API, hotel booking site APIs (e.g., Booking.com API)

[0818] Generative AI: OpenAI's GPT-3 or a similar generative AI model.

[0819] Emotion engine: Affectiva's Emotion AI, etc.

[0820] System operation

[0821] 1. The user enters the address of their planned business trip location into the terminal interface. For example, they might enter an address such as "Chiyoda-ku, Tokyo".

[0822] 2. The device sends the entered address information to the server as an HTTP request.

[0823] 3. The server uses the entered address information to call the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[0824] 4. The server then uses an API to collect additional detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[0825] 5. The server uses a generative AI (e.g., GPT-3) to collect reviews and ratings from across the network and calculate an overall rating for each facility.

[0826] 6. The server uses a generation AI to generate visual images of the accommodations. Specifically, it generates high-resolution visual images based on the collected image data and links them to information about each facility.

[0827] 7. The server recognizes the user's emotions using an emotion engine (e.g., Affectiva's Emotion AI). It analyzes comments entered by the user into the system, the speed and frequency of their actions, etc., to evaluate the user's emotions.

[0828] 8. The server suggests the most suitable accommodation based on the collected data and visual images, as well as the user's sentiment analysis results. For example, if the user is feeling stressed, it will suggest relaxing facilities or highly-rated accommodations.

[0829] 9. The terminal displays suggested information received from the server to the user. Based on this information, the user can select their final accommodation and further specify conditions (e.g., room type, budget, etc.).

[0830] 10. The user selects the most suitable accommodation and confirms the reservation from their device.

[0831] 11. The device sends the reservation information to the server, and the server works with the hotel's reservation system (e.g., Booking.com API) to finalize the reservation.

[0832] 12. The server sends a reservation completion notification to the terminal and notifies the user.

[0833] Specific example

[0834] Let's take the example of a user entering "Chiyoda-ku, Tokyo". The following are possible prompt messages.

[0835] Example of a prompt:

[0836] "Please conduct an emotionally-based evaluation of accommodations and suggest relaxing hotels near Chiyoda Ward, Tokyo."

[0837] By using the system of this invention, the process of selecting accommodations for business trips and vacations is significantly simplified. Furthermore, by incorporating the user's emotions, it becomes possible to suggest accommodations that better suit individual needs, thereby improving the user experience.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] The user enters the address of their planned business trip destination into the terminal's interface.

[0841] Specific operation: The user uses the keyboard to enter a specific address, such as "Chiyoda-ku, Tokyo," and clicks the "Search" button on the interface.

[0842] Input: Address of the planned business trip location.

[0843] Output: Data including address information.

[0844] Step 2:

[0845] The terminal sends the entered address information to the server in the form of an HTTP request.

[0846] Specific operation: The device converts the address information into JSON format and sends it to the server's API endpoint as an HTTP POST request.

[0847] Input: Address information in JSON format.

[0848] Output: Request data received by the server.

[0849] Step 3:

[0850] The server uses the received address information to send requests to the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[0851] Specific operation: The server sends requests containing address information to the Google Maps API and hotel booking site APIs and receives search results.

[0852] Input: Address information.

[0853] Output: List of accommodations.

[0854] Step 4:

[0855] The server uses an API to collect detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[0856] Specific operation: The server calls the API for each accommodation facility, retrieves detailed information, and saves it to the database.

[0857] Input: List of accommodations.

[0858] Output: Detailed information on each facility.

[0859] Step 5:

[0860] The server uses AI generation to collect reviews and ratings from across the internet and calculates an overall rating for each facility.

[0861] Specific operation: The server collects review information using web scraping tools and APIs, and analyzes it using a generative AI model.

[0862] Input: URLs and review site information for each facility.

[0863] Output: Overall evaluation data for each facility.

[0864] Step 6:

[0865] The server uses a generation AI to create visual images of the accommodation facilities.

[0866] Specific operation: The server uses collected image data to generate high-resolution visual images using AI and saves them to a database.

[0867] Input: Image data for each facility.

[0868] Output: High-resolution visual image.

[0869] Step 7:

[0870] The server uses an emotion engine to recognize the user's emotions.

[0871] Specific operation: The server inputs the user's past activity history and comments into the sentiment engine and receives the analysis results.

[0872] Input: User's operation history and comment data.

[0873] Output: User sentiment data.

[0874] Step 8:

[0875] The server suggests the most suitable accommodation based on the collected data, visual images, and user sentiment analysis results.

[0876] Specific operation: The server aggregates all data, uses an algorithm to select the most suitable accommodation, and creates a recommendation list.

[0877] Input: Accommodation details, overall rating data, visual images, and user sentiment data.

[0878] Output: Recommended accommodation list.

[0879] Step 9:

[0880] The terminal displays the suggested information received from the server to the user.

[0881] Specific operation: The terminal displays a recommendation list in a GUI, allowing the user to specify additional conditions.

[0882] Input: List of recommended accommodations.

[0883] Output: Data display that the user can view.

[0884] Step 10:

[0885] Users select the most suitable accommodation and confirm their reservation from their device.

[0886] Specific operation: The user selects an accommodation from the recommendation list, sets the booking details (room type, budget, etc.), and clicks the "Book Now" button.

[0887] Input: User-selected accommodation information and reservation details.

[0888] Output: Booking confirmation request data.

[0889] Step 11:

[0890] The terminal sends the reservation information to the server, which then works with the hotel's reservation system to finalize the reservation.

[0891] Specific operation: The device sends the reservation information to the server in JSON format, and the server calls the hotel reservation system's API to confirm the reservation.

[0892] Input: Booking confirmation request data.

[0893] Output: Confirmed reservation information sent to the hotel reservation system.

[0894] Step 12:

[0895] The server sends a reservation completion notification to the terminal, informing the user.

[0896] Specific operation: The server sends a reservation completion notification to the terminal as an HTTP response, and the terminal notifies the user via a pop-up or email.

[0897] Input: Confirmed reservation information.

[0898] Output: Reservation complete notification.

[0899] (Application Example 2)

[0900] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0901] Conventional work support systems struggle to quickly and efficiently optimize equipment placement within a specific work area. Furthermore, they often fail to consider user emotions, potentially leading to user stress and decreased work efficiency. Additionally, the lack of visual confirmation of the work environment makes it time-consuming and cumbersome to grasp the actual situation.

[0902] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting the address of the planned business trip location, means for searching for nearby accommodations based on the input address information, means for collecting detailed information, ratings, and reviews of nearby accommodations, means for generating a visual image of the accommodation using a generation AI, means for suggesting the most suitable accommodation to the user based on the collected information and visual image, means for inputting the planned work location, means for searching for work support equipment within a specific range based on the input information, means for collecting detailed information, ratings, and reviews of work support equipment within a specific range, and means for recognizing the user's emotions using an emotion engine and suggesting the most suitable work support equipment based on that. As a result, optimal equipment placement within the work area can be performed quickly and efficiently, work support that takes into account the user's emotions can be provided, and work efficiency can be improved and stress can be reduced.

[0903] "Means for entering the address of a planned business trip location" refers to an interface for users to input the address of a specific planned business trip location into the system.

[0904] "A means of searching for nearby accommodations based on entered address information" refers to a function that searches for accommodations located in the vicinity of an entered address.

[0905] "Means for collecting detailed information, ratings, and reviews of nearby accommodations" refers to the function of collecting information on facilities, rates, services, and user ratings and reviews related to the searched accommodations.

[0906] "Method for generating visual images of accommodations using generative AI" refers to a function that utilizes generative AI to generate high-resolution visual images of accommodations.

[0907] "A means of suggesting the most suitable accommodation to users based on collected information and visual images" refers to a function that suggests the most suitable accommodation to users based on collected information and generated visual images.

[0908] "Means for inputting work locations" refers to an interface for users to input information about specific work locations.

[0909] "A means of searching for work support equipment within a specific range based on input information" refers to a function that searches for work support equipment located in the vicinity of a planned work location based on the input information about that location.

[0910] "Means for collecting detailed information, evaluations, and reviews of work assistance equipment within a specific range" refers to a function for collecting detailed information, performance evaluations, and reviews of searched work assistance equipment.

[0911] "A means of recognizing the user's emotions using an emotion engine and proposing the most suitable work support equipment based on those emotions" refers to a function that uses an emotion engine to analyze the user's emotions and then proposes the most suitable work support equipment based on the results.

[0912] Specific embodiments for carrying out the present invention are described in detail below. This system allows users to input the address of their planned business trip destination, search for, evaluate, and review nearby accommodations, and provides visual images to suggest the most suitable place to stay. The same method can also be applied to work arrangements within a factory.

[0913] Overview of program processing

[0914] This system takes the address of a planned business trip location or planned work location within a factory as input, and based on that, it searches, evaluates, and reviews nearby accommodations and work support equipment, and provides visual images to offer the most suitable suggestions to the user. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[0915] Hardware and software to be used

[0916] Hardware: Autonomous mobile robots, control terminals in the factory (PCs, tablets), user terminals (smartphones)

[0917] Software: Google Maps API, hotel booking site API, factory equipment management system API, generative AI (e.g., OpenAI GPT-4), emotion engine (Emotion AI)

[0918] System Processing Procedure

[0919] The user logs into the system and enters their planned business trip location or planned work location within the factory. The entered information is sent to the server, which uses this information to search for accommodations and work support equipment within the specified area.

[0920] Search and evaluation

[0921] The server uses an API to collect detailed information about accommodations and work assistance equipment within a specified range. This includes facilities, rates, services offered, operational status, performance, and maintenance status. The server also uses generative AI to collect and analyze reviews and ratings of accommodations and work assistance equipment to calculate an overall rating.

[0922] Visual image generation

[0923] Next, the server uses a generation AI to create visual images of each accommodation and work support equipment, linking them to detailed information. In this process, high-resolution visual previews are created based on the collected image data.

[0924] Using an Emotion Engine

[0925] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[0926] Specific usage examples

[0927] Example 1: Optimizing work layout within a factory

[0928] When the operator inputs "Section A of the third manufacturing line" into the robot, the robot sends that information to the server. The server uses the factory equipment management system API to list support robots and equipment within 50 meters. The server collects detailed information on each piece of equipment and uses generative AI to calculate an evaluation of each piece of equipment. Furthermore, it uses generative AI to generate a visual image of the site, and after recognizing the user's emotions using an emotion engine, it suggests the most suitable support equipment.

[0929] Example of a prompt:

[0930] "Here, we need to provide information to optimally position work support robots and equipment operating within a designated factory area. Users will be assisted in selecting the most suitable equipment based on comments and operation history entered using an emotion engine."

[0931] In summary, the present invention aims to significantly improve work efficiency and provide a more comfortable experience for users by making optimal suggestions that take into account the user's feelings when selecting accommodation during business trips and arranging work support equipment within factories.

[0932] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0933] Step 1:

[0934] The user logs into the system and enters their planned business trip location or planned work location within the factory. The address and work location information is entered from the user's terminal and sent to the server.

[0935] Input: Address of the planned business trip location or information of the planned work site.

[0936] Output: Input information is sent to the server.

[0937] Step 2:

[0938] The server searches for accommodations or work support equipment within a specified range based on the entered information. This search is performed using the Google Maps API, hotel booking site APIs, and factory equipment management system APIs.

[0939] Input: Address or work location information

[0940] Output: List of accommodations and work support equipment within the specified range.

[0941] Step 3:

[0942] Based on the acquired list, the server collects detailed information about each accommodation and work support equipment. This detailed information includes facilities, rates, services offered, operational status, performance, and maintenance status.

[0943] Input: List of accommodations and work support equipment

[0944] Output: Detailed information on each facility and piece of equipment.

[0945] Step 4:

[0946] The server uses generation AI to collect and analyze internet-wide reviews of each accommodation and work support equipment. This allows it to calculate ratings for each facility and piece of equipment.

[0947] Input: Detailed information and reviews

[0948] Output: Overall evaluation of each facility and piece of equipment.

[0949] Step 5:

[0950] The server uses a generation AI to generate visual images of each accommodation facility and work support equipment. It processes the collected image data to generate high-resolution images and links them to detailed information.

[0951] Input: Image data

[0952] Output: Visual image

[0953] Step 6:

[0954] The server uses an emotion engine to recognize emotions from comments and activity history entered by the user. This allows the server to evaluate what emotions the user is feeling.

[0955] Input: Comments and operation history

[0956] Output: User sentiment rating

[0957] Step 7:

[0958] The server synthesizes this information and suggests the most suitable accommodation and work support equipment to the user. The suggestions also take into account the user's emotional evaluation.

[0959] Input: Detailed information, overall rating, visual image, sentiment rating

[0960] Output: Suggestions for optimal accommodation and work support equipment.

[0961] Step 8:

[0962] The user terminal receives suggested information from the server and displays it to the user. The user reviews detailed information and visual images of the suggested accommodations and work support equipment and makes a final selection.

[0963] Input: Suggestion information from the server

[0964] Output: Information displayed to the user

[0965] Step 9:

[0966] Once the user makes their final selection and decides on a reservation or work order, that information is sent to the server via the user's terminal. The server then works in conjunction with the hotel reservation system or work management system to confirm the final reservation or order.

[0967] Input: User's last selection

[0968] Output: Confirmation of reservation or work order

[0969] Step 10:

[0970] The server sends information about the completion of a reservation or work order to the user's terminal and notifies the user.

[0971] Input: Completion information for reservations or work orders

[0972] Output: User notification

[0973] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0974] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0975] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0976] [Third Embodiment]

[0977] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0978] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0979] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0980] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0981] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0982] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0983] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0984] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0985] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0986] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0987] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0988] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0989] The embodiments for carrying out the present invention will be described in detail below.

[0990] Program processing overview:

[0991] The system of this invention allows users to input the address of their planned business trip destination and then search for, rate, review, and provide visual images of nearby accommodations to suggest the most suitable place to stay.

[0992] Program details:

[0993] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[0994] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[0995] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[0996] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[0997] The server then uses the collected ratings, features, and visual images to suggest the most suitable accommodations to the user. These suggestions are optimized based on the user's conditions and requirements. For example, facilities with high ratings, within the user's budget, and judged to be clean based on their visual images might be selected.

[0998] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[0999] Specific example:

[1000] Example 1: In the case of a business trip to Tokyo

[1001] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1002] 2. The device sends that information to the server.

[1003] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1004] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1005] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1006] 6. The server then uses a generation AI to generate visual images of each facility.

[1007] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[1008] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1009] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[1010] 10. The server sends reservation completion information to the terminal and notifies the user.

[1011] The present invention, in the form described above, is a system that significantly reduces the time and effort required to select accommodation during business trips and efficiently provides users with the most suitable accommodation.

[1012] The following describes the processing flow.

[1013] Step 1:

[1014] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[1015] Step 2:

[1016] The terminal sends the entered address information to the server. The terminal then sends this information to the server as a request.

[1017] Step 3:

[1018] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[1019] Step 4:

[1020] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[1021] Step 5:

[1022] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[1023] Step 6:

[1024] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[1025] Step 7:

[1026] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[1027] Step 8:

[1028] The server determines the best accommodation for the user based on collected ratings, features, and visual images. For example, it selects the best accommodation based on ratings, price, and user preferences.

[1029] Step 9:

[1030] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[1031] Step 10:

[1032] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[1033] Step 11:

[1034] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[1035] Step 12:

[1036] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[1037] Step 13:

[1038] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[1039] Step 14:

[1040] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[1041] (Example 1)

[1042] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1043] In modern business and travel planning, choosing the right accommodation is crucial. However, finding the optimal accommodation from the vast amount of information available online is time-consuming and laborious. Furthermore, the scattered nature of reviews and ratings makes it difficult to gather reliable information in one place. Additionally, the lack of visual information makes it difficult to grasp the actual condition of the facilities.

[1044] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1045] In this invention, the server includes means for inputting address information, means for searching for nearby facilities based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby facilities, means for generating visual information of facilities using a generative AI model, and means for presenting appropriate facilities to the user based on the acquired information and visual information. As a result, users can easily select accommodations based on reliable and detailed information and can also confirm visual images, thereby improving the accuracy and efficiency of their selection.

[1046] "Address information" refers to location information that users enter as their destination for business trips or travel.

[1047] "Nearby facilities" refers to accommodations and other related facilities located within a specific area, which are searched based on the entered address information.

[1048] "Detailed information" refers to information about the specific characteristics and services offered by surrounding facilities, such as equipment, fees, and services.

[1049] "Ratings" refer to scores or ranks assigned to surrounding facilities by users or third parties, indicating the quality of the facilities and the level of satisfaction with the services provided.

[1050] A "review" refers to a written account of user or third-party experiences and feedback regarding nearby facilities.

[1051] A "generative AI model" refers to a model that analyzes and generates data using artificial intelligence technology, and in this invention, it is particularly used for generating visual information of accommodation facilities and for analyzing evaluations and reviews.

[1052] "Visual information" refers to high-resolution images and visual representations of facilities generated using generative AI models.

[1053] "Means of presenting appropriate facilities to users" refers to a function that selects and suggests the most suitable accommodation for the user based on acquired detailed information, ratings, reviews, and visual information.

[1054] The embodiments for carrying out the present invention will be described in detail below.

[1055] The system of this invention allows the user to input the address of their planned business trip destination, search for, evaluate, and collect reviews of accommodations in the surrounding area, generate visual images of the accommodations using a generative AI model, and suggest the most suitable place to stay.

[1056] First, the user logs into the terminal and enters the specific address information of their planned business trip location. For example, the user can enter a specific address such as "Chiyoda-ku, Tokyo." At this point, the address information is entered into the terminal.

[1057] Next, the terminal sends the entered address information to the server. The server receives this address information and uses it to search for nearby facilities. For the search, it uses existing search services such as the Google Maps API or the APIs of accommodation booking sites to list accommodations within a specific range (e.g., within 10km).

[1058] The server uses the API again to collect detailed information about each accommodation based on the acquired list of accommodations. This information includes facilities, rates, and services offered. Next, a generative AI model is used to collect reviews and ratings from across the internet and analyze and calculate an overall rating for each accommodation. Data acquisition from review sites and social media is considered for review collection.

[1059] Furthermore, the server uses a generative AI model to generate visual images of the accommodations. Based on the collected image data, it generates high-resolution visual images and links them to detailed information about each accommodation.

[1060] The server suggests the most suitable accommodation to the user based on collected ratings, features, and visual images. These suggestions are optimized based on the user's conditions and requirements (e.g., budget, cleanliness, high ratings, etc.). For example, facilities with high ratings, within budget, and high cleanliness based on visual images will be selected.

[1061] Finally, the terminal receives suggested information from the server and displays it to the user. The user can review detailed information and visual images of the suggested accommodations and further specify conditions (e.g., room type, price range, etc.). When the user finally selects an accommodation and decides to make a reservation, that information is sent via the terminal to the server, which then works with the accommodation's reservation system to confirm the final reservation. After the reservation is complete, the server sends a reservation confirmation notification to the terminal, notifying the user.

[1062] Specific example

[1063] Example 1: In the case of a business trip to Tokyo

[1064] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1065] 2. The device sends that information to the server.

[1066] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1067] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1068] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1069] 6. The server then uses a generation AI to generate visual images of each facility.

[1070] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[1071] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1072] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[1073] 10. The server sends reservation completion information to the terminal and notifies the user.

[1074] An example of a prompt is a sentence that you input to the generating AI depending on a specific destination or condition, and is shown as follows:

[1075] "Please recommend highly-rated accommodations in and around Chiyoda Ward, Tokyo, that are within my budget."

[1076] The above describes the embodiments for carrying out the present invention, which significantly reduces the time and effort required to select accommodations during business trips and makes it possible to efficiently provide users with the most suitable accommodations.

[1077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1078] Step 1:

[1079] The user logs into the device and enters the address of their planned business trip location.

[1080] Specifically, the user enters their username and password on the terminal's login screen to access the system. Next, they enter the address of their planned business trip location (e.g., "Chiyoda-ku, Tokyo") into the input field.

[1081] Input: User information (username, password), address of the planned business trip location.

[1082] Output: Login authentication result, address information.

[1083] Step 2:

[1084] The terminal sends the entered address information to the server.

[1085] Specifically, the terminal converts the entered address information into an appropriate format (e.g., JSON format) and sends a request to the server.

[1086] Input: Address information.

[1087] Output: Request containing address information.

[1088] Step 3:

[1089] The server searches for accommodations near the planned business trip location.

[1090] Specifically, the server uses the Google Maps API and the APIs of accommodation booking websites to search for accommodations within a specified range (e.g., within 10km) based on the entered address. The search results are then stored in a list format.

[1091] Input: Request including address information.

[1092] Output: List of accommodations.

[1093] Step 4:

[1094] The server collects detailed information about accommodations and analyzes ratings and reviews.

[1095] Specifically, the server uses the acquired list of accommodations to collect detailed information about each facility (such as facilities, prices, and services) via an API. Next, it uses a generative AI model to collect and analyze reviews and ratings from across the internet to calculate an overall rating.

[1096] Input: List of accommodations.

[1097] Output: Detailed information on accommodations, and analysis results of ratings and reviews for each property.

[1098] Step 5:

[1099] The server generates a visual image of the accommodation.

[1100] Specifically, the server uses a generative AI model to generate high-resolution visual images based on the collected image data, and links these images to detailed information about each accommodation.

[1101] Input: Accommodation details and image data.

[1102] Output: Detailed information about the accommodation, including visual images.

[1103] Step 6:

[1104] The server selects and suggests the most suitable accommodation.

[1105] In terms of specific operations, the server considers the user's conditions (budget, rating, cleanliness, etc.) and selects the most suitable accommodation based on collected ratings, features, and visual images. It then generates a recommendation and sends it to the terminal.

[1106] Input: User criteria, detailed information about the accommodation including visual images.

[1107] Output: Suggestions for the most suitable accommodation.

[1108] Step 7:

[1109] The device displays suggestions to the user.

[1110] Specifically, the terminal displays the suggested information received from the server on the user interface, allowing the user to view the details.

[1111] Input: Proposal information.

[1112] Output: Suggestion information displayed in the user interface.

[1113] Step 8:

[1114] The user selects an accommodation and makes a reservation.

[1115] In terms of specific actions, the user reviews the details of the suggested accommodations, considers conditions such as room type and price range, and makes a selection. After making a selection, they confirm the final reservation details on the reservation confirmation screen.

[1116] Input: Suggested information, user selection.

[1117] Output: Final reservation confirmed.

[1118] Step 9:

[1119] The device sends the reservation information to the server, completing the reservation.

[1120] Specifically, the terminal converts the user's reservation confirmation information into the appropriate format and sends it to the server. The server then integrates the received reservation information with the accommodation's reservation system and confirms the reservation.

[1121] Input: Final reservation confirmation information.

[1122] Output: Reservation confirmation information.

[1123] Step 10:

[1124] The server sends a reservation completion notification to the terminal, informing the user.

[1125] Specifically, the server generates reservation completion information and sends it to the terminal. The terminal then displays a reservation completion notification to the user.

[1126] Input: Reservation confirmation information.

[1127] Output: Reservation confirmation.

[1128] (Application Example 1)

[1129] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1130] Currently, finding dining options while on a business trip or staying at a hotel is time-consuming and laborious. Furthermore, the need to check reviews and ratings individually makes it difficult to make the best choice. Choosing dining options in an unfamiliar place is particularly stressful, highlighting the need for a system that efficiently suggests optimal choices.

[1131] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1132] In this invention, the server includes means for inputting the address of the planned business trip destination or place of stay; means for searching for nearby dining options based on the input address information; means for collecting detailed information, ratings, and reviews of nearby dining options; means for generating visual images of dining options using a generative AI model; and means for suggesting the most suitable dining option to the user based on the collected information and visual images. This enables the user to efficiently find a highly-rated and appropriate dining option, saving time and effort.

[1133] "Planned business trip location" refers to a place where the user will temporarily stay for business purposes.

[1134] "Place of stay" refers to the address or location where a user will temporarily stay or reside.

[1135] "Address information" refers to information that indicates a specific geographical location, including the city name and street number.

[1136] "Food and beverage options" refer to restaurants and delivery services offered within a specific area.

[1137] "Detailed information" refers to information about food and beverage options, such as facilities, menus, and services.

[1138] "Rating" refers to the overall value or score that users or customers give to food and beverage options.

[1139] A "review" is a written record and publication of a user's experience and opinion regarding a food and beverage option.

[1140] A "generative AI model" is an artificial intelligence algorithm that generates information from text and images.

[1141] "Visual image" refers to the appearance of food and beverage options, as well as visual images and videos of the meals being served.

[1142] "Proposal" refers to the act of presenting suitable options to users based on collected information and AI analysis results.

[1143] "Feedback" refers to responses and answers such as opinions, evaluations, and requests received from users.

[1144] "Additional conditions" refer to specific requests or requirements from the user, which may result in adjustments to the services provided.

[1145] "Overall rating" refers to a general evaluation score calculated based on multiple reviews and evaluation data.

[1146] The embodiments for carrying out the present invention will be described in detail below.

[1147] System Configuration

[1148] The system of the present invention includes the following components:

[1149] 1. User terminal: A device such as a smartphone or tablet.

[1150] 2. Server: A system for cloud-based data management and processing.

[1151] 3. APIs: Google Maps API and APIs for restaurant delivery services

[1152] 4. Generative AI Models: AI algorithms that generate information from text and images.

[1153] Program Processing Overview

[1154] Address input via user terminal

[1155] First, the user logs into their terminal and enters the address of their planned business trip destination or place of stay. The entered address information is then sent from the user's terminal to the server.

[1156] Information retrieval and collection by server

[1157] The server uses the Google Maps API to convert address information into latitude and longitude. Then, it utilizes a restaurant delivery service API to search for dining options around the specified latitude and longitude. The search results include detailed information about the dining options (menu, prices, services, etc.).

[1158] Server-based evaluation and review collection and analysis

[1159] The server uses a generative AI model to collect and analyze reviews from across the internet based on the detailed information of the food and beverage options it has gathered. The generative AI model uses prompts like the following to calculate an overall rating for each food and beverage option.

[1160] Example of a prompt

[1161] Please calculate the overall rating of the restaurant based on the following reviews:

[1162] "The food was delicious, and the service was excellent."

[1163] "Considering the high price, the taste was disappointing."

[1164] "The staff were friendly, and the waiting time was short."

[1165] "The delivery was fast, and the food was still warm."

[1166] Server-based generation of visual images

[1167] Furthermore, the server uses a generative AI model to generate high-resolution visual images based on the collected image data of food and beverage options. These visual images are linked as detailed information.

[1168] Suggestions and feedback for users

[1169] The server suggests optimal food and beverage options to the user based on the collected information and generated visual images. The suggestions are displayed on the user's terminal, allowing the user to review the suggestions, specify further conditions, and provide feedback.

[1170] Hardware and software to be used

[1171] User devices: Smartphones, tablets, etc.

[1172] Server: Cloud-based database system

[1173] APIs: Google Maps API, Restaurant Delivery Service API

[1174] Generative AI models: Natural language processing and image generation models such as the OpenAI API.

[1175] Specific example

[1176] For example, if a user enters "Chiyoda-ku, Tokyo," the process would be as follows:

[1177] 1. Address information is sent from the user's terminal to the server.

[1178] 2. The server uses the Google Maps API to convert the address to latitude and longitude.

[1179] 3. Use a restaurant delivery service API to search for food and beverage options within a specified range (e.g., within 5km).

[1180] 4. The server collects and analyzes reviews from across the internet using a generation AI model and calculates an overall rating.

[1181] 5. Generate visual images using a generative AI model.

[1182] 6. Based on this information, suggest the most suitable dining options to the user.

[1183] Thus, the system of the present invention enables users to efficiently find highly-rated and appropriate dining options.

[1184] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1185] Step 1:

[1186] The user logs into the terminal and enters the address of their planned business trip destination or place of stay. This information includes the city name and street number. For example, they might enter "Chiyoda-ku, Tokyo".

[1187] Step 2:

[1188] The terminal sends the entered address information to the server. The transmitted address information is then prepared for processing on the server.

[1189] Step 3:

[1190] The server uses the Google Maps API to convert address information into latitude and longitude. This process converts the entered text-based address information into numerical latitude and longitude coordinates. The output latitude and longitude coordinates are then used in the next step.

[1191] Step 4:

[1192] The server uses the acquired latitude and longitude information to search for food and beverage options within a specified range (e.g., within 5km) using a restaurant delivery service API. The input is latitude and longitude information, and the output is a list of food and beverage options as search results.

[1193] Step 5:

[1194] The server then uses the API to collect detailed information (menu, prices, services, etc.) for each food and beverage option from the search results. In this collection process, the list of search results is input into the API, and a list of detailed information is output.

[1195] Step 6:

[1196] The server uses a generative AI model to collect reviews from across the internet, analyzes them, and calculates an overall rating for each dining option. Specifically, the review content is input to the generative AI model as prompts, and the rating is obtained as output. An example of a prompt used is as follows:

[1197] Please calculate the overall rating of the restaurant based on the following reviews:

[1198] "The food was delicious, and the service was excellent."

[1199] "Considering the high price, the taste was disappointing."

[1200] "The staff were friendly, and the waiting time was short."

[1201] "The delivery was fast, and the food was still warm."

[1202] Step 7:

[1203] The server uses a generative AI model to generate high-resolution visual images based on collected image data. These generated visual images are linked to detailed information about food and beverage options. The input is image data, and the output is the generated high-resolution visual image.

[1204] Step 8:

[1205] The server suggests the most suitable dining options to the user based on collected ratings, characteristics, and visual images. These suggestions are optimized based on the user's conditions and requirements. The input is the collected ratings, characteristics, and visual images, and the output is the suggested optimal dining options.

[1206] Step 9:

[1207] The terminal receives suggestion information from the server and displays it to the user. The user reviews the detailed information and visual images of the suggested food and beverage options and selects their desired menu and order. The input is the suggestion information, and the output is the selected menu and order information.

[1208] Step 10:

[1209] The terminal sends the user's selected order information to the server, which then confirms the order in conjunction with the delivery service system. The server then sends order completion information to the terminal, notifying the user. The input is the order information, and the output is the order completion notification.

[1210] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1211] The embodiments for carrying out the present invention will be described in detail below.

[1212] Overview of program processing

[1213] The system of this invention suggests the most suitable accommodation for the user by having them input the address of their planned business trip destination and then searching for, rating, reviewing, and providing visual images of nearby accommodations. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[1214] Program details

[1215] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[1216] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[1217] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[1218] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[1219] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1220] The server then works with an emotion engine to suggest the best accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[1221] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[1222] Specific example

[1223] Example 1: In the case of a business trip to Tokyo

[1224] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1225] 2. The device sends that information to the server.

[1226] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1227] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1228] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1229] 6. The server then uses a generation AI to generate visual images of each facility.

[1230] 7. The server uses an emotion engine to recognize the user's emotions. For example, it analyzes comments the user enters into the system or actions such as repeatedly clicking on facilities that the user is interested in.

[1231] 8. The server combines this information and suggests "Hotel A" to the user. For example, if the hotel has a rating of 4.5, is within the budget, appears clean based on its visual image, and the user is determined to be seeking relaxation.

[1232] 9. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1233] 10. The device sends that information to the server, which then works with the hotel reservation system to complete the reservation.

[1234] 11. The server sends reservation completion information to the terminal and notifies the user.

[1235] The present invention aims to provide users with a more comfortable experience by offering optimal suggestions for selecting accommodation during business trips, taking into account the user's feelings, thereby significantly reducing time and effort.

[1236] The following describes the processing flow.

[1237] Step 1:

[1238] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[1239] Step 2:

[1240] The terminal sends the entered address information to the server.

[1241] Step 3:

[1242] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[1243] Step 4:

[1244] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[1245] Step 5:

[1246] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[1247] Step 6:

[1248] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[1249] Step 7:

[1250] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[1251] Step 8:

[1252] The server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1253] Step 9:

[1254] The server works in conjunction with an emotion engine to suggest the most suitable accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[1255] Step 10:

[1256] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[1257] Step 11:

[1258] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[1259] Step 12:

[1260] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[1261] Step 13:

[1262] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[1263] Step 14:

[1264] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[1265] Step 15:

[1266] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[1267] Step 16:

[1268] The server uses an emotion engine to evaluate user satisfaction after a reservation. For example, after a reservation, the system prompts the user to input feedback, and then analyzes their emotions based on those comments and their operation history.

[1269] Step 17:

[1270] The server saves the user's emotional state and feedback in the system so that it can be incorporated into future suggestions. This will enable more personalized suggestions tailored to the user's preferences and satisfaction level.

[1271] (Example 2)

[1272] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1273] When selecting appropriate accommodation for business trips or vacations, users face the challenge of having to gather and compare a large amount of information, which is time-consuming and laborious. Furthermore, the information provided often does not adequately address the individual feelings and needs of users, making it difficult to choose the best accommodation.

[1274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1275] In this invention, the server includes means for inputting the address of the planned business trip destination, means for searching for nearby accommodations based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby accommodations, means for creating a visual image of the accommodation using generative AI, means for recognizing the user's emotions, and means for suggesting the most suitable place to stay based on the collected information and visual image, as well as the user's emotions. This makes it possible for the user to easily find the most suitable accommodation that suits their emotions and needs.

[1276] "Method for entering the address of a planned business trip location" refers to an interface that allows users to enter the address of a place they plan to visit during a business trip or vacation in text format.

[1277] "Methods for searching for nearby accommodations" refers to systems or algorithms that search for accommodations in the vicinity of a specified address.

[1278] "Means of obtaining detailed information, ratings, and reviews" refers to the function of collecting information such as accommodation overviews, user ratings, and user review comments from the internet.

[1279] "Generative AI" is artificial intelligence that uses machine learning algorithms based on large amounts of data to automatically perform specific tasks.

[1280] "Means for creating visual images of accommodation facilities" refers to systems or programs that generate high-resolution visual images using collected image data.

[1281] "Means of recognizing user emotions" refers to systems and algorithms that analyze and determine a user's emotional state at a given time based on their input data and operation history.

[1282] "A means of suggesting the optimal accommodation" refers to a system that selects and displays the most suitable accommodation for the user based on collected information and the user's sentiments.

[1283] "A means of analyzing reviews across the entire internet using generative AI" refers to a function that uses generative AI to analyze review information existing on the internet and calculate an overall rating.

[1284] Modes for carrying out the invention

[1285] This invention is a system that, by entering the address of a planned business trip destination, searches for, rates, reviews, and provides visual images of nearby accommodations, and suggests the most suitable place to stay. Furthermore, by using an emotion engine that recognizes the user's emotions, it achieves more personalized suggestions.

[1286] The overall system

[1287] This system allows users to enter an address, which is then used to search for nearby accommodations, collecting detailed information, ratings, and reviews for each property. It then uses a generative AI to create visual images of the accommodations and, based on this data, suggests the most suitable place for the user. Furthermore, it uses an emotion engine to analyze the user's emotions and uses that information to provide even more optimal suggestions.

[1288] Hardware and software configuration

[1289] hardware

[1290] User devices: Laptops, desktop PCs, smartphones, tablets, etc.

[1291] Server: High-performance cloud server (e.g., AWS, Google Cloud)

[1292] software

[1293] API services: Google Maps API, hotel booking site APIs (e.g., Booking.com API)

[1294] Generative AI: OpenAI's GPT-3 or a similar generative AI model.

[1295] Emotion engine: Affectiva's Emotion AI, etc.

[1296] System operation

[1297] 1. The user enters the address of their planned business trip location into the terminal interface. For example, they might enter an address such as "Chiyoda-ku, Tokyo".

[1298] 2. The device sends the entered address information to the server as an HTTP request.

[1299] 3. The server uses the entered address information to call the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[1300] 4. The server then uses an API to collect additional detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[1301] 5. The server uses a generative AI (e.g., GPT-3) to collect reviews and ratings from across the network and calculate an overall rating for each facility.

[1302] 6. The server uses a generation AI to generate visual images of the accommodations. Specifically, it generates high-resolution visual images based on the collected image data and links them to information about each facility.

[1303] 7. The server recognizes the user's emotions using an emotion engine (e.g., Affectiva's Emotion AI). It analyzes comments entered by the user into the system, the speed and frequency of their actions, etc., to evaluate the user's emotions.

[1304] 8. The server suggests the most suitable accommodation based on the collected data and visual images, as well as the user's sentiment analysis results. For example, if the user is feeling stressed, it will suggest relaxing facilities or highly-rated accommodations.

[1305] 9. The terminal displays suggested information received from the server to the user. Based on this information, the user can select their final accommodation and further specify conditions (e.g., room type, budget, etc.).

[1306] 10. The user selects the most suitable accommodation and confirms the reservation from their device.

[1307] 11. The device sends the reservation information to the server, and the server works with the hotel's reservation system (e.g., Booking.com API) to finalize the reservation.

[1308] 12. The server sends a reservation completion notification to the terminal and notifies the user.

[1309] Specific example

[1310] Let's take the example of a user entering "Chiyoda-ku, Tokyo". The following are possible prompt messages.

[1311] Example of a prompt:

[1312] "Please conduct an emotionally-based evaluation of accommodations and suggest relaxing hotels near Chiyoda Ward, Tokyo."

[1313] By using the system of this invention, the process of selecting accommodations for business trips and vacations is significantly simplified. Furthermore, by incorporating the user's emotions, it becomes possible to suggest accommodations that better suit individual needs, thereby improving the user experience.

[1314] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1315] Step 1:

[1316] The user enters the address of their planned business trip destination into the terminal's interface.

[1317] Specific operation: The user uses the keyboard to enter a specific address, such as "Chiyoda-ku, Tokyo," and clicks the "Search" button on the interface.

[1318] Input: Address of the planned business trip location.

[1319] Output: Data including address information.

[1320] Step 2:

[1321] The terminal sends the entered address information to the server in the form of an HTTP request.

[1322] Specific operation: The device converts the address information into JSON format and sends it to the server's API endpoint as an HTTP POST request.

[1323] Input: Address information in JSON format.

[1324] Output: Request data received by the server.

[1325] Step 3:

[1326] The server uses the received address information to send requests to the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[1327] Specific operation: The server sends requests containing address information to the Google Maps API and hotel booking site APIs and receives search results.

[1328] Input: Address information.

[1329] Output: List of accommodations.

[1330] Step 4:

[1331] The server uses an API to collect detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[1332] Specific operation: The server calls the API for each accommodation facility, retrieves detailed information, and saves it to the database.

[1333] Input: List of accommodations.

[1334] Output: Detailed information on each facility.

[1335] Step 5:

[1336] The server uses AI generation to collect reviews and ratings from across the internet and calculates an overall rating for each facility.

[1337] Specific operation: The server collects review information using web scraping tools and APIs, and analyzes it using a generative AI model.

[1338] Input: URLs and review site information for each facility.

[1339] Output: Overall evaluation data for each facility.

[1340] Step 6:

[1341] The server uses a generation AI to create visual images of the accommodation facilities.

[1342] Specific operation: The server uses collected image data to generate high-resolution visual images using AI and saves them to a database.

[1343] Input: Image data for each facility.

[1344] Output: High-resolution visual image.

[1345] Step 7:

[1346] The server uses an emotion engine to recognize the user's emotions.

[1347] Specific operation: The server inputs the user's past activity history and comments into the sentiment engine and receives the analysis results.

[1348] Input: User's operation history and comment data.

[1349] Output: User sentiment data.

[1350] Step 8:

[1351] The server suggests the most suitable accommodation based on the collected data, visual images, and user sentiment analysis results.

[1352] Specific operation: The server aggregates all data, uses an algorithm to select the most suitable accommodation, and creates a recommendation list.

[1353] Input: Accommodation details, overall rating data, visual images, and user sentiment data.

[1354] Output: Recommended accommodation list.

[1355] Step 9:

[1356] The terminal displays the suggested information received from the server to the user.

[1357] Specific operation: The terminal displays a recommendation list in a GUI, allowing the user to specify additional conditions.

[1358] Input: List of recommended accommodations.

[1359] Output: Data display that the user can view.

[1360] Step 10:

[1361] Users select the most suitable accommodation and confirm their reservation from their device.

[1362] Specific operation: The user selects an accommodation from the recommendation list, sets the booking details (room type, budget, etc.), and clicks the "Book Now" button.

[1363] Input: User-selected accommodation information and reservation details.

[1364] Output: Booking confirmation request data.

[1365] Step 11:

[1366] The terminal sends the reservation information to the server, which then works with the hotel's reservation system to finalize the reservation.

[1367] Specific operation: The device sends the reservation information to the server in JSON format, and the server calls the hotel reservation system's API to confirm the reservation.

[1368] Input: Booking confirmation request data.

[1369] Output: Confirmed reservation information sent to the hotel reservation system.

[1370] Step 12:

[1371] The server sends a reservation completion notification to the terminal, informing the user.

[1372] Specific operation: The server sends a reservation completion notification to the terminal as an HTTP response, and the terminal notifies the user via a pop-up or email.

[1373] Input: Confirmed reservation information.

[1374] Output: Reservation complete notification.

[1375] (Application Example 2)

[1376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1377] Conventional work support systems struggle to quickly and efficiently optimize equipment placement within a specific work area. Furthermore, they often fail to consider user emotions, potentially leading to user stress and decreased work efficiency. Additionally, the lack of visual confirmation of the work environment makes it time-consuming and cumbersome to grasp the actual situation.

[1378] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting the address of the planned business trip location, means for searching for nearby accommodations based on the input address information, means for collecting detailed information, ratings, and reviews of nearby accommodations, means for generating a visual image of the accommodation using a generation AI, means for suggesting the most suitable accommodation to the user based on the collected information and visual image, means for inputting the planned work location, means for searching for work support equipment within a specific range based on the input information, means for collecting detailed information, ratings, and reviews of work support equipment within a specific range, and means for recognizing the user's emotions using an emotion engine and suggesting the most suitable work support equipment based on that. As a result, optimal equipment placement within the work area can be performed quickly and efficiently, work support that takes into account the user's emotions can be provided, and work efficiency can be improved and stress can be reduced.

[1379] "Means for entering the address of a planned business trip location" refers to an interface for users to input the address of a specific planned business trip location into the system.

[1380] "A means of searching for nearby accommodations based on entered address information" refers to a function that searches for accommodations located in the vicinity of an entered address.

[1381] "Means for collecting detailed information, ratings, and reviews of nearby accommodations" refers to the function of collecting information on facilities, rates, services, and user ratings and reviews related to the searched accommodations.

[1382] "Method for generating visual images of accommodations using generative AI" refers to a function that utilizes generative AI to generate high-resolution visual images of accommodations.

[1383] "A means of suggesting the most suitable accommodation to users based on collected information and visual images" refers to a function that suggests the most suitable accommodation to users based on collected information and generated visual images.

[1384] "Means for inputting work locations" refers to an interface for users to input information about specific work locations.

[1385] "A means of searching for work support equipment within a specific range based on input information" refers to a function that searches for work support equipment located in the vicinity of a planned work location based on the input information about that location.

[1386] "Means for collecting detailed information, evaluations, and reviews of work assistance equipment within a specific range" refers to a function for collecting detailed information, performance evaluations, and reviews of searched work assistance equipment.

[1387] "A means of recognizing the user's emotions using an emotion engine and proposing the most suitable work support equipment based on those emotions" refers to a function that uses an emotion engine to analyze the user's emotions and then proposes the most suitable work support equipment based on the results.

[1388] Specific embodiments for carrying out the present invention are described in detail below. This system allows users to input the address of their planned business trip destination, search for, evaluate, and review nearby accommodations, and provides visual images to suggest the most suitable place to stay. The same method can also be applied to work arrangements within a factory.

[1389] Overview of program processing

[1390] This system takes the address of a planned business trip location or planned work location within a factory as input, and based on that, it searches, evaluates, and reviews nearby accommodations and work support equipment, and provides visual images to offer the most suitable suggestions to the user. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[1391] Hardware and software to be used

[1392] Hardware: Autonomous mobile robots, control terminals in the factory (PCs, tablets), user terminals (smartphones)

[1393] Software: Google Maps API, hotel booking site API, factory equipment management system API, generative AI (e.g., OpenAI GPT-4), emotion engine (Emotion AI)

[1394] System Processing Procedure

[1395] The user logs into the system and enters their planned business trip location or planned work location within the factory. The entered information is sent to the server, which uses this information to search for accommodations and work support equipment within the specified area.

[1396] Search and evaluation

[1397] The server uses an API to collect detailed information about accommodations and work assistance equipment within a specified range. This includes facilities, rates, services offered, operational status, performance, and maintenance status. The server also uses generative AI to collect and analyze reviews and ratings of accommodations and work assistance equipment to calculate an overall rating.

[1398] Visual image generation

[1399] Next, the server uses a generation AI to create visual images of each accommodation and work support equipment, linking them to detailed information. In this process, high-resolution visual previews are created based on the collected image data.

[1400] Using an Emotion Engine

[1401] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1402] Specific usage examples

[1403] Example 1: Optimizing work layout within a factory

[1404] When the operator inputs "Section A of the third manufacturing line" into the robot, the robot sends that information to the server. The server uses the factory equipment management system API to list support robots and equipment within 50 meters. The server collects detailed information on each piece of equipment and uses generative AI to calculate an evaluation of each piece of equipment. Furthermore, it uses generative AI to generate a visual image of the site, and after recognizing the user's emotions using an emotion engine, it suggests the most suitable support equipment.

[1405] Example of a prompt:

[1406] "Here, we need to provide information to optimally position work support robots and equipment operating within a designated factory area. Users will be assisted in selecting the most suitable equipment based on comments and operation history entered using an emotion engine."

[1407] In summary, the present invention aims to significantly improve work efficiency and provide a more comfortable experience for users by making optimal suggestions that take into account the user's feelings when selecting accommodation during business trips and arranging work support equipment within factories.

[1408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1409] Step 1:

[1410] The user logs into the system and enters their planned business trip location or planned work location within the factory. The address and work location information is entered from the user's terminal and sent to the server.

[1411] Input: Address of the planned business trip location or information of the planned work site.

[1412] Output: Input information is sent to the server.

[1413] Step 2:

[1414] The server searches for accommodations or work support equipment within a specified range based on the entered information. This search is performed using the Google Maps API, hotel booking site APIs, and factory equipment management system APIs.

[1415] Input: Address or work location information

[1416] Output: List of accommodations and work support equipment within the specified range.

[1417] Step 3:

[1418] Based on the acquired list, the server collects detailed information about each accommodation and work support equipment. This detailed information includes facilities, rates, services offered, operational status, performance, and maintenance status.

[1419] Input: List of accommodations and work support equipment

[1420] Output: Detailed information on each facility and piece of equipment.

[1421] Step 4:

[1422] The server uses generation AI to collect and analyze internet-wide reviews of each accommodation and work support equipment. This allows it to calculate ratings for each facility and piece of equipment.

[1423] Input: Detailed information and reviews

[1424] Output: Overall evaluation of each facility and piece of equipment.

[1425] Step 5:

[1426] The server uses a generation AI to generate visual images of each accommodation facility and work support equipment. It processes the collected image data to generate high-resolution images and links them to detailed information.

[1427] Input: Image data

[1428] Output: Visual image

[1429] Step 6:

[1430] The server uses an emotion engine to recognize emotions from comments and activity history entered by the user. This allows the server to evaluate what emotions the user is feeling.

[1431] Input: Comments and operation history

[1432] Output: User sentiment rating

[1433] Step 7:

[1434] The server synthesizes this information and suggests the most suitable accommodation and work support equipment to the user. The suggestions also take into account the user's emotional evaluation.

[1435] Input: Detailed information, overall rating, visual image, sentiment rating

[1436] Output: Suggestions for optimal accommodation and work support equipment.

[1437] Step 8:

[1438] The user terminal receives suggested information from the server and displays it to the user. The user reviews detailed information and visual images of the suggested accommodations and work support equipment and makes a final selection.

[1439] Input: Suggestion information from the server

[1440] Output: Information displayed to the user

[1441] Step 9:

[1442] Once the user makes their final selection and decides on a reservation or work order, that information is sent to the server via the user's terminal. The server then works in conjunction with the hotel reservation system or work management system to confirm the final reservation or order.

[1443] Input: User's last selection

[1444] Output: Confirmation of reservation or work order

[1445] Step 10:

[1446] The server sends information about the completion of a reservation or work order to the user's terminal and notifies the user.

[1447] Input: Completion information for reservations or work orders

[1448] Output: User notification

[1449] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1450] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1451] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1452] [Fourth Embodiment]

[1453] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1454] As shown in Figure 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.

[1455] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1456] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1457] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1458] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1459] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1460] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1461] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1462] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[1463] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1464] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1465] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1466] The embodiments for carrying out the present invention will be described in detail below.

[1467] Program processing overview:

[1468] The system of this invention allows users to input the address of their planned business trip destination and then search for, rate, review, and provide visual images of nearby accommodations to suggest the most suitable place to stay.

[1469] Program details:

[1470] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[1471] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[1472] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[1473] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[1474] The server then uses the collected ratings, features, and visual images to suggest the most suitable accommodations to the user. These suggestions are optimized based on the user's conditions and requirements. For example, facilities with high ratings, within the user's budget, and judged to be clean based on their visual images might be selected.

[1475] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[1476] Specific example:

[1477] Example 1: In the case of a business trip to Tokyo

[1478] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1479] 2. The device sends that information to the server.

[1480] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1481] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1482] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1483] 6. The server then uses a generation AI to generate visual images of each facility.

[1484] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[1485] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1486] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[1487] 10. The server sends reservation completion information to the terminal and notifies the user.

[1488] The present invention, in the form described above, is a system that significantly reduces the time and effort required to select accommodation during business trips and efficiently provides users with the most suitable accommodation.

[1489] The following describes the processing flow.

[1490] Step 1:

[1491] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[1492] Step 2:

[1493] The terminal sends the entered address information to the server. The terminal then sends this information to the server as a request.

[1494] Step 3:

[1495] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[1496] Step 4:

[1497] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[1498] Step 5:

[1499] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[1500] Step 6:

[1501] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[1502] Step 7:

[1503] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[1504] Step 8:

[1505] The server determines the best accommodation for the user based on collected ratings, features, and visual images. For example, it selects the best accommodation based on ratings, price, and user preferences.

[1506] Step 9:

[1507] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[1508] Step 10:

[1509] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[1510] Step 11:

[1511] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[1512] Step 12:

[1513] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[1514] Step 13:

[1515] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[1516] Step 14:

[1517] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[1518] (Example 1)

[1519] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1520] In modern business and travel planning, choosing the right accommodation is crucial. However, finding the optimal accommodation from the vast amount of information available online is time-consuming and laborious. Furthermore, the scattered nature of reviews and ratings makes it difficult to gather reliable information in one place. Additionally, the lack of visual information makes it difficult to grasp the actual condition of the facilities.

[1521] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1522] In this invention, the server includes means for inputting address information, means for searching for nearby facilities based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby facilities, means for generating visual information of facilities using a generative AI model, and means for presenting appropriate facilities to the user based on the acquired information and visual information. As a result, users can easily select accommodations based on reliable and detailed information and can also confirm visual images, thereby improving the accuracy and efficiency of their selection.

[1523] "Address information" refers to location information that users enter as their destination for business trips or travel.

[1524] "Nearby facilities" refers to accommodations and other related facilities located within a specific area, which are searched based on the entered address information.

[1525] "Detailed information" refers to information about the specific characteristics and services offered by surrounding facilities, such as equipment, fees, and services.

[1526] "Ratings" refer to scores or ranks assigned to surrounding facilities by users or third parties, indicating the quality of the facilities and the level of satisfaction with the services provided.

[1527] A "review" refers to a written account of user or third-party experiences and feedback regarding nearby facilities.

[1528] A "generative AI model" refers to a model that analyzes and generates data using artificial intelligence technology, and in this invention, it is particularly used for generating visual information of accommodation facilities and for analyzing evaluations and reviews.

[1529] "Visual information" refers to high-resolution images and visual representations of facilities generated using generative AI models.

[1530] "Means of presenting appropriate facilities to users" refers to a function that selects and suggests the most suitable accommodation for the user based on acquired detailed information, ratings, reviews, and visual information.

[1531] The embodiments for carrying out the present invention will be described in detail below.

[1532] The system of this invention allows the user to input the address of their planned business trip destination, search for, evaluate, and collect reviews of accommodations in the surrounding area, generate visual images of the accommodations using a generative AI model, and suggest the most suitable place to stay.

[1533] First, the user logs into the terminal and enters the specific address information of their planned business trip location. For example, the user can enter a specific address such as "Chiyoda-ku, Tokyo." At this point, the address information is entered into the terminal.

[1534] Next, the terminal sends the entered address information to the server. The server receives this address information and uses it to search for nearby facilities. For the search, it uses existing search services such as the Google Maps API or the APIs of accommodation booking sites to list accommodations within a specific range (e.g., within 10km).

[1535] The server uses the API again to collect detailed information about each accommodation based on the acquired list of accommodations. This information includes facilities, rates, and services offered. Next, a generative AI model is used to collect reviews and ratings from across the internet and analyze and calculate an overall rating for each accommodation. Data acquisition from review sites and social media is considered for review collection.

[1536] Furthermore, the server uses a generative AI model to generate visual images of the accommodations. Based on the collected image data, it generates high-resolution visual images and links them to detailed information about each accommodation.

[1537] The server suggests the most suitable accommodation to the user based on collected ratings, features, and visual images. These suggestions are optimized based on the user's conditions and requirements (e.g., budget, cleanliness, high ratings, etc.). For example, facilities with high ratings, within budget, and high cleanliness based on visual images will be selected.

[1538] Finally, the terminal receives suggested information from the server and displays it to the user. The user can review detailed information and visual images of the suggested accommodations and further specify conditions (e.g., room type, price range, etc.). When the user finally selects an accommodation and decides to make a reservation, that information is sent via the terminal to the server, which then works with the accommodation's reservation system to confirm the final reservation. After the reservation is complete, the server sends a reservation confirmation notification to the terminal, notifying the user.

[1539] Specific example

[1540] Example 1: In the case of a business trip to Tokyo

[1541] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1542] 2. The device sends that information to the server.

[1543] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1544] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1545] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1546] 6. The server then uses a generation AI to generate visual images of each facility.

[1547] 7. Based on this information, the server suggests "Hotel A" to the user. For example, it might be a facility selected for reasons such as a rating of 4.5, a price within budget, and high cleanliness.

[1548] 8. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1549] 9. The device sends the information to the server, which then works with the hotel reservation system to complete the reservation.

[1550] 10. The server sends reservation completion information to the terminal and notifies the user.

[1551] An example of a prompt is a sentence that you input to the generating AI depending on a specific destination or condition, and is shown as follows:

[1552] "Please recommend highly-rated accommodations in and around Chiyoda Ward, Tokyo, that are within my budget."

[1553] The above describes the embodiments for carrying out the present invention, which significantly reduces the time and effort required to select accommodations during business trips and makes it possible to efficiently provide users with the most suitable accommodations.

[1554] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1555] Step 1:

[1556] The user logs into the device and enters the address of their planned business trip location.

[1557] Specifically, the user enters their username and password on the terminal's login screen to access the system. Next, they enter the address of their planned business trip location (e.g., "Chiyoda-ku, Tokyo") into the input field.

[1558] Input: User information (username, password), address of the planned business trip location.

[1559] Output: Login authentication result, address information.

[1560] Step 2:

[1561] The terminal sends the entered address information to the server.

[1562] Specifically, the terminal converts the entered address information into an appropriate format (e.g., JSON format) and sends a request to the server.

[1563] Input: Address information.

[1564] Output: Request containing address information.

[1565] Step 3:

[1566] The server searches for accommodations near the planned business trip location.

[1567] Specifically, the server uses the Google Maps API and the APIs of accommodation booking websites to search for accommodations within a specified range (e.g., within 10km) based on the entered address. The search results are then stored in a list format.

[1568] Input: Request including address information.

[1569] Output: List of accommodations.

[1570] Step 4:

[1571] The server collects detailed information about accommodations and analyzes ratings and reviews.

[1572] Specifically, the server uses the acquired list of accommodations to collect detailed information about each facility (such as facilities, prices, and services) via an API. Next, it uses a generative AI model to collect and analyze reviews and ratings from across the internet to calculate an overall rating.

[1573] Input: List of accommodations.

[1574] Output: Detailed information on accommodations, and analysis results of ratings and reviews for each property.

[1575] Step 5:

[1576] The server generates a visual image of the accommodation.

[1577] Specifically, the server uses a generative AI model to generate high-resolution visual images based on the collected image data, and links these images to detailed information about each accommodation.

[1578] Input: Accommodation details and image data.

[1579] Output: Detailed information about the accommodation, including visual images.

[1580] Step 6:

[1581] The server selects and suggests the most suitable accommodation.

[1582] In terms of specific operations, the server considers the user's conditions (budget, rating, cleanliness, etc.) and selects the most suitable accommodation based on collected ratings, features, and visual images. It then generates a recommendation and sends it to the terminal.

[1583] Input: User criteria, detailed information about the accommodation including visual images.

[1584] Output: Suggestions for the most suitable accommodation.

[1585] Step 7:

[1586] The device displays suggestions to the user.

[1587] Specifically, the terminal displays the suggested information received from the server on the user interface, allowing the user to view the details.

[1588] Input: Proposal information.

[1589] Output: Suggestion information displayed in the user interface.

[1590] Step 8:

[1591] The user selects an accommodation and makes a reservation.

[1592] In terms of specific actions, the user reviews the details of the suggested accommodations, considers conditions such as room type and price range, and makes a selection. After making a selection, they confirm the final reservation details on the reservation confirmation screen.

[1593] Input: Suggested information, user selection.

[1594] Output: Final reservation confirmed.

[1595] Step 9:

[1596] The device sends the reservation information to the server, completing the reservation.

[1597] Specifically, the terminal converts the user's reservation confirmation information into the appropriate format and sends it to the server. The server then integrates the received reservation information with the accommodation's reservation system and confirms the reservation.

[1598] Input: Final reservation confirmation information.

[1599] Output: Reservation confirmation information.

[1600] Step 10:

[1601] The server sends a reservation completion notification to the terminal, informing the user.

[1602] Specifically, the server generates reservation completion information and sends it to the terminal. The terminal then displays a reservation completion notification to the user.

[1603] Input: Reservation confirmation information.

[1604] Output: Reservation confirmation.

[1605] (Application Example 1)

[1606] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1607] Currently, finding dining options while on a business trip or staying at a hotel is time-consuming and laborious. Furthermore, the need to check reviews and ratings individually makes it difficult to make the best choice. Choosing dining options in an unfamiliar place is particularly stressful, highlighting the need for a system that efficiently suggests optimal choices.

[1608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1609] In this invention, the server includes means for inputting the address of the planned business trip destination or place of stay; means for searching for nearby dining options based on the input address information; means for collecting detailed information, ratings, and reviews of nearby dining options; means for generating visual images of dining options using a generative AI model; and means for suggesting the most suitable dining option to the user based on the collected information and visual images. This enables the user to efficiently find a highly-rated and appropriate dining option, saving time and effort.

[1610] "Planned business trip location" refers to a place where the user will temporarily stay for business purposes.

[1611] "Place of stay" refers to the address or location where a user will temporarily stay or reside.

[1612] "Address information" refers to information that indicates a specific geographical location, including the city name and street number.

[1613] "Food and beverage options" refer to restaurants and delivery services offered within a specific area.

[1614] "Detailed information" refers to information about food and beverage options, such as facilities, menus, and services.

[1615] "Rating" refers to the overall value or score that users or customers give to food and beverage options.

[1616] A "review" is a written record and publication of a user's experience and opinion regarding a food and beverage option.

[1617] A "generative AI model" is an artificial intelligence algorithm that generates information from text and images.

[1618] "Visual image" refers to the appearance of food and beverage options, as well as visual images and videos of the meals being served.

[1619] "Proposal" refers to the act of presenting suitable options to users based on collected information and AI analysis results.

[1620] "Feedback" refers to responses and answers such as opinions, evaluations, and requests received from users.

[1621] "Additional conditions" refer to specific requests or requirements from the user, which may result in adjustments to the services provided.

[1622] "Overall rating" refers to a general evaluation score calculated based on multiple reviews and evaluation data.

[1623] The embodiments for carrying out the present invention will be described in detail below.

[1624] System Configuration

[1625] The system of the present invention includes the following components:

[1626] 1. User terminal: A device such as a smartphone or tablet.

[1627] 2. Server: A system for cloud-based data management and processing.

[1628] 3. APIs: Google Maps API and APIs for restaurant delivery services

[1629] 4. Generative AI Models: AI algorithms that generate information from text and images.

[1630] Program Processing Overview

[1631] Address input via user terminal

[1632] First, the user logs into their terminal and enters the address of their planned business trip destination or place of stay. The entered address information is then sent from the user's terminal to the server.

[1633] Information retrieval and collection by server

[1634] The server uses the Google Maps API to convert address information into latitude and longitude. Then, it utilizes a restaurant delivery service API to search for dining options around the specified latitude and longitude. The search results include detailed information about the dining options (menu, prices, services, etc.).

[1635] Server-based evaluation and review collection and analysis

[1636] The server uses a generative AI model to collect and analyze reviews from across the internet based on the detailed information of the food and beverage options it has gathered. The generative AI model uses prompts like the following to calculate an overall rating for each food and beverage option.

[1637] Example of a prompt

[1638] Please calculate the overall rating of the restaurant based on the following reviews:

[1639] "The food was delicious, and the service was excellent."

[1640] "Considering the high price, the taste was disappointing."

[1641] "The staff were friendly, and the waiting time was short."

[1642] "The delivery was fast, and the food was still warm."

[1643] Server-based generation of visual images

[1644] Furthermore, the server uses a generative AI model to generate high-resolution visual images based on the collected image data of food and beverage options. These visual images are linked as detailed information.

[1645] Suggestions and feedback for users

[1646] The server suggests optimal food and beverage options to the user based on the collected information and generated visual images. The suggestions are displayed on the user's terminal, allowing the user to review the suggestions, specify further conditions, and provide feedback.

[1647] Hardware and software to be used

[1648] User devices: Smartphones, tablets, etc.

[1649] Server: Cloud-based database system

[1650] APIs: Google Maps API, Restaurant Delivery Service API

[1651] Generative AI models: Natural language processing and image generation models such as the OpenAI API.

[1652] Specific example

[1653] For example, if a user enters "Chiyoda-ku, Tokyo," the process would be as follows:

[1654] 1. Address information is sent from the user's terminal to the server.

[1655] 2. The server uses the Google Maps API to convert the address to latitude and longitude.

[1656] 3. Use a restaurant delivery service API to search for food and beverage options within a specified range (e.g., within 5km).

[1657] 4. The server collects and analyzes reviews from across the internet using a generation AI model and calculates an overall rating.

[1658] 5. Generate visual images using a generative AI model.

[1659] 6. Based on this information, suggest the most suitable dining options to the user.

[1660] Thus, the system of the present invention enables users to efficiently find highly-rated and appropriate dining options.

[1661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1662] Step 1:

[1663] The user logs into the terminal and enters the address of their planned business trip destination or place of stay. This information includes the city name and street number. For example, they might enter "Chiyoda-ku, Tokyo".

[1664] Step 2:

[1665] The terminal sends the entered address information to the server. The transmitted address information is then prepared for processing on the server.

[1666] Step 3:

[1667] The server uses the Google Maps API to convert address information into latitude and longitude. This process converts the entered text-based address information into numerical latitude and longitude coordinates. The output latitude and longitude coordinates are then used in the next step.

[1668] Step 4:

[1669] The server uses the acquired latitude and longitude information to search for food and beverage options within a specified range (e.g., within 5km) using a restaurant delivery service API. The input is latitude and longitude information, and the output is a list of food and beverage options as search results.

[1670] Step 5:

[1671] The server then uses the API to collect detailed information (menu, prices, services, etc.) for each food and beverage option from the search results. In this collection process, the list of search results is input into the API, and a list of detailed information is output.

[1672] Step 6:

[1673] The server uses a generative AI model to collect reviews from across the internet, analyzes them, and calculates an overall rating for each dining option. Specifically, the review content is input to the generative AI model as prompts, and the rating is obtained as output. An example of a prompt used is as follows:

[1674] Please calculate the overall rating of the restaurant based on the following reviews:

[1675] "The food was delicious, and the service was excellent."

[1676] "Considering the high price, the taste was disappointing."

[1677] "The staff were friendly, and the waiting time was short."

[1678] "The delivery was fast, and the food was still warm."

[1679] Step 7:

[1680] The server uses a generative AI model to generate high-resolution visual images based on collected image data. These generated visual images are linked to detailed information about food and beverage options. The input is image data, and the output is the generated high-resolution visual image.

[1681] Step 8:

[1682] The server suggests the most suitable dining options to the user based on collected ratings, characteristics, and visual images. These suggestions are optimized based on the user's conditions and requirements. The input is the collected ratings, characteristics, and visual images, and the output is the suggested optimal dining options.

[1683] Step 9:

[1684] The terminal receives suggestion information from the server and displays it to the user. The user reviews the detailed information and visual images of the suggested food and beverage options and selects their desired menu and order. The input is the suggestion information, and the output is the selected menu and order information.

[1685] Step 10:

[1686] The terminal sends the user's selected order information to the server, which then confirms the order in conjunction with the delivery service system. The server then sends order completion information to the terminal, notifying the user. The input is the order information, and the output is the order completion notification.

[1687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1688] The embodiments for carrying out the present invention will be described in detail below.

[1689] Overview of program processing

[1690] The system of this invention suggests the most suitable accommodation for the user by having them input the address of their planned business trip destination and then searching for, rating, reviewing, and providing visual images of nearby accommodations. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[1691] Program details

[1692] First, the user logs into their device and enters the address of their planned business trip location. For example, they might enter a specific address such as "Chiyoda-ku, Tokyo."

[1693] Next, the terminal sends the entered address information to the server. The server uses this address information to search for accommodations within a specific range (e.g., within 10km). Existing search services, such as the Google Maps API or hotel booking site APIs, are used for the search.

[1694] The server uses the API to collect detailed information about each accommodation based on the acquired list. This detailed information includes the facility's amenities, rates, and services. Then, it uses a generative AI to collect and analyze online reviews and ratings for each accommodation. The generative AI collects data from review sites and social media to calculate an overall rating for each accommodation.

[1695] Furthermore, the server uses a generative AI to generate visual images of the accommodations. In this process, high-resolution visual images are generated based on the collected image data and linked to detailed information about each accommodation.

[1696] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1697] The server then works with an emotion engine to suggest the best accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[1698] Finally, the terminal receives the suggested information from the server and displays it to the user. The user can review the detailed information and visual images of the suggested accommodations and specify further conditions (e.g., room type, price range, etc.). Once the user makes a final selection and decides to book, that information is sent via the terminal to the server, which then works with the hotel's reservation system to confirm the final booking. Once the booking is complete, the server sends a booking confirmation notification to the terminal, notifying the user.

[1699] Specific example

[1700] Example 1: In the case of a business trip to Tokyo

[1701] 1. The user enters "Chiyoda-ku, Tokyo" into the device.

[1702] 2. The device sends that information to the server.

[1703] 3. The server uses the Google Maps API and hotel booking site APIs to list accommodations within a 10km radius.

[1704] 4. The server collects detailed information about each facility. This includes information about equipment, fees, and services offered.

[1705] 5. The server uses a generation AI to collect and analyze reviews from across the internet and calculate an overall rating for each facility.

[1706] 6. The server then uses a generation AI to generate visual images of each facility.

[1707] 7. The server uses an emotion engine to recognize the user's emotions. For example, it analyzes comments the user enters into the system or actions such as repeatedly clicking on facilities that the user is interested in.

[1708] 8. The server combines this information and suggests "Hotel A" to the user. For example, if the hotel has a rating of 4.5, is within the budget, appears clean based on its visual image, and the user is determined to be seeking relaxation.

[1709] 9. The terminal displays suggestions to the user, who then selects the room type, price, etc., and confirms the reservation.

[1710] 10. The device sends that information to the server, which then works with the hotel reservation system to complete the reservation.

[1711] 11. The server sends reservation completion information to the terminal and notifies the user.

[1712] The present invention aims to provide users with a more comfortable experience by offering optimal suggestions for selecting accommodation during business trips, taking into account the user's feelings, thereby significantly reducing time and effort.

[1713] The following describes the processing flow.

[1714] Step 1:

[1715] The user enters the address of their planned business trip location into the device. For example, they might enter "Chiyoda-ku, Tokyo".

[1716] Step 2:

[1717] The terminal sends the entered address information to the server.

[1718] Step 3:

[1719] The server uses specific APIs (such as the Google Maps API or hotel booking site APIs) to search for nearby accommodations based on the entered address. The search range is, for example, within 10km.

[1720] Step 4:

[1721] The server compiles accommodation information obtained from the API into a list format. The list includes basic information such as hotel name, address, price, and facilities.

[1722] Step 5:

[1723] The server makes additional API requests to retrieve more detailed information about the listed accommodations. This information includes amenities, services, and availability.

[1724] Step 6:

[1725] The server uses a generation AI to collect reviews and ratings from across the internet. This is a process that analyzes data from review sites and social media to generate rating points for each accommodation.

[1726] Step 7:

[1727] The server uses a generation AI to generate visual images of the accommodation. Based on the collected image data, it creates high-resolution visual images.

[1728] Step 8:

[1729] The server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1730] Step 9:

[1731] The server works in conjunction with an emotion engine to suggest the most suitable accommodation based on the user's emotions. For example, if the user is feeling stressed, relaxing facilities or highly-rated accommodations will be suggested.

[1732] Step 10:

[1733] The server sends information about the selected accommodation to the terminal. This information includes detailed information about the accommodation, visual images, prices, ratings, etc.

[1734] Step 11:

[1735] The device displays information about suggested accommodations to the user. The user can view detailed information and visual images, and further specify room type and additional conditions.

[1736] Step 12:

[1737] The user selects their final accommodation and confirms the reservation. The user reviews the selected accommodation, room type, price, etc., and confirms the reservation.

[1738] Step 13:

[1739] The terminal sends the user's final selection to the server. The reservation request is sent to the server.

[1740] Step 14:

[1741] The server integrates with the hotel's reservation system to finalize the reservation. Once the reservation is complete, the server sends the reservation confirmation information to the terminal.

[1742] Step 15:

[1743] The device notifies the user that the reservation is complete. The user confirms that the reservation is confirmed and that accommodation for their business trip has been secured.

[1744] Step 16:

[1745] The server uses an emotion engine to evaluate user satisfaction after a reservation. For example, after a reservation, the system prompts the user to input feedback, and then analyzes their emotions based on those comments and their operation history.

[1746] Step 17:

[1747] The server saves the user's emotional state and feedback in the system so that it can be incorporated into future suggestions. This will enable more personalized suggestions tailored to the user's preferences and satisfaction level.

[1748] (Example 2)

[1749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1750] When selecting appropriate accommodation for business trips or vacations, users face the challenge of having to gather and compare a large amount of information, which is time-consuming and laborious. Furthermore, the information provided often does not adequately address the individual feelings and needs of users, making it difficult to choose the best accommodation.

[1751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1752] In this invention, the server includes means for inputting the address of the planned business trip destination, means for searching for nearby accommodations based on the input address information, means for obtaining detailed information, ratings, and reviews of nearby accommodations, means for creating a visual image of the accommodation using generative AI, means for recognizing the user's emotions, and means for suggesting the most suitable place to stay based on the collected information and visual image, as well as the user's emotions. This makes it possible for the user to easily find the most suitable accommodation that suits their emotions and needs.

[1753] "Method for entering the address of a planned business trip location" refers to an interface that allows users to enter the address of a place they plan to visit during a business trip or vacation in text format.

[1754] "Methods for searching for nearby accommodations" refers to systems or algorithms that search for accommodations in the vicinity of a specified address.

[1755] "Means of obtaining detailed information, ratings, and reviews" refers to the function of collecting information such as accommodation overviews, user ratings, and user review comments from the internet.

[1756] "Generative AI" is artificial intelligence that uses machine learning algorithms based on large amounts of data to automatically perform specific tasks.

[1757] "Means for creating visual images of accommodation facilities" refers to systems or programs that generate high-resolution visual images using collected image data.

[1758] "Means of recognizing user emotions" refers to systems and algorithms that analyze and determine a user's emotional state at a given time based on their input data and operation history.

[1759] "A means of suggesting the optimal accommodation" refers to a system that selects and displays the most suitable accommodation for the user based on collected information and the user's sentiments.

[1760] "A means of analyzing reviews across the entire internet using generative AI" refers to a function that uses generative AI to analyze review information existing on the internet and calculate an overall rating.

[1761] Modes for carrying out the invention

[1762] This invention is a system that, by entering the address of a planned business trip destination, searches for, rates, reviews, and provides visual images of nearby accommodations, and suggests the most suitable place to stay. Furthermore, by using an emotion engine that recognizes the user's emotions, it achieves more personalized suggestions.

[1763] The overall system

[1764] This system allows users to enter an address, which is then used to search for nearby accommodations, collecting detailed information, ratings, and reviews for each property. It then uses a generative AI to create visual images of the accommodations and, based on this data, suggests the most suitable place for the user. Furthermore, it uses an emotion engine to analyze the user's emotions and uses that information to provide even more optimal suggestions.

[1765] Hardware and software configuration

[1766] hardware

[1767] User devices: Laptops, desktop PCs, smartphones, tablets, etc.

[1768] Server: High-performance cloud server (e.g., AWS, Google Cloud)

[1769] software

[1770] API services: Google Maps API, hotel booking site APIs (e.g., Booking.com API)

[1771] Generative AI: OpenAI's GPT-3 or a similar generative AI model.

[1772] Emotion engine: Affectiva's Emotion AI, etc.

[1773] System operation

[1774] 1. The user enters the address of their planned business trip location into the terminal interface. For example, they might enter an address such as "Chiyoda-ku, Tokyo".

[1775] 2. The device sends the entered address information to the server as an HTTP request.

[1776] 3. The server uses the entered address information to call the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[1777] 4. The server then uses an API to collect additional detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[1778] 5. The server uses a generative AI (e.g., GPT-3) to collect reviews and ratings from across the network and calculate an overall rating for each facility.

[1779] 6. The server uses a generation AI to generate visual images of the accommodations. Specifically, it generates high-resolution visual images based on the collected image data and links them to information about each facility.

[1780] 7. The server recognizes the user's emotions using an emotion engine (e.g., Affectiva's Emotion AI). It analyzes comments entered by the user into the system, the speed and frequency of their actions, etc., to evaluate the user's emotions.

[1781] 8. The server suggests the most suitable accommodation based on the collected data and visual images, as well as the user's sentiment analysis results. For example, if the user is feeling stressed, it will suggest relaxing facilities or highly-rated accommodations.

[1782] 9. The terminal displays suggested information received from the server to the user. Based on this information, the user can select their final accommodation and further specify conditions (e.g., room type, budget, etc.).

[1783] 10. The user selects the most suitable accommodation and confirms the reservation from their device.

[1784] 11. The device sends the reservation information to the server, and the server works with the hotel's reservation system (e.g., Booking.com API) to finalize the reservation.

[1785] 12. The server sends a reservation completion notification to the terminal and notifies the user.

[1786] Specific example

[1787] Let's take the example of a user entering "Chiyoda-ku, Tokyo". The following are possible prompt messages.

[1788] Example of a prompt:

[1789] "Please conduct an emotionally-based evaluation of accommodations and suggest relaxing hotels near Chiyoda Ward, Tokyo."

[1790] By using the system of this invention, the process of selecting accommodations for business trips and vacations is significantly simplified. Furthermore, by incorporating the user's emotions, it becomes possible to suggest accommodations that better suit individual needs, thereby improving the user experience.

[1791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1792] Step 1:

[1793] The user enters the address of their planned business trip destination into the terminal's interface.

[1794] Specific operation: The user uses the keyboard to enter a specific address, such as "Chiyoda-ku, Tokyo," and clicks the "Search" button on the interface.

[1795] Input: Address of the planned business trip location.

[1796] Output: Data including address information.

[1797] Step 2:

[1798] The terminal sends the entered address information to the server in the form of an HTTP request.

[1799] Specific operation: The device converts the address information into JSON format and sends it to the server's API endpoint as an HTTP POST request.

[1800] Input: Address information in JSON format.

[1801] Output: Request data received by the server.

[1802] Step 3:

[1803] The server uses the received address information to send requests to the Google Maps API and hotel booking site APIs to search for accommodations within a 10km radius.

[1804] Specific operation: The server sends requests containing address information to the Google Maps API and hotel booking site APIs and receives search results.

[1805] Input: Address information.

[1806] Output: List of accommodations.

[1807] Step 4:

[1808] The server uses an API to collect detailed information (facilities, rates, services, etc.) for each facility included in the acquired list of accommodations.

[1809] Specific operation: The server calls the API for each accommodation facility, retrieves detailed information, and saves it to the database.

[1810] Input: List of accommodations.

[1811] Output: Detailed information on each facility.

[1812] Step 5:

[1813] The server uses AI generation to collect reviews and ratings from across the internet and calculates an overall rating for each facility.

[1814] Specific operation: The server collects review information using web scraping tools and APIs, and analyzes it using a generative AI model.

[1815] Input: URLs and review site information for each facility.

[1816] Output: Overall evaluation data for each facility.

[1817] Step 6:

[1818] The server uses a generation AI to create visual images of the accommodation facilities.

[1819] Specific operation: The server uses collected image data to generate high-resolution visual images using AI and saves them to a database.

[1820] Input: Image data for each facility.

[1821] Output: High-resolution visual image.

[1822] Step 7:

[1823] The server uses an emotion engine to recognize the user's emotions.

[1824] Specific operation: The server inputs the user's past activity history and comments into the sentiment engine and receives the analysis results.

[1825] Input: User's operation history and comment data.

[1826] Output: User sentiment data.

[1827] Step 8:

[1828] The server suggests the most suitable accommodation based on the collected data, visual images, and user sentiment analysis results.

[1829] Specific operation: The server aggregates all data, uses an algorithm to select the most suitable accommodation, and creates a recommendation list.

[1830] Input: Accommodation details, overall rating data, visual images, and user sentiment data.

[1831] Output: Recommended accommodation list.

[1832] Step 9:

[1833] The terminal displays the suggested information received from the server to the user.

[1834] Specific operation: The terminal displays a recommendation list in a GUI, allowing the user to specify additional conditions.

[1835] Input: List of recommended accommodations.

[1836] Output: Data display that the user can view.

[1837] Step 10:

[1838] Users select the most suitable accommodation and confirm their reservation from their device.

[1839] Specific operation: The user selects an accommodation from the recommendation list, sets the booking details (room type, budget, etc.), and clicks the "Book Now" button.

[1840] Input: User-selected accommodation information and reservation details.

[1841] Output: Booking confirmation request data.

[1842] Step 11:

[1843] The terminal sends the reservation information to the server, which then works with the hotel's reservation system to finalize the reservation.

[1844] Specific operation: The device sends the reservation information to the server in JSON format, and the server calls the hotel reservation system's API to confirm the reservation.

[1845] Input: Booking confirmation request data.

[1846] Output: Confirmed reservation information sent to the hotel reservation system.

[1847] Step 12:

[1848] The server sends a reservation completion notification to the terminal, informing the user.

[1849] Specific operation: The server sends a reservation completion notification to the terminal as an HTTP response, and the terminal notifies the user via a pop-up or email.

[1850] Input: Confirmed reservation information.

[1851] Output: Reservation complete notification.

[1852] (Application Example 2)

[1853] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1854] Conventional work support systems struggle to quickly and efficiently optimize equipment placement within a specific work area. Furthermore, they often fail to consider user emotions, potentially leading to user stress and decreased work efficiency. Additionally, the lack of visual confirmation of the work environment makes it time-consuming and cumbersome to grasp the actual situation.

[1855] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting the address of the planned business trip location, means for searching for nearby accommodations based on the input address information, means for collecting detailed information, ratings, and reviews of nearby accommodations, means for generating a visual image of the accommodation using a generation AI, means for suggesting the most suitable accommodation to the user based on the collected information and visual image, means for inputting the planned work location, means for searching for work support equipment within a specific range based on the input information, means for collecting detailed information, ratings, and reviews of work support equipment within a specific range, and means for recognizing the user's emotions using an emotion engine and suggesting the most suitable work support equipment based on that. As a result, optimal equipment placement within the work area can be performed quickly and efficiently, work support that takes into account the user's emotions can be provided, and work efficiency can be improved and stress can be reduced.

[1856] "Means for entering the address of a planned business trip location" refers to an interface for users to input the address of a specific planned business trip location into the system.

[1857] "A means of searching for nearby accommodations based on entered address information" refers to a function that searches for accommodations located in the vicinity of an entered address.

[1858] "Means for collecting detailed information, ratings, and reviews of nearby accommodations" refers to the function of collecting information on facilities, rates, services, and user ratings and reviews related to the searched accommodations.

[1859] "Method for generating visual images of accommodations using generative AI" refers to a function that utilizes generative AI to generate high-resolution visual images of accommodations.

[1860] "A means of suggesting the most suitable accommodation to users based on collected information and visual images" refers to a function that suggests the most suitable accommodation to users based on collected information and generated visual images.

[1861] "Means for inputting work locations" refers to an interface for users to input information about specific work locations.

[1862] "A means of searching for work support equipment within a specific range based on input information" refers to a function that searches for work support equipment located in the vicinity of a planned work location based on the input information about that location.

[1863] "Means for collecting detailed information, evaluations, and reviews of work assistance equipment within a specific range" refers to a function for collecting detailed information, performance evaluations, and reviews of searched work assistance equipment.

[1864] "A means of recognizing the user's emotions using an emotion engine and proposing the most suitable work support equipment based on those emotions" refers to a function that uses an emotion engine to analyze the user's emotions and then proposes the most suitable work support equipment based on the results.

[1865] Specific embodiments for carrying out the present invention are described in detail below. This system allows users to input the address of their planned business trip destination, search for, evaluate, and review nearby accommodations, and provides visual images to suggest the most suitable place to stay. The same method can also be applied to work arrangements within a factory.

[1866] Overview of program processing

[1867] This system takes the address of a planned business trip location or planned work location within a factory as input, and based on that, it searches, evaluates, and reviews nearby accommodations and work support equipment, and provides visual images to offer the most suitable suggestions to the user. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it achieves even more personalized suggestions.

[1868] Hardware and software to be used

[1869] Hardware: Autonomous mobile robots, control terminals in the factory (PCs, tablets), user terminals (smartphones)

[1870] Software: Google Maps API, hotel booking site API, factory equipment management system API, generative AI (e.g., OpenAI GPT-4), emotion engine (Emotion AI)

[1871] System Processing Procedure

[1872] The user logs into the system and enters their planned business trip location or planned work location within the factory. The entered information is sent to the server, which uses this information to search for accommodations and work support equipment within the specified area.

[1873] Search and evaluation

[1874] The server uses an API to collect detailed information about accommodations and work assistance equipment within a specified range. This includes facilities, rates, services offered, operational status, performance, and maintenance status. The server also uses generative AI to collect and analyze reviews and ratings of accommodations and work assistance equipment to calculate an overall rating.

[1875] Visual image generation

[1876] Next, the server uses a generation AI to create visual images of each accommodation and work support equipment, linking them to detailed information. In this process, high-resolution visual previews are created based on the collected image data.

[1877] Using an Emotion Engine

[1878] Furthermore, the server uses an emotion engine to recognize emotions from the user's input and action history. The emotion engine analyzes the user's comments, the speed and frequency of actions, etc., and evaluates what emotions the user is feeling.

[1879] Specific usage examples

[1880] Example 1: Optimizing work layout within a factory

[1881] When the operator inputs "Section A of the third manufacturing line" into the robot, the robot sends that information to the server. The server uses the factory equipment management system API to list support robots and equipment within 50 meters. The server collects detailed information on each piece of equipment and uses generative AI to calculate an evaluation of each piece of equipment. Furthermore, it uses generative AI to generate a visual image of the site, and after recognizing the user's emotions using an emotion engine, it suggests the most suitable support equipment.

[1882] Example of a prompt:

[1883] "Here, we need to provide information to optimally position work support robots and equipment operating within a designated factory area. Users will be assisted in selecting the most suitable equipment based on comments and operation history entered using an emotion engine."

[1884] In summary, the present invention aims to significantly improve work efficiency and provide a more comfortable experience for users by making optimal suggestions that take into account the user's feelings when selecting accommodation during business trips and arranging work support equipment within factories.

[1885] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1886] Step 1:

[1887] The user logs into the system and enters their planned business trip location or planned work location within the factory. The address and work location information is entered from the user's terminal and sent to the server.

[1888] Input: Address of the planned business trip location or information of the planned work site.

[1889] Output: Input information is sent to the server.

[1890] Step 2:

[1891] The server searches for accommodations or work support equipment within a specified range based on the entered information. This search is performed using the Google Maps API, hotel booking site APIs, and factory equipment management system APIs.

[1892] Input: Address or work location information

[1893] Output: List of accommodations and work support equipment within the specified range.

[1894] Step 3:

[1895] Based on the acquired list, the server collects detailed information about each accommodation and work support equipment. This detailed information includes facilities, rates, services offered, operational status, performance, and maintenance status.

[1896] Input: List of accommodations and work support equipment

[1897] Output: Detailed information on each facility and piece of equipment.

[1898] Step 4:

[1899] The server uses generation AI to collect and analyze internet-wide reviews of each accommodation and work support equipment. This allows it to calculate ratings for each facility and piece of equipment.

[1900] Input: Detailed information and reviews

[1901] Output: Overall evaluation of each facility and piece of equipment.

[1902] Step 5:

[1903] The server uses a generation AI to generate visual images of each accommodation facility and work support equipment. It processes the collected image data to generate high-resolution images and links them to detailed information.

[1904] Input: Image data

[1905] Output: Visual image

[1906] Step 6:

[1907] The server uses an emotion engine to recognize emotions from comments and activity history entered by the user. This allows the server to evaluate what emotions the user is feeling.

[1908] Input: Comments and operation history

[1909] Output: User sentiment rating

[1910] Step 7:

[1911] The server synthesizes this information and suggests the most suitable accommodation and work support equipment to the user. The suggestions also take into account the user's emotional evaluation.

[1912] Input: Detailed information, overall rating, visual image, sentiment rating

[1913] Output: Suggestions for optimal accommodation and work support equipment.

[1914] Step 8:

[1915] The user terminal receives suggested information from the server and displays it to the user. The user reviews detailed information and visual images of the suggested accommodations and work support equipment and makes a final selection.

[1916] Input: Suggestion information from the server

[1917] Output: Information displayed to the user

[1918] Step 9:

[1919] Once the user makes their final selection and decides on a reservation or work order, that information is sent to the server via the user's terminal. The server then works in conjunction with the hotel reservation system or work management system to confirm the final reservation or order.

[1920] Input: User's last selection

[1921] Output: Confirmation of reservation or work order

[1922] Step 10:

[1923] The server sends information about the completion of a reservation or work order to the user's terminal and notifies the user.

[1924] Input: Completion information for reservations or work orders

[1925] Output: User notification

[1926] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1927] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1928] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1929] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1930] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1931] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1932] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1933] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1934] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1935] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1936] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1937] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1938] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1939] 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.

[1940] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1941] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1942] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1943] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1944] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1945] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of ...

Claims

1. A way to enter the address of the planned business trip location, A method for searching for nearby accommodations based on the entered address information, Means for collecting detailed information, ratings, and reviews of nearby accommodations, A means of generating visual images of accommodations using generation AI, A means of suggesting the most suitable accommodation to users based on collected information and visual images, A system that includes this.

2. The system according to claim 1, further comprising means for obtaining user feedback and customizing the selection of accommodations based on additional conditions.

3. The system according to claim 1, including a generating AI that analyzes online reviews in order to calculate an overall rating for each accommodation facility.

Images