system

Abstract

We provide the system. [Solution] Collect fish finder data, By integrating past weather conditions, seasons, and fishing records, and analyzing them with artificial intelligence, A means for notifying an information processing device of information indicating the location of the fishing grounds in coordinates, A means to function as a platform for information recipients to act efficiently, with the aim of managing urban resources. Based on the analyzed data, this information will be provided to fishermen as a means to optimize resource utilization within urban areas. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional fishing, fishermen mainly rely on fish finders to search for fish, but it is difficult to immediately find a suitable fishing ground, often resulting in inefficient fishing. As a result, productivity decreases, and it may be difficult to ship the planned quantity. And there is a problem that the supply to the market based on fishing information is unstable, causing price fluctuations.

Means for Solving the Problems

[0005] This invention is a system that collects real-time data from a fish finder, integrates it with historical temperature, season, and catch data, and analyzes it using AI to accurately identify fishing grounds and notifies the user's terminal of the information, indicated by latitude and longitude. This system improves fishing efficiency by scoring the analysis results and providing the user with only highly accurate results. Furthermore, in integrating real-time and historical data, missing or outlier values ​​are imputed to achieve accurate and reliable analysis.

[0006] "Fish finder data" refers to information such as the location and density of fish detected by a fish finder at sea, as well as water temperature.

[0007] "Historical temperature data" refers to information about temperatures recorded in the past for a specific region or period.

[0008] "Seasonal data" refers to data that represents information related to the four seasons and climate patterns in a particular region.

[0009] "Catch data" refers to information about the types, quantities, timing, and locations of fish caught in past fishing activities.

[0010] "Analyzing with AI" refers to the process of using artificial intelligence technology to analyze data and derive patterns or predictions.

[0011] "Fishing ground location" refers to a specific point in a body of water where a school of fish is predicted to exist, indicated by its latitude and longitude.

[0012] "Notifying the device" refers to sending the information obtained as a result of the analysis to a device such as a smartphone or tablet so that the user can receive it.

[0013] "Scoring analysis results" refers to the process of evaluating the reliability and effectiveness of results obtained during the data analysis process, and then assigning priority and importance scores based on those evaluations.

[0014] "Real-time data" refers to data that sequentially obtains the latest information at that point in time.

[0015] "Compensation for deficiencies and anomalies" refers to the process of supplementing and correcting missing or inaccurate information in the data.

Brief Explanation of Drawings

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0037] The system according to the present invention integrates real-time data collected from a fish finder with historical temperature, season, and catch data, and performs AI-based analysis to provide users with fishing grounds. The roles of the server, terminal, and user are described below.

[0038] Data collection and integration

[0039] The server receives fish-finding data transmitted from each vessel in real time. This data includes fish density, current water temperature, and latitude / longitude information. Simultaneously, the server periodically retrieves historical temperature and seasonal information, as well as historical catch data, from a database and integrates them. The integrated data is then formatted for AI analysis.

[0040] AI analysis

[0041] The server uses AI to perform analysis based on integrated data. The AI ​​compares past and present data patterns to predict the movement and current state of fish schools. As a result of the AI ​​analysis, the location (latitude and longitude) of dense fishing grounds where predicted high-density fish schools exist is identified. The analysis results are scored according to their confidence level, and only the most accurate results are selected.

[0042] Notification of results

[0043] The identified fishing grounds are transmitted from the server to the terminal and displayed as push notifications on the user's smartphone. This allows users to instantly obtain information about new fishing grounds.

[0044] User behavior

[0045] Based on the analysis results displayed on the terminal, users can efficiently fish by directing their boats to newly identified fishing grounds. Users can also check the latitude and longitude information of fishing grounds, allowing them to reach their target fishing grounds in a short amount of time.

[0046] As a concrete example of this system, suppose that during an analysis on a particular day, the location "35.6895°N 139.6917°E" is identified as a fishing ground and notified to the user. Based on this information, the user can head directly to an area they haven't explored before, enabling them to catch a large quantity of fish in a short amount of time.

[0047] This invention allows fishermen to achieve their planned catch with less effort and ensure a stable supply to the market.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server receives real-time data from fish finders transmitted from each vessel. This data includes fish density, water temperature, and the vessel's latitude and longitude. The server temporarily stores the received data in storage.

[0051] Step 2:

[0052] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is then converted into a format that can be integrated with the latest real-time data for analysis.

[0053] Step 3:

[0054] The server integrates collected real-time and historical data and performs data refinement. This process involves imputing missing data and filtering outliers.

[0055] Step 4:

[0056] The server provides the prepared data as input to the AI ​​model and performs the analysis. The AI ​​compares past fish school patterns with the current data to identify the predicted location of the fishing grounds.

[0057] Step 5:

[0058] The server scores the AI-generated analysis results and selects the most reliable ones. The selected analysis results are compiled in latitude and longitude format.

[0059] Step 6:

[0060] The server sends the selected analysis results to the terminal and delivers a push notification to the user's smartphone.

[0061] Step 7:

[0062] The user checks the information displayed on the terminal, operates the boat based on the notified fishing grounds, and prepares to head to a new fishing ground.

[0063] Step 8:

[0064] After arriving at the fishing grounds, users re-examine the fish finder data and begin fishing at the recommended location. This enables efficient fishing.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] There is a need to effectively utilize real-time data obtained from fish finders along with historical environmental and catch information to provide more accurate location information for fishing grounds. However, conventional technologies have made it difficult to obtain highly accurate results in data integration and analysis. As a result, fishermen have been unable to fish efficiently and have suffered economic losses.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for collecting fish finder data and analyzing it using a computational model in conjunction with past temperature, season, and catch history; means for assigning a reliability index to the analysis results and selecting the most reliable result; and means for integrating real-time fish school information with historical information, and analyzing it after supplementing missing and anomaly data. This enables fishermen to quickly obtain highly accurate and reliable fishing ground information and achieve efficient fishing.

[0070] "Fish finder data" refers to information obtained from devices used to measure the presence and density of fish, as well as water depth, underwater.

[0071] "Past temperature, season, and fishing history" refers to data on temperature changes, seasonal environmental conditions, and fishing results that have been recorded to date.

[0072] A "computational model" is a mathematical or algorithmic framework used to analyze complex information.

[0073] "Assigning a reliability index" means applying numerical values ​​or evaluation criteria to the analysis results to assess their accuracy and reliability.

[0074] "Real-time fish school information" refers to data on fish schools that reflects the current situation and provides immediate updates.

[0075] "Completing missing and abnormal data" means supplementing or correcting incomplete or abnormal data with necessary information.

[0076] "Highly accurate and reliable fishing ground information" refers to data on the location of fishing grounds where the analysis results are extremely accurate and reliable in relation to the actual situation.

[0077] This invention relates to a system that acquires fish finder data in real time and analyzes it in conjunction with past temperature, season, and catch history. The server plays a central role in this system and uses a computer with a powerful processor and sufficient memory to perform high-speed and efficient data processing. Python is used as the programming language for data processing, and generative AI model-based frameworks such as TENSORFLOW® and PyTorch are utilized for analysis.

[0078] The server first receives real-time data transmitted from the ship via the fish finder and stores it in a database. This data includes fish density, water temperature, and latitude / longitude information. Next, it retrieves historical temperature, season, and catch data from the database and integrates it with the real-time data. During this process, any missing or abnormal data is filled in.

[0079] The server uses integrated data to perform analysis using an AI model. The AI ​​compares historical data patterns with current real-time data to predict the movement of fish schools and dense fishing grounds. Based on these results, it assigns a confidence score and notifies the information display device of the most reliable result.

[0080] The information display device is typically the user's smartphone, which receives push notifications from the server. Based on these notifications, the user can steer their boat towards the identified fishing grounds and conduct efficient fishing.

[0081] As a concrete example, one day, a server analysis identifies "35.6895°N 139.6917°E" as a fishing ground with a high density of fish, and this information is sent to the user via push notification. Based on this information, the user heads directly to the selected fishing ground and achieves a large catch in a short amount of time.

[0082] An example of a prompt message for the generating AI model might be: "Based on real-time fish finder data integrated with historical temperature, season, and catch data, output the location of the fishing ground where the next high-density fish school is predicted to occur, in latitude and longitude."

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server receives data in real time from the fish finder. This data includes information on fish density, water temperature, and latitude / longitude. The input is sensor data transmitted from the vessel, and the output is raw data stored in the database. The server prevents data loss by immediately saving the information received via the network to the database.

[0086] Step 2:

[0087] The server retrieves historical data on temperature, season, and catch from the database. This involves query operations utilizing the database management system. The input is historical information within the database, and the output is an integrated dataset containing this information. The server integrates the retrieved historical data with real-time data and formats it into a format suitable for analysis.

[0088] Step 3:

[0089] The server uses an integrated dataset to perform analysis with a generative AI model. The AI ​​model is implemented using TensorFlow and PyTorch, and predicts the movement of fish schools by comparing past data patterns with current data. The input is an integrated dataset, and the output is latitude and longitude information and confidence scores for high-density fishing grounds. The server feeds the data into the AI ​​model and receives the output from the model in an analyzable format.

[0090] Step 4:

[0091] The server scores the AI ​​analysis results and selects the most reliable results. Here, a confidence score is assigned to the analysis results, and the server filters the results based on this score. The input is the analysis results from the AI ​​model, and the output is the selected, highly reliable fishing ground information. The server evaluates the reliability of the results and extracts only the most relevant information.

[0092] Step 5:

[0093] The server sends the selected fishing ground information to the terminal. Specifically, it uses a push notification function to notify the user's information display device of the results. The input is reliable fishing ground information, and the output is a notification message displayed on the user's terminal. The server sends notifications via an API, allowing the user to receive the information quickly.

[0094] Step 6:

[0095] The user directs the vessel to a designated fishing ground based on fishing ground information displayed on the terminal. This enables efficient fishing. The input is the notification information displayed on the terminal, and the output is the user's decision to move. The user enters the notified latitude and longitude into the vessel's navigation system and proceeds to the target fishing ground.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] There is a need to address the inefficient use of fishery resources in urban areas and the resulting unsustainable resource management challenges. Currently, fishermen have limited means of obtaining effective fishing ground information, hindering optimal resource utilization. To address these problems, it is necessary to leverage real-time data and historical insights to streamline resource management across cities.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] This invention includes a server that collects fish finder data and analyzes it using artificial intelligence in conjunction with past weather conditions, timing, and fishing history; a platform that functions for efficient action by information recipients for the purpose of urban resource management; and a means that provides useful information to fishermen based on the analyzed data and optimizes resource use within the city. This enables efficient resource use and sustainable urban management in real time.

[0101] "Fish finder data" refers to information about the presence and density of fish obtained using a fish finder.

[0102] "Past weather conditions" refers to historical information about weather patterns such as previous temperatures and precipitation.

[0103] "Period" refers to time information that indicates a specific time period or season.

[0104] "Fishing history" refers to records of the amount and types of fish caught in past fishing operations.

[0105] "Analyzing with artificial intelligence" refers to the process of interpreting data using AI technology to identify patterns and make predictions.

[0106] "Coordinates" are a combination of numbers and signs used to indicate a specific location.

[0107] An "information processing device" refers to hardware and software used for receiving, analyzing, and notifying data.

[0108] An "information recipient" is a user or organization that receives and utilizes the analyzed information.

[0109] "Urban resource management" refers to various activities aimed at optimizing the use of natural resources, human resources, information resources, and other resources within a city.

[0110] The system that realizes this invention uses AI to analyze fish finder data, past weather conditions, time of year, and fishing history. Specifically, the server, terminal, and user configurations each play a crucial role.

[0111] The server receives real-time data transmitted from fish finders and integrates it with information from past weather conditions and fishing history databases. This is done using a database management system (e.g., MySQL® or PostgreSQL). The integrated data is analyzed using artificial intelligence models (such as TensorFlow or PyTorch) to identify fishing ground locations using coordinates.

[0112] The analyzed results are sent as push notifications to the terminal via the information processing device. The terminal allows the user to receive the analysis results and use them to decide on the most efficient fishing grounds to move to. Push notification services such as Firebase Cloud Messaging are used for this notification.

[0113] Based on the received fishing ground information, users can efficiently utilize fishing grounds as part of urban resource management. This promotes sustainable resource use within cities.

[0114] As a concrete example, consider a scenario where, on a particular summer day, the temperature is higher than average, and AI analyzes the likelihood of fish schools gathering in a specific warm water area. This information is then sent to the user's device, allowing them to quickly access that fishing ground and optimize their catch.

[0115] Furthermore, an example of an input prompt statement for the generating AI model used to realize this invention is shown below.

[0116] "Create a program that collects real-time data using a fish finder and analyzes it with AI. Please describe in detail the specifications of the app that will send push notifications to a smartphone about predicted fishing grounds."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives data transmitted in real time from the fish finder. This data includes fish density, current water temperature, and latitude / longitude information. The input data is recorded in a database and prepared for integration with historical weather conditions and fishing history data.

[0120] Step 2:

[0121] The server retrieves historical weather conditions, timing, and fishing history information from a database management system. This historical data is then integrated with real-time data. Here, the time-series data is organized, outliers and missing values ​​are imputed, and the data is formatted for analysis.

[0122] Step 3:

[0123] The server inputs the integrated dataset into an artificial intelligence model (e.g., a model utilizing TensorFlow) and performs data analysis. Based on this analysis, it extracts patterns in the movement of fish schools and predicts future fishing ground locations. The output is the coordinate information of the predicted fishing ground locations.

[0124] Step 4:

[0125] The server prioritizes the AI ​​analysis results and selects the most accurate ones. A confidence scoring algorithm is used for this selection. The selected results are then prepared for notification to the user.

[0126] Step 5:

[0127] The server pushes the selected fishing ground location information to the terminal via an information processing device. By using a notification service such as Firebase Cloud Messaging, it is configured to deliver the information to the user's terminal immediately.

[0128] Step 6:

[0129] Users check the fishing ground location information received on their devices. Based on the information provided, they plan efficient navigation to fishing grounds. This allows users to sustainably utilize urban fishing resources.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention combines a system that uses AI to analyze data collected from a fish finder with an emotion engine that recognizes user emotions. This system is mainly composed of three entities: a server, a terminal, and a user.

[0132] Data collection and analysis

[0133] The server receives fish finder data transmitted from ships in real time and integrates it with historical temperature, season, and catch data. This data is analyzed by AI and formatted to provide fishermen with information on optimal fishing ground locations. The AI ​​learns from past patterns and indicates areas where fish schools are predicted to be concentrated in the form of latitude and longitude.

[0134] How the emotion engine works

[0135] The emotion engine installed on the server recognizes the user's past emotional patterns and current emotional state. This makes it possible to optimize the content and timing of notifications based on the user's emotions. Specifically, it adjusts the information provided, for example, by offering concise information when the user is stressed and detailed information when the user is relaxed.

[0136] Information provision and user response

[0137] The device provides users with push notifications indicating fishing ground locations (latitude and longitude) and related information. These notifications are delivered to the user in the most appropriate format based on analysis by an emotion engine.

[0138] Based on the information received from the device, the user moves to the fishing grounds and fishes effectively. For example, if the user's emotional state is elevated, a notification is sent with an encouraging message to support their motivation. On the other hand, if an emotion indicating fatigue is detected, a concise notification containing only the facts is sent.

[0139] Specific example

[0140] At a certain point, based on fish finder data, the AI ​​identifies "40.7128°N 74.0060°W" as the optimal fishing ground. Based on this analysis, the server analyzes the user's emotional state and notifies them of their location along with an encouraging message, such as "It looks like we'll have a great catch today! The next spot is 40.7128°N 74.0060°W." In this way, the combination of emotion engines allows for personalized notifications, improving both fishing efficiency and user experience.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The server acquires fish finder data transmitted in real time from the vessel. The acquired data includes fish density, water temperature, and the vessel's position information.

[0144] Step 2:

[0145] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is prepared as base data to be used for analysis along with real-time data.

[0146] Step 3:

[0147] The server integrates real-time and historical data, filling in missing or outlier values. This prepares a dataset suitable for AI analysis.

[0148] Step 4:

[0149] The server performs AI analysis to identify the optimal fishing grounds based on the movement and predictions of fish schools. These fishing grounds are shown in latitude and longitude format.

[0150] Step 5:

[0151] The server uses an emotion engine to analyze the user's current emotional state. It also considers past emotional data to determine the user's stress and motivation levels.

[0152] Step 6:

[0153] The server customizes the content and format of notifications based on the analyzed emotional state. For example, it adds encouraging words to highly motivated users and prepares concise notifications for fatigued users.

[0154] Step 7:

[0155] The server sends customized fishing ground information and messages to the device. The device receives this and displays it to the user as a unique push notification.

[0156] Step 8:

[0157] Based on the information received from the device, the user steers the boat towards the designated fishing grounds. The user can then begin effective fishing based on the notified points.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0160] While conventional fish finder systems can provide real-time fishing ground locations and high-precision data analysis, they fail to optimize information based on the user's psychological state. This lack of flexibility in providing information tailored to the user's current situation makes them particularly difficult to use effectively when stressed or fatigued.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for collecting acoustic device data and integrating it with past weather data, temporal information, and fishing information for analysis in an information processing system; means for analyzing the user's emotional state and optimizing notification content and timing; and means for generating optimized information tailored to the user and providing it via an information terminal. This enables flexible and effective information provision based on the user's emotional state.

[0163] "Acoustic device data" refers to information based on sound waves used to measure the location and density of schools of fish.

[0164] An "information processing system" is an electronic system that analyzes collected data and generates information useful to users.

[0165] A "coordinate system" is a combination of latitude and longitude used to indicate a specific location on Earth.

[0166] An "information terminal" is an electronic device used by users to access and receive information.

[0167] "User emotional state" refers to the collective psychological reactions and emotions that individual users exhibit in specific situations.

[0168] "Notification content" refers to the content of messages and information transmitted to users through information terminals.

[0169] "Optimization" refers to adjusting or improving information in a way that is best suited to a specific purpose or condition.

[0170] "High-precision data analysis" is the process of deriving accurate and reliable results through statistical or computational analysis of data.

[0171] This system supports users' efficient fishing activities based on acoustic data transmitted from ships. Users can utilize this data in real time and receive information tailored to their emotional state.

[0172] The server first receives data provided by acoustic devices. This data indicates the location and density of fish schools in the ocean and is measured using sound waves. The server then integrates this data with previously recorded weather data, temporal information, and fishing data. This data is analyzed by an information processing system, and a generative AI model is used to identify areas where fish school concentrations are predicted in the form of latitude and longitude.

[0173] Next, the server uses an emotion engine to understand the user's past emotional data and current emotional state. This allows it to optimize the content and timing of notifications, generating information in a format suitable for the user.

[0174] The device provides users with optimized information sent from the server via push notifications. These notifications may include encouraging messages such as, "Today is a good day for fishing. The next point is 40.7128°N 74.0060°W."

[0175] Based on the information received from their device, users can move to designated fishing grounds and fish effectively. When users are feeling excited, encouraging messages included in notifications further support their motivation, while when they are feeling fatigued, only the minimum necessary information is provided, enabling efficient decision-making.

[0176] An example of a prompt message for this system is: "Please provide the optimal fishing ground location predicted based on fish finder data, and optimized notification content based on the user's sentiment."

[0177] Thus, this invention enables flexible information provision that takes into account the emotional state of users, thereby improving the efficiency of fishing.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server receives fish school data transmitted from acoustic devices in real time. This data measures the location and density of fish schools in the ocean using sound waves. The server receives this data as input and stores it in a database. This storage process enables subsequent data integration and analysis.

[0181] Step 2:

[0182] The server integrates received fish school data with historical weather data, temporal information, and catch information. This data consists of historical data obtained from various sensors and past fishing activities. The server prepares the integrated data as a dataset for analysis and inputs it into the generating AI model. During this process, missing or outlier data are automatically imputed, improving the accuracy of the analysis.

[0183] Step 3:

[0184] The server analyzes the integrated dataset using a generative AI model. The AI ​​model learns from past patterns and identifies areas where fish schools are predicted to be concentrated. Specifically, the AI ​​performs pattern recognition and predicts the optimal fishing ground location as latitude and longitude coordinates. This predicted data is output from the server.

[0185] Step 4:

[0186] The server uses an emotion engine to analyze past user emotion data and the user's current emotional state. The user's emotional state is extracted from past user feedback and current input data. Based on this analysis, the content and timing of notifications are optimized. If speed is a priority, a concise message focusing on the key points is generated.

[0187] Step 5:

[0188] The device receives optimized information transmitted from the server. This information includes fishing ground data (latitude and longitude) and optimized messages based on the user's emotions. The device displays this information on the screen and provides it to the user via push notifications. By receiving these notifications, the user can select fishing grounds that are appropriate for the situation.

[0189] Step 6:

[0190] Users receive information from their devices and move to fishing grounds based on the provided coordinates. They then use emotionally appropriate messages contained within the information to guide their fishing activities. For example, encouraging messages may prompt them to take action. Based on this information, users can efficiently carry out their activities in the actual fishing grounds.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In modern fisheries, while there is a demand for increased efficiency in selecting fishing grounds, there is also a need to provide information that is tailored to the emotional state of the information recipients, such as fishermen and urban dwellers. However, current systems simply analyze and notify fishing ground and urban information without optimizing the information based on the user's emotions, resulting in challenges such as low information acceptance and utility. Moreover, urban residents encounter a wide range of information on a daily basis, making it difficult to select useful and appropriate information from among them.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] This invention includes a server that collects fish finder data, analyzes it using an intelligent system in conjunction with past weather conditions, temporal factors, and catch data, and notifies an information terminal of information indicating the location of fishing grounds in geographic coordinates; an emotion recognition engine that recognizes the emotional state of the user and provides optimized information notifications; and an environmental data set in a city that collects information based on the emotional state of the residents. This enables efficient selection of fishing grounds in fisheries and the provision of information tailored to the emotional state of users in urban life.

[0196] "Fish finder data" refers to data collected from devices that acquire information about the location and movement of schools of fish.

[0197] "Past weather conditions" refers to information about environmental factors such as past temperatures, wind speeds, and precipitation.

[0198] "Temporal factors" are elements related to a specific time, such as the season or date and time.

[0199] "Catch data" refers to information about the types and quantities of fish obtained in the past through fishing activities.

[0200] An "intelligent system" is a technology or system that uses artificial intelligence to analyze data and derive useful information.

[0201] "Geographic coordinates" are numerical information consisting of latitude and longitude that indicates a specific location on Earth.

[0202] An "information terminal" is an electronic device used to receive and display information, and includes smartphones and tablets.

[0203] An "emotion recognition engine" is a technology that uses sensing technology and algorithms to identify a person's emotional state and enables the provision of information based on that identification.

[0204] "Users" refers to a diverse range of users, including fishermen and urban residents, who utilize this system.

[0205] "Environmental data" refers to various types of information about local conditions, such as traffic information, weather information, and event information in cities.

[0206] "Personalization" refers to customizing and providing information in a way that is suitable for a specific user.

[0207] This system consists of three main elements: a server, a terminal, and a user. The server receives fish school data transmitted from a fish finder and analyzes it using an intelligent system, integrating it with past weather conditions, temporal factors, and catch data. This analysis generates information indicating the location of the optimal fishing grounds in geographical coordinates. The server then transmits this information to the terminal. The terminal receives the information and notifies the user. The notified information is adjusted by an emotion recognition engine based on the user's emotional state. For example, if the user is stressed, concise information is provided; if relaxed, detailed information is provided. Cloud services such as AWS (registered trademark) are used in this process. Cloud services help process large amounts of data quickly and generate the necessary information. As a concrete example, on the day an event is held in the city, this system can be used to notify urban dwellers of traffic congestion information and alternative routes at the appropriate time. Furthermore, the generating AI model uses prompt sentences in the form of "What activities would you recommend when the user is relaxed?" based on the user's current emotional state. This makes it possible to provide information tailored to the user's needs.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server receives data in real time from the fish finder. This data is input and combined with weather conditions and catch data to generate a dataset. This then forms the basis for the next analysis.

[0211] Step 2:

[0212] The server uses the dataset to perform analysis with an intelligent system. The input here is an integrated dataset, and the AI ​​model learns from past patterns and outputs the predicted locations of fish school concentrations in latitude and longitude format.

[0213] Step 3:

[0214] The server evaluates the analysis results and extracts only the information with high accuracy. The input is multiple predicted locations generated by the AI, and the output is fishing ground location information selected from the scored data based on accuracy.

[0215] Step 4:

[0216] The server uses an emotion recognition engine to analyze the user's emotional state. The input here is the user's emotional pattern and current state information, and the output is a guideline for determining the appropriate style of communication.

[0217] Step 5:

[0218] The terminal receives data sent from the server and notifies the user. Specific inputs include fishing ground information (including latitude and longitude) and guidelines for communication style, while the output is a push notification to the user's terminal.

[0219] Step 6:

[0220] Users receive notifications and use them to plan their fishing trips or urban life. They receive notification information from their devices as input and use it to select the optimal action. For example, they can start moving to a recommended fishing spot.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] The system according to the present invention integrates real-time data collected from a fish finder with historical temperature, season, and catch data, and performs AI-based analysis to provide users with fishing grounds. The roles of the server, terminal, and user are described below.

[0238] Data collection and integration

[0239] The server receives fish-finding data transmitted from each vessel in real time. This data includes fish density, current water temperature, and latitude / longitude information. Simultaneously, the server periodically retrieves historical temperature and seasonal information, as well as historical catch data, from a database and integrates them. The integrated data is then formatted for AI analysis.

[0240] AI analysis

[0241] The server uses AI to perform analysis based on integrated data. The AI ​​compares past and present data patterns to predict the movement and current state of fish schools. As a result of the AI ​​analysis, the location (latitude and longitude) of dense fishing grounds where predicted high-density fish schools exist is identified. The analysis results are scored according to their confidence level, and only the most accurate results are selected.

[0242] Notification of results

[0243] The identified fishing grounds are transmitted from the server to the terminal and displayed as push notifications on the user's smartphone. This allows users to instantly obtain information about new fishing grounds.

[0244] User behavior

[0245] Based on the analysis results displayed on the terminal, users can efficiently fish by directing their boats to newly identified fishing grounds. Users can also check the latitude and longitude information of fishing grounds, allowing them to reach their target fishing grounds in a short amount of time.

[0246] As a concrete example of this system, suppose that during an analysis on a particular day, the location "35.6895°N 139.6917°E" is identified as a fishing ground and notified to the user. Based on this information, the user can head directly to an area they haven't explored before, enabling them to catch a large quantity of fish in a short amount of time.

[0247] This invention allows fishermen to achieve their planned catch with less effort and ensure a stable supply to the market.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The server receives real-time data from fish finders transmitted from each vessel. This data includes fish density, water temperature, and the vessel's latitude and longitude. The server temporarily stores the received data in storage.

[0251] Step 2:

[0252] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is then converted into a format that can be integrated with the latest real-time data for analysis.

[0253] Step 3:

[0254] The server integrates collected real-time and historical data and performs data refinement. This process involves imputing missing data and filtering outliers.

[0255] Step 4:

[0256] The server provides the prepared data as input to the AI ​​model and performs the analysis. The AI ​​compares past fish school patterns with the current data to identify the predicted location of the fishing grounds.

[0257] Step 5:

[0258] The server scores the AI-generated analysis results and selects the most reliable ones. The selected analysis results are compiled in latitude and longitude format.

[0259] Step 6:

[0260] The server sends the selected analysis results to the terminal and delivers a push notification to the user's smartphone.

[0261] Step 7:

[0262] The user checks the information displayed on the terminal, operates the boat based on the notified fishing grounds, and prepares to head to a new fishing ground.

[0263] Step 8:

[0264] After arriving at the fishing grounds, users re-examine the fish finder data and begin fishing at the recommended location. This enables efficient fishing.

[0265] (Example 1)

[0266] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] There is a need to effectively utilize real-time data obtained from fish finders along with historical environmental and catch information to provide more accurate location information for fishing grounds. However, conventional technologies have made it difficult to obtain highly accurate results in data integration and analysis. As a result, fishermen have been unable to fish efficiently and have suffered economic losses.

[0268] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0269] In this invention, the server includes means for collecting fish finder data and analyzing it using a computational model in conjunction with past temperature, season, and catch history; means for assigning a reliability index to the analysis results and selecting the most reliable result; and means for integrating real-time fish school information with historical information, and analyzing it after supplementing missing and anomaly data. This enables fishermen to quickly obtain highly accurate and reliable fishing ground information and achieve efficient fishing.

[0270] "Fish finder data" refers to information obtained from devices used to measure the presence and density of fish, as well as water depth, underwater.

[0271] "Past temperature, season, and fishing history" refers to data on temperature changes, seasonal environmental conditions, and fishing results that have been recorded to date.

[0272] A "computational model" is a mathematical or algorithmic framework used to analyze complex information.

[0273] "Assigning a reliability index" means applying numerical values ​​or evaluation criteria to the analysis results to assess their accuracy and reliability.

[0274] "Real-time fish school information" refers to data on fish schools that reflects the current situation and provides immediate updates.

[0275] "Completing missing and abnormal data" means supplementing or correcting incomplete or abnormal data with necessary information.

[0276] "Highly accurate and reliable fishing ground information" refers to data on the location of fishing grounds where the analysis results are extremely accurate and reliable in relation to the actual situation.

[0277] This invention relates to a system that acquires fish finder data in real time and analyzes it in conjunction with past temperature, season, and catch history. The server plays a central role in this system and uses a computer with a powerful processor and sufficient memory to perform high-speed and efficient data processing. Python is used as the programming language for data processing, and generative AI model-based frameworks such as TensorFlow and PyTorch are utilized for analysis.

[0278] The server first receives the real-time data transmitted from the ship through the fish finder and stores it in the database. This data includes fish density, water temperature, and latitude / longitude information. Subsequently, the server retrieves the historical data of past air temperatures, seasons, and fishing catches from the database and integrates it with the real-time data. In this process, any missing or abnormal data is complemented.

[0279] The server uses the integrated data to perform analysis by an AI model. The AI compares the past data patterns with the current real-time data to predict the movement of the fish school and the concentrated fishing grounds. Based on this result, a confidence score is assigned, and the most reliable result is notified to the information display device.

[0280] The information display device is usually the user's smartphone, which receives push notifications from the server. Based on this notification, the user can navigate the ship towards the specified fishing ground and conduct efficient fishing.

[0281] As a specific example, in an analysis conducted by the server on a certain day, "35.6895°N 139.6917°E" is identified as a fishing ground with a high-density fish school, and this information is push-notified to the user. Based on this information, the user sails directly to the selected fishing ground and achieves a large catch in a short time.

[0282] As an example of the prompt text for the generated AI model, content such as "Based on the past air temperature, season, and fishing catch data integrated with the real-time fish finder data, please output the location of the fishing ground where the next high-density fish school is predicted in latitude / longitude." can be considered.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The server receives data in real time from the fish school detector. This data includes information on fish density, water temperature, latitude and longitude. The input is sensor data transmitted from a ship, and the output is raw data stored in a database. The server immediately saves the information received via the network to the database to prevent data loss.

[0286] Step 2:

[0287] The server retrieves historical data on past air temperatures, seasons, and fishing catches from the database. This includes query operations performed using a database management system. The input is historical information within the database, and the output is an integrated dataset containing this information. The server integrates the retrieved historical data with the real-time data and formats it into a form suitable for analysis.

[0288] Step 3:

[0289] The server uses the integrated dataset to perform analysis with a generated AI model. The AI model is implemented using TensorFlow or PyTorch and predicts the movement of fish schools by comparing past data patterns with current data. The input is the integrated dataset, and the output is the latitude and longitude information of high-density fishing grounds and a confidence score. The server inputs data into the AI model and receives the output from the model in an analyzable format.

[0290] Step 4:

[0291] The server scores the AI analysis results and selects the most reliable results. Here, a confidence score is given to the analysis results, and based on this, the top results are filtered. The input is the analysis results from the AI model, and the output is the selected reliable fishing ground information. The server evaluates the reliability of the results and extracts only the optimal information.

[0292] Step 5:

[0293] The server sends the selected fishing ground information to the terminal. Specifically, it uses a push notification function to notify the user's information display device of the results. The input is reliable fishing ground information, and the output is a notification message displayed on the user's terminal. The server sends notifications via an API, allowing the user to receive the information quickly.

[0294] Step 6:

[0295] The user directs the vessel to a designated fishing ground based on fishing ground information displayed on the terminal. This enables efficient fishing. The input is the notification information displayed on the terminal, and the output is the user's decision to move. The user enters the notified latitude and longitude into the vessel's navigation system and proceeds to the target fishing ground.

[0296] (Application Example 1)

[0297] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] There is a need to address the inefficient use of fishery resources in urban areas and the resulting unsustainable resource management challenges. Currently, fishermen have limited means of obtaining effective fishing ground information, hindering optimal resource utilization. To address these problems, it is necessary to leverage real-time data and historical insights to streamline resource management across cities.

[0299] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0300] In this invention, the server includes means for collecting fish finder data, integrating it with past weather conditions, time periods, and fishing histories, and analyzing it using artificial intelligence; means for functioning as a platform for the efficient operation of information recipients for the purpose of urban resource management; and means for providing information beneficial to fishermen based on the analyzed data and optimizing resource utilization within the city. This enables efficient real-time resource utilization and sustainable urban management.

[0301] "Fish finder data" refers to information regarding the presence and density of fish obtained using a fish finder.

[0302] "Past weather conditions" refers to historical information regarding weather patterns such as previous temperatures and precipitation.

[0303] "Time period" refers to time information indicating a specific time zone or season.

[0304] "Fishing history" refers to recorded information regarding the catch amount and types obtained in previous fishing operations.

[0305] "Analysis using artificial intelligence" refers to the process of interpreting data and making patterns and predictions using AI technology.

[0306] "Coordinates" refers to a combination of numerical values and symbols used to indicate a specific location.

[0307] "Information processing device" refers to the hardware and software for receiving, analyzing, and notifying data.

[0308] "Information recipient" refers to a user or organization that receives and utilizes the analyzed information.

[0309] "Urban resource management" refers to various activities for optimizing the utilization of natural resources, human resources, information resources, etc. within the city.

[0310] The system that realizes this invention uses AI to analyze fish finder data along with past weather conditions, time of year, and fishing history. Specifically, the server, terminal, and user configurations each play a crucial role.

[0311] The server receives real-time data transmitted from fish finders and integrates it with information from past weather conditions and fishing history databases. This is done using a database management system (e.g., MySQL or PostgreSQL). The integrated data is analyzed using artificial intelligence models (such as TensorFlow or PyTorch) to identify fishing ground locations using coordinates.

[0312] The analyzed results are sent as push notifications to the terminal via the information processing device. The terminal allows the user to receive the analysis results and use them to decide on the most efficient fishing grounds to move to. Push notification services such as Firebase Cloud Messaging are used for this notification.

[0313] Based on the received fishing ground information, users can efficiently utilize fishing grounds as part of urban resource management. This promotes sustainable resource use within cities.

[0314] As a concrete example, consider a scenario where, on a particular summer day, the temperature is higher than average, and AI analyzes the likelihood of fish schools gathering in a specific warm water area. This information is then sent to the user's device, allowing them to quickly access that fishing ground and optimize their catch.

[0315] Furthermore, an example of an input prompt statement for the generating AI model used to realize this invention is shown below.

[0316] "Create a program that collects real-time data using a fish finder and analyzes it with AI. Please describe in detail the specifications of the app that will send push notifications to a smartphone about predicted fishing grounds."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The server receives data transmitted in real time from the fish finder. This data includes fish density, current water temperature, and latitude / longitude information. The input data is recorded in a database and prepared for integration with historical weather conditions and fishing history data.

[0320] Step 2:

[0321] The server retrieves historical weather conditions, timing, and fishing history information from a database management system. This historical data is then integrated with real-time data. Here, the time-series data is organized, outliers and missing values ​​are imputed, and the data is formatted for analysis.

[0322] Step 3:

[0323] The server inputs the integrated dataset into an artificial intelligence model (e.g., a model utilizing TensorFlow) and performs data analysis. Based on this analysis, it extracts patterns in the movement of fish schools and predicts future fishing ground locations. The output is the coordinate information of the predicted fishing ground locations.

[0324] Step 4:

[0325] The server prioritizes the AI ​​analysis results and selects the most accurate ones. A confidence scoring algorithm is used for this selection. The selected results are then prepared for notification to the user.

[0326] Step 5:

[0327] The server pushes the selected fishing ground location information to the terminal via an information processing device. By using a notification service such as Firebase Cloud Messaging, it is configured to deliver the information to the user's terminal immediately.

[0328] Step 6:

[0329] Users check the fishing ground location information received on their devices. Based on the information provided, they plan efficient navigation to fishing grounds. This allows users to sustainably utilize urban fishing resources.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention combines a system that uses AI to analyze data collected from a fish finder with an emotion engine that recognizes user emotions. This system is mainly composed of three entities: a server, a terminal, and a user.

[0332] Data collection and analysis

[0333] The server receives fish finder data transmitted from ships in real time and integrates it with historical temperature, season, and catch data. This data is analyzed by AI and formatted to provide fishermen with information on optimal fishing ground locations. The AI ​​learns from past patterns and indicates areas where fish schools are predicted to be concentrated in the form of latitude and longitude.

[0334] How the emotion engine works

[0335] The emotion engine installed on the server recognizes the user's past emotional patterns and current emotional state. This makes it possible to optimize the content and timing of notifications based on the user's emotions. Specifically, it adjusts the information provided, for example, by offering concise information when the user is stressed and detailed information when the user is relaxed.

[0336] Information provision and user response

[0337] The device provides users with push notifications indicating fishing ground locations (indicated by latitude and longitude) and related information. These notifications are delivered to the user in the most appropriate format based on analysis by an emotion engine.

[0338] Based on the information received from the device, the user moves to the fishing grounds and fishes effectively. For example, if the user's emotional state is elevated, a notification is sent with an encouraging message to support their motivation. On the other hand, if an emotion indicating fatigue is detected, a concise notification containing only the facts is sent.

[0339] Specific example

[0340] At a certain point, based on fish finder data, the AI ​​identifies "40.7128°N 74.0060°W" as the optimal fishing ground. Based on this analysis, the server analyzes the user's emotional state and notifies them of their location along with an encouraging message, such as "It looks like we'll have a great catch today! The next spot is 40.7128°N 74.0060°W." In this way, the combination of emotion engines allows for personalized notifications, improving both fishing efficiency and user experience.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The server acquires fish finder data transmitted in real time from the vessel. The acquired data includes fish density, water temperature, and the vessel's position information.

[0344] Step 2:

[0345] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is prepared as base data to be used for analysis along with real-time data.

[0346] Step 3:

[0347] The server integrates real-time and historical data, filling in missing or outlier values. This prepares a dataset suitable for AI analysis.

[0348] Step 4:

[0349] The server performs AI analysis to identify the optimal fishing grounds based on the movement and predictions of fish schools. These fishing grounds are shown in latitude and longitude format.

[0350] Step 5:

[0351] The server uses an emotion engine to analyze the user's current emotional state. It also considers past emotional data to determine the user's stress and motivation levels.

[0352] Step 6:

[0353] The server customizes the content and format of notifications based on the analyzed emotional state. For example, it adds encouraging words to highly motivated users and prepares concise notifications for fatigued users.

[0354] Step 7:

[0355] The server sends customized fishing ground information and messages to the device. The device receives this and displays it to the user as a unique push notification.

[0356] Step 8:

[0357] Based on the information received from the device, the user steers the boat toward the designated fishing grounds. The user can then begin effective fishing based on the notified points.

[0358] (Example 2)

[0359] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0360] While conventional fish finder systems can provide real-time fishing ground locations and high-precision data analysis, they fail to optimize information based on the user's psychological state. This lack of flexibility in providing information tailored to the user's current situation makes them particularly difficult to use effectively when stressed or fatigued.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0362] In this invention, the server includes means for collecting acoustic device data and integrating it with past weather data, temporal information, and fishing information for analysis in an information processing system; means for analyzing the user's emotional state and optimizing notification content and timing; and means for generating optimized information tailored to the user and providing it via an information terminal. This enables flexible and effective information provision based on the user's emotional state.

[0363] "Acoustic device data" refers to information based on sound waves used to measure the location and density of schools of fish.

[0364] An "information processing system" is an electronic system that analyzes collected data and generates information useful to users.

[0365] A "coordinate system" is a combination of latitude and longitude used to indicate a specific location on Earth.

[0366] An "information terminal" is an electronic device that users access and receive information from.

[0367] "User emotional state" refers to the collective psychological reactions and emotions that individual users exhibit in specific situations.

[0368] "Notification content" refers to the content of messages and information transmitted to users through information terminals.

[0369] "Optimization" refers to adjusting or improving information in a way that is best suited to a specific purpose or condition.

[0370] "High-precision data analysis" is the process of deriving accurate and reliable results through statistical or computational analysis of data.

[0371] This system supports users' efficient fishing activities based on acoustic data transmitted from ships. Users can utilize this data in real time and receive information tailored to their emotional state.

[0372] The server first receives data provided by acoustic devices. This data indicates the location and density of fish schools in the ocean and is measured using sound waves. The server then integrates this data with previously recorded weather data, temporal information, and fishing data. This data is analyzed by an information processing system, and a generative AI model is used to identify areas where fish school concentrations are predicted in the form of latitude and longitude.

[0373] Next, the server uses an emotion engine to understand the user's past emotional data and current emotional state. This allows it to optimize the content and timing of notifications, generating information in a format suitable for the user.

[0374] The device provides users with optimized information sent from the server via push notifications. These notifications may include encouraging messages such as, "Today is a good day for fishing. The next point is 40.7128°N 74.0060°W."

[0375] Based on the information received from their device, users can move to designated fishing grounds and fish effectively. When users are feeling excited, encouraging messages included in notifications further support their motivation, while when they are feeling fatigued, only the minimum necessary information is provided, enabling efficient decision-making.

[0376] An example of a prompt message for this system is: "Please provide the optimal fishing ground location predicted based on fish finder data, and optimized notification content based on the user's sentiment."

[0377] Thus, this invention enables flexible information provision that takes into account the emotional state of users, thereby improving the efficiency of fishing.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The server receives fish school data transmitted from acoustic devices in real time. This data measures the location and density of fish schools in the ocean using sound waves. The server receives this data as input and stores it in a database. This storage process enables subsequent data integration and analysis.

[0381] Step 2:

[0382] The server integrates received fish school data with historical weather data, temporal information, and catch information. This data consists of historical data obtained from various sensors and past fishing activities. The server prepares the integrated data as a dataset for analysis and inputs it into the generating AI model. During this process, missing or outlier data are automatically imputed, improving the accuracy of the analysis.

[0383] Step 3:

[0384] The server analyzes the integrated dataset using a generative AI model. The AI ​​model learns from past patterns and identifies areas where fish schools are predicted to be concentrated. Specifically, the AI ​​performs pattern recognition and predicts the optimal fishing ground location as latitude and longitude coordinates. This predicted data is output from the server.

[0385] Step 4:

[0386] The server uses an emotion engine to analyze past user emotion data and the user's current emotional state. The user's emotional state is extracted from past user feedback and current input data. Based on this analysis, the content and timing of notifications are optimized. If speed is a priority, a concise message focusing on the key points is generated.

[0387] Step 5:

[0388] The device receives optimized information transmitted from the server. This information includes fishing ground data (latitude and longitude) and optimized messages based on the user's emotions. The device displays this information on the screen and provides it to the user via push notifications. By receiving these notifications, the user can select fishing grounds that are appropriate for the situation.

[0389] Step 6:

[0390] Users receive information from their devices and move to fishing grounds based on the provided coordinates. They then use emotionally appropriate messages contained within the information to guide their fishing activities. For example, encouraging messages may prompt them to take action. Based on this information, users can efficiently carry out their activities in the actual fishing grounds.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0393] In modern fisheries, while there is a demand for increased efficiency in selecting fishing grounds, there is also a need to provide information that is tailored to the emotional state of the information recipients, such as fishermen and urban dwellers. However, current systems simply analyze and notify fishing ground and urban information without optimizing the information based on the user's emotions, resulting in challenges such as low information acceptance and utility. Moreover, urban residents encounter a wide range of information on a daily basis, making it difficult to select useful and appropriate information from among them.

[0394] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0395] This invention includes a server that collects fish finder data, analyzes it using an intelligent system in conjunction with past weather conditions, temporal factors, and catch data, and notifies an information terminal of information indicating the location of fishing grounds in geographic coordinates; an emotion recognition engine that recognizes the emotional state of the user and provides optimized information notifications; and an environmental data set in a city that collects information based on the emotional state of the residents. This enables efficient selection of fishing grounds in fisheries and the provision of information tailored to the emotional state of users in urban life.

[0396] "Fish finder data" refers to data collected from devices that acquire information about the location and movement of schools of fish.

[0397] "Past weather conditions" refers to information about environmental factors such as past temperatures, wind speeds, and precipitation.

[0398] "Temporal factors" are elements related to a specific time, such as the season or date and time.

[0399] "Catch data" refers to information about the types and quantities of fish obtained in the past through fishing activities.

[0400] An "intelligent system" is a technology or system that uses artificial intelligence to analyze data and derive useful information.

[0401] "Geographic coordinates" are numerical information consisting of latitude and longitude that indicates a specific location on Earth.

[0402] An "information terminal" is an electronic device used to receive and display information, and includes smartphones and tablets.

[0403] An "emotion recognition engine" is a technology that uses sensing technology and algorithms to identify a person's emotional state and enables the provision of information based on that identification.

[0404] "Users" refers to a diverse range of users, including fishermen and urban residents, who utilize this system.

[0405] "Environmental data" refers to various types of information about local conditions, such as traffic information, weather information, and event information in cities.

[0406] "Personalization" refers to customizing and providing information in a way that is suitable for a specific user.

[0407] This system consists of three main elements: a server, a terminal, and a user. The server receives fish school data transmitted from a fish finder and analyzes it using an intelligent system, integrating it with past weather conditions, temporal factors, and catch data. This analysis generates information indicating the location of the optimal fishing grounds in geographical coordinates. The server then transmits this information to the terminal. The terminal receives the information and notifies the user. The notified information is adjusted by an emotion recognition engine based on the user's emotional state. For example, if the user is stressed, concise information is provided; if relaxed, detailed information is provided. Cloud services such as AWS are used in this process. Cloud services help process large amounts of data quickly and generate the necessary information. As a concrete example, on the day an event is held in the city, this system can be used to notify urban dwellers of traffic congestion information and alternative routes at the appropriate time. Furthermore, the generating AI model uses prompt sentences in the form of "What activities would you recommend when the user is relaxed?" based on the user's current emotional state. This makes it possible to provide information tailored to the user's needs.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The server receives data in real time from the fish finder. This data is input and combined with weather conditions and catch data to generate a dataset. This then forms the basis for the next analysis.

[0411] Step 2:

[0412] The server uses the dataset to perform analysis with an intelligent system. The input here is an integrated dataset, and the AI ​​model learns from past patterns and outputs the predicted locations of fish school concentrations in latitude and longitude format.

[0413] Step 3:

[0414] The server evaluates the analysis results and extracts only the information with high accuracy. The input is multiple predicted locations generated by the AI, and the output is fishing ground location information selected from the scored data based on accuracy.

[0415] Step 4:

[0416] The server uses an emotion recognition engine to analyze the user's emotional state. The input here is the user's emotional pattern and current state information, and the output is a guideline for determining the appropriate style of communication.

[0417] Step 5:

[0418] The terminal receives data sent from the server and notifies the user. Specific inputs include fishing ground information (including latitude and longitude) and guidelines for communication style, while the output is a push notification to the user's terminal.

[0419] Step 6:

[0420] Users receive notifications and use them to plan their fishing trips or urban life. They receive notification information from their devices as input and use it to select the optimal action. For example, they can start moving to a recommended fishing spot.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0427] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] The system according to the present invention integrates real-time data collected from a fish finder with historical temperature, season, and catch data, and performs AI-based analysis to provide users with fishing grounds. The roles of the server, terminal, and user are described below.

[0438] Data collection and integration

[0439] The server receives fish-finding data transmitted from each vessel in real time. This data includes fish density, current water temperature, and latitude / longitude information. Simultaneously, the server periodically retrieves historical temperature and seasonal information, as well as historical catch data, from a database and integrates them. The integrated data is then formatted for AI analysis.

[0440] AI analysis

[0441] The server uses AI to perform analysis based on integrated data. The AI ​​compares past and present data patterns to predict the movement and current state of fish schools. As a result of the AI ​​analysis, the location (latitude and longitude) of dense fishing grounds where predicted high-density fish schools exist is identified. The analysis results are scored according to their confidence level, and only the most accurate results are selected.

[0442] Notification of results

[0443] The identified fishing grounds are transmitted from the server to the terminal and displayed as push notifications on the user's smartphone. This allows users to instantly obtain information about new fishing grounds.

[0444] User behavior

[0445] Based on the analysis results displayed on the terminal, users can efficiently fish by directing their boats to newly identified fishing grounds. Users can also check the latitude and longitude information of fishing grounds, allowing them to reach their target fishing grounds in a short amount of time.

[0446] As a concrete example of this system, suppose that during an analysis on a particular day, the location "35.6895°N 139.6917°E" is identified as a fishing ground and notified to the user. Based on this information, the user can head directly to an area they haven't explored before, enabling them to catch a large quantity of fish in a short amount of time.

[0447] This invention allows fishermen to achieve their planned catch with less effort and ensure a stable supply to the market.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The server receives real-time data from fish finders transmitted from each vessel. This data includes fish density, water temperature, and the vessel's latitude and longitude. The server temporarily stores the received data in storage.

[0451] Step 2:

[0452] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is then converted into a format that can be integrated with the latest real-time data for analysis.

[0453] Step 3:

[0454] The server integrates collected real-time and historical data and performs data refinement. This process involves imputing missing data and filtering outliers.

[0455] Step 4:

[0456] The server provides the prepared data as input to the AI ​​model and performs the analysis. The AI ​​compares past fish school patterns with the current data to identify the predicted location of the fishing grounds.

[0457] Step 5:

[0458] The server scores the AI-generated analysis results and selects the most reliable ones. The selected analysis results are compiled in latitude and longitude format.

[0459] Step 6:

[0460] The server sends the selected analysis results to the terminal and delivers a push notification to the user's smartphone.

[0461] Step 7:

[0462] The user checks the information displayed on the terminal, operates the boat based on the notified fishing grounds, and prepares to head to a new fishing ground.

[0463] Step 8:

[0464] After arriving at the fishing grounds, users re-examine the fish finder data and begin fishing at the recommended location. This enables efficient fishing.

[0465] (Example 1)

[0466] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] There is a need to effectively utilize real-time data obtained from fish finders along with historical environmental and catch information to provide more accurate location information for fishing grounds. However, conventional technologies have made it difficult to obtain highly accurate results in data integration and analysis. As a result, fishermen have been unable to fish efficiently and have suffered economic losses.

[0468] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0469] In this invention, the server includes means for collecting fish finder data and analyzing it using a computational model in conjunction with past temperature, season, and catch history; means for assigning a reliability index to the analysis results and selecting the most reliable result; and means for integrating real-time fish school information with historical information, and analyzing it after supplementing missing and anomaly data. This enables fishermen to quickly obtain highly accurate and reliable fishing ground information and achieve efficient fishing.

[0470] "Fish finder data" refers to information obtained from devices used to measure the presence and density of fish, as well as water depth, underwater.

[0471] "Past temperature, season, and fishing history" refers to data on temperature changes, seasonal environmental conditions, and fishing results that have been recorded to date.

[0472] A "computational model" is a mathematical or algorithmic framework used to analyze complex information.

[0473] "Assigning a reliability index" means applying numerical values ​​or evaluation criteria to the analysis results to assess their accuracy and reliability.

[0474] "Real-time fish school information" refers to data on fish schools that reflects the current situation and provides immediate updates.

[0475] "Completing missing and abnormal data" means supplementing or correcting incomplete or abnormal data with necessary information.

[0476] "Highly accurate and reliable fishing ground information" refers to data on the location of fishing grounds where the analysis results are extremely accurate and reliable in relation to the actual situation.

[0477] This invention relates to a system that acquires fish finder data in real time and analyzes it in conjunction with past temperature, season, and catch history. The server plays a central role in this system and uses a computer with a powerful processor and sufficient memory to perform high-speed and efficient data processing. Python is used as the programming language for data processing, and generative AI model-based frameworks such as TensorFlow and PyTorch are utilized for analysis.

[0478] The server first receives real-time data transmitted from the ship via the fish finder and stores it in a database. This data includes fish density, water temperature, and latitude / longitude information. Next, it retrieves historical temperature, season, and catch data from the database and integrates it with the real-time data. During this process, any missing or abnormal data is filled in.

[0479] The server uses integrated data to perform analysis using an AI model. The AI ​​compares historical data patterns with current real-time data to predict the movement of fish schools and dense fishing grounds. Based on these results, it assigns a confidence score and notifies the information display device of the most reliable result.

[0480] The information display device is typically the user's smartphone, which receives push notifications from the server. Based on these notifications, the user can steer their boat towards the identified fishing grounds and conduct efficient fishing.

[0481] As a concrete example, one day, a server analysis identifies "35.6895°N 139.6917°E" as a fishing ground with a high density of fish, and this information is sent to the user via push notification. Based on this information, the user heads directly to the selected fishing ground and achieves a large catch in a short amount of time.

[0482] An example of a prompt message for the generating AI model might be: "Based on real-time fish finder data integrated with historical temperature, season, and catch data, output the latitude and longitude of the fishing ground where the next high-density fish school is predicted to appear."

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The server receives data in real time from the fish finder. This data includes information on fish density, water temperature, and latitude / longitude. The input is sensor data transmitted from the vessel, and the output is raw data stored in the database. The server prevents data loss by immediately saving the information received via the network to the database.

[0486] Step 2:

[0487] The server retrieves historical data on temperature, season, and catch from the database. This involves query operations utilizing the database management system. The input is historical information within the database, and the output is an integrated dataset containing this information. The server integrates the retrieved historical data with real-time data and formats it into a format suitable for analysis.

[0488] Step 3:

[0489] The server uses an integrated dataset to perform analysis with a generative AI model. The AI ​​model is implemented using TensorFlow and PyTorch, and predicts the movement of fish schools by comparing past data patterns with current data. The input is an integrated dataset, and the output is latitude and longitude information and confidence scores for high-density fishing grounds. The server feeds the data into the AI ​​model and receives the output from the model in an analyzable format.

[0490] Step 4:

[0491] The server scores the AI ​​analysis results and selects the most reliable results. Here, a confidence score is assigned to the analysis results, and the server filters the results based on this score. The input is the analysis results from the AI ​​model, and the output is the selected, highly reliable fishing ground information. The server evaluates the reliability of the results and extracts only the most relevant information.

[0492] Step 5:

[0493] The server sends the selected fishing ground information to the terminal. Specifically, it uses a push notification function to notify the user's information display device of the results. The input is reliable fishing ground information, and the output is a notification message displayed on the user's terminal. The server sends notifications via an API, allowing the user to receive the information quickly.

[0494] Step 6:

[0495] The user directs the vessel to a designated fishing ground based on fishing ground information displayed on the terminal. This enables efficient fishing. The input is the notification information displayed on the terminal, and the output is the user's decision to move. The user enters the notified latitude and longitude into the vessel's navigation system and proceeds to the target fishing ground.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] There is a need to address the inefficient use of fishery resources in urban areas and the resulting unsustainable resource management challenges. Currently, fishermen have limited means of obtaining effective fishing ground information, hindering optimal resource utilization. To address these problems, it is necessary to leverage real-time data and historical insights to streamline resource management across cities.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] This invention includes a server that collects fish finder data and analyzes it using artificial intelligence in conjunction with past weather conditions, timing, and fishing history; a platform that functions for efficient action by information recipients for the purpose of urban resource management; and a means that provides useful information to fishermen based on the analyzed data and optimizes resource use within the city. This enables efficient resource use and sustainable urban management in real time.

[0501] "Fish finder data" refers to information about the presence and density of fish obtained using a fish finder.

[0502] "Past weather conditions" refers to historical information about weather patterns such as previous temperatures and precipitation.

[0503] "Period" refers to time information that indicates a specific time period or season.

[0504] "Fishing history" refers to records of the amount and types of fish caught in past fishing operations.

[0505] "Analyzing with artificial intelligence" refers to the process of interpreting data using AI technology to identify patterns and make predictions.

[0506] "Coordinates" are a combination of numbers and signs used to indicate a specific location.

[0507] An "information processing device" refers to hardware and software used for receiving, analyzing, and notifying data.

[0508] An "information recipient" is a user or organization that receives and utilizes the analyzed information.

[0509] "Urban resource management" refers to various activities aimed at optimizing the use of natural resources, human resources, information resources, and other resources within a city.

[0510] The system that realizes this invention uses AI to analyze fish finder data along with past weather conditions, time of year, and fishing history. Specifically, the server, terminal, and user configurations each play a crucial role.

[0511] The server receives real-time data transmitted from fish finders and integrates it with information from past weather conditions and fishing history databases. This is done using a database management system (e.g., MySQL or PostgreSQL). The integrated data is analyzed using artificial intelligence models (such as TensorFlow or PyTorch) to identify fishing ground locations using coordinates.

[0512] The analyzed results are sent as push notifications to the terminal via the information processing device. The terminal allows the user to receive the analysis results and use them to decide on the most efficient fishing grounds to move to. Push notification services such as Firebase Cloud Messaging are used for this notification.

[0513] Based on the received fishing ground information, users can efficiently utilize fishing grounds as part of urban resource management. This promotes sustainable resource use within cities.

[0514] As a concrete example, consider a scenario where, on a particular summer day, the temperature is higher than average, and AI analyzes the likelihood of fish schools gathering in a specific warm water area. This information is then sent to the user's device, allowing them to quickly access that fishing ground and optimize their catch.

[0515] Furthermore, an example of an input prompt statement for the generating AI model used to realize this invention is shown below.

[0516] "Create a program that collects real-time data using a fish finder and analyzes it with AI. Please describe in detail the specifications of the app that will send push notifications to a smartphone about predicted fishing grounds."

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The server receives data transmitted in real time from the fish finder. This data includes fish density, current water temperature, and latitude / longitude information. The input data is recorded in a database and prepared for integration with historical weather conditions and fishing history data.

[0520] Step 2:

[0521] The server retrieves historical weather conditions, timing, and fishing history information from a database management system. This historical data is then integrated with real-time data. Here, the time-series data is organized, outliers and missing values ​​are imputed, and the data is formatted for analysis.

[0522] Step 3:

[0523] The server inputs the integrated dataset into an artificial intelligence model (e.g., a model utilizing TensorFlow) and performs data analysis. Based on this analysis, it extracts patterns in the movement of fish schools and predicts future fishing ground locations. The output is the coordinate information of the predicted fishing ground locations.

[0524] Step 4:

[0525] The server prioritizes the AI ​​analysis results and selects the most accurate ones. A confidence scoring algorithm is used for this selection. The selected results are then prepared for notification to the user.

[0526] Step 5:

[0527] The server pushes the selected fishing ground location information to the terminal via an information processing device. By using a notification service such as Firebase Cloud Messaging, it is configured to deliver the information to the user's terminal immediately.

[0528] Step 6:

[0529] Users check the fishing ground location information received on their devices. Based on the information provided, they plan efficient navigation to fishing grounds. This allows users to sustainably utilize urban fishing resources.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention combines a system that uses AI to analyze data collected from a fish finder with an emotion engine that recognizes user emotions. This system is mainly composed of three entities: a server, a terminal, and a user.

[0532] Data collection and analysis

[0533] The server receives fish finder data transmitted from ships in real time and integrates it with historical temperature, season, and catch data. This data is analyzed by AI and formatted to provide fishermen with information on optimal fishing ground locations. The AI ​​learns from past patterns and indicates areas where fish schools are predicted to be concentrated in the form of latitude and longitude.

[0534] How the emotion engine works

[0535] The emotion engine installed on the server recognizes the user's past emotional patterns and current emotional state. This makes it possible to optimize the content and timing of notifications based on the user's emotions. Specifically, it adjusts the information provided, for example, by offering concise information when the user is stressed and detailed information when the user is relaxed.

[0536] Information provision and user response

[0537] The device provides users with push notifications indicating fishing ground locations (indicated by latitude and longitude) and related information. These notifications are delivered to the user in the most appropriate format based on analysis by an emotion engine.

[0538] Based on the information received from the device, the user moves to the fishing grounds and fishes effectively. For example, if the user's emotional state is elevated, a notification is sent with an encouraging message to support their motivation. On the other hand, if an emotion indicating fatigue is detected, a concise notification containing only the facts is sent.

[0539] Specific example

[0540] At a certain point, based on fish finder data, the AI ​​identifies "40.7128°N 74.0060°W" as the optimal fishing ground. Based on this analysis, the server analyzes the user's emotional state and notifies them of their location along with an encouraging message, such as "It looks like we'll have a great catch today! The next spot is 40.7128°N 74.0060°W." In this way, the combination of emotion engines allows for personalized notifications, improving both fishing efficiency and user experience.

[0541] The following describes the processing flow.

[0542] Step 1:

[0543] The server acquires fish finder data transmitted in real time from the vessel. The acquired data includes fish density, water temperature, and the vessel's position information.

[0544] Step 2:

[0545] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is prepared as base data to be used for analysis along with real-time data.

[0546] Step 3:

[0547] The server integrates real-time and historical data, filling in missing or outlier values. This prepares a dataset suitable for AI analysis.

[0548] Step 4:

[0549] The server performs AI analysis to identify the optimal fishing grounds based on the movement and predictions of fish schools. These fishing grounds are shown in latitude and longitude format.

[0550] Step 5:

[0551] The server uses an emotion engine to analyze the user's current emotional state. It also considers past emotional data to determine the user's stress and motivation levels.

[0552] Step 6:

[0553] The server customizes the content and format of notifications based on the analyzed emotional state. For example, it adds encouraging words to highly motivated users and prepares concise notifications for fatigued users.

[0554] Step 7:

[0555] The server sends customized fishing ground information and messages to the device. The device receives this and displays it to the user as a unique push notification.

[0556] Step 8:

[0557] Based on the information received from the device, the user steers the boat toward the designated fishing grounds. The user can then begin effective fishing based on the notified points.

[0558] (Example 2)

[0559] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] While conventional fish finder systems can provide real-time fishing ground locations and high-precision data analysis, they fail to optimize information based on the user's psychological state. This lack of flexibility in providing information tailored to the user's current situation makes them particularly difficult to use effectively when stressed or fatigued.

[0561] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0562] In this invention, the server includes means for collecting acoustic device data and integrating it with past weather data, temporal information, and fishing information for analysis in an information processing system; means for analyzing the user's emotional state and optimizing notification content and timing; and means for generating optimized information tailored to the user and providing it via an information terminal. This enables flexible and effective information provision based on the user's emotional state.

[0563] "Acoustic device data" refers to information based on sound waves used to measure the location and density of schools of fish.

[0564] An "information processing system" is an electronic system that analyzes collected data and generates information useful to users.

[0565] A "coordinate system" is a combination of latitude and longitude used to indicate a specific location on Earth.

[0566] An "information terminal" is an electronic device that users access and receive information from.

[0567] "User emotional state" refers to the collective psychological reactions and emotions that individual users exhibit in specific situations.

[0568] "Notification content" refers to the content of messages and information transmitted to users through information terminals.

[0569] "Optimization" refers to adjusting or improving information in a way that is best suited to a specific purpose or condition.

[0570] "High-precision data analysis" is the process of deriving accurate and reliable results through statistical or computational analysis of data.

[0571] This system supports users' efficient fishing activities based on acoustic data transmitted from ships. Users can utilize this data in real time and receive information tailored to their emotional state.

[0572] The server first receives data provided by acoustic devices. This data indicates the location and density of fish schools in the ocean and is measured using sound waves. The server then integrates this data with previously recorded weather data, temporal information, and fishing data. This data is analyzed by an information processing system, and a generative AI model is used to identify areas where fish school concentrations are predicted in the form of latitude and longitude.

[0573] Next, the server uses an emotion engine to understand the user's past emotional data and current emotional state. This allows it to optimize the content and timing of notifications, generating information in a format suitable for the user.

[0574] The device provides users with optimized information sent from the server via push notifications. These notifications may include encouraging messages such as, "Today is a good day for fishing. The next point is 40.7128°N 74.0060°W."

[0575] Based on the information received from their device, users can move to designated fishing grounds and fish effectively. When users are feeling excited, encouraging messages included in notifications further support their motivation, while when they are feeling fatigued, only the minimum necessary information is provided, enabling efficient decision-making.

[0576] An example of a prompt message for this system is: "Please provide the optimal fishing ground location predicted based on fish finder data, and optimized notification content based on the user's sentiment."

[0577] Thus, this invention enables flexible information provision that takes into account the emotional state of users, thereby improving the efficiency of fishing.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The server receives fish school data transmitted from acoustic devices in real time. This data measures the location and density of fish schools in the ocean using sound waves. The server receives this data as input and stores it in a database. This storage process enables subsequent data integration and analysis.

[0581] Step 2:

[0582] The server integrates received fish school data with historical weather data, temporal information, and catch information. This data consists of historical data obtained from various sensors and past fishing activities. The server prepares the integrated data as a dataset for analysis and inputs it into the generating AI model. During this process, missing or outlier data are automatically imputed, improving the accuracy of the analysis.

[0583] Step 3:

[0584] The server analyzes the integrated dataset using a generative AI model. The AI ​​model learns from past patterns and identifies areas where fish schools are predicted to be concentrated. Specifically, the AI ​​performs pattern recognition and predicts the optimal fishing ground location as latitude and longitude coordinates. This predicted data is output from the server.

[0585] Step 4:

[0586] The server uses an emotion engine to analyze past user emotion data and the user's current emotional state. The user's emotional state is extracted from past user feedback and current input data. Based on this analysis, the content and timing of notifications are optimized. If speed is a priority, a concise message focusing on the key points is generated.

[0587] Step 5:

[0588] The device receives optimized information transmitted from the server. This information includes fishing ground data (latitude and longitude) and optimized messages based on the user's emotions. The device displays this information on the screen and provides it to the user via push notifications. By receiving these notifications, the user can select fishing grounds that are appropriate for the situation.

[0589] Step 6:

[0590] Users receive information from their devices and move to fishing grounds based on the provided coordinates. They then use emotionally appropriate messages contained within the information to guide their fishing activities. For example, encouraging messages may prompt them to take action. Based on this information, users can efficiently carry out their activities in the actual fishing grounds.

[0591] (Application Example 2)

[0592] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0593] In modern fisheries, while there is a demand for increased efficiency in selecting fishing grounds, there is also a need to provide information that is tailored to the emotional state of the information recipients, such as fishermen and urban dwellers. However, current systems simply analyze and notify fishing ground and urban information without optimizing the information based on the user's emotions, resulting in challenges such as low information acceptance and utility. Moreover, urban residents encounter a wide range of information on a daily basis, making it difficult to select useful and appropriate information from among them.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0595] This invention includes a server that collects fish finder data, analyzes it using an intelligent system in conjunction with past weather conditions, temporal factors, and catch data, and notifies an information terminal of information indicating the location of fishing grounds in geographic coordinates; an emotion recognition engine that recognizes the emotional state of the user and provides optimized information notifications; and an environmental data set in a city that collects information based on the emotional state of the residents. This enables efficient selection of fishing grounds in fisheries and the provision of information tailored to the emotional state of users in urban life.

[0596] "Fish finder data" refers to data collected from devices that acquire information about the location and movement of schools of fish.

[0597] "Past weather conditions" refers to information about environmental factors such as past temperatures, wind speeds, and precipitation.

[0598] "Temporal factors" are elements related to a specific time, such as the season or date and time.

[0599] "Catch data" refers to information about the types and quantities of fish obtained in the past through fishing activities.

[0600] An "intelligent system" is a technology or system that uses artificial intelligence to analyze data and derive useful information.

[0601] "Geographic coordinates" are numerical information consisting of latitude and longitude that indicates a specific location on Earth.

[0602] An "information terminal" is an electronic device used to receive and display information, and includes smartphones and tablets.

[0603] An "emotion recognition engine" is a technology that uses sensing technology and algorithms to identify a person's emotional state and enables the provision of information based on that identification.

[0604] "Users" refers to a diverse range of users, including fishermen and urban residents, who utilize this system.

[0605] "Environmental data" refers to various types of information about local conditions, such as traffic information, weather information, and event information in cities.

[0606] "Personalization" refers to customizing and providing information in a way that is suitable for a specific user.

[0607] This system consists of three main elements: a server, a terminal, and a user. The server receives fish school data transmitted from a fish finder and analyzes it using an intelligent system, integrating it with past weather conditions, temporal factors, and catch data. This analysis generates information indicating the location of the optimal fishing grounds in geographical coordinates. The server then transmits this information to the terminal. The terminal receives the information and notifies the user. The notified information is adjusted by an emotion recognition engine based on the user's emotional state. For example, if the user is stressed, concise information is provided; if relaxed, detailed information is provided. Cloud services such as AWS are used in this process. Cloud services help process large amounts of data quickly and generate the necessary information. As a concrete example, on the day an event is held in the city, this system can be used to notify urban dwellers of traffic congestion information and alternative routes at the appropriate time. Furthermore, the generating AI model uses prompt sentences in the form of "What activities would you recommend when the user is relaxed?" based on the user's current emotional state. This makes it possible to provide information tailored to the user's needs.

[0608] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0609] Step 1:

[0610] The server receives data in real time from the fish finder. This data is input and combined with weather conditions and catch data to generate a dataset. This then forms the basis for the next analysis.

[0611] Step 2:

[0612] The server uses the dataset to perform analysis with an intelligent system. The input here is an integrated dataset, and the AI ​​model learns from past patterns and outputs the predicted locations of fish school concentrations in latitude and longitude format.

[0613] Step 3:

[0614] The server evaluates the analysis results and extracts only the information with high accuracy. The input is multiple predicted locations generated by the AI, and the output is fishing ground location information selected from the scored data based on accuracy.

[0615] Step 4:

[0616] The server uses an emotion recognition engine to analyze the user's emotional state. The input here is the user's emotional pattern and current state information, and the output is a guideline for determining the appropriate style of communication.

[0617] Step 5:

[0618] The terminal receives data sent from the server and notifies the user. Specific inputs include fishing ground information (including latitude and longitude) and guidelines for communication style, while the output is a push notification to the user's terminal.

[0619] Step 6:

[0620] Users receive notifications and use them to plan their fishing trips or urban life. They receive notification information from their devices as input and use it to select the optimal action. For example, they can start moving to a recommended fishing spot.

[0621] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0627] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0629] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0630] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0631] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0632] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0633] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0634] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0635] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0636] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0637] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] The system according to the present invention integrates real-time data collected from a fish finder with historical temperature, season, and catch data, and performs AI-based analysis to provide users with fishing grounds. The roles of the server, terminal, and user are described below.

[0639] Data collection and integration

[0640] The server receives fish-finding data transmitted from each vessel in real time. This data includes fish density, current water temperature, and latitude / longitude information. Simultaneously, the server periodically retrieves historical temperature and seasonal information, as well as historical catch data, from a database and integrates them. The integrated data is then formatted for AI analysis.

[0641] AI analysis

[0642] The server uses AI to perform analysis based on integrated data. The AI ​​compares past and present data patterns to predict the movement and current state of fish schools. As a result of the AI ​​analysis, the location (latitude and longitude) of dense fishing grounds where predicted high-density fish schools exist is identified. The analysis results are scored according to their confidence level, and only the most accurate results are selected.

[0643] Notification of results

[0644] The identified fishing grounds are transmitted from the server to the terminal and displayed as push notifications on the user's smartphone. This allows users to instantly obtain information about new fishing grounds.

[0645] User behavior

[0646] Based on the analysis results displayed on the terminal, users can efficiently fish by directing their boats to newly identified fishing grounds. Users can also check the latitude and longitude information of fishing grounds, allowing them to reach their target fishing grounds in a short amount of time.

[0647] As a concrete example of this system, suppose that during an analysis on a particular day, the location "35.6895°N 139.6917°E" is identified as a fishing ground and notified to the user. Based on this information, the user can head directly to an area they haven't explored before, enabling them to catch a large quantity of fish in a short amount of time.

[0648] This invention allows fishermen to achieve their planned catch with less effort and ensure a stable supply to the market.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The server receives real-time data from fish finders transmitted from each vessel. This data includes fish density, water temperature, and the vessel's latitude and longitude. The server temporarily stores the received data in storage.

[0652] Step 2:

[0653] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is then converted into a format that can be integrated with the latest real-time data for analysis.

[0654] Step 3:

[0655] The server integrates collected real-time and historical data and performs data refinement. This process involves imputing missing data and filtering outliers.

[0656] Step 4:

[0657] The server provides the prepared data as input to the AI ​​model and performs the analysis. The AI ​​compares past fish school patterns with the current data to identify the predicted location of the fishing grounds.

[0658] Step 5:

[0659] The server scores the AI-generated analysis results and selects the most reliable ones. The selected analysis results are compiled in latitude and longitude format.

[0660] Step 6:

[0661] The server sends the selected analysis results to the terminal and delivers a push notification to the user's smartphone.

[0662] Step 7:

[0663] The user checks the information displayed on the terminal, operates the boat based on the notified fishing grounds, and prepares to head to a new fishing ground.

[0664] Step 8:

[0665] After arriving at the fishing grounds, users re-examine the fish finder data and begin fishing at the recommended location. This enables efficient fishing.

[0666] (Example 1)

[0667] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] There is a need to effectively utilize real-time data obtained from fish finders along with historical environmental and catch information to provide more accurate location information for fishing grounds. However, conventional technologies have made it difficult to obtain highly accurate results in data integration and analysis. As a result, fishermen have been unable to fish efficiently and have suffered economic losses.

[0669] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0670] In this invention, the server includes means for collecting fish finder data and analyzing it using a computational model in conjunction with past temperature, season, and catch history; means for assigning a reliability index to the analysis results and selecting the most reliable result; and means for integrating real-time fish school information with historical information, and analyzing it after supplementing missing and anomaly data. This enables fishermen to quickly obtain highly accurate and reliable fishing ground information and achieve efficient fishing.

[0671] "Fish finder data" refers to information obtained from devices used to measure the presence and density of fish, as well as water depth, underwater.

[0672] "Past temperature, season, and fishing history" refers to data on temperature changes, seasonal environmental conditions, and fishing results that have been recorded to date.

[0673] A "computational model" is a mathematical or algorithmic framework used to analyze complex information.

[0674] "Assigning a reliability index" means applying numerical values ​​or evaluation criteria to the analysis results to assess their accuracy and reliability.

[0675] "Real-time fish school information" refers to data on fish schools that reflects the current situation and provides immediate updates.

[0676] "Completing missing and abnormal data" means supplementing or correcting incomplete or abnormal data with necessary information.

[0677] "Highly accurate and reliable fishing ground information" refers to data on the location of fishing grounds where the analysis results are extremely accurate and reliable in relation to the actual situation.

[0678] This invention relates to a system that acquires fish finder data in real time and analyzes it in conjunction with past temperature, season, and catch history. The server plays a central role in this system and uses a computer with a powerful processor and sufficient memory to perform high-speed and efficient data processing. Python is used as the programming language for data processing, and generative AI model-based frameworks such as TensorFlow and PyTorch are utilized for analysis.

[0679] The server first receives real-time data transmitted from the ship via the fish finder and stores it in a database. This data includes fish density, water temperature, and latitude / longitude information. Next, it retrieves historical temperature, season, and catch data from the database and integrates it with the real-time data. During this process, any missing or abnormal data is filled in.

[0680] The server uses integrated data to perform analysis using an AI model. The AI ​​compares historical data patterns with current real-time data to predict the movement of fish schools and dense fishing grounds. Based on these results, it assigns a confidence score and notifies the information display device of the most reliable result.

[0681] The information display device is typically the user's smartphone, which receives push notifications from the server. Based on these notifications, the user can steer their boat towards the identified fishing grounds and conduct efficient fishing.

[0682] As a concrete example, one day, a server analysis identifies "35.6895°N 139.6917°E" as a fishing ground with a high density of fish, and this information is sent to the user via push notification. Based on this information, the user heads directly to the selected fishing ground and achieves a large catch in a short amount of time.

[0683] An example of a prompt message for the generating AI model might be: "Based on real-time fish finder data integrated with historical temperature, season, and catch data, output the latitude and longitude of the fishing ground where the next high-density fish school is predicted to appear."

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] The server receives data in real time from the fish finder. This data includes information on fish density, water temperature, and latitude / longitude. The input is sensor data transmitted from the vessel, and the output is raw data stored in the database. The server prevents data loss by immediately saving the information received via the network to the database.

[0687] Step 2:

[0688] The server retrieves historical data on temperature, season, and catch from the database. This involves query operations utilizing the database management system. The input is historical information within the database, and the output is an integrated dataset containing this information. The server integrates the retrieved historical data with real-time data and formats it into a format suitable for analysis.

[0689] Step 3:

[0690] The server uses an integrated dataset to perform analysis with a generative AI model. The AI ​​model is implemented using TensorFlow and PyTorch, and predicts the movement of fish schools by comparing past data patterns with current data. The input is an integrated dataset, and the output is latitude and longitude information and confidence scores for high-density fishing grounds. The server feeds the data into the AI ​​model and receives the output from the model in an analyzable format.

[0691] Step 4:

[0692] The server scores the AI ​​analysis results and selects the most reliable results. Here, a confidence score is assigned to the analysis results, and the server filters the results based on this score. The input is the analysis results from the AI ​​model, and the output is the selected, highly reliable fishing ground information. The server evaluates the reliability of the results and extracts only the most relevant information.

[0693] Step 5:

[0694] The server sends the selected fishing ground information to the terminal. Specifically, it uses a push notification function to notify the user's information display device of the results. The input is reliable fishing ground information, and the output is a notification message displayed on the user's terminal. The server sends notifications via an API, allowing the user to receive the information quickly.

[0695] Step 6:

[0696] The user directs the vessel to a designated fishing ground based on fishing ground information displayed on the terminal. This enables efficient fishing. The input is the notification information displayed on the terminal, and the output is the user's decision to move. The user enters the notified latitude and longitude into the vessel's navigation system and proceeds to the target fishing ground.

[0697] (Application Example 1)

[0698] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] There is a need to address the inefficient use of fishery resources in urban areas and the resulting unsustainable resource management challenges. Currently, fishermen have limited means of obtaining effective fishing ground information, hindering optimal resource utilization. To address these problems, it is necessary to leverage real-time data and historical insights to streamline resource management across cities.

[0700] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0701] This invention includes a server that collects fish finder data and analyzes it using artificial intelligence in conjunction with past weather conditions, timing, and fishing history; a platform that functions for efficient action by information recipients for the purpose of urban resource management; and a means that provides useful information to fishermen based on the analyzed data and optimizes resource use within the city. This enables efficient resource use and sustainable urban management in real time.

[0702] "Fish finder data" refers to information about the presence and density of fish obtained using a fish finder.

[0703] "Past weather conditions" refers to historical information about weather patterns such as previous temperatures and precipitation.

[0704] "Period" refers to time information that indicates a specific time period or season.

[0705] "Fishing history" refers to records of the amount and types of fish caught in past fishing operations.

[0706] "Analyzing with artificial intelligence" refers to the process of interpreting data using AI technology to identify patterns and make predictions.

[0707] "Coordinates" are a combination of numbers and signs used to indicate a specific location.

[0708] An "information processing device" refers to hardware and software used for receiving, analyzing, and notifying data.

[0709] An "information recipient" is a user or organization that receives and utilizes the analyzed information.

[0710] "Urban resource management" refers to various activities aimed at optimizing the use of natural resources, human resources, information resources, and other resources within a city.

[0711] The system that realizes this invention uses AI to analyze fish finder data along with past weather conditions, time of year, and fishing history. Specifically, the server, terminal, and user configurations each play a crucial role.

[0712] The server receives real-time data transmitted from fish finders and integrates it with information from past weather conditions and fishing history databases. This is done using a database management system (e.g., MySQL or PostgreSQL). The integrated data is analyzed using artificial intelligence models (such as TensorFlow or PyTorch) to identify fishing ground locations using coordinates.

[0713] The analyzed results are sent as push notifications to the terminal via the information processing device. The terminal allows the user to receive the analysis results and use them to decide on the most efficient fishing grounds to move to. Push notification services such as Firebase Cloud Messaging are used for this notification.

[0714] Based on the received fishing ground information, users can efficiently utilize fishing grounds as part of urban resource management. This promotes sustainable resource use within cities.

[0715] As a concrete example, consider a scenario where, on a particular summer day, the temperature is higher than average, and AI analyzes the likelihood of fish schools gathering in a specific warm water area. This information is then sent to the user's device, allowing them to quickly access that fishing ground and optimize their catch.

[0716] Furthermore, an example of an input prompt statement for the generating AI model used to realize this invention is shown below.

[0717] "Create a program that collects real-time data using a fish finder and analyzes it with AI. Please describe in detail the specifications of the app that will send push notifications to a smartphone about predicted fishing grounds."

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The server receives data transmitted in real time from the fish finder. This data includes fish density, current water temperature, and latitude / longitude information. The input data is recorded in a database and prepared for integration with historical weather conditions and fishing history data.

[0721] Step 2:

[0722] The server retrieves historical weather conditions, timing, and fishing history information from a database management system. This historical data is then integrated with real-time data. Here, the time-series data is organized, outliers and missing values ​​are imputed, and the data is formatted for analysis.

[0723] Step 3:

[0724] The server inputs the integrated dataset into an artificial intelligence model (e.g., a model utilizing TensorFlow) and performs data analysis. Based on this analysis, it extracts patterns in the movement of fish schools and predicts future fishing ground locations. The output is the coordinate information of the predicted fishing ground locations.

[0725] Step 4:

[0726] The server prioritizes the AI ​​analysis results and selects the most accurate ones. A confidence scoring algorithm is used for this selection. The selected results are then prepared for notification to the user.

[0727] Step 5:

[0728] The server pushes the selected fishing ground location information to the terminal via an information processing device. By using a notification service such as Firebase Cloud Messaging, it is configured to deliver the information to the user's terminal immediately.

[0729] Step 6:

[0730] Users check the fishing ground location information received on their devices. Based on the information provided, they plan efficient navigation to fishing grounds. This allows users to sustainably utilize urban fishing resources.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention combines a system that uses AI to analyze data collected from a fish finder with an emotion engine that recognizes user emotions. This system is mainly composed of three entities: a server, a terminal, and a user.

[0733] Data collection and analysis

[0734] The server receives fish finder data transmitted from ships in real time and integrates it with historical temperature, season, and catch data. This data is analyzed by AI and formatted to provide fishermen with information on optimal fishing ground locations. The AI ​​learns from past patterns and indicates areas where fish schools are predicted to be concentrated in the form of latitude and longitude.

[0735] How the emotion engine works

[0736] The emotion engine installed on the server recognizes the user's past emotional patterns and current emotional state. This makes it possible to optimize the content and timing of notifications based on the user's emotions. Specifically, it adjusts the information provided, for example, by offering concise information when the user is stressed and detailed information when the user is relaxed.

[0737] Information provision and user response

[0738] The device provides users with push notifications indicating fishing ground locations (indicated by latitude and longitude) and related information. These notifications are delivered to the user in the most appropriate format based on analysis by an emotion engine.

[0739] Based on the information received from the device, the user moves to the fishing grounds and fishes effectively. For example, if the user's emotional state is elevated, a notification is sent with an encouraging message to support their motivation. On the other hand, if an emotion indicating fatigue is detected, a concise notification containing only the facts is sent.

[0740] Specific example

[0741] At a certain point, based on fish finder data, the AI ​​identifies "40.7128°N 74.0060°W" as the optimal fishing ground. Based on this analysis, the server analyzes the user's emotional state and notifies them of their location along with an encouraging message, such as "It looks like we'll have a great catch today! The next spot is 40.7128°N 74.0060°W." In this way, the combination of emotion engines allows for personalized notifications, improving both fishing efficiency and user experience.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The server acquires fish finder data transmitted in real time from the vessel. The acquired data includes fish density, water temperature, and the vessel's position information.

[0745] Step 2:

[0746] The server retrieves historical temperature data, seasonal variation data, and fishing catch data from the database. This data is prepared as base data to be used for analysis along with real-time data.

[0747] Step 3:

[0748] The server integrates real-time and historical data, filling in missing or outlier values. This prepares a dataset suitable for AI analysis.

[0749] Step 4:

[0750] The server performs AI analysis to identify the optimal fishing grounds based on the movement and predictions of fish schools. These fishing grounds are shown in latitude and longitude format.

[0751] Step 5:

[0752] The server uses an emotion engine to analyze the user's current emotional state. It also considers past emotional data to determine the user's stress and motivation levels.

[0753] Step 6:

[0754] The server customizes the content and format of notifications based on the analyzed emotional state. For example, it adds encouraging words to highly motivated users and prepares concise notifications for fatigued users.

[0755] Step 7:

[0756] The server sends customized fishing ground information and messages to the device. The device receives this and displays it to the user as a unique push notification.

[0757] Step 8:

[0758] Based on the information received from the device, the user steers the boat toward the designated fishing grounds. The user can then begin effective fishing based on the notified points.

[0759] (Example 2)

[0760] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0761] While conventional fish finder systems can provide real-time fishing ground locations and high-precision data analysis, they fail to optimize information based on the user's psychological state. This lack of flexibility in providing information tailored to the user's current situation makes them particularly difficult to use effectively when stressed or fatigued.

[0762] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0763] In this invention, the server includes means for collecting acoustic device data and integrating it with past weather data, temporal information, and fishing information for analysis in an information processing system; means for analyzing the user's emotional state and optimizing notification content and timing; and means for generating optimized information tailored to the user and providing it via an information terminal. This enables flexible and effective information provision based on the user's emotional state.

[0764] "Acoustic device data" refers to information based on sound waves used to measure the location and density of schools of fish.

[0765] An "information processing system" is an electronic system that analyzes collected data and generates information useful to users.

[0766] A "coordinate system" is a combination of latitude and longitude used to indicate a specific location on Earth.

[0767] An "information terminal" is an electronic device that users access and receive information from.

[0768] "User emotional state" refers to the collective psychological reactions and emotions that individual users exhibit in specific situations.

[0769] "Notification content" refers to the content of messages and information transmitted to users through information terminals.

[0770] "Optimization" refers to adjusting or improving information in a way that is best suited to a specific purpose or condition.

[0771] "High-precision data analysis" is the process of deriving accurate and reliable results through statistical or computational analysis of data.

[0772] This system supports users' efficient fishing activities based on acoustic data transmitted from ships. Users can utilize this data in real time and receive information tailored to their emotional state.

[0773] The server first receives data provided by acoustic devices. This data indicates the location and density of fish schools in the ocean and is measured using sound waves. The server then integrates this data with previously recorded weather data, temporal information, and fishing data. This data is analyzed by an information processing system, and a generative AI model is used to identify areas where fish school concentrations are predicted in the form of latitude and longitude.

[0774] Next, the server uses an emotion engine to understand the user's past emotional data and current emotional state. This allows it to optimize the content and timing of notifications, generating information in a format suitable for the user.

[0775] The device provides users with optimized information sent from the server via push notifications. These notifications may include encouraging messages such as, "Today is a good day for fishing. The next point is 40.7128°N 74.0060°W."

[0776] Based on the information received from their device, users can move to designated fishing grounds and fish effectively. When users are feeling excited, encouraging messages included in notifications further support their motivation, while when they are feeling fatigued, only the minimum necessary information is provided, enabling efficient decision-making.

[0777] An example of a prompt message for this system is: "Please provide the optimal fishing ground location predicted based on fish finder data, and optimized notification content based on the user's sentiment."

[0778] Thus, this invention enables flexible information provision that takes into account the emotional state of users, thereby improving the efficiency of fishing.

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] The server receives fish school data transmitted from acoustic devices in real time. This data measures the location and density of fish schools in the ocean using sound waves. The server receives this data as input and stores it in a database. This storage process enables subsequent data integration and analysis.

[0782] Step 2:

[0783] The server integrates received fish school data with historical weather data, temporal information, and catch information. This data consists of historical data obtained from various sensors and past fishing activities. The server prepares the integrated data as a dataset for analysis and inputs it into the generating AI model. During this process, missing or outlier data are automatically imputed, improving the accuracy of the analysis.

[0784] Step 3:

[0785] The server analyzes the integrated dataset using a generative AI model. The AI ​​model learns from past patterns and identifies areas where fish schools are predicted to be concentrated. Specifically, the AI ​​performs pattern recognition and predicts the optimal fishing ground location as latitude and longitude coordinates. This predicted data is output from the server.

[0786] Step 4:

[0787] The server uses an emotion engine to analyze past user emotion data and the user's current emotional state. The user's emotional state is extracted from past user feedback and current input data. Based on this analysis, the content and timing of notifications are optimized. If speed is a priority, a concise message focusing on the key points is generated.

[0788] Step 5:

[0789] The device receives optimized information transmitted from the server. This information includes fishing ground data (latitude and longitude) and optimized messages based on the user's emotions. The device displays this information on the screen and provides it to the user via push notifications. By receiving these notifications, the user can select fishing grounds that are appropriate for the situation.

[0790] Step 6:

[0791] Users receive information from their devices and move to fishing grounds based on the provided coordinates. They then use emotionally appropriate messages contained within the information to guide their fishing activities. For example, encouraging messages may prompt them to take action. Based on this information, users can efficiently carry out their activities in the actual fishing grounds.

[0792] (Application Example 2)

[0793] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0794] In modern fisheries, while there is a demand for increased efficiency in selecting fishing grounds, there is also a need to provide information that is tailored to the emotional state of the information recipients, such as fishermen and urban dwellers. However, current systems simply analyze and notify fishing ground and urban information without optimizing the information based on the user's emotions, resulting in challenges such as low information acceptance and utility. Moreover, urban residents encounter a wide range of information on a daily basis, making it difficult to select useful and appropriate information from among them.

[0795] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0796] This invention includes a server that collects fish finder data, analyzes it using an intelligent system in conjunction with past weather conditions, temporal factors, and catch data, and notifies an information terminal of information indicating the location of fishing grounds in geographic coordinates; an emotion recognition engine that recognizes the emotional state of the user and provides optimized information notifications; and an environmental data set in a city that collects information based on the emotional state of the residents. This enables efficient selection of fishing grounds in fisheries and the provision of information tailored to the emotional state of users in urban life.

[0797] "Fish finder data" refers to data collected from devices that acquire information about the location and movement of schools of fish.

[0798] "Past weather conditions" refers to information about environmental factors such as past temperatures, wind speeds, and precipitation.

[0799] "Temporal factors" are elements related to a specific time, such as the season or date and time.

[0800] "Catch data" refers to information about the types and quantities of fish obtained in the past through fishing activities.

[0801] An "intelligent system" is a technology or system that uses artificial intelligence to analyze data and derive useful information.

[0802] "Geographic coordinates" are numerical information consisting of latitude and longitude that indicates a specific location on Earth.

[0803] An "information terminal" is an electronic device used to receive and display information, and includes smartphones and tablets.

[0804] An "emotion recognition engine" is a technology that uses sensing technology and algorithms to identify a person's emotional state and enables the provision of information based on that identification.

[0805] "Users" refers to a diverse range of users, including fishermen and urban residents, who utilize this system.

[0806] "Environmental data" refers to various types of information about local conditions, such as traffic information, weather information, and event information in cities.

[0807] "Personalization" refers to customizing and providing information in a way that is suitable for a specific user.

[0808] This system consists of three main elements: a server, a terminal, and a user. The server receives fish school data transmitted from a fish finder and analyzes it using an intelligent system, integrating it with past weather conditions, temporal factors, and catch data. This analysis generates information indicating the location of the optimal fishing grounds in geographical coordinates. The server then transmits this information to the terminal. The terminal receives the information and notifies the user. The notified information is adjusted by an emotion recognition engine based on the user's emotional state. For example, if the user is stressed, concise information is provided; if relaxed, detailed information is provided. Cloud services such as AWS are used in this process. Cloud services help process large amounts of data quickly and generate the necessary information. As a concrete example, on the day an event is held in the city, this system can be used to notify urban dwellers of traffic congestion information and alternative routes at the appropriate time. Furthermore, the generating AI model uses prompt sentences in the form of "What activities would you recommend when the user is relaxed?" based on the user's current emotional state. This makes it possible to provide information tailored to the user's needs.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The server receives data in real time from the fish finder. This data is input and combined with weather conditions and catch data to generate a dataset. This then forms the basis for the next analysis.

[0812] Step 2:

[0813] The server uses the dataset to perform analysis with an intelligent system. The input here is an integrated dataset, and the AI ​​model learns from past patterns and outputs the predicted locations of fish school concentrations in latitude and longitude format.

[0814] Step 3:

[0815] The server evaluates the analysis results and extracts only the information with high accuracy. The input is multiple predicted locations generated by the AI, and the output is fishing ground location information selected from the scored data based on accuracy.

[0816] Step 4:

[0817] The server uses an emotion recognition engine to analyze the user's emotional state. The input here is the user's emotional pattern and current state information, and the output is a guideline for determining the appropriate style of communication.

[0818] Step 5:

[0819] The terminal receives data sent from the server and notifies the user. Specific inputs include fishing ground information (including latitude and longitude) and guidelines for communication style, while the output is a push notification to the user's terminal.

[0820] Step 6:

[0821] Users receive notifications and use them to plan their fishing trips or urban life. They receive notification information from their devices as input and use it to select the optimal action. For example, they can start moving to a recommended fishing spot.

[0822] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0828] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0829] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0830] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0831] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0832] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0833] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0834] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0835] 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.

[0836] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0837] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0838] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0839] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0840] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0841] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] Collect fish finder data,

[0846] By integrating past temperature, season, and catch data with AI analysis,

[0847] The terminal receives information indicating the location of the fishing grounds in terms of latitude and longitude.

[0848] A system that includes the means.

[0849] (Claim 2)

[0850] The analysis results are scored, and only the highly accurate results are notified to the user.

[0851] The system according to claim 1.

[0852] (Claim 3)

[0853] The system integrates real-time fish school data with historical data, and then analyzes it after imputing missing and anomaly data.

[0854] The system according to claim 1.

[0855] "Example 1"

[0856] (Claim 1)

[0857] Collect fish finder data,

[0858] By integrating past temperature, season, and fishing history data, and analyzing it using a computational model,

[0859] A means for notifying an information display device of information indicating the location of the fishing grounds in terms of latitude and longitude,

[0860] A method for assigning a reliability index to the analysis results and selecting the most reliable result,

[0861] A method for integrating real-time fish school information with historical data, and then analyzing it after supplementing missing and anomaly information,

[0862] A system including means for sending results to an information display device via push notification.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising means for moving to a specified fishing ground based on analysis results notified to the user on an information display device.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for analyzing past and present data patterns using a generative AI model to predict the next high-density fish school.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] Collect fish finder data,

[0870] By integrating past weather conditions, seasons, and fishing records, and analyzing them with artificial intelligence,

[0871] The information processing device is notified of the location of the fishing grounds, indicated by coordinates.

[0872] means and

[0873] It serves as a platform for information recipients to act efficiently, with the aim of managing urban resources.

[0874] means and

[0875] Based on the analyzed data, we will provide useful information to fishermen and optimize resource utilization within urban areas.

[0876] means and

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The analysis results are prioritized, and only highly accurate results are notified to the information recipient.

[0880] The system according to claim 1.

[0881] (Claim 3)

[0882] We integrate real-time fish school data with historical information, correct for missing or anomaly data, perform analysis, and provide it as information to support urban sustainability.

[0883] The system according to claim 1.

[0884] "Example 2 of combining an emotion engine"

[0885] (Claim 1)

[0886] Collect acoustic device data.

[0887] Past weather data, temporal information, and fishing catch information are integrated and analyzed using an information processing system.

[0888] The system notifies the information terminal of the location of the area in coordinate format.

[0889] means and

[0890] Analyze the user's emotional state and optimize notification content and timing.

[0891] means and

[0892] It generates optimized information tailored to the user and provides it via information terminals.

[0893] means and

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The analysis results are evaluated, and only highly accurate results are notified to the user.

[0897] The system according to claim 1.

[0898] (Claim 3)

[0899] Real-time acoustic data and historical data are integrated, and analysis is performed after imputing missing and anomaly data.

[0900] The system according to claim 1.

[0901] "Application example 2 when combining with an emotional engine"

[0902] (Claim 1)

[0903] Collect fish finder data,

[0904] By integrating past weather conditions, temporal factors, and catch data, and analyzing them with an intelligent system,

[0905] A means of notifying an information terminal of information indicating the location of the fishing grounds in geographical coordinates,

[0906] A means equipped with an emotion recognition engine that recognizes the user's emotional state and provides optimized information notifications,

[0907] A means of collecting environmental data in cities and personalizing information based on the emotional state of residents,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which evaluates the analysis results and notifies the user only of the results with high accuracy.

[0911] (Claim 3)

[0912] The system according to claim 1, which integrates real-time data with historical data and analyzes data by supplementing missing and anomaly data. [Explanation of Symbols]

[0913] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Collect fish finder data, By integrating past weather conditions, seasons, and fishing records, and analyzing them with artificial intelligence, A means for notifying an information processing device of information indicating the location of the fishing grounds in coordinates, A means to function as a platform for information recipients to act efficiently, with the aim of managing urban resources. Based on the analyzed data, this information will be provided to fishermen as a means to optimize resource utilization within urban areas. A system that includes this.

2. The analysis results are prioritized, and only highly accurate results are notified to the information recipient. The system according to claim 1.

3. We integrate real-time fish school data with historical information, correct for missing or anomaly data, perform analysis, and provide it as information to support urban sustainability. The system according to claim 1.

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