System

Abstract

A system is provided.SOLUTION: This system is provided with a means for acquiring the position information of a user, a means for acquiring the schedule information of the user, a means for acquiring weather forecast and traffic information, a means for generating an action plan optimal to the user by performing analysis based on the acquired information, and a means for displaying the generated action plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, where we are bombarded with information, it is difficult for users to quickly and efficiently obtain the most useful information at any given time. Similarly, it is not easy to optimize daily plans by appropriately utilizing external data such as weather forecasts and traffic information. Furthermore, there is a lack of systems that provide greater convenience by automatically adjusting the information obtained based on the user's situation and mood. Therefore, there is a need for a system that provides users with the information they truly need in a timely manner and efficiently supports their daily lives. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means. First, the system includes a means for acquiring user location information, a means for acquiring user schedule information, and a means for acquiring external data such as weather forecasts and traffic information. Next, the system includes a means for analyzing the acquired information and generating an optimal action plan for the user. The system also includes a means for displaying the generated action plan to the user. Furthermore, the system includes a means for acquiring feedback from the user, analyzing the feedback, and improving the accuracy of future action plan generation, as well as a means for changing the displayed information and action plan depending on the user's mood and situation. This allows the user to obtain the most useful information at any given time, and the system evolves based on user feedback, making it possible to improve the efficiency and convenience of everyday life.

[0006] "User" refers to the individual who wears and uses this system.

[0007] "Location information" is data that indicates a user's current geographic location.

[0008] "Schedule information" is data that indicates the user's plans and plans.

[0009] "Weather forecast" refers to information that predicts future weather.

[0010] "Traffic information" refers to information showing current traffic conditions and forecasts.

[0011] "Analysis" is the process of performing calculations and comparisons based on acquired information to arrive at a certain conclusion or result.

[0012] An "action plan" is a proposal that instructs the user on specific actions and preparations to be taken.

[0013] "Feedback" refers to information such as usage status and opinions that users provide to the system.

[0014] "Accuracy" refers to the accuracy and reliability of the action plans and information provided by the system.

[0015] "Mood" refers to the user's emotional and mental state at any given time.

[0016] "Situation" refers to the environment or conditions in which the user finds himself or herself. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. To implement this system, the following steps can be taken.

[0039] 1. Collection of User Information

[0040] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, it obtains the user's schedule information from a calendar app or similar. This information is then sent to the server.

[0041] 2. Acquiring external data

[0042] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0043] 3. Data integration and analysis

[0044] The server then analyzes the collected location information, schedule information, weather forecasts, and traffic information, and generates an optimal action plan for the user based on this analysis.

[0045] 4. Generating Recommendations

[0046] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation such as "Bring an umbrella." It may also make suggestions based on traffic information, such as "Get on the train earlier."

[0047] 5. Displaying Recommendations

[0048] The generated recommendations are then sent from the server to the device (glasses), which visually displays this information to the user, who can then review the displayed recommendations and make decisions based on them.

[0049] 6. Collecting User Feedback

[0050] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0051] 7. Recommendation optimization

[0052] The server analyzes the feedback and learns to improve the accuracy of future recommendations, allowing the system to continually provide the best information for users.

[0053] Specific examples

[0054] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home) and then checks the user's schedule (work) for the day. The server obtains the weather forecast and confirms that it will rain that day. Based on traffic information, the server also determines that the user needs to catch the train earlier than usual.

[0055] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella" or "Traffic may be congested, so you should leave earlier than usual." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0056] After arriving at work, users can use the glasses' "consider" feature to provide feedback, which the server analyzes and uses to improve future recommendations.

[0057] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0061] Step 2:

[0062] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0063] Step 3:

[0064] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0065] Step 4:

[0066] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0067] Step 5:

[0068] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0069] Step 6:

[0070] The server analyzes the location information, schedule information, weather forecast, and traffic information it receives, and generates an optimal action plan for the user based on this analysis.

[0071] Step 7:

[0072] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain, the system may suggest "take an umbrella," or based on traffic information, "get on the train earlier."

[0073] Step 8:

[0074] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0075] Step 9:

[0076] The user reviews the displayed recommendations and selects or adjusts an action based on them, for example, taking a specific action such as carrying an umbrella or changing the train time.

[0077] Step 10:

[0078] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0079] Step 11:

[0080] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. Based on the feedback information, it updates the model to improve the accuracy of future recommendations.

[0081] Step 12:

[0082] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] Conventional systems are insufficient in optimizing users' daily life action plans and rarely provide specific recommendations tailored to specific situations. Furthermore, they lack the technology to effectively utilize feedback to improve the accuracy of future action plans. This reduces user convenience and limits the value of the system.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for the user to input feedback on the action plan provided, and means for analyzing using a machine learning model to improve the accuracy of subsequent action plans based on the feedback. This makes it possible to optimize the user's daily life action plan and provide recommendations tailored to specific situations. Furthermore, by utilizing the feedback, the accuracy of subsequent action plans is improved, improving user convenience.

[0088] "Means for obtaining user location information" refers to devices or technologies that use a GPS module to identify the user's current geographical location.

[0089] "Means for acquiring user schedule information" refers to devices or technologies that acquire a user's plans and schedules from a calendar app or other time management software.

[0090] "Means for obtaining weather forecasts and traffic information" refers to devices and technologies that use external APIs and databases to obtain current and future weather and traffic information.

[0091] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a set of software and algorithms that integrates the collected user location information, schedule information, weather forecast, and traffic information, analyzes this data, and generates an optimal action plan.

[0092] The "means for displaying the generated action plan to the user" refers to a display or a display device for presenting the generated action plan to the user.

[0093] The "means for the user to input feedback on the provided action plan" refers to an interface or device that allows the user to evaluate the usefulness and suitability of the received action plan and input feedback.

[0094] The "means for performing analysis using a machine learning model to improve the accuracy of subsequent action plans based on the feedback" refers to software and technology that analyzes feedback data collected from users and uses machine learning algorithms to improve the accuracy of generating subsequent action plans.

[0095] The system of the present invention supports the user's daily life and provides optimal information through a glasses-type terminal worn by the user. The system of the present invention can be implemented according to the following procedure.

[0096] First, the device (glasses) collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which is used to obtain the user's current location. The device also connects to a calendar app to obtain the user's schedule information. This information is then sent to the server in real time.

[0097] The server obtains weather forecasts and traffic information using external APIs. Specifically, it uses OpenWeatherMap as the weather forecast API and Google Maps API as the traffic information API. This information is used to optimize the user's daily activities.

[0098] The server then integrates and analyzes the user's location, schedule, weather forecast, and traffic information. Generative AI models and machine learning algorithms are used in the analysis to generate an optimal action plan for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation that the user should "bring an umbrella," and based on traffic information, it will make a suggestion that the user should "board the train earlier."

[0099] The generated recommendations are sent from the server to the device (glasses), which then visually displays them to the user. The user can decide what to do based on the displayed recommendations. At the same time, the user can also enter feedback after taking action. The feedback is sent to the server via the device, and the server analyzes it to improve the accuracy of future recommendations.

[0100] As a concrete example, consider the case where a user puts on glasses before going to work in the morning. The device obtains the user's current location (home) and checks the user's schedule for the day (work). The server obtains the weather forecast, confirms that the weather for that day will be rainy, and then obtains traffic information to determine that the user should leave earlier than usual. The server generates recommendations such as "It's going to rain today, so you should take an umbrella" and "You should leave earlier than usual," and sends these to the device to display on the glasses' display. After arriving at work, the user enters feedback using the "Consider" function on the glasses, and the server analyzes this feedback and updates the machine learning model to improve the accuracy of the next recommendation.

[0101] An example of a prompt sentence could be, "Please obtain the user's current location and schedule information, and generate an optimal plan of action based on the weather forecast and traffic information. For example, could you suggest taking an umbrella if it's raining, or leaving earlier if traffic is heavy, thereby optimizing the user's daily activities?"

[0102] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information according to individual circumstances.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1: Collect user information

[0105] The device uses a GPS module to obtain the user's current location. It receives GPS data as input and generates the user's geographical location information as output. Next, the device connects to a database such as a calendar app to obtain the user's schedule information. Here, the input is data from the calendar app, and the output is the schedule information for that day. This collected information is sent to the server in real time.

[0106] Step 2: Retrieving external data

[0107] The server uses external APIs to obtain weather and traffic information. It inputs weather data from a weather API (e.g., OpenWeatherMap) and outputs weather forecast information. Similarly, it inputs traffic data from a traffic information API (e.g., Google Maps API) and outputs traffic information. These data are stored for subsequent analysis.

[0108] Step 3: Data synthesis and analysis

[0109] The server integrates the user's location information, schedule information, weather forecast, and traffic information it has acquired. It takes in various pieces of information (location information, schedule information, weather forecast, traffic information) as input and generates integrated data. The server then performs analysis based on this integrated data. Generative AI models and machine learning algorithms are used for the analysis, and the analysis results are used as output to generate an optimal action plan.

[0110] Step 4: Generate recommendations

[0111] The server generates recommendations to provide to the user based on the analysis results. It takes the analysis results as input and outputs suggestions (recommendations) for optimizing the user's daily activities. For example, if it is raining, it may generate specific suggestions such as "take an umbrella" or "leave earlier" if traffic is congested.

[0112] Step 5: View recommendations

[0113] The server generates recommendations and sends them to the device. Recommendation data is taken as input and sent to the device as output. The device then visually displays this information to the user. Specifically, the suggestions are displayed on the glasses' display for the user to review.

[0114] Step 6: Gather user feedback

[0115] After the user completes an action based on the provided recommendation, they enter feedback. The user's evaluation data is taken as input, and the feedback information is sent from the device to the server as output. The glasses' "Consider" function makes it easy to enter feedback.

[0116] Step 7: Optimize your recommendations

[0117] The server analyzes the collected feedback. It takes the feedback data as input and outputs the analysis results to improve the accuracy of future recommendations. The server uses a machine learning algorithm to update the model based on the feedback, optimizing future recommendations to be more accurate. This continuous feedback loop allows the entire system to provide a higher level of convenience.

[0118] (Application example 1)

[0119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] Conventional behavioral planning systems based on location and schedule information have been effective in supporting users' daily lives. However, they lack the ability to provide specific information to improve the shopping experience in brick-and-mortar stores, such as sale and promotion information in real time. They also lack recommendation functions that take into account in-store special events and inventory information. A system that can address these shortcomings and further improve users' shopping experience is needed.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0122] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring store sales, promotions, and inventory information, and means for generating an action plan based on the sales, promotions, and inventory information. This allows the user to receive optimal action plans in real time that take into account sales and promotion information and store inventory status. Special event and sale information is also notified in real time, further enhancing the user's shopping experience.

[0123] "Means for obtaining user location information" refers to a device for obtaining the geographical location of the user's current location using GPS or other location identification technology.

[0124] The "means for acquiring user schedule information" is a device that has the function of acquiring the user's plans and dates from a calendar application or schedule management system.

[0125] "Means for obtaining weather forecasts and traffic information" refers to means for obtaining information about weather forecasts and traffic conditions from external APIs or data sources.

[0126] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or algorithm for analyzing data such as location information, schedule information, weather forecasts, and traffic information, and automatically generating an optimal action plan to provide to the user.

[0127] The "means for displaying the generated action plan to the user" refers to a display device or interface for visually showing the generated action plan to the user.

[0128] "Means for obtaining in-store sales information, promotions, and inventory information" refers to systems and database access means for obtaining special sales information, promotions, and product inventory information in physical stores.

[0129] The "means for generating an action plan based on the sale information, promotion, and inventory information" refers to a system or algorithm for automatically generating an optimal action plan for the user based on the acquired sale information, promotion information, and inventory information.

[0130] The "means for obtaining feedback from users" refers to an interface or input device for collecting opinions and evaluations provided by users.

[0131] The "means for analyzing the feedback and improving the accuracy of subsequent action plan generation" refers to a system or method for analyzing collected feedback and improving the algorithm for generating subsequent recommendations and action plans.

[0132] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to a function or system that takes into account the user's current mood and situation and flexibly changes the displayed information and generated action plans based on that.

[0133] "Means of obtaining information about special events and sales in real time and notifying users at the optimal time" refers to a notification system or interface that obtains information about special events and sales taking place in physical stores in real time and allows users to receive that information at the optimal time.

[0134] The system of the present invention is designed to improve the shopping experience of users in physical stores. Specific embodiments for implementing this system are as follows.

[0135] Collection of User Information

[0136] First, when a user wears smart glasses, the device (smart glasses) collects the user's location information. Specifically, it uses a GPS module to obtain the user's current geographical location. It also obtains the user's schedule information from a calendar application. This allows the user's schedule and shopping list to be displayed.

[0137] Retrieving External Data

[0138] The server then retrieves weather and traffic information from an external API. The weather forecast includes today's weather and temperature, and the traffic information includes current congestion and traffic incidents. It also retrieves in-store sales, promotions, and inventory information. This includes a list of special offers, current promotions, and the stock status of each item.

[0139] Data integration and analysis

[0140] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, sale information, promotion information, and inventory information. This generates an optimal action plan for the user. For example, the server may guide the user to a location where products are on sale, or recommend products based on promotion information.

[0141] Generating recommendations

[0142] Based on the analysis results, the server generates recommendations appropriate for the user. For example, it generates information such as "refrigerated food is low in stock, so it is recommended to purchase early" or "there are currently promotional items." It also takes into account information about busy cash registers to recommend the most suitable cash register.

[0143] Viewing Recommendations

[0144] The generated recommendations are sent from the server to the smart glasses, where users can visually check the information and make optimal purchasing decisions. They are also notified of special events and sales information in stores in real time.

[0145] User feedback collection and optimization

[0146] After completing their shopping, the user provides feedback through the smart glasses, including information on whether the provided action plan and recommendations were useful. The server analyzes this feedback and improves the analysis algorithm to improve the accuracy of future action plan generation.

[0147] Hardware and Software Used

[0148] Hardware: Smart glasses (with GPS module)

[0149] Software: Python, external API (weather forecast, traffic information)

[0150] Specific examples

[0151] For example, the following prompts can be used as input to a generative AI model to optimize recommendations:

[0152] Example prompt sentence:

[0153] If the current weather is rainy and traffic is heavy, generate an appropriate plan of action for the user, taking into account the user's location, the travel time from home to the nearest station, and their schedule for the day.

[0154] In this way, the system of the present invention can efficiently support the user's daily life and shopping experience and provide optimal information.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1: Collect user information

[0157] The device first obtains the user's location information. The input is the user's current geographic location, and data is collected using a GPS module. The output is the user's precise current location. Next, the device obtains the user's schedule information from a calendar application. This input data includes the user's appointments and shopping list. The output is the user's specific schedule information.

[0158] Step 2: Retrieving external data

[0159] The server retrieves weather forecast information using an external API. The input is a request to the API that provides the weather forecast, and the output is the weather information returned by the API. Similarly, the server retrieves traffic information from an external API. The input is a request to the API that provides traffic information, and the output is information about current traffic conditions. The server also retrieves in-store sales, promotions, and inventory information. The input is a query to the store's database, and the output is the relevant sales, promotions, and inventory information.

[0160] Step 3: Data synthesis and analysis

[0161] The server integrates and analyzes all collected location information, schedule information, weather forecasts, traffic information, sale information, promotion information, and inventory information. The input data is each of the above information, which is processed by the data analysis algorithm on the server. The output is a specific action plan. This plan includes guidance to products on sale or promotion, and the optimal transportation method taking into account current traffic conditions.

[0162] Step 4: Generate recommendations

[0163] The server generates an action plan appropriate for the user based on the analysis results. The input is the analyzed data, and the output is a specific recommendation. For example, it may include recommendations such as "Here are the items currently on promotion" or "The cash register is busy, so you should use another one."

[0164] Step 5: View recommendations

[0165] The server sends the generated recommendations to the user's device. The input is the generated action plan, and the output is the specific recommendations displayed on the device. The user can visually check this information through the smart glasses and optimize their shopping behavior.

[0166] Step 6: Gather user feedback

[0167] After completing their shopping, the user provides feedback through the terminal. The input is feedback data from the user, including their evaluation of the usefulness of the recommendations. The output is feedback information sent to the server.

[0168] Step 7: Feedback analysis and optimization

[0169] The server analyzes user feedback and learns to improve the accuracy of future action plan generation. The input is the collected feedback data, which is processed by a data analysis algorithm. The output is an optimized action plan generation algorithm. This allows the system to continuously improve and provide more useful information to users.

[0170] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0171] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. Furthermore, by combining it with an emotion engine, it is possible to make recommendations that take into account the user's emotional state. An embodiment of this system is described below.

[0172] 1. Collection of User Information

[0173] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, the device obtains the user's schedule information from a calendar app or similar. This information is sent to the server in real time.

[0174] 2. Acquiring external data

[0175] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0176] 3. Use of Emotion Engine

[0177] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0178] 4. Data integration and analysis

[0179] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information, and generates an optimal action plan for the user based on the analysis results.

[0180] 5. Generating Recommendations

[0181] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain and the user feels tired, the server will generate recommendations such as "You should bring an umbrella" or "You should take a break at a cafe to refresh yourself."

[0182] 6. Displaying Recommendations

[0183] The generated recommendations are sent from the server to the device (glasses), which visually displays this information to the user. The user then checks the displayed recommendations and decides what to do based on them.

[0184] 7. Collecting User Feedback

[0185] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0186] 8. Recommendation optimization

[0187] The server analyzes the feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine to improve the accuracy of future recommendations.

[0188] Specific examples

[0189] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home), then checks the schedule for the day (work). Furthermore, the emotion engine recognizes the user's mood as "tired." The server obtains the weather forecast and confirms that it will rain that day. It also determines, based on traffic information, that the user needs to catch the train earlier than usual.

[0190] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic may be congested, so you should leave earlier than usual," or "You're tired, so you should take a short break at a cafe in front of the station." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0191] After arriving at work, users can use the glasses' "Consider" function to input feedback, such as "I brought an umbrella," "I changed my train time," or "I took a break at a cafe." The server receives and analyzes this feedback and uses it to improve the accuracy of future recommendations.

[0192] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information. Furthermore, by using the emotion engine, it is possible to provide personalized advice according to the user's mood and situation.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0196] Step 2:

[0197] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0198] Step 3:

[0199] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0200] Step 4:

[0201] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0202] Step 5:

[0203] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0204] Step 6:

[0205] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0206] Step 7:

[0207] The server analyzes the location information, schedule information, weather forecast, traffic information, and emotion information it receives, and generates an optimal action plan for the user based on this analysis.

[0208] Step 8:

[0209] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain and the user is feeling tired, the server will make recommendations such as "You should take an umbrella" or "You should take a break at a cafe to refresh yourself."

[0210] Step 9:

[0211] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0212] Step 10:

[0213] The user reviews the displayed recommendations and selects or adjusts an action based on them, such as taking an umbrella, changing the train time, or taking a break at a cafe.

[0214] Step 11:

[0215] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0216] Step 12:

[0217] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine, updating the model to improve the accuracy of future recommendations.

[0218] Step 13:

[0219] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0220] Example 2

[0221] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0222] Conventional information provision systems can provide recommendations based on the user's location and schedule information, but they do not adequately provide personalized information or generate action plans that take the user's emotional state into account.In addition, their functionality for utilizing feedback to improve accuracy in future visits was limited, making it difficult to provide optimal information for the user.

[0223] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0224] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for recognizing the user's emotional state and acquiring emotional information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring feedback from the user, means for analyzing the feedback and improving the accuracy of generating subsequent action plans, and means for changing the displayed information and action plan according to the user's mood and situation, thereby enabling the provision of optimal information and the generation of an action plan taking into account the user's emotional state and feedback.

[0225] "Means for acquiring user location information" refers to hardware or software for identifying the user's current location and collecting that information.

[0226] "Means for acquiring user schedule information" refers to a system or application for acquiring schedule and calendar information entered by a user.

[0227] "Means for obtaining weather and traffic information" means software or systems for collecting weather and traffic conditions from external APIs or data sources.

[0228] "Means for recognizing a user's emotional state and acquiring emotional information" refers to hardware and software for analyzing a user's facial expressions, voice, and other biometric signals to identify their emotional state.

[0229] "Means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or program that integrates collected data and uses algorithms or machine learning models to create an optimal action plan.

[0230] The "means for displaying the generated action plan to the user" refers to a display device such as a display or a head-mounted display for visually presenting the generated action plan to the user.

[0231] "Means for obtaining user feedback" refers to hardware or software for inputting user-provided ratings and opinions.

[0232] "Means for analyzing the feedback and improving the accuracy of future action plans" refers to algorithms or machine learning models that analyze the obtained feedback data and improve the quality of future action plans.

[0233] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to software and systems for dynamically adjusting the information provided and action plans based on the user's current situation and emotions.

[0234] The system of the present invention supports users' daily lives and provides optimal information through a glasses-type device worn by the user. This system includes functions for providing recommendations based on the user's current location, schedule information, external weather forecasts and traffic information, and the user's emotional state.

[0235] Collection of User Information

[0236] First, the device collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which acquires the user's current location. The device also acquires the user's schedule information from a calendar app. For example, if the user has entered a schedule in their calendar that says "I have a meeting in the office at 9 o'clock," this information is acquired. All collected information is sent to the server in real time.

[0237] Retrieving External Data

[0238] The server then uses external APIs to obtain weather and traffic information. The server calls a specific weather API (e.g., OpenWeatherMap API) to obtain weather forecast data based on the user's current location. For traffic information, the server uses Google Maps API to collect delay and congestion information. This provides data to optimize the user's daily activities.

[0239] Using the Emotion Engine

[0240] The emotion engine installed in the device analyzes the user's facial expressions and voice to recognize the user's current emotional state. The device's camera and microphone capture the user's facial expressions and voice, and this data is analyzed by the emotion engine. For example, if the user feels "tired," the emotion is recognized from the user's facial expressions and tone of voice. The recognized emotion information is sent to the server.

[0241] Generating and displaying recommendations

[0242] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information. The server uses data analysis tools such as Python and TensorFlow. Based on the analysis results, an optimal action plan is generated for the user. For example, recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic will be congested, so you should leave early," or "You're tired, so you should take a break at a cafe" may be generated. This information is sent from the server to the device and displayed on the display of the glasses device.

[0243] Collecting and incorporating user feedback

[0244] After completing an action, the user enters feedback using the "Consider" function on the glasses device. For example, they can enter information such as "I took an umbrella," "I changed my train time," or "I took a break at a cafe." This feedback information is sent to the server, which analyzes it and improves the accuracy of future recommendations. The server learns the user's behavioral patterns and preferences, and also integrates emotional information from the emotion engine to further optimize future recommendations.

[0245] Examples of prompt statements

[0246] Below are examples of prompts used within the system.

[0247] Example prompt for getting weather forecast information:

[0248] "Please fetch the current weather forecast for the user's location."

[0249] Example prompts for using the Emotion Engine:

[0250] "Analyze the user's current emotional state based on their facial expressions and vocal tones, and provide a summary."

[0251] Example prompt for getting traffic information:

[0252] "Retrieve the latest traffic conditions for the user's commute route."

[0253] In this way, the system of the present invention can efficiently support the user's daily life and provide more personalized information.

[0254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0255] Step 1:

[0256] The user wears the glasses device

[0257] Input: The user puts on the glasses device.

[0258] Operation: The system starts when the user wears the glasses-type device.

[0259] Output: System startup

[0260] Step 2:

[0261] The device acquires user location information

[0262] Input: Built-in GPS module

[0263] How it works: The device's GPS module measures the user's current location and collects that data.

[0264] Output: Current location data of the user

[0265] Step 3:

[0266] The device obtains the user's schedule information

[0267] Input: Calendar app

[0268] Operation: The device references the calendar app and obtains the user's schedule information.

[0269] Output: User's schedule data

[0270] Step 4:

[0271] The device sends the collected information to a server

[0272] Input: User's current location data, schedule data

[0273] Operation: The data acquired by the device is sent to the server using the communication module.

[0274] Output: Location and schedule information sent to the server

[0275] Step 5:

[0276] The server retrieves the external data

[0277] Input: Weather forecast API, traffic information API

[0278] How it works: The server calls an external API to get weather and traffic information.

[0279] Output: Weather forecast data, traffic information data

[0280] Step 6:

[0281] The device acquires emotion data

[0282] Input: Built-in camera and microphone, emotion engine

[0283] How it works: The device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0284] Output: User's emotional information

[0285] Step 7:

[0286] The device sends emotional information to the server.

[0287] Input: User's emotional information

[0288] Operation: The device sends the acquired emotional information to the server.

[0289] Output: Emotion information sent to the server

[0290] Step 8:

[0291] The server integrates and analyzes the data

[0292] Input: location information, schedule information, weather forecast data, traffic information data, emotional information

[0293] How it works: The server aggregates all the data and analyzes it using Python and TensorFlow.

[0294] Output: Analysis results (optimal action plan)

[0295] Step 9:

[0296] The server generates the recommendations

[0297] Input: Analysis results

[0298] Operation: The server uses a recommendation algorithm to create an optimal action plan for the user.

[0299] Output: Recommendation data

[0300] Step 10:

[0301] The server sends the recommendations to the device.

[0302] Input: Recommendation data

[0303] Operation: The server generates recommendation data and sends it to the device.

[0304] Output: Recommendation data sent to the device

[0305] Step 11:

[0306] The device displays the recommendations to the user.

[0307] Input: Recommendation data

[0308] Action: Display the recommendation content on the device display.

[0309] Output: Visual presentation to the user (displayed recommendations)

[0310] Step 12:

[0311] User enters feedback

[0312] Input: User behavior and sentiment

[0313] How it works: The user uses the "consider" feature on the glasses device to provide feedback on the provided recommendation.

[0314] Output: User feedback data

[0315] Step 13:

[0316] The device sends feedback to the server

[0317] Input: User feedback data

[0318] Action: The device sends user feedback to the server.

[0319] Output: Feedback data sent to the server

[0320] Step 14:

[0321] The server analyzes the feedback and improves the accuracy of the next recommendation.

[0322] Input: Feedback data, emotional information

[0323] How it works: The server analyzes the feedback data and updates the machine learning algorithm to improve the accuracy of future recommendations.

[0324] Output: An optimized recommendation model

[0325] In this way, the system of the present invention can provide the user with an optimal action plan and utilize feedback and emotional information to improve accuracy in future attempts.

[0326] (Application example 2)

[0327] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0328] Today's consumers seek efficient and comfortable shopping experiences amid their busy daily lives. However, traditional stores have difficulty providing personalized services that take into account the customer's emotional state and real-time circumstances. Furthermore, one-way information provision often makes it difficult to link this information to actual purchasing behavior. Therefore, a system that proposes optimal action plans that are stress-free for consumers is needed.

[0329] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for analyzing the user's facial expression and tone of voice to recognize their emotional state, means for acquiring in-store inventory information, campaign information, and congestion status, means for performing analysis based on the acquired information and generating an optimal action plan for the user, and means for displaying the generated action plan to the user. This makes it possible to provide personalized recommendations based on the consumer's emotional state and real-time in-store conditions.

[0330] "Location information" is information that identifies the user's current location.

[0331] "Schedule information" is information including the user's plans and plans.

[0332] "Weather forecast" is information predicting current and future weather conditions.

[0333] "Traffic information" refers to information about current traffic conditions.

[0334] "Emotional state" refers to the psychological state of the user as perceived from their facial expression and tone of voice.

[0335] "Inventory information" refers to the list and quantity of products available in the store.

[0336] "Campaign Information" is information about special promotions and discounts being held in stores.

[0337] "Crowding status" is information about the number of people in the store and the degree of congestion.

[0338] An "action plan" is an optimal action plan proposed to the user based on the acquired information.

[0339] The "display means" refers to a device or method for visually presenting the generated action plan and information to the user.

[0340] "Feedback" refers to evaluations and reaction information provided by users in response to actions or suggestions.

[0341] The system of the present invention optimizes a user's daily activities and in-store activities through smart glasses worn by the user. Specific embodiments of the system will be described below.

[0342] First, the smart glasses terminal includes a camera, microphone, display, and Wi-Fi module. The user puts on the smart glasses and activates them. The terminal uses the camera to capture the user's face and the microphone to record their voice. It also connects to the store's network via the Wi-Fi module and transmits data to the server in real time.

[0343] The server acquires multiple pieces of information and integrates and analyzes them. As a specific example, the server includes the following means:

[0344] 1. User Information Collection:

[0345] Identify the user's current location (where they are in the store). You can use the location information API.

[0346] Obtain the user's schedule information based on data sent from a calendar app or similar.

[0347] Obtain weather forecasts and traffic information to help optimize users' daily activities.

[0348] 2. Using the Emotion Engine:

[0349] The smart glasses' camera and microphone are used to capture and analyze the user's facial expressions and tone of voice to recognize their emotional state, using on-device AI engines such as Google Cloud Vision API and IBM Watson.

[0350] 3. Acquiring external data:

[0351] In-store inventory information, campaign information, and congestion status are obtained via API.

[0352] 4. Data integration and analysis:

[0353] The server integrates and analyzes the acquired location information, schedule information, emotional information, and in-store data. Based on this information, it generates optimal action plans and recommendations for users. The analysis is performed on a cloud-based analysis server.

[0354] 5. Displaying Recommendations:

[0355] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user decides on an action based on the displayed information.

[0356] For example, when a user enters a store, the smart glasses will acquire the user's current location and facial expression (emotional state). Based on this information, as well as real-time store information (stock, campaigns, and crowding), the server will suggest the most suitable products to the user and guide them to the appropriate section. In this way, users can enjoy a personalized shopping experience tailored to their individual situation and the store's circumstances.

[0357] Examples of prompt statements

[0358] A user is wearing smart glasses. Design a system that analyzes the user's emotional state from their facial expressions and tone of voice, and integrates their current location (where they are in the store) with store inventory, campaign information, and congestion information to provide the user with appropriate product suggestions and service information. Provide specific recommendations based on the user's emotional state and the integrated data.

[0359] As described above, the present invention can efficiently and comfortably support users' daily lives and shopping experiences.

[0360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0361] Step 1:

[0362] Collection of User Information

[0363] First, the smart glasses, which are the device, capture the user's face using a camera and record audio using a microphone. Video data from the camera and audio data from the microphone are input. The user's current location is obtained via a location information API, and the user's schedule information is obtained from a calendar app. This information is then sent to the server via a Wi-Fi module.

[0364] Input: Camera video, microphone audio, location information, schedule information

[0365] Output: Consolidated data sent to the server

[0366] Step 2:

[0367] Retrieving External Data

[0368] The server uses external APIs to retrieve in-store inventory information, campaign information, crowding status, and relevant weather and traffic information, thereby gathering all the necessary external data in real time.

[0369] Input: External API call

[0370] Output: Inventory information, campaign information, congestion status, weather forecast, traffic information

[0371] Step 3:

[0372] Using the Emotion Engine

[0373] The system receives camera footage and audio data sent from the device and runs it through an emotion engine to recognize the user's emotional state. This analysis is performed using Google Cloud Vision API and IBM Watson. After the emotional state is recognized, the results are also sent to the server.

[0374] Input: Camera video, audio data

[0375] Output: Emotional state data

[0376] Step 4:

[0377] Data integration

[0378] The server integrates the user information sent in step 1, the external data acquired in step 2, and the emotional state data recognized in step 3. This aggregates the user's current situation and the external environment into a single data set.

[0379] Input: User information (location, schedule), external data (inventory, campaigns, crowds, weather forecast, traffic information), emotional state data

[0380] Output: Unified dataset

[0381] Step 5:

[0382] Analyzing data and generating an action plan

[0383] Based on the integrated data set, the server analyzes and generates a user action plan. For example, if the user is recognized as having fun, it may recommend a specific section or special discount information. This analysis is performed using a cloud-based analysis server.

[0384] Input: Unified dataset

[0385] Output: Action plan, recommendation

[0386] Step 6:

[0387] Viewing Recommendations

[0388] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user makes decisions based on this information.

[0389] Input: Action plan, recommendation

[0390] Output: Information displayed on the smart glasses display

[0391] Step 7:

[0392] Collecting user feedback

[0393] After a user takes action based on a displayed recommendation, they can use the smart glasses' "feedback" function to enter their evaluation of that action or suggestion, which is then sent to the server and used to improve the accuracy of future recommendations.

[0394] Input: User feedback

[0395] Output: Feedback data sent to the server

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0397] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0402] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0404] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0407] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0408] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0409] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0411] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0412] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. To implement this system, the following steps can be taken.

[0413] 1. Collection of User Information

[0414] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, it obtains the user's schedule information from a calendar app or similar. This information is then sent to the server.

[0415] 2. Acquiring external data

[0416] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0417] 3. Data integration and analysis

[0418] The server then analyzes the collected location information, schedule information, weather forecasts, and traffic information, and generates an optimal action plan for the user based on this analysis.

[0419] 4. Generating Recommendations

[0420] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation such as "Bring an umbrella." It may also make suggestions based on traffic information, such as "Get on the train earlier."

[0421] 5. Displaying Recommendations

[0422] The generated recommendations are then sent from the server to the device (glasses), which visually displays this information to the user, who can then review the displayed recommendations and make decisions based on them.

[0423] 6. Collecting User Feedback

[0424] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0425] 7. Recommendation optimization

[0426] The server analyzes the feedback and learns to improve the accuracy of future recommendations, allowing the system to continually provide the best information for users.

[0427] Specific examples

[0428] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home) and then checks the user's schedule (work) for the day. The server obtains the weather forecast and confirms that it will rain that day. Based on traffic information, the server also determines that the user needs to catch the train earlier than usual.

[0429] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella" or "Traffic may be congested, so you should leave earlier than usual." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0430] After arriving at work, users can use the glasses' "consider" feature to provide feedback, which the server analyzes and uses to improve future recommendations.

[0431] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information.

[0432] The processing flow will be explained below.

[0433] Step 1:

[0434] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0435] Step 2:

[0436] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0437] Step 3:

[0438] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0439] Step 4:

[0440] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0441] Step 5:

[0442] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0443] Step 6:

[0444] The server analyzes the location information, schedule information, weather forecast, and traffic information it receives, and generates an optimal action plan for the user based on this analysis.

[0445] Step 7:

[0446] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain, the system may suggest "take an umbrella," or based on traffic information, "get on the train earlier."

[0447] Step 8:

[0448] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0449] Step 9:

[0450] The user reviews the displayed recommendations and selects or adjusts an action based on them, for example, taking a specific action such as carrying an umbrella or changing the train time.

[0451] Step 10:

[0452] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0453] Step 11:

[0454] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. Based on the feedback information, it updates the model to improve the accuracy of future recommendations.

[0455] Step 12:

[0456] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0457] Example 1

[0458] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0459] Conventional systems are insufficient in optimizing users' daily life action plans and rarely provide specific recommendations tailored to specific situations. Furthermore, they lack the technology to effectively utilize feedback to improve the accuracy of future action plans. This reduces user convenience and limits the value of the system.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0461] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for the user to input feedback on the action plan provided, and means for analyzing using a machine learning model to improve the accuracy of subsequent action plans based on the feedback. This makes it possible to optimize the user's daily life action plan and provide recommendations tailored to specific situations. Furthermore, by utilizing the feedback, the accuracy of subsequent action plans is improved, improving user convenience.

[0462] "Means for obtaining user location information" refers to devices or technologies that use a GPS module to identify the user's current geographical location.

[0463] "Means for acquiring user schedule information" refers to devices or technologies that acquire a user's plans and schedules from a calendar app or other time management software.

[0464] "Means for obtaining weather forecasts and traffic information" refers to devices and technologies that use external APIs and databases to obtain current and future weather and traffic information.

[0465] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a set of software and algorithms that integrates the collected user location information, schedule information, weather forecast, and traffic information, analyzes this data, and generates an optimal action plan.

[0466] The "means for displaying the generated action plan to the user" refers to a display or a display device for presenting the generated action plan to the user.

[0467] The "means for the user to input feedback on the provided action plan" refers to an interface or device that allows the user to evaluate the usefulness and suitability of the received action plan and input feedback.

[0468] The "means for performing analysis using a machine learning model to improve the accuracy of subsequent action plans based on the feedback" refers to software and technology that analyzes feedback data collected from users and uses machine learning algorithms to improve the accuracy of generating subsequent action plans.

[0469] The system of the present invention supports the user's daily life and provides optimal information through a glasses-type terminal worn by the user. The system of the present invention can be implemented according to the following procedure.

[0470] First, the device (glasses) collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which is used to obtain the user's current location. The device also connects to a calendar app to obtain the user's schedule information. This information is then sent to the server in real time.

[0471] The server obtains weather forecasts and traffic information using external APIs. Specifically, it uses OpenWeatherMap as the weather forecast API and Google Maps API as the traffic information API. This information is used to optimize the user's daily activities.

[0472] The server then integrates and analyzes the user's location, schedule, weather forecast, and traffic information. Generative AI models and machine learning algorithms are used in the analysis to generate an optimal action plan for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation that the user should "bring an umbrella," and based on traffic information, it will make a suggestion that the user should "board the train earlier."

[0473] The generated recommendations are sent from the server to the device (glasses), which then visually displays them to the user. The user can decide what to do based on the displayed recommendations. At the same time, the user can also enter feedback after taking action. The feedback is sent to the server via the device, and the server analyzes it to improve the accuracy of future recommendations.

[0474] As a concrete example, consider the case where a user puts on glasses before going to work in the morning. The device obtains the user's current location (home) and checks the user's schedule for the day (work). The server obtains the weather forecast, confirms that the weather for that day will be rainy, and then obtains traffic information to determine that the user should leave earlier than usual. The server generates recommendations such as "It's going to rain today, so you should take an umbrella" and "You should leave earlier than usual," and sends these to the device to display on the glasses' display. After arriving at work, the user enters feedback using the "Consider" function on the glasses, and the server analyzes this feedback and updates the machine learning model to improve the accuracy of the next recommendation.

[0475] An example of a prompt sentence could be, "Please obtain the user's current location and schedule information, and generate an optimal plan of action based on the weather forecast and traffic information. For example, could you suggest taking an umbrella if it's raining, or leaving earlier if traffic is heavy, thereby optimizing the user's daily activities?"

[0476] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information according to individual circumstances.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1: Collect user information

[0479] The device uses a GPS module to obtain the user's current location. It receives GPS data as input and generates the user's geographical location information as output. Next, the device connects to a database such as a calendar app to obtain the user's schedule information. Here, the input is data from the calendar app, and the output is the schedule information for that day. This collected information is sent to the server in real time.

[0480] Step 2: Retrieving external data

[0481] The server uses external APIs to obtain weather and traffic information. It inputs weather data from a weather API (e.g., OpenWeatherMap) and outputs weather forecast information. Similarly, it inputs traffic data from a traffic information API (e.g., Google Maps API) and outputs traffic information. These data are stored for subsequent analysis.

[0482] Step 3: Data synthesis and analysis

[0483] The server integrates the user's location information, schedule information, weather forecast, and traffic information it has acquired. It takes in various pieces of information (location information, schedule information, weather forecast, traffic information) as input and generates integrated data. The server then performs analysis based on this integrated data. Generative AI models and machine learning algorithms are used for the analysis, and the analysis results are used as output to generate an optimal action plan.

[0484] Step 4: Generate recommendations

[0485] The server generates recommendations to provide to the user based on the analysis results. It takes the analysis results as input and outputs suggestions (recommendations) for optimizing the user's daily activities. For example, if it is raining, it may generate specific suggestions such as "take an umbrella" or "leave earlier" if traffic is congested.

[0486] Step 5: View recommendations

[0487] The server generates recommendations and sends them to the device. Recommendation data is taken as input and sent to the device as output. The device then visually displays this information to the user. Specifically, the suggestions are displayed on the glasses' display for the user to review.

[0488] Step 6: Gather user feedback

[0489] After the user completes an action based on the provided recommendation, they enter feedback. The user's evaluation data is taken as input, and the feedback information is sent from the device to the server as output. The glasses' "Consider" function makes it easy to enter feedback.

[0490] Step 7: Optimize your recommendations

[0491] The server analyzes the collected feedback. It takes the feedback data as input and outputs the analysis results to improve the accuracy of future recommendations. The server uses a machine learning algorithm to update the model based on the feedback, optimizing future recommendations to be more accurate. This continuous feedback loop allows the entire system to provide a higher level of convenience.

[0492] (Application example 1)

[0493] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] Conventional behavioral planning systems based on location and schedule information have been effective in supporting users' daily lives. However, they lack the ability to provide specific information to improve the shopping experience in brick-and-mortar stores, such as sale and promotion information in real time. They also lack recommendation functions that take into account in-store special events and inventory information. A system that can address these shortcomings and further improve users' shopping experience is needed.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0496] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring store sales, promotions, and inventory information, and means for generating an action plan based on the sales, promotions, and inventory information. This allows the user to receive optimal action plans in real time that take into account sales and promotion information and store inventory status. Special event and sale information is also notified in real time, further enhancing the user's shopping experience.

[0497] "Means for obtaining user location information" refers to a device for obtaining the geographical location of the user's current location using GPS or other location identification technology.

[0498] The "means for acquiring user schedule information" is a device that has the function of acquiring the user's plans and dates from a calendar application or schedule management system.

[0499] "Means for obtaining weather forecasts and traffic information" refers to means for obtaining information about weather forecasts and traffic conditions from external APIs or data sources.

[0500] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or algorithm for analyzing data such as location information, schedule information, weather forecasts, and traffic information, and automatically generating an optimal action plan to provide to the user.

[0501] The "means for displaying the generated action plan to the user" refers to a display device or interface for visually showing the generated action plan to the user.

[0502] "Means for obtaining in-store sales information, promotions, and inventory information" refers to systems and database access means for obtaining special sales information, promotions, and product inventory information in physical stores.

[0503] The "means for generating an action plan based on the sale information, promotion, and inventory information" refers to a system or algorithm for automatically generating an optimal action plan for the user based on the acquired sale information, promotion information, and inventory information.

[0504] The "means for obtaining feedback from users" refers to an interface or input device for collecting opinions and evaluations provided by users.

[0505] The "means for analyzing the feedback and improving the accuracy of subsequent action plan generation" refers to a system or method for analyzing collected feedback and improving the algorithm for generating subsequent recommendations and action plans.

[0506] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to a function or system that takes into account the user's current mood and situation and flexibly changes the displayed information and generated action plans based on that.

[0507] "Means of obtaining information about special events and sales in real time and notifying users at the optimal time" refers to a notification system or interface that obtains information about special events and sales taking place in physical stores in real time and allows users to receive that information at the optimal time.

[0508] The system of the present invention is designed to improve the shopping experience of users in physical stores. Specific embodiments for implementing this system are as follows.

[0509] Collection of User Information

[0510] First, when a user wears smart glasses, the device (smart glasses) collects the user's location information. Specifically, it uses a GPS module to obtain the user's current geographical location. It also obtains the user's schedule information from a calendar application. This allows the user's schedule and shopping list to be displayed.

[0511] Retrieving External Data

[0512] The server then retrieves weather and traffic information from an external API. The weather forecast includes today's weather and temperature, and the traffic information includes current congestion and traffic incidents. It also retrieves in-store sales, promotions, and inventory information. This includes a list of special offers, current promotions, and the stock status of each item.

[0513] Data integration and analysis

[0514] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, sale information, promotion information, and inventory information. This generates an optimal action plan for the user. For example, the server may guide the user to a location where products are on sale, or recommend products based on promotion information.

[0515] Generating recommendations

[0516] Based on the analysis results, the server generates recommendations appropriate for the user. For example, it generates information such as "refrigerated food is low in stock, so it is recommended to purchase early" or "there are currently promotional items." It also takes into account information about busy cash registers to recommend the most suitable cash register.

[0517] Viewing Recommendations

[0518] The generated recommendations are sent from the server to the smart glasses, where users can visually check the information and make optimal purchasing decisions. They are also notified of special events and sales information in stores in real time.

[0519] User feedback collection and optimization

[0520] After completing their shopping, the user provides feedback through the smart glasses, including information on whether the provided action plan and recommendations were useful. The server analyzes this feedback and improves the analysis algorithm to improve the accuracy of future action plan generation.

[0521] Hardware and Software Used

[0522] Hardware: Smart glasses (with GPS module)

[0523] Software: Python, external API (weather forecast, traffic information)

[0524] Specific examples

[0525] For example, the following prompts can be used as input to a generative AI model to optimize recommendations:

[0526] Example prompt sentence:

[0527] If the current weather is rainy and traffic is heavy, generate an appropriate plan of action for the user, taking into account the user's location, the travel time from home to the nearest station, and their schedule for the day.

[0528] In this way, the system of the present invention can efficiently support the user's daily life and shopping experience and provide optimal information.

[0529] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0530] Step 1: Collect user information

[0531] The device first obtains the user's location information. The input is the user's current geographic location, and data is collected using a GPS module. The output is the user's precise current location. Next, the device obtains the user's schedule information from a calendar application. This input data includes the user's appointments and shopping list. The output is the user's specific schedule information.

[0532] Step 2: Retrieving external data

[0533] The server retrieves weather forecast information using an external API. The input is a request to the API that provides the weather forecast, and the output is the weather information returned by the API. Similarly, the server retrieves traffic information from an external API. The input is a request to the API that provides traffic information, and the output is information about current traffic conditions. The server also retrieves in-store sales, promotions, and inventory information. The input is a query to the store's database, and the output is the relevant sales, promotions, and inventory information.

[0534] Step 3: Data synthesis and analysis

[0535] The server integrates and analyzes all collected location information, schedule information, weather forecasts, traffic information, sale information, promotion information, and inventory information. The input data is each of the above information, which is processed by the data analysis algorithm on the server. The output is a specific action plan. This plan includes guidance to products on sale or promotion, and the optimal transportation method taking into account current traffic conditions.

[0536] Step 4: Generate recommendations

[0537] The server generates an action plan appropriate for the user based on the analysis results. The input is the analyzed data, and the output is a specific recommendation. For example, it may include recommendations such as "Here are the items currently on promotion" or "The cash register is busy, so you should use another one."

[0538] Step 5: View recommendations

[0539] The server sends the generated recommendations to the user's device. The input is the generated action plan, and the output is the specific recommendations displayed on the device. The user can visually check this information through the smart glasses and optimize their shopping behavior.

[0540] Step 6: Gather user feedback

[0541] After completing their shopping, the user provides feedback through the terminal. The input is feedback data from the user, including their evaluation of the usefulness of the recommendations. The output is feedback information sent to the server.

[0542] Step 7: Feedback analysis and optimization

[0543] The server analyzes user feedback and learns to improve the accuracy of future action plan generation. The input is the collected feedback data, which is processed by a data analysis algorithm. The output is an optimized action plan generation algorithm. This allows the system to continuously improve and provide more useful information to users.

[0544] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0545] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. Furthermore, by combining it with an emotion engine, it is possible to make recommendations that take into account the user's emotional state. An embodiment of this system is described below.

[0546] 1. Collection of User Information

[0547] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, the device obtains the user's schedule information from a calendar app or similar. This information is sent to the server in real time.

[0548] 2. Acquiring external data

[0549] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0550] 3. Use of Emotion Engine

[0551] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0552] 4. Data integration and analysis

[0553] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information, and generates an optimal action plan for the user based on the analysis results.

[0554] 5. Generating Recommendations

[0555] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain and the user feels tired, the server will generate recommendations such as "You should bring an umbrella" or "You should take a break at a cafe to refresh yourself."

[0556] 6. Displaying Recommendations

[0557] The generated recommendations are sent from the server to the device (glasses), which visually displays this information to the user. The user then checks the displayed recommendations and decides what to do based on them.

[0558] 7. Collecting User Feedback

[0559] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0560] 8. Recommendation optimization

[0561] The server analyzes the feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine to improve the accuracy of future recommendations.

[0562] Specific examples

[0563] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home), then checks the schedule for the day (work). Furthermore, the emotion engine recognizes the user's mood as "tired." The server obtains the weather forecast and confirms that it will rain that day. It also determines, based on traffic information, that the user needs to catch the train earlier than usual.

[0564] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic may be congested, so you should leave earlier than usual," or "You're tired, so you should take a short break at a cafe in front of the station." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0565] After arriving at work, users can use the glasses' "Consider" function to input feedback, such as "I brought an umbrella," "I changed my train time," or "I took a break at a cafe." The server receives and analyzes this feedback and uses it to improve the accuracy of future recommendations.

[0566] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information. Furthermore, by using the emotion engine, it is possible to provide personalized advice according to the user's mood and situation.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0570] Step 2:

[0571] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0572] Step 3:

[0573] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0574] Step 4:

[0575] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0576] Step 5:

[0577] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0578] Step 6:

[0579] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0580] Step 7:

[0581] The server analyzes the location information, schedule information, weather forecast, traffic information, and emotion information it receives, and generates an optimal action plan for the user based on this analysis.

[0582] Step 8:

[0583] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain and the user is feeling tired, the server will make recommendations such as "You should take an umbrella" or "You should take a break at a cafe to refresh yourself."

[0584] Step 9:

[0585] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0586] Step 10:

[0587] The user reviews the displayed recommendations and selects or adjusts an action based on them, such as taking an umbrella, changing the train time, or taking a break at a cafe.

[0588] Step 11:

[0589] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0590] Step 12:

[0591] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine, updating the model to improve the accuracy of future recommendations.

[0592] Step 13:

[0593] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0594] Example 2

[0595] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0596] Conventional information provision systems can provide recommendations based on the user's location and schedule information, but they do not adequately provide personalized information or generate action plans that take the user's emotional state into account.In addition, their functionality for utilizing feedback to improve accuracy in future visits was limited, making it difficult to provide optimal information for the user.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0598] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for recognizing the user's emotional state and acquiring emotional information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring feedback from the user, means for analyzing the feedback and improving the accuracy of generating subsequent action plans, and means for changing the displayed information and action plan according to the user's mood and situation, thereby enabling the provision of optimal information and the generation of an action plan taking into account the user's emotional state and feedback.

[0599] "Means for acquiring user location information" refers to hardware or software for identifying the user's current location and collecting that information.

[0600] "Means for acquiring user schedule information" refers to a system or application for acquiring schedule and calendar information entered by a user.

[0601] "Means for obtaining weather and traffic information" means software or systems for collecting weather and traffic conditions from external APIs or data sources.

[0602] "Means for recognizing a user's emotional state and acquiring emotional information" refers to hardware and software for analyzing a user's facial expressions, voice, and other biometric signals to identify their emotional state.

[0603] "Means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or program that integrates collected data and uses algorithms or machine learning models to create an optimal action plan.

[0604] The "means for displaying the generated action plan to the user" refers to a display device such as a display or a head-mounted display for visually presenting the generated action plan to the user.

[0605] "Means for obtaining user feedback" refers to hardware or software for inputting user-provided ratings and opinions.

[0606] "Means for analyzing the feedback and improving the accuracy of future action plans" refers to algorithms or machine learning models that analyze the obtained feedback data and improve the quality of future action plans.

[0607] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to software and systems for dynamically adjusting the information provided and action plans based on the user's current situation and emotions.

[0608] The system of the present invention supports users' daily lives and provides optimal information through a glasses-type device worn by the user. This system includes functions for providing recommendations based on the user's current location, schedule information, external weather forecasts and traffic information, and the user's emotional state.

[0609] Collection of User Information

[0610] First, the device collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which acquires the user's current location. The device also acquires the user's schedule information from a calendar app. For example, if the user has entered a schedule in their calendar that says "I have a meeting in the office at 9 o'clock," this information is acquired. All collected information is sent to the server in real time.

[0611] Retrieving External Data

[0612] The server then uses external APIs to obtain weather and traffic information. The server calls a specific weather API (e.g., OpenWeatherMap API) to obtain weather forecast data based on the user's current location. For traffic information, the server uses Google Maps API to collect delay and congestion information. This provides data to optimize the user's daily activities.

[0613] Using the Emotion Engine

[0614] The emotion engine installed in the device analyzes the user's facial expressions and voice to recognize the user's current emotional state. The device's camera and microphone capture the user's facial expressions and voice, and this data is analyzed by the emotion engine. For example, if the user feels "tired," the emotion is recognized from the user's facial expressions and tone of voice. The recognized emotion information is sent to the server.

[0615] Generating and displaying recommendations

[0616] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information. The server uses data analysis tools such as Python and TensorFlow. Based on the analysis results, an optimal action plan is generated for the user. For example, recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic will be congested, so you should leave early," or "You're tired, so you should take a break at a cafe" may be generated. This information is sent from the server to the device and displayed on the display of the glasses device.

[0617] Collecting and incorporating user feedback

[0618] After completing an action, the user enters feedback using the "Consider" function on the glasses device. For example, they can enter information such as "I took an umbrella," "I changed my train time," or "I took a break at a cafe." This feedback information is sent to the server, which analyzes it and improves the accuracy of future recommendations. The server learns the user's behavioral patterns and preferences, and also integrates emotional information from the emotion engine to further optimize future recommendations.

[0619] Examples of prompt statements

[0620] Below are examples of prompts used within the system.

[0621] Example prompt for getting weather forecast information:

[0622] "Please fetch the current weather forecast for the user's location."

[0623] Example prompts for using the Emotion Engine:

[0624] "Analyze the user's current emotional state based on their facial expressions and vocal tones, and provide a summary."

[0625] Example prompt for getting traffic information:

[0626] "Retrieve the latest traffic conditions for the user's commute route."

[0627] In this way, the system of the present invention can efficiently support the user's daily life and provide more personalized information.

[0628] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0629] Step 1:

[0630] The user wears the glasses device

[0631] Input: The user puts on the glasses device.

[0632] Operation: The system starts when the user wears the glasses-type device.

[0633] Output: System startup

[0634] Step 2:

[0635] The device acquires user location information

[0636] Input: Built-in GPS module

[0637] How it works: The device's GPS module measures the user's current location and collects that data.

[0638] Output: Current location data of the user

[0639] Step 3:

[0640] The device obtains the user's schedule information

[0641] Input: Calendar app

[0642] Operation: The device references the calendar app and obtains the user's schedule information.

[0643] Output: User's schedule data

[0644] Step 4:

[0645] The device sends the collected information to a server

[0646] Input: User's current location data, schedule data

[0647] Operation: The data acquired by the device is sent to the server using the communication module.

[0648] Output: Location and schedule information sent to the server

[0649] Step 5:

[0650] The server retrieves the external data

[0651] Input: Weather forecast API, traffic information API

[0652] How it works: The server calls an external API to get weather and traffic information.

[0653] Output: Weather forecast data, traffic information data

[0654] Step 6:

[0655] The device acquires emotion data

[0656] Input: Built-in camera and microphone, emotion engine

[0657] How it works: The device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0658] Output: User's emotional information

[0659] Step 7:

[0660] The device sends emotional information to the server.

[0661] Input: User's emotional information

[0662] Operation: The device sends the acquired emotional information to the server.

[0663] Output: Emotion information sent to the server

[0664] Step 8:

[0665] The server integrates and analyzes the data

[0666] Input: location information, schedule information, weather forecast data, traffic information data, emotional information

[0667] How it works: The server aggregates all the data and analyzes it using Python and TensorFlow.

[0668] Output: Analysis results (optimal action plan)

[0669] Step 9:

[0670] The server generates the recommendations

[0671] Input: Analysis results

[0672] Operation: The server uses a recommendation algorithm to create an optimal action plan for the user.

[0673] Output: Recommendation data

[0674] Step 10:

[0675] The server sends the recommendations to the device.

[0676] Input: Recommendation data

[0677] Operation: The server generates recommendation data and sends it to the device.

[0678] Output: Recommendation data sent to the device

[0679] Step 11:

[0680] The device displays the recommendations to the user.

[0681] Input: Recommendation data

[0682] Action: Display the recommendation content on the device display.

[0683] Output: Visual presentation to the user (displayed recommendations)

[0684] Step 12:

[0685] User enters feedback

[0686] Input: User behavior and sentiment

[0687] How it works: The user uses the "consider" feature on the glasses device to provide feedback on the provided recommendation.

[0688] Output: User feedback data

[0689] Step 13:

[0690] The device sends feedback to the server

[0691] Input: User feedback data

[0692] Action: The device sends user feedback to the server.

[0693] Output: Feedback data sent to the server

[0694] Step 14:

[0695] The server analyzes the feedback and improves the accuracy of the next recommendation.

[0696] Input: Feedback data, emotional information

[0697] How it works: The server analyzes the feedback data and updates the machine learning algorithm to improve the accuracy of future recommendations.

[0698] Output: An optimized recommendation model

[0699] In this way, the system of the present invention can provide the user with an optimal action plan and utilize feedback and emotional information to improve accuracy in future attempts.

[0700] (Application example 2)

[0701] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] Today's consumers seek efficient and comfortable shopping experiences amid their busy daily lives. However, traditional stores have difficulty providing personalized services that take into account the customer's emotional state and real-time circumstances. Furthermore, one-way information provision often makes it difficult to link this information to actual purchasing behavior. Therefore, a system that proposes optimal action plans that are stress-free for consumers is needed.

[0703] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for analyzing the user's facial expression and tone of voice to recognize their emotional state, means for acquiring in-store inventory information, campaign information, and congestion status, means for performing analysis based on the acquired information and generating an optimal action plan for the user, and means for displaying the generated action plan to the user. This makes it possible to provide personalized recommendations based on the consumer's emotional state and real-time in-store conditions.

[0704] "Location information" is information that identifies the user's current location.

[0705] "Schedule information" is information including the user's plans and plans.

[0706] "Weather forecast" is information predicting current and future weather conditions.

[0707] "Traffic information" refers to information about current traffic conditions.

[0708] "Emotional state" refers to the psychological state of the user as perceived from their facial expression and tone of voice.

[0709] "Inventory information" refers to the list and quantity of products available in the store.

[0710] "Campaign Information" is information about special promotions and discounts being held in stores.

[0711] "Crowding status" is information about the number of people in the store and the degree of congestion.

[0712] An "action plan" is an optimal action plan proposed to the user based on the acquired information.

[0713] The "display means" refers to a device or method for visually presenting the generated action plan and information to the user.

[0714] "Feedback" refers to evaluations and reaction information provided by users in response to actions or suggestions.

[0715] The system of the present invention optimizes a user's daily activities and in-store activities through smart glasses worn by the user. Specific embodiments of the system will be described below.

[0716] First, the smart glasses terminal includes a camera, microphone, display, and Wi-Fi module. The user puts on the smart glasses and activates them. The terminal uses the camera to capture the user's face and the microphone to record their voice. It also connects to the store's network via the Wi-Fi module and transmits data to the server in real time.

[0717] The server acquires multiple pieces of information and integrates and analyzes them. As a specific example, the server includes the following means:

[0718] 1. User Information Collection:

[0719] Identify the user's current location (where they are in the store). You can use the location information API.

[0720] Obtain the user's schedule information based on data sent from a calendar app or similar.

[0721] Obtain weather forecasts and traffic information to help optimize users' daily activities.

[0722] 2. Using the Emotion Engine:

[0723] The smart glasses' camera and microphone are used to capture and analyze the user's facial expressions and tone of voice to recognize their emotional state, using on-device AI engines such as Google Cloud Vision API and IBM Watson.

[0724] 3. Acquiring external data:

[0725] In-store inventory information, campaign information, and congestion status are obtained via API.

[0726] 4. Data integration and analysis:

[0727] The server integrates and analyzes the acquired location information, schedule information, emotional information, and in-store data. Based on this information, it generates optimal action plans and recommendations for users. The analysis is performed on a cloud-based analysis server.

[0728] 5. Displaying Recommendations:

[0729] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user decides on an action based on the displayed information.

[0730] For example, when a user enters a store, the smart glasses will acquire the user's current location and facial expression (emotional state). Based on this information, as well as real-time store information (stock, campaigns, and crowding), the server will suggest the most suitable products to the user and guide them to the appropriate section. In this way, users can enjoy a personalized shopping experience tailored to their individual situation and the store's circumstances.

[0731] Examples of prompt statements

[0732] A user is wearing smart glasses. Design a system that analyzes the user's emotional state from their facial expressions and tone of voice, and integrates their current location (where they are in the store) with store inventory, campaign information, and congestion information to provide the user with appropriate product suggestions and service information. Provide specific recommendations based on the user's emotional state and the integrated data.

[0733] As described above, the present invention can efficiently and comfortably support users' daily lives and shopping experiences.

[0734] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0735] Step 1:

[0736] Collection of User Information

[0737] First, the smart glasses, which are the device, capture the user's face using a camera and record audio using a microphone. Video data from the camera and audio data from the microphone are input. The user's current location is obtained via a location information API, and the user's schedule information is obtained from a calendar app. This information is then sent to the server via a Wi-Fi module.

[0738] Input: Camera video, microphone audio, location information, schedule information

[0739] Output: Consolidated data sent to the server

[0740] Step 2:

[0741] Retrieving External Data

[0742] The server uses external APIs to retrieve in-store inventory information, campaign information, crowding status, and relevant weather and traffic information, thereby gathering all the necessary external data in real time.

[0743] Input: External API call

[0744] Output: Inventory information, campaign information, congestion status, weather forecast, traffic information

[0745] Step 3:

[0746] Using the Emotion Engine

[0747] The system receives camera footage and audio data sent from the device and runs it through an emotion engine to recognize the user's emotional state. This analysis is performed using Google Cloud Vision API and IBM Watson. After the emotional state is recognized, the results are also sent to the server.

[0748] Input: Camera video, audio data

[0749] Output: Emotional state data

[0750] Step 4:

[0751] Data integration

[0752] The server integrates the user information sent in step 1, the external data acquired in step 2, and the emotional state data recognized in step 3. This aggregates the user's current situation and the external environment into a single data set.

[0753] Input: User information (location, schedule), external data (inventory, campaigns, crowds, weather forecast, traffic information), emotional state data

[0754] Output: Unified dataset

[0755] Step 5:

[0756] Analyzing data and generating an action plan

[0757] Based on the integrated data set, the server analyzes and generates a user action plan. For example, if the user is recognized as having fun, it may recommend a specific section or special discount information. This analysis is performed using a cloud-based analysis server.

[0758] Input: Unified dataset

[0759] Output: Action plan, recommendation

[0760] Step 6:

[0761] Viewing Recommendations

[0762] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user makes decisions based on this information.

[0763] Input: Action plan, recommendation

[0764] Output: Information displayed on the smart glasses display

[0765] Step 7:

[0766] Collecting user feedback

[0767] After a user takes action based on a displayed recommendation, they can use the smart glasses' "feedback" function to enter their evaluation of that action or suggestion, which is then sent to the server and used to improve the accuracy of future recommendations.

[0768] Input: User feedback

[0769] Output: Feedback data sent to the server

[0770] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0771] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0772] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0773] [Third embodiment]

[0774] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0775] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0776] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0777] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0778] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0779] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0780] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0781] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0782] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0783] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0784] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0785] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0786] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. To implement this system, the following steps can be taken.

[0787] 1. Collection of User Information

[0788] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, it obtains the user's schedule information from a calendar app or similar. This information is then sent to the server.

[0789] 2. Acquiring external data

[0790] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0791] 3. Data integration and analysis

[0792] The server then analyzes the collected location information, schedule information, weather forecasts, and traffic information, and generates an optimal action plan for the user based on this analysis.

[0793] 4. Generating Recommendations

[0794] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation such as "Bring an umbrella." It may also make suggestions based on traffic information, such as "Get on the train earlier."

[0795] 5. Displaying Recommendations

[0796] The generated recommendations are then sent from the server to the device (glasses), which visually displays this information to the user, who can then review the displayed recommendations and make decisions based on them.

[0797] 6. Collecting User Feedback

[0798] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0799] 7. Recommendation optimization

[0800] The server analyzes the feedback and learns to improve the accuracy of future recommendations, allowing the system to continually provide the best information for users.

[0801] Specific examples

[0802] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home) and then checks the user's schedule (work) for the day. The server obtains the weather forecast and confirms that it will rain that day. Based on traffic information, the server also determines that the user needs to catch the train earlier than usual.

[0803] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella" or "Traffic may be congested, so you should leave earlier than usual." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0804] After arriving at work, users can use the glasses' "consider" feature to provide feedback, which the server analyzes and uses to improve future recommendations.

[0805] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0809] Step 2:

[0810] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0811] Step 3:

[0812] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0813] Step 4:

[0814] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0815] Step 5:

[0816] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0817] Step 6:

[0818] The server analyzes the location information, schedule information, weather forecast, and traffic information it receives, and generates an optimal action plan for the user based on this analysis.

[0819] Step 7:

[0820] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain, the system may suggest "take an umbrella," or based on traffic information, "get on the train earlier."

[0821] Step 8:

[0822] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0823] Step 9:

[0824] The user reviews the displayed recommendations and selects or adjusts an action based on them, for example, taking a specific action such as carrying an umbrella or changing the train time.

[0825] Step 10:

[0826] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0827] Step 11:

[0828] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. Based on the feedback information, it updates the model to improve the accuracy of future recommendations.

[0829] Step 12:

[0830] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0831] Example 1

[0832] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0833] Conventional systems are insufficient in optimizing users' daily life action plans and rarely provide specific recommendations tailored to specific situations. Furthermore, they lack the technology to effectively utilize feedback to improve the accuracy of future action plans. This reduces user convenience and limits the value of the system.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0835] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for the user to input feedback on the action plan provided, and means for analyzing using a machine learning model to improve the accuracy of subsequent action plans based on the feedback. This makes it possible to optimize the user's daily life action plan and provide recommendations tailored to specific situations. Furthermore, by utilizing the feedback, the accuracy of subsequent action plans is improved, improving user convenience.

[0836] "Means for obtaining user location information" refers to devices or technologies that use a GPS module to identify the user's current geographical location.

[0837] "Means for acquiring user schedule information" refers to devices or technologies that acquire a user's plans and schedules from a calendar app or other time management software.

[0838] "Means for obtaining weather forecasts and traffic information" refers to devices and technologies that use external APIs and databases to obtain current and future weather and traffic information.

[0839] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a set of software and algorithms that integrates the collected user location information, schedule information, weather forecast, and traffic information, analyzes this data, and generates an optimal action plan.

[0840] The "means for displaying the generated action plan to the user" refers to a display or a display device for presenting the generated action plan to the user.

[0841] The "means for the user to input feedback on the provided action plan" refers to an interface or device that allows the user to evaluate the usefulness and suitability of the received action plan and input feedback.

[0842] The "means for performing analysis using a machine learning model to improve the accuracy of subsequent action plans based on the feedback" refers to software and technology that analyzes feedback data collected from users and uses machine learning algorithms to improve the accuracy of generating subsequent action plans.

[0843] The system of the present invention supports the user's daily life and provides optimal information through a glasses-type terminal worn by the user. The system of the present invention can be implemented according to the following procedure.

[0844] First, the device (glasses) collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which is used to obtain the user's current location. The device also connects to a calendar app to obtain the user's schedule information. This information is then sent to the server in real time.

[0845] The server obtains weather forecasts and traffic information using external APIs. Specifically, it uses OpenWeatherMap as the weather forecast API and Google Maps API as the traffic information API. This information is used to optimize the user's daily activities.

[0846] The server then integrates and analyzes the user's location, schedule, weather forecast, and traffic information. Generative AI models and machine learning algorithms are used in the analysis to generate an optimal action plan for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation that the user should "bring an umbrella," and based on traffic information, it will make a suggestion that the user should "board the train earlier."

[0847] The generated recommendations are sent from the server to the device (glasses), which then visually displays them to the user. The user can decide what to do based on the displayed recommendations. At the same time, the user can also enter feedback after taking action. The feedback is sent to the server via the device, and the server analyzes it to improve the accuracy of future recommendations.

[0848] As a concrete example, consider the case where a user puts on glasses before going to work in the morning. The device obtains the user's current location (home) and checks the user's schedule for the day (work). The server obtains the weather forecast, confirms that the weather for that day will be rainy, and then obtains traffic information to determine that the user should leave earlier than usual. The server generates recommendations such as "It's going to rain today, so you should take an umbrella" and "You should leave earlier than usual," and sends these to the device to display on the glasses' display. After arriving at work, the user enters feedback using the "Consider" function on the glasses, and the server analyzes this feedback and updates the machine learning model to improve the accuracy of the next recommendation.

[0849] An example of a prompt sentence could be, "Please obtain the user's current location and schedule information, and generate an optimal plan of action based on the weather forecast and traffic information. For example, could you suggest taking an umbrella if it's raining, or leaving earlier if traffic is heavy, thereby optimizing the user's daily activities?"

[0850] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information according to individual circumstances.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1: Collect user information

[0853] The device uses a GPS module to obtain the user's current location. It receives GPS data as input and generates the user's geographical location information as output. Next, the device connects to a database such as a calendar app to obtain the user's schedule information. Here, the input is data from the calendar app, and the output is the schedule information for that day. This collected information is sent to the server in real time.

[0854] Step 2: Retrieving external data

[0855] The server uses external APIs to obtain weather and traffic information. It inputs weather data from a weather API (e.g., OpenWeatherMap) and outputs weather forecast information. Similarly, it inputs traffic data from a traffic information API (e.g., Google Maps API) and outputs traffic information. These data are stored for subsequent analysis.

[0856] Step 3: Data synthesis and analysis

[0857] The server integrates the user's location information, schedule information, weather forecast, and traffic information it has acquired. It takes in various pieces of information (location information, schedule information, weather forecast, traffic information) as input and generates integrated data. The server then performs analysis based on this integrated data. Generative AI models and machine learning algorithms are used for the analysis, and the analysis results are used as output to generate an optimal action plan.

[0858] Step 4: Generate recommendations

[0859] The server generates recommendations to provide to the user based on the analysis results. It takes the analysis results as input and outputs suggestions (recommendations) for optimizing the user's daily activities. For example, if it is raining, it may generate specific suggestions such as "take an umbrella" or "leave earlier" if traffic is congested.

[0860] Step 5: View recommendations

[0861] The server generates recommendations and sends them to the device. Recommendation data is taken as input and sent to the device as output. The device then visually displays this information to the user. Specifically, the suggestions are displayed on the glasses' display for the user to review.

[0862] Step 6: Gather user feedback

[0863] After the user completes an action based on the provided recommendation, they enter feedback. The user's evaluation data is taken as input, and the feedback information is sent from the device to the server as output. The glasses' "Consider" function makes it easy to enter feedback.

[0864] Step 7: Optimize your recommendations

[0865] The server analyzes the collected feedback. It takes the feedback data as input and outputs the analysis results to improve the accuracy of future recommendations. The server uses a machine learning algorithm to update the model based on the feedback, optimizing future recommendations to be more accurate. This continuous feedback loop allows the entire system to provide a higher level of convenience.

[0866] (Application example 1)

[0867] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0868] Conventional behavioral planning systems based on location and schedule information have been effective in supporting users' daily lives. However, they lack the ability to provide specific information to improve the shopping experience in brick-and-mortar stores, such as sale and promotion information in real time. They also lack recommendation functions that take into account in-store special events and inventory information. A system that can address these shortcomings and further improve users' shopping experience is needed.

[0869] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0870] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring store sales, promotions, and inventory information, and means for generating an action plan based on the sales, promotions, and inventory information. This allows the user to receive optimal action plans in real time that take into account sales and promotion information and store inventory status. Special event and sale information is also notified in real time, further enhancing the user's shopping experience.

[0871] "Means for obtaining user location information" refers to a device for obtaining the geographical location of the user's current location using GPS or other location identification technology.

[0872] The "means for acquiring user schedule information" is a device that has the function of acquiring the user's plans and dates from a calendar application or schedule management system.

[0873] "Means for obtaining weather forecasts and traffic information" refers to means for obtaining information about weather forecasts and traffic conditions from external APIs or data sources.

[0874] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or algorithm for analyzing data such as location information, schedule information, weather forecasts, and traffic information, and automatically generating an optimal action plan to provide to the user.

[0875] The "means for displaying the generated action plan to the user" refers to a display device or interface for visually showing the generated action plan to the user.

[0876] "Means for obtaining in-store sales information, promotions, and inventory information" refers to systems and database access means for obtaining special sales information, promotions, and product inventory information in physical stores.

[0877] The "means for generating an action plan based on the sale information, promotion, and inventory information" refers to a system or algorithm for automatically generating an optimal action plan for the user based on the acquired sale information, promotion information, and inventory information.

[0878] The "means for obtaining feedback from users" refers to an interface or input device for collecting opinions and evaluations provided by users.

[0879] The "means for analyzing the feedback and improving the accuracy of subsequent action plan generation" refers to a system or method for analyzing collected feedback and improving the algorithm for generating subsequent recommendations and action plans.

[0880] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to a function or system that takes into account the user's current mood and situation and flexibly changes the displayed information and generated action plans based on that.

[0881] "Means of obtaining information about special events and sales in real time and notifying users at the optimal time" refers to a notification system or interface that obtains information about special events and sales taking place in physical stores in real time and allows users to receive that information at the optimal time.

[0882] The system of the present invention is designed to improve the shopping experience of users in physical stores. Specific embodiments for implementing this system are as follows.

[0883] Collection of User Information

[0884] First, when a user wears smart glasses, the device (smart glasses) collects the user's location information. Specifically, it uses a GPS module to obtain the user's current geographical location. It also obtains the user's schedule information from a calendar application. This allows the user's schedule and shopping list to be displayed.

[0885] Retrieving External Data

[0886] The server then retrieves weather and traffic information from an external API. The weather forecast includes today's weather and temperature, and the traffic information includes current congestion and traffic incidents. It also retrieves in-store sales, promotions, and inventory information. This includes a list of special offers, current promotions, and the stock status of each item.

[0887] Data integration and analysis

[0888] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, sale information, promotion information, and inventory information. This generates an optimal action plan for the user. For example, the server may guide the user to a location where products are on sale, or recommend products based on promotion information.

[0889] Generating recommendations

[0890] Based on the analysis results, the server generates recommendations appropriate for the user. For example, it generates information such as "refrigerated food is low in stock, so it is recommended to purchase early" or "there are currently promotional items." It also takes into account information about busy cash registers to recommend the most suitable cash register.

[0891] Viewing Recommendations

[0892] The generated recommendations are sent from the server to the smart glasses, where users can visually check the information and make optimal purchasing decisions. They are also notified of special events and sales information in stores in real time.

[0893] User feedback collection and optimization

[0894] After completing their shopping, the user provides feedback through the smart glasses, including information on whether the provided action plan and recommendations were useful. The server analyzes this feedback and improves the analysis algorithm to improve the accuracy of future action plan generation.

[0895] Hardware and Software Used

[0896] Hardware: Smart glasses (with GPS module)

[0897] Software: Python, external API (weather forecast, traffic information)

[0898] Specific examples

[0899] For example, the following prompts can be used as input to a generative AI model to optimize recommendations:

[0900] Example prompt sentence:

[0901] If the current weather is rainy and traffic is heavy, generate an appropriate plan of action for the user, taking into account the user's location, the travel time from home to the nearest station, and their schedule for the day.

[0902] In this way, the system of the present invention can efficiently support the user's daily life and shopping experience and provide optimal information.

[0903] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0904] Step 1: Collect user information

[0905] The device first obtains the user's location information. The input is the user's current geographic location, and data is collected using a GPS module. The output is the user's precise current location. Next, the device obtains the user's schedule information from a calendar application. This input data includes the user's appointments and shopping list. The output is the user's specific schedule information.

[0906] Step 2: Retrieving external data

[0907] The server retrieves weather forecast information using an external API. The input is a request to the API that provides the weather forecast, and the output is the weather information returned by the API. Similarly, the server retrieves traffic information from an external API. The input is a request to the API that provides traffic information, and the output is information about current traffic conditions. The server also retrieves in-store sales, promotions, and inventory information. The input is a query to the store's database, and the output is the relevant sales, promotions, and inventory information.

[0908] Step 3: Data synthesis and analysis

[0909] The server integrates and analyzes all collected location information, schedule information, weather forecasts, traffic information, sale information, promotion information, and inventory information. The input data is each of the above information, which is processed by the data analysis algorithm on the server. The output is a specific action plan. This plan includes guidance to products on sale or promotion, and the optimal transportation method taking into account current traffic conditions.

[0910] Step 4: Generate recommendations

[0911] The server generates an action plan appropriate for the user based on the analysis results. The input is the analyzed data, and the output is a specific recommendation. For example, it may include recommendations such as "Here are the items currently on promotion" or "The cash register is busy, so you should use another one."

[0912] Step 5: View recommendations

[0913] The server sends the generated recommendations to the user's device. The input is the generated action plan, and the output is the specific recommendations displayed on the device. The user can visually check this information through the smart glasses and optimize their shopping behavior.

[0914] Step 6: Gather user feedback

[0915] After completing their shopping, the user provides feedback through the terminal. The input is feedback data from the user, including their evaluation of the usefulness of the recommendations. The output is feedback information sent to the server.

[0916] Step 7: Feedback analysis and optimization

[0917] The server analyzes user feedback and learns to improve the accuracy of future action plan generation. The input is the collected feedback data, which is processed by a data analysis algorithm. The output is an optimized action plan generation algorithm. This allows the system to continuously improve and provide more useful information to users.

[0918] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0919] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. Furthermore, by combining it with an emotion engine, it is possible to make recommendations that take into account the user's emotional state. An embodiment of this system is described below.

[0920] 1. Collection of User Information

[0921] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, the device obtains the user's schedule information from a calendar app or similar. This information is sent to the server in real time.

[0922] 2. Acquiring external data

[0923] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[0924] 3. Use of Emotion Engine

[0925] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0926] 4. Data integration and analysis

[0927] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information, and generates an optimal action plan for the user based on the analysis results.

[0928] 5. Generating Recommendations

[0929] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain and the user feels tired, the server will generate recommendations such as "You should bring an umbrella" or "You should take a break at a cafe to refresh yourself."

[0930] 6. Displaying Recommendations

[0931] The generated recommendations are sent from the server to the device (glasses), which visually displays this information to the user. The user then checks the displayed recommendations and decides what to do based on them.

[0932] 7. Collecting User Feedback

[0933] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[0934] 8. Recommendation optimization

[0935] The server analyzes the feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine to improve the accuracy of future recommendations.

[0936] Specific examples

[0937] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home), then checks the schedule for the day (work). Furthermore, the emotion engine recognizes the user's mood as "tired." The server obtains the weather forecast and confirms that it will rain that day. It also determines, based on traffic information, that the user needs to catch the train earlier than usual.

[0938] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic may be congested, so you should leave earlier than usual," or "You're tired, so you should take a short break at a cafe in front of the station." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[0939] After arriving at work, users can use the glasses' "Consider" function to input feedback, such as "I brought an umbrella," "I changed my train time," or "I took a break at a cafe." The server receives and analyzes this feedback and uses it to improve the accuracy of future recommendations.

[0940] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information. Furthermore, by using the emotion engine, it is possible to provide personalized advice according to the user's mood and situation.

[0941] The processing flow will be explained below.

[0942] Step 1:

[0943] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[0944] Step 2:

[0945] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[0946] Step 3:

[0947] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[0948] Step 4:

[0949] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[0950] Step 5:

[0951] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[0952] Step 6:

[0953] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[0954] Step 7:

[0955] The server analyzes the location information, schedule information, weather forecast, traffic information, and emotion information it receives, and generates an optimal action plan for the user based on this analysis.

[0956] Step 8:

[0957] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain and the user is feeling tired, the server will make recommendations such as "You should take an umbrella" or "You should take a break at a cafe to refresh yourself."

[0958] Step 9:

[0959] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[0960] Step 10:

[0961] The user reviews the displayed recommendations and selects or adjusts an action based on them, such as taking an umbrella, changing the train time, or taking a break at a cafe.

[0962] Step 11:

[0963] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[0964] Step 12:

[0965] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine, updating the model to improve the accuracy of future recommendations.

[0966] Step 13:

[0967] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[0968] Example 2

[0969] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0970] Conventional information provision systems can provide recommendations based on the user's location and schedule information, but they do not adequately provide personalized information or generate action plans that take the user's emotional state into account.In addition, their functionality for utilizing feedback to improve accuracy in future visits was limited, making it difficult to provide optimal information for the user.

[0971] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0972] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for recognizing the user's emotional state and acquiring emotional information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring feedback from the user, means for analyzing the feedback and improving the accuracy of generating subsequent action plans, and means for changing the displayed information and action plan according to the user's mood and situation, thereby enabling the provision of optimal information and the generation of an action plan taking into account the user's emotional state and feedback.

[0973] "Means for acquiring user location information" refers to hardware or software for identifying the user's current location and collecting that information.

[0974] "Means for acquiring user schedule information" refers to a system or application for acquiring schedule and calendar information entered by a user.

[0975] "Means for obtaining weather and traffic information" means software or systems for collecting weather and traffic conditions from external APIs or data sources.

[0976] "Means for recognizing a user's emotional state and acquiring emotional information" refers to hardware and software for analyzing a user's facial expressions, voice, and other biometric signals to identify their emotional state.

[0977] "Means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or program that integrates collected data and uses algorithms or machine learning models to create an optimal action plan.

[0978] The "means for displaying the generated action plan to the user" refers to a display device such as a display or a head-mounted display for visually presenting the generated action plan to the user.

[0979] "Means for obtaining user feedback" refers to hardware or software for inputting user-provided ratings and opinions.

[0980] "Means for analyzing the feedback and improving the accuracy of future action plans" refers to algorithms or machine learning models that analyze the obtained feedback data and improve the quality of future action plans.

[0981] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to software and systems for dynamically adjusting the information provided and action plans based on the user's current situation and emotions.

[0982] The system of the present invention supports users' daily lives and provides optimal information through a glasses-type device worn by the user. This system includes functions for providing recommendations based on the user's current location, schedule information, external weather forecasts and traffic information, and the user's emotional state.

[0983] Collection of User Information

[0984] First, the device collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which acquires the user's current location. The device also acquires the user's schedule information from a calendar app. For example, if the user has entered a schedule in their calendar that says "I have a meeting in the office at 9 o'clock," this information is acquired. All collected information is sent to the server in real time.

[0985] Retrieving External Data

[0986] The server then uses external APIs to obtain weather and traffic information. The server calls a specific weather API (e.g., OpenWeatherMap API) to obtain weather forecast data based on the user's current location. For traffic information, the server uses Google Maps API to collect delay and congestion information. This provides data to optimize the user's daily activities.

[0987] Using the Emotion Engine

[0988] The emotion engine installed in the device analyzes the user's facial expressions and voice to recognize the user's current emotional state. The device's camera and microphone capture the user's facial expressions and voice, and this data is analyzed by the emotion engine. For example, if the user feels "tired," the emotion is recognized from the user's facial expressions and tone of voice. The recognized emotion information is sent to the server.

[0989] Generating and displaying recommendations

[0990] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information. The server uses data analysis tools such as Python and TensorFlow. Based on the analysis results, an optimal action plan is generated for the user. For example, recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic will be congested, so you should leave early," or "You're tired, so you should take a break at a cafe" may be generated. This information is sent from the server to the device and displayed on the display of the glasses device.

[0991] Collecting and incorporating user feedback

[0992] After completing an action, the user enters feedback using the "Consider" function on the glasses device. For example, they can enter information such as "I took an umbrella," "I changed my train time," or "I took a break at a cafe." This feedback information is sent to the server, which analyzes it and improves the accuracy of future recommendations. The server learns the user's behavioral patterns and preferences, and also integrates emotional information from the emotion engine to further optimize future recommendations.

[0993] Examples of prompt statements

[0994] Below are examples of prompts used within the system.

[0995] Example prompt for getting weather forecast information:

[0996] "Please fetch the current weather forecast for the user's location."

[0997] Example prompts for using the Emotion Engine:

[0998] "Analyze the user's current emotional state based on their facial expressions and vocal tones, and provide a summary."

[0999] Example prompt for getting traffic information:

[1000] "Retrieve the latest traffic conditions for the user's commute route."

[1001] In this way, the system of the present invention can efficiently support the user's daily life and provide more personalized information.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] The user wears the glasses device

[1005] Input: The user puts on the glasses device.

[1006] Operation: The system starts when the user wears the glasses-type device.

[1007] Output: System startup

[1008] Step 2:

[1009] The device acquires user location information

[1010] Input: Built-in GPS module

[1011] How it works: The device's GPS module measures the user's current location and collects that data.

[1012] Output: Current location data of the user

[1013] Step 3:

[1014] The device obtains the user's schedule information

[1015] Input: Calendar app

[1016] Operation: The device references the calendar app and obtains the user's schedule information.

[1017] Output: User's schedule data

[1018] Step 4:

[1019] The device sends the collected information to a server

[1020] Input: User's current location data, schedule data

[1021] Operation: The data acquired by the device is sent to the server using the communication module.

[1022] Output: Location and schedule information sent to the server

[1023] Step 5:

[1024] The server retrieves the external data

[1025] Input: Weather forecast API, traffic information API

[1026] How it works: The server calls an external API to get weather and traffic information.

[1027] Output: Weather forecast data, traffic information data

[1028] Step 6:

[1029] The device acquires emotion data

[1030] Input: Built-in camera and microphone, emotion engine

[1031] How it works: The device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1032] Output: User's emotional information

[1033] Step 7:

[1034] The device sends emotional information to the server.

[1035] Input: User's emotional information

[1036] Operation: The device sends the acquired emotional information to the server.

[1037] Output: Emotion information sent to the server

[1038] Step 8:

[1039] The server integrates and analyzes the data

[1040] Input: location information, schedule information, weather forecast data, traffic information data, emotional information

[1041] How it works: The server aggregates all the data and analyzes it using Python and TensorFlow.

[1042] Output: Analysis results (optimal action plan)

[1043] Step 9:

[1044] The server generates the recommendations

[1045] Input: Analysis results

[1046] Operation: The server uses a recommendation algorithm to create an optimal action plan for the user.

[1047] Output: Recommendation data

[1048] Step 10:

[1049] The server sends the recommendations to the device.

[1050] Input: Recommendation data

[1051] Operation: The server generates recommendation data and sends it to the device.

[1052] Output: Recommendation data sent to the device

[1053] Step 11:

[1054] The device displays the recommendations to the user.

[1055] Input: Recommendation data

[1056] Action: Display the recommendation content on the device display.

[1057] Output: Visual presentation to the user (displayed recommendations)

[1058] Step 12:

[1059] User enters feedback

[1060] Input: User behavior and sentiment

[1061] How it works: The user uses the "consider" feature on the glasses device to provide feedback on the provided recommendation.

[1062] Output: User feedback data

[1063] Step 13:

[1064] The device sends feedback to the server

[1065] Input: User feedback data

[1066] Action: The device sends user feedback to the server.

[1067] Output: Feedback data sent to the server

[1068] Step 14:

[1069] The server analyzes the feedback and improves the accuracy of the next recommendation.

[1070] Input: Feedback data, emotional information

[1071] How it works: The server analyzes the feedback data and updates the machine learning algorithm to improve the accuracy of future recommendations.

[1072] Output: An optimized recommendation model

[1073] In this way, the system of the present invention can provide the user with an optimal action plan and utilize feedback and emotional information to improve accuracy in future attempts.

[1074] (Application example 2)

[1075] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1076] Today's consumers seek efficient and comfortable shopping experiences amid their busy daily lives. However, traditional stores have difficulty providing personalized services that take into account the customer's emotional state and real-time circumstances. Furthermore, one-way information provision often makes it difficult to link this information to actual purchasing behavior. Therefore, a system that proposes optimal action plans that are stress-free for consumers is needed.

[1077] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for analyzing the user's facial expression and tone of voice to recognize their emotional state, means for acquiring in-store inventory information, campaign information, and congestion status, means for performing analysis based on the acquired information and generating an optimal action plan for the user, and means for displaying the generated action plan to the user. This makes it possible to provide personalized recommendations based on the consumer's emotional state and real-time in-store conditions.

[1078] "Location information" is information that identifies the user's current location.

[1079] "Schedule information" is information including the user's plans and plans.

[1080] "Weather forecast" is information predicting current and future weather conditions.

[1081] "Traffic information" refers to information about current traffic conditions.

[1082] "Emotional state" refers to the psychological state of the user as perceived from their facial expression and tone of voice.

[1083] "Inventory information" refers to the list and quantity of products available in the store.

[1084] "Campaign Information" is information about special promotions and discounts being held in stores.

[1085] "Crowding status" is information about the number of people in the store and the degree of congestion.

[1086] An "action plan" is an optimal action plan proposed to the user based on the acquired information.

[1087] The "display means" refers to a device or method for visually presenting the generated action plan and information to the user.

[1088] "Feedback" refers to evaluations and reaction information provided by users in response to actions or suggestions.

[1089] The system of the present invention optimizes a user's daily activities and in-store activities through smart glasses worn by the user. Specific embodiments of the system will be described below.

[1090] First, the smart glasses terminal includes a camera, microphone, display, and Wi-Fi module. The user puts on the smart glasses and activates them. The terminal uses the camera to capture the user's face and the microphone to record their voice. It also connects to the store's network via the Wi-Fi module and transmits data to the server in real time.

[1091] The server acquires multiple pieces of information and integrates and analyzes them. As a specific example, the server includes the following means:

[1092] 1. User Information Collection:

[1093] Identify the user's current location (where they are in the store). You can use the location information API.

[1094] Obtain the user's schedule information based on data sent from a calendar app or similar.

[1095] Obtain weather forecasts and traffic information to help optimize users' daily activities.

[1096] 2. Using the Emotion Engine:

[1097] The smart glasses' camera and microphone are used to capture and analyze the user's facial expressions and tone of voice to recognize their emotional state, using on-device AI engines such as Google Cloud Vision API and IBM Watson.

[1098] 3. Acquiring external data:

[1099] In-store inventory information, campaign information, and congestion status are obtained via API.

[1100] 4. Data integration and analysis:

[1101] The server integrates and analyzes the acquired location information, schedule information, emotional information, and in-store data. Based on this information, it generates optimal action plans and recommendations for users. The analysis is performed on a cloud-based analysis server.

[1102] 5. Displaying Recommendations:

[1103] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user decides on an action based on the displayed information.

[1104] For example, when a user enters a store, the smart glasses will acquire the user's current location and facial expression (emotional state). Based on this information, as well as real-time store information (stock, campaigns, and crowding), the server will suggest the most suitable products to the user and guide them to the appropriate section. In this way, users can enjoy a personalized shopping experience tailored to their individual situation and the store's circumstances.

[1105] Examples of prompt statements

[1106] A user is wearing smart glasses. Design a system that analyzes the user's emotional state from their facial expressions and tone of voice, and integrates their current location (where they are in the store) with store inventory, campaign information, and congestion information to provide the user with appropriate product suggestions and service information. Provide specific recommendations based on the user's emotional state and the integrated data.

[1107] As described above, the present invention can efficiently and comfortably support users' daily lives and shopping experiences.

[1108] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1109] Step 1:

[1110] Collection of User Information

[1111] First, the smart glasses, which are the device, capture the user's face using a camera and record audio using a microphone. Video data from the camera and audio data from the microphone are input. The user's current location is obtained via a location information API, and the user's schedule information is obtained from a calendar app. This information is then sent to the server via a Wi-Fi module.

[1112] Input: Camera video, microphone audio, location information, schedule information

[1113] Output: Consolidated data sent to the server

[1114] Step 2:

[1115] Retrieving External Data

[1116] The server uses external APIs to retrieve in-store inventory information, campaign information, crowding status, and relevant weather and traffic information, thereby gathering all the necessary external data in real time.

[1117] Input: External API call

[1118] Output: Inventory information, campaign information, congestion status, weather forecast, traffic information

[1119] Step 3:

[1120] Using the Emotion Engine

[1121] The system receives camera footage and audio data sent from the device and runs it through an emotion engine to recognize the user's emotional state. This analysis is performed using Google Cloud Vision API and IBM Watson. After the emotional state is recognized, the results are also sent to the server.

[1122] Input: Camera video, audio data

[1123] Output: Emotional state data

[1124] Step 4:

[1125] Data integration

[1126] The server integrates the user information sent in step 1, the external data acquired in step 2, and the emotional state data recognized in step 3. This aggregates the user's current situation and the external environment into a single data set.

[1127] Input: User information (location, schedule), external data (inventory, campaigns, crowds, weather forecast, traffic information), emotional state data

[1128] Output: Unified dataset

[1129] Step 5:

[1130] Analyzing data and generating an action plan

[1131] Based on the integrated data set, the server analyzes and generates a user action plan. For example, if the user is recognized as having fun, it may recommend a specific section or special discount information. This analysis is performed using a cloud-based analysis server.

[1132] Input: Unified dataset

[1133] Output: Action plan, recommendation

[1134] Step 6:

[1135] Viewing Recommendations

[1136] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user makes decisions based on this information.

[1137] Input: Action plan, recommendation

[1138] Output: Information displayed on the smart glasses display

[1139] Step 7:

[1140] Collecting user feedback

[1141] After a user takes action based on a displayed recommendation, they can use the smart glasses' "feedback" function to enter their evaluation of that action or suggestion, which is then sent to the server and used to improve the accuracy of future recommendations.

[1142] Input: User feedback

[1143] Output: Feedback data sent to the server

[1144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1146] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1147] [Fourth embodiment]

[1148] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1152] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1157] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1159] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1160] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1161] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. To implement this system, the following steps can be taken.

[1162] 1. Collection of User Information

[1163] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, it obtains the user's schedule information from a calendar app or similar. This information is then sent to the server.

[1164] 2. Acquiring external data

[1165] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[1166] 3. Data integration and analysis

[1167] The server then analyzes the collected location information, schedule information, weather forecasts, and traffic information, and generates an optimal action plan for the user based on this analysis.

[1168] 4. Generating Recommendations

[1169] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation such as "Bring an umbrella." It may also make suggestions based on traffic information, such as "Get on the train earlier."

[1170] 5. Displaying Recommendations

[1171] The generated recommendations are then sent from the server to the device (glasses), which visually displays this information to the user, who can then review the displayed recommendations and make decisions based on them.

[1172] 6. Collecting User Feedback

[1173] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[1174] 7. Recommendation optimization

[1175] The server analyzes the feedback and learns to improve the accuracy of future recommendations, allowing the system to continually provide the best information for users.

[1176] Specific examples

[1177] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home) and then checks the user's schedule (work) for the day. The server obtains the weather forecast and confirms that it will rain that day. Based on traffic information, the server also determines that the user needs to catch the train earlier than usual.

[1178] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella" or "Traffic may be congested, so you should leave earlier than usual." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[1179] After arriving at work, users can use the glasses' "consider" feature to provide feedback, which the server analyzes and uses to improve future recommendations.

[1180] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information.

[1181] The processing flow will be explained below.

[1182] Step 1:

[1183] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[1184] Step 2:

[1185] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[1186] Step 3:

[1187] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[1188] Step 4:

[1189] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[1190] Step 5:

[1191] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[1192] Step 6:

[1193] The server analyzes the location information, schedule information, weather forecast, and traffic information it receives, and generates an optimal action plan for the user based on this analysis.

[1194] Step 7:

[1195] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain, the system may suggest "take an umbrella," or based on traffic information, "get on the train earlier."

[1196] Step 8:

[1197] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[1198] Step 9:

[1199] The user reviews the displayed recommendations and selects or adjusts an action based on them, for example, taking a specific action such as carrying an umbrella or changing the train time.

[1200] Step 10:

[1201] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[1202] Step 11:

[1203] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. Based on the feedback information, it updates the model to improve the accuracy of future recommendations.

[1204] Step 12:

[1205] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[1206] Example 1

[1207] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1208] Conventional systems are insufficient in optimizing users' daily life action plans and rarely provide specific recommendations tailored to specific situations. Furthermore, they lack the technology to effectively utilize feedback to improve the accuracy of future action plans. This reduces user convenience and limits the value of the system.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1210] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for the user to input feedback on the action plan provided, and means for analyzing using a machine learning model to improve the accuracy of subsequent action plans based on the feedback. This makes it possible to optimize the user's daily life action plan and provide recommendations tailored to specific situations. Furthermore, by utilizing the feedback, the accuracy of subsequent action plans is improved, improving user convenience.

[1211] "Means for obtaining user location information" refers to devices or technologies that use a GPS module to identify the user's current geographical location.

[1212] "Means for acquiring user schedule information" refers to devices or technologies that acquire a user's plans and schedules from a calendar app or other time management software.

[1213] "Means for obtaining weather forecasts and traffic information" refers to devices and technologies that use external APIs and databases to obtain current and future weather and traffic information.

[1214] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a set of software and algorithms that integrates the collected user location information, schedule information, weather forecast, and traffic information, analyzes this data, and generates an optimal action plan.

[1215] The "means for displaying the generated action plan to the user" refers to a display or a display device for presenting the generated action plan to the user.

[1216] The "means for the user to input feedback on the provided action plan" refers to an interface or device that allows the user to evaluate the usefulness and suitability of the received action plan and input feedback.

[1217] The "means for performing analysis using a machine learning model to improve the accuracy of subsequent action plans based on the feedback" refers to software and technology that analyzes feedback data collected from users and uses machine learning algorithms to improve the accuracy of generating subsequent action plans.

[1218] The system of the present invention supports the user's daily life and provides optimal information through a glasses-type terminal worn by the user. The system of the present invention can be implemented according to the following procedure.

[1219] First, the device (glasses) collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which is used to obtain the user's current location. The device also connects to a calendar app to obtain the user's schedule information. This information is then sent to the server in real time.

[1220] The server obtains weather forecasts and traffic information using external APIs. Specifically, it uses OpenWeatherMap as the weather forecast API and Google Maps API as the traffic information API. This information is used to optimize the user's daily activities.

[1221] The server then integrates and analyzes the user's location, schedule, weather forecast, and traffic information. Generative AI models and machine learning algorithms are used in the analysis to generate an optimal action plan for the user. For example, if the weather forecast predicts rain, the server will generate a recommendation that the user should "bring an umbrella," and based on traffic information, it will make a suggestion that the user should "board the train earlier."

[1222] The generated recommendations are sent from the server to the device (glasses), which then visually displays them to the user. The user can decide what to do based on the displayed recommendations. At the same time, the user can also enter feedback after taking action. The feedback is sent to the server via the device, and the server analyzes it to improve the accuracy of future recommendations.

[1223] As a concrete example, consider the case where a user puts on glasses before going to work in the morning. The device obtains the user's current location (home) and checks the user's schedule for the day (work). The server obtains the weather forecast, confirms that the weather for that day will be rainy, and then obtains traffic information to determine that the user should leave earlier than usual. The server generates recommendations such as "It's going to rain today, so you should take an umbrella" and "You should leave earlier than usual," and sends these to the device to display on the glasses' display. After arriving at work, the user enters feedback using the "Consider" function on the glasses, and the server analyzes this feedback and updates the machine learning model to improve the accuracy of the next recommendation.

[1224] An example of a prompt sentence could be, "Please obtain the user's current location and schedule information, and generate an optimal plan of action based on the weather forecast and traffic information. For example, could you suggest taking an umbrella if it's raining, or leaving earlier if traffic is heavy, thereby optimizing the user's daily activities?"

[1225] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information according to individual circumstances.

[1226] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1227] Step 1: Collect user information

[1228] The device uses a GPS module to obtain the user's current location. It receives GPS data as input and generates the user's geographical location information as output. Next, the device connects to a database such as a calendar app to obtain the user's schedule information. Here, the input is data from the calendar app, and the output is the schedule information for that day. This collected information is sent to the server in real time.

[1229] Step 2: Retrieving external data

[1230] The server uses external APIs to obtain weather and traffic information. It inputs weather data from a weather API (e.g., OpenWeatherMap) and outputs weather forecast information. Similarly, it inputs traffic data from a traffic information API (e.g., Google Maps API) and outputs traffic information. These data are stored for subsequent analysis.

[1231] Step 3: Data synthesis and analysis

[1232] The server integrates the user's location information, schedule information, weather forecast, and traffic information it has acquired. It takes in various pieces of information (location information, schedule information, weather forecast, traffic information) as input and generates integrated data. The server then performs analysis based on this integrated data. Generative AI models and machine learning algorithms are used for the analysis, and the analysis results are used as output to generate an optimal action plan.

[1233] Step 4: Generate recommendations

[1234] The server generates recommendations to provide to the user based on the analysis results. It takes the analysis results as input and outputs suggestions (recommendations) for optimizing the user's daily activities. For example, if it is raining, it may generate specific suggestions such as "take an umbrella" or "leave earlier" if traffic is congested.

[1235] Step 5: View recommendations

[1236] The server generates recommendations and sends them to the device. Recommendation data is taken as input and sent to the device as output. The device then visually displays this information to the user. Specifically, the suggestions are displayed on the glasses' display for the user to review.

[1237] Step 6: Gather user feedback

[1238] After the user completes an action based on the provided recommendation, they enter feedback. The user's evaluation data is taken as input, and the feedback information is sent from the device to the server as output. The glasses' "Consider" function makes it easy to enter feedback.

[1239] Step 7: Optimize your recommendations

[1240] The server analyzes the collected feedback. It takes the feedback data as input and outputs the analysis results to improve the accuracy of future recommendations. The server uses a machine learning algorithm to update the model based on the feedback, optimizing future recommendations to be more accurate. This continuous feedback loop allows the entire system to provide a higher level of convenience.

[1241] (Application example 1)

[1242] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1243] Conventional behavioral planning systems based on location and schedule information have been effective in supporting users' daily lives. However, they lack the ability to provide specific information to improve the shopping experience in brick-and-mortar stores, such as sale and promotion information in real time. They also lack recommendation functions that take into account in-store special events and inventory information. A system that can address these shortcomings and further improve users' shopping experience is needed.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1245] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecasts and traffic information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring store sales, promotions, and inventory information, and means for generating an action plan based on the sales, promotions, and inventory information. This allows the user to receive optimal action plans in real time that take into account sales and promotion information and store inventory status. Special event and sale information is also notified in real time, further enhancing the user's shopping experience.

[1246] "Means for obtaining user location information" refers to a device for obtaining the geographical location of the user's current location using GPS or other location identification technology.

[1247] The "means for acquiring user schedule information" is a device that has the function of acquiring the user's plans and dates from a calendar application or schedule management system.

[1248] "Means for obtaining weather forecasts and traffic information" refers to means for obtaining information about weather forecasts and traffic conditions from external APIs or data sources.

[1249] The "means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or algorithm for analyzing data such as location information, schedule information, weather forecasts, and traffic information, and automatically generating an optimal action plan to provide to the user.

[1250] The "means for displaying the generated action plan to the user" refers to a display device or interface for visually showing the generated action plan to the user.

[1251] "Means for obtaining in-store sales information, promotions, and inventory information" refers to systems and database access means for obtaining special sales information, promotions, and product inventory information in physical stores.

[1252] The "means for generating an action plan based on the sale information, promotion, and inventory information" refers to a system or algorithm for automatically generating an optimal action plan for the user based on the acquired sale information, promotion information, and inventory information.

[1253] The "means for obtaining feedback from users" refers to an interface or input device for collecting opinions and evaluations provided by users.

[1254] The "means for analyzing the feedback and improving the accuracy of subsequent action plan generation" refers to a system or method for analyzing collected feedback and improving the algorithm for generating subsequent recommendations and action plans.

[1255] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to a function or system that takes into account the user's current mood and situation and flexibly changes the displayed information and generated action plans based on that.

[1256] "Means of obtaining information about special events and sales in real time and notifying users at the optimal time" refers to a notification system or interface that obtains information about special events and sales taking place in physical stores in real time and allows users to receive that information at the optimal time.

[1257] The system of the present invention is designed to improve the shopping experience of users in physical stores. Specific embodiments for implementing this system are as follows.

[1258] Collection of User Information

[1259] First, when a user wears smart glasses, the device (smart glasses) collects the user's location information. Specifically, it uses a GPS module to obtain the user's current geographical location. It also obtains the user's schedule information from a calendar application. This allows the user's schedule and shopping list to be displayed.

[1260] Retrieving External Data

[1261] The server then retrieves weather and traffic information from an external API. The weather forecast includes today's weather and temperature, and the traffic information includes current congestion and traffic incidents. It also retrieves in-store sales, promotions, and inventory information. This includes a list of special offers, current promotions, and the stock status of each item.

[1262] Data integration and analysis

[1263] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, sale information, promotion information, and inventory information. This generates an optimal action plan for the user. For example, the server may guide the user to a location where products are on sale, or recommend products based on promotion information.

[1264] Generating recommendations

[1265] Based on the analysis results, the server generates recommendations appropriate for the user. For example, it generates information such as "refrigerated food is low in stock, so it is recommended to purchase early" or "there are currently promotional items." It also takes into account information about busy cash registers to recommend the most suitable cash register.

[1266] Viewing Recommendations

[1267] The generated recommendations are sent from the server to the smart glasses, where users can visually check the information and make optimal purchasing decisions. They are also notified of special events and sales information in stores in real time.

[1268] User feedback collection and optimization

[1269] After completing their shopping, the user provides feedback through the smart glasses, including information on whether the provided action plan and recommendations were useful. The server analyzes this feedback and improves the analysis algorithm to improve the accuracy of future action plan generation.

[1270] Hardware and Software Used

[1271] Hardware: Smart glasses (with GPS module)

[1272] Software: Python, external API (weather forecast, traffic information)

[1273] Specific examples

[1274] For example, the following prompts can be used as input to a generative AI model to optimize recommendations:

[1275] Example prompt sentence:

[1276] If the current weather is rainy and traffic is heavy, generate an appropriate plan of action for the user, taking into account the user's location, the travel time from home to the nearest station, and their schedule for the day.

[1277] In this way, the system of the present invention can efficiently support the user's daily life and shopping experience and provide optimal information.

[1278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1279] Step 1: Collect user information

[1280] The device first obtains the user's location information. The input is the user's current geographic location, and data is collected using a GPS module. The output is the user's precise current location. Next, the device obtains the user's schedule information from a calendar application. This input data includes the user's appointments and shopping list. The output is the user's specific schedule information.

[1281] Step 2: Retrieving external data

[1282] The server retrieves weather forecast information using an external API. The input is a request to the API that provides the weather forecast, and the output is the weather information returned by the API. Similarly, the server retrieves traffic information from an external API. The input is a request to the API that provides traffic information, and the output is information about current traffic conditions. The server also retrieves in-store sales, promotions, and inventory information. The input is a query to the store's database, and the output is the relevant sales, promotions, and inventory information.

[1283] Step 3: Data synthesis and analysis

[1284] The server integrates and analyzes all collected location information, schedule information, weather forecasts, traffic information, sale information, promotion information, and inventory information. The input data is each of the above information, which is processed by the data analysis algorithm on the server. The output is a specific action plan. This plan includes guidance to products on sale or promotion, and the optimal transportation method taking into account current traffic conditions.

[1285] Step 4: Generate recommendations

[1286] The server generates an action plan appropriate for the user based on the analysis results. The input is the analyzed data, and the output is a specific recommendation. For example, it may include recommendations such as "Here are the items currently on promotion" or "The cash register is busy, so you should use another one."

[1287] Step 5: View recommendations

[1288] The server sends the generated recommendations to the user's device. The input is the generated action plan, and the output is the specific recommendations displayed on the device. The user can visually check this information through the smart glasses and optimize their shopping behavior.

[1289] Step 6: Gather user feedback

[1290] After completing their shopping, the user provides feedback through the terminal. The input is feedback data from the user, including their evaluation of the usefulness of the recommendations. The output is feedback information sent to the server.

[1291] Step 7: Feedback analysis and optimization

[1292] The server analyzes user feedback and learns to improve the accuracy of future action plan generation. The input is the collected feedback data, which is processed by a data analysis algorithm. The output is an optimized action plan generation algorithm. This allows the system to continuously improve and provide more useful information to users.

[1293] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1294] The system of the present invention supports the user's daily life and provides optimal information through a glasses terminal worn by the user. Furthermore, by combining it with an emotion engine, it is possible to make recommendations that take into account the user's emotional state. An embodiment of this system is described below.

[1295] 1. Collection of User Information

[1296] First, the device (glasses) collects the necessary information from the user. Specifically, the device uses a GPS module to obtain the user's current location. Next, the device obtains the user's schedule information from a calendar app or similar. This information is sent to the server in real time.

[1297] 2. Acquiring external data

[1298] The server then uses external APIs to retrieve weather and traffic information, which is then used to optimize the user's daily routine.

[1299] 3. Use of Emotion Engine

[1300] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[1301] 4. Data integration and analysis

[1302] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information, and generates an optimal action plan for the user based on the analysis results.

[1303] 5. Generating Recommendations

[1304] Based on the analysis results, the server generates recommendations appropriate for the user. For example, if the weather forecast predicts rain and the user feels tired, the server will generate recommendations such as "You should bring an umbrella" or "You should take a break at a cafe to refresh yourself."

[1305] 6. Displaying Recommendations

[1306] The generated recommendations are sent from the server to the device (glasses), which visually displays this information to the user. The user then checks the displayed recommendations and decides what to do based on them.

[1307] 7. Collecting User Feedback

[1308] After users complete an action, they use the glasses' "consider" feature to enter feedback, including whether the provided recommendation was useful.

[1309] 8. Recommendation optimization

[1310] The server analyzes the feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine to improve the accuracy of future recommendations.

[1311] Specific examples

[1312] For example, suppose a user puts on glasses before going to work in the morning. In this case, the device first obtains the user's current location (home), then checks the schedule for the day (work). Furthermore, the emotion engine recognizes the user's mood as "tired." The server obtains the weather forecast and confirms that it will rain that day. It also determines, based on traffic information, that the user needs to catch the train earlier than usual.

[1313] The server integrates this information and generates recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic may be congested, so you should leave earlier than usual," or "You're tired, so you should take a short break at a cafe in front of the station." This information is sent to the device and displayed on the glasses' display. The user can then adjust their behavior based on this information.

[1314] After arriving at work, users can use the glasses' "Consider" function to input feedback, such as "I brought an umbrella," "I changed my train time," or "I took a break at a cafe." The server receives and analyzes this feedback and uses it to improve the accuracy of future recommendations.

[1315] In this way, the system of the present invention can efficiently support the user's daily life and provide optimal information. Furthermore, by using the emotion engine, it is possible to provide personalized advice according to the user's mood and situation.

[1316] The processing flow will be explained below.

[1317] Step 1:

[1318] The user puts on the glasses and enters their user ID and password to log in. The device sends this login information to the server.

[1319] Step 2:

[1320] The server authenticates the login information and starts the session if authentication is successful, otherwise it returns an error message to the terminal.

[1321] Step 3:

[1322] The device uses the GPS module to obtain the user's current location, which is then sent to the server in real time.

[1323] Step 4:

[1324] The device retrieves the user's schedule information for the day from a calendar app or related external services, and sends the retrieved schedule information to the server.

[1325] Step 5:

[1326] The server receives location and schedule information, and uses external APIs to retrieve weather and traffic information, which are then used for analysis as information relevant to the user.

[1327] Step 6:

[1328] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, etc. to recognize the user's current emotion. The recognized emotion information is also sent to the server.

[1329] Step 7:

[1330] The server analyzes the location information, schedule information, weather forecast, traffic information, and emotion information it receives, and generates an optimal action plan for the user based on this analysis.

[1331] Step 8:

[1332] Recommendations are made based on the action plan generated by the server. For example, if the weather forecast predicts rain and the user is feeling tired, the server will make recommendations such as "You should take an umbrella" or "You should take a break at a cafe to refresh yourself."

[1333] Step 9:

[1334] The server sends the generated recommendations to the device, which displays them on the glasses' display.

[1335] Step 10:

[1336] The user reviews the displayed recommendations and selects or adjusts an action based on them, such as taking an umbrella, changing the train time, or taking a break at a cafe.

[1337] Step 11:

[1338] After the user completes the action, they can use the "Consider" function on the glasses to enter feedback on the recommendation, which the device then sends to the server.

[1339] Step 12:

[1340] The server analyzes the received feedback and learns the user's behavioral patterns and preferences. It also integrates and analyzes emotional information from the emotion engine, updating the model to improve the accuracy of future recommendations.

[1341] Step 13:

[1342] The server will then use the updated model to further optimize future user responses, ensuring that the next time the user uses the service, more accurate information will be provided.

[1343] Example 2

[1344] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1345] Conventional information provision systems can provide recommendations based on the user's location and schedule information, but they do not adequately provide personalized information or generate action plans that take the user's emotional state into account.In addition, their functionality for utilizing feedback to improve accuracy in future visits was limited, making it difficult to provide optimal information for the user.

[1346] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1347] In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for recognizing the user's emotional state and acquiring emotional information, means for analyzing the acquired information and generating an optimal action plan for the user, means for displaying the generated action plan to the user, means for acquiring feedback from the user, means for analyzing the feedback and improving the accuracy of generating subsequent action plans, and means for changing the displayed information and action plan according to the user's mood and situation, thereby enabling the provision of optimal information and the generation of an action plan taking into account the user's emotional state and feedback.

[1348] "Means for acquiring user location information" refers to hardware or software for identifying the user's current location and collecting that information.

[1349] "Means for acquiring user schedule information" refers to a system or application for acquiring schedule and calendar information entered by a user.

[1350] "Means for obtaining weather and traffic information" means software or systems for collecting weather and traffic conditions from external APIs or data sources.

[1351] "Means for recognizing a user's emotional state and acquiring emotional information" refers to hardware and software for analyzing a user's facial expressions, voice, and other biometric signals to identify their emotional state.

[1352] "Means for analyzing the acquired information and generating an optimal action plan for the user" refers to a system or program that integrates collected data and uses algorithms or machine learning models to create an optimal action plan.

[1353] The "means for displaying the generated action plan to the user" refers to a display device such as a display or a head-mounted display for visually presenting the generated action plan to the user.

[1354] "Means for obtaining user feedback" refers to hardware or software for inputting user-provided ratings and opinions.

[1355] "Means for analyzing the feedback and improving the accuracy of future action plans" refers to algorithms or machine learning models that analyze the obtained feedback data and improve the quality of future action plans.

[1356] "Means for changing the displayed information and action plans according to the user's mood and situation" refers to software and systems for dynamically adjusting the information provided and action plans based on the user's current situation and emotions.

[1357] The system of the present invention supports users' daily lives and provides optimal information through a glasses-type device worn by the user. This system includes functions for providing recommendations based on the user's current location, schedule information, external weather forecasts and traffic information, and the user's emotional state.

[1358] Collection of User Information

[1359] First, the device collects the necessary information from the user. Specifically, the device is equipped with a GPS module, which acquires the user's current location. The device also acquires the user's schedule information from a calendar app. For example, if the user has entered a schedule in their calendar that says "I have a meeting in the office at 9 o'clock," this information is acquired. All collected information is sent to the server in real time.

[1360] Retrieving External Data

[1361] The server then uses external APIs to obtain weather and traffic information. The server calls a specific weather API (e.g., OpenWeatherMap API) to obtain weather forecast data based on the user's current location. For traffic information, the server uses Google Maps API to collect delay and congestion information. This provides data to optimize the user's daily activities.

[1362] Using the Emotion Engine

[1363] The emotion engine installed in the device analyzes the user's facial expressions and voice to recognize the user's current emotional state. The device's camera and microphone capture the user's facial expressions and voice, and this data is analyzed by the emotion engine. For example, if the user feels "tired," the emotion is recognized from the user's facial expressions and tone of voice. The recognized emotion information is sent to the server.

[1364] Generating and displaying recommendations

[1365] The server integrates and analyzes the collected location information, schedule information, weather forecast, traffic information, and emotional information. The server uses data analysis tools such as Python and TensorFlow. Based on the analysis results, an optimal action plan is generated for the user. For example, recommendations such as "It's going to rain today, so you should take an umbrella," "Traffic will be congested, so you should leave early," or "You're tired, so you should take a break at a cafe" may be generated. This information is sent from the server to the device and displayed on the display of the glasses device.

[1366] Collecting and incorporating user feedback

[1367] After completing an action, the user enters feedback using the "Consider" function on the glasses device. For example, they can enter information such as "I took an umbrella," "I changed my train time," or "I took a break at a cafe." This feedback information is sent to the server, which analyzes it and improves the accuracy of future recommendations. The server learns the user's behavioral patterns and preferences, and also integrates emotional information from the emotion engine to further optimize future recommendations.

[1368] Examples of prompt statements

[1369] Below are examples of prompts used within the system.

[1370] Example prompt for getting weather forecast information:

[1371] "Please fetch the current weather forecast for the user's location."

[1372] Example prompts for using the Emotion Engine:

[1373] "Analyze the user's current emotional state based on their facial expressions and vocal tones, and provide a summary."

[1374] Example prompt for getting traffic information:

[1375] "Retrieve the latest traffic conditions for the user's commute route."

[1376] In this way, the system of the present invention can efficiently support the user's daily life and provide more personalized information.

[1377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1378] Step 1:

[1379] The user wears the glasses device

[1380] Input: The user puts on the glasses device.

[1381] Operation: The system starts when the user wears the glasses-type device.

[1382] Output: System startup

[1383] Step 2:

[1384] The device acquires user location information

[1385] Input: Built-in GPS module

[1386] How it works: The device's GPS module measures the user's current location and collects that data.

[1387] Output: Current location data of the user

[1388] Step 3:

[1389] The device obtains the user's schedule information

[1390] Input: Calendar app

[1391] Operation: The device references the calendar app and obtains the user's schedule information.

[1392] Output: User's schedule data

[1393] Step 4:

[1394] The device sends the collected information to a server

[1395] Input: User's current location data, schedule data

[1396] Operation: The data acquired by the device is sent to the server using the communication module.

[1397] Output: Location and schedule information sent to the server

[1398] Step 5:

[1399] The server retrieves the external data

[1400] Input: Weather forecast API, traffic information API

[1401] How it works: The server calls an external API to get weather and traffic information.

[1402] Output: Weather forecast data, traffic information data

[1403] Step 6:

[1404] The device acquires emotion data

[1405] Input: Built-in camera and microphone, emotion engine

[1406] How it works: The device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1407] Output: User's emotional information

[1408] Step 7:

[1409] The device sends emotional information to the server.

[1410] Input: User's emotional information

[1411] Operation: The device sends the acquired emotional information to the server.

[1412] Output: Emotion information sent to the server

[1413] Step 8:

[1414] The server integrates and analyzes the data

[1415] Input: location information, schedule information, weather forecast data, traffic information data, emotional information

[1416] How it works: The server aggregates all the data and analyzes it using Python and TensorFlow.

[1417] Output: Analysis results (optimal action plan)

[1418] Step 9:

[1419] The server generates the recommendations

[1420] Input: Analysis results

[1421] Operation: The server uses a recommendation algorithm to create an optimal action plan for the user.

[1422] Output: Recommendation data

[1423] Step 10:

[1424] The server sends the recommendations to the device.

[1425] Input: Recommendation data

[1426] Operation: The server generates recommendation data and sends it to the device.

[1427] Output: Recommendation data sent to the device

[1428] Step 11:

[1429] The device displays the recommendations to the user.

[1430] Input: Recommendation data

[1431] Action: Display the recommendation content on the device display.

[1432] Output: Visual presentation to the user (displayed recommendations)

[1433] Step 12:

[1434] User enters feedback

[1435] Input: User behavior and sentiment

[1436] How it works: The user uses the "consider" feature on the glasses device to provide feedback on the provided recommendation.

[1437] Output: User feedback data

[1438] Step 13:

[1439] The device sends feedback to the server

[1440] Input: User feedback data

[1441] Action: The device sends user feedback to the server.

[1442] Output: Feedback data sent to the server

[1443] Step 14:

[1444] The server analyzes the feedback and improves the accuracy of the next recommendation.

[1445] Input: Feedback data, emotional information

[1446] How it works: The server analyzes the feedback data and updates the machine learning algorithm to improve the accuracy of future recommendations.

[1447] Output: An optimized recommendation model

[1448] In this way, the system of the present invention can provide the user with an optimal action plan and utilize feedback and emotional information to improve accuracy in future attempts.

[1449] (Application example 2)

[1450] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1451] Today's consumers seek efficient and comfortable shopping experiences amid their busy daily lives. However, traditional stores have difficulty providing personalized services that take into account the customer's emotional state and real-time circumstances. Furthermore, one-way information provision often makes it difficult to link this information to actual purchasing behavior. Therefore, a system that proposes optimal action plans that are stress-free for consumers is needed.

[1452] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user location information, means for acquiring user schedule information, means for acquiring weather forecast and traffic information, means for analyzing the user's facial expression and tone of voice to recognize their emotional state, means for acquiring in-store inventory information, campaign information, and congestion status, means for performing analysis based on the acquired information and generating an optimal action plan for the user, and means for displaying the generated action plan to the user. This makes it possible to provide personalized recommendations based on the consumer's emotional state and real-time in-store conditions.

[1453] "Location information" is information that identifies the user's current location.

[1454] "Schedule information" is information including the user's plans and plans.

[1455] "Weather forecast" is information predicting current and future weather conditions.

[1456] "Traffic information" refers to information about current traffic conditions.

[1457] "Emotional state" refers to the psychological state of the user as perceived from their facial expression and tone of voice.

[1458] "Inventory information" refers to the list and quantity of products available in the store.

[1459] "Campaign Information" is information about special promotions and discounts being held in stores.

[1460] "Crowding status" is information about the number of people in the store and the degree of congestion.

[1461] An "action plan" is an optimal action plan proposed to the user based on the acquired information.

[1462] The "display means" refers to a device or method for visually presenting the generated action plan and information to the user.

[1463] "Feedback" refers to evaluations and reaction information provided by users in response to actions or suggestions.

[1464] The system of the present invention optimizes a user's daily activities and in-store activities through smart glasses worn by the user. Specific embodiments of the system will be described below.

[1465] First, the smart glasses terminal includes a camera, microphone, display, and Wi-Fi module. The user puts on the smart glasses and activates them. The terminal uses the camera to capture the user's face and the microphone to record their voice. It also connects to the store's network via the Wi-Fi module and transmits data to the server in real time.

[1466] The server acquires multiple pieces of information and integrates and analyzes them. As a specific example, the server includes the following means:

[1467] 1. User Information Collection:

[1468] Identify the user's current location (where they are in the store). You can use the location information API.

[1469] Obtain the user's schedule information based on data sent from a calendar app or similar.

[1470] Obtain weather forecasts and traffic information to help optimize users' daily activities.

[1471] 2. Using the Emotion Engine:

[1472] The smart glasses' camera and microphone are used to capture and analyze the user's facial expressions and tone of voice to recognize their emotional state, using on-device AI engines such as Google Cloud Vision API and IBM Watson.

[1473] 3. Acquiring external data:

[1474] In-store inventory information, campaign information, and congestion status are obtained via API.

[1475] 4. Data integration and analysis:

[1476] The server integrates and analyzes the acquired location information, schedule information, emotional information, and in-store data. Based on this information, it generates optimal action plans and recommendations for users. The analysis is performed on a cloud-based analysis server.

[1477] 5. Displaying Recommendations:

[1478] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user decides on an action based on the displayed information.

[1479] For example, when a user enters a store, the smart glasses will acquire the user's current location and facial expression (emotional state). Based on this information, as well as real-time store information (stock, campaigns, and crowding), the server will suggest the most suitable products to the user and guide them to the appropriate section. In this way, users can enjoy a personalized shopping experience tailored to their individual situation and the store's circumstances.

[1480] Examples of prompt statements

[1481] A user is wearing smart glasses. Design a system that analyzes the user's emotional state from their facial expressions and tone of voice, and integrates their current location (where they are in the store) with store inventory, campaign information, and congestion information to provide the user with appropriate product suggestions and service information. Provide specific recommendations based on the user's emotional state and the integrated data.

[1482] As described above, the present invention can efficiently and comfortably support users' daily lives and shopping experiences.

[1483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1484] Step 1:

[1485] Collection of User Information

[1486] First, the smart glasses, which are the device, capture the user's face using a camera and record audio using a microphone. Video data from the camera and audio data from the microphone are input. The user's current location is obtained via a location information API, and the user's schedule information is obtained from a calendar app. This information is then sent to the server via a Wi-Fi module.

[1487] Input: Camera video, microphone audio, location information, schedule information

[1488] Output: Consolidated data sent to the server

[1489] Step 2:

[1490] Retrieving External Data

[1491] The server uses external APIs to retrieve in-store inventory information, campaign information, crowding status, and relevant weather and traffic information, thereby gathering all the necessary external data in real time.

[1492] Input: External API call

[1493] Output: Inventory information, campaign information, congestion status, weather forecast, traffic information

[1494] Step 3:

[1495] Using the Emotion Engine

[1496] The system receives camera footage and audio data sent from the device and runs it through an emotion engine to recognize the user's emotional state. This analysis is performed using Google Cloud Vision API and IBM Watson. After the emotional state is recognized, the results are also sent to the server.

[1497] Input: Camera video, audio data

[1498] Output: Emotional state data

[1499] Step 4:

[1500] Data integration

[1501] The server integrates the user information sent in step 1, the external data acquired in step 2, and the emotional state data recognized in step 3. This aggregates the user's current situation and the external environment into a single data set.

[1502] Input: User information (location, schedule), external data (inventory, campaigns, crowds, weather forecast, traffic information), emotional state data

[1503] Output: Unified dataset

[1504] Step 5:

[1505] Analyzing data and generating an action plan

[1506] Based on the integrated data set, the server analyzes and generates a user action plan. For example, if the user is recognized as having fun, it may recommend a specific section or special discount information. This analysis is performed using a cloud-based analysis server.

[1507] Input: Unified dataset

[1508] Output: Action plan, recommendation

[1509] Step 6:

[1510] Viewing Recommendations

[1511] The generated action plans and recommendations are visually displayed on the smart glasses display, and the user makes decisions based on this information.

[1512] Input: Action plan, recommendation

[1513] Output: Information displayed on the smart glasses display

[1514] Step 7:

[1515] Collecting user feedback

[1516] After a user takes action based on a displayed recommendation, they can use the smart glasses' "feedback" function to enter their evaluation of that action or suggestion, which is then sent to the server and used to improve the accuracy of future recommendations.

[1517] Input: User feedback

[1518] Output: Feedback data sent to the server

[1519] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1520] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1521] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1522] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1523] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1524] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1525] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1526] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1527] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1528] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1529] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1530] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1531] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1532] 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.

[1533] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1534] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1535] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1536] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1537] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1538] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1539] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1540] The following is further disclosed regarding the above embodiment.

[1541] (Claim 1)

[1542] A means for acquiring user location information;

[1543] A means for obtaining schedule information of a user;

[1544] means for obtaining weather and traffic information;

[1545] means for performing analysis based on the acquired information and generating an optimal action plan for the user;

[1546] The system includes means for displaying the generated action plan to a user.

[1547] (Claim 2)

[1548] a means for obtaining feedback from users;

[1549] The system of claim 1 further comprising means for analyzing the feedback to improve accuracy of subsequent action plan generation.

[1550] (Claim 3)

[1551] 10. The system of claim 1, further comprising means for changing the displayed information and action plan depending on the user's mood and situation.

[1552] "Example 1"

[1553] (Claim 1)

[1554] A means for acquiring user location information;

[1555] A means for obtaining schedule information of a user;

[1556] means for obtaining weather and traffic information;

[1557] means for performing analysis based on the acquired information and generating an optimal action plan for the user;

[1558] means for displaying the generated action plan to a user;

[1559] a means for the user to input feedback on the provided action plan;

[1560] A means for performing analysis using a machine learning model to improve the accuracy of subsequent action plans based on the feedback;

[1561] A system including:

[1562] (Claim 2)

[1563] a means for obtaining feedback from users;

[1564] The system of claim 1 further comprising means for analyzing the feedback to improve accuracy of subsequent action plan generation.

[1565] (Claim 3)

[1566] 10. The system of claim 1, further comprising means for changing the displayed information and action plan depending on the user's mood and situation.

[1567] "Application Example 1"

[1568] (Claim 1)

[1569] A means for acquiring user location information;

[1570] A means for obtaining schedule information of a user;

[1571] means for obtaining weather and traffic information;

[1572] means for performing analysis based on the acquired information and generating an optimal action plan for the user;

[1573] means for displaying the generated action plan to a user;

[1574] a means for obtaining in-store special offers, promotions and inventory information;

[1575] means for generating an action plan based on the sale information, promotions, and inventory information;

[1576] A system including:

[1577] (Claim 2)

[1578] a means for obtaining feedback from users;

[1579] The system of claim 1 further comprising means for analyzing the feedback to improve accuracy of subsequent action plan generation.

[1580] (Claim 3)

[1581] A means for changing the displayed information and action plan depending on the user's mood and situation;

[1582] 2. The system according to claim 1, further comprising means for obtaining information on special events and sales in the store in real time and notifying the user at an optimal time.

[1583] "Example 2: Combining Emotion Engines"

[1584] (Claim 1)

[1585] A means for acquiring user location information;

[1586] A means for obtaining schedule information of a user;

[1587] means for obtaining weather and traffic information;

[1588] means for performing analysis based on the acquired information and generating an optimal action plan for the user;

[1589] means for recognizing a user's emotional state and acquiring emotional information;

[1590] The system includes means for displaying the generated action plan to a user.

[1591] (Claim 2)

[1592] a means for obtaining feedback from users;

[1593] The system of claim 1 further comprising means for analyzing the feedback to improve accuracy of subsequent action plan generation.

[1594] (Claim 3)

[1595] 10. The system of claim 1, further comprising means for changing the displayed information and action plan depending on the user's mood and situation.

[1596] "Application example 2 when combining emotion engines"

[1597] (Claim 1)

[1598] A means for acquiring user location information;

[1599] A means for obtaining schedule information of a user;

[1600] means for obtaining weather and traffic information;

[1601] A means for recognizing a user's emotional state by analyzing facial expressions and tone of voice;

[1602] A means of obtaining in-store inventory information, campaign information, and congestion status;

[1603] means for performing analysis based on the acquired information and generating an optimal action plan for the user;

[1604] The system includes means for displaying the generated action plan to a user.

[1605] (Claim 2)

[1606] a means for obtaining feedback from users;

[1607] The system of claim 1 further comprising means for analyzing the feedback to improve accuracy of subsequent action plan generation.

[1608] (Claim 3)

[1609] 10. The system of claim 1, further comprising means for changing the displayed information and action plan depending on the user's mood and situation. [Explanation of symbols]

[1610] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring user location information; A means for obtaining schedule information of a user; means for obtaining weather and traffic information; means for performing analysis based on the acquired information and generating an optimal action plan for the user; The system includes means for displaying the generated action plan to a user.

2. a means for obtaining feedback from users; The system of claim 1 further comprising means for analyzing the feedback to improve the accuracy of subsequent generation of action plans.

3. 2. The system according to claim 1, further comprising means for changing the displayed information and action plan according to the user's mood and situation.

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