system

Abstract

We provide the system. [Solution] As a means of data collection, it acquires image data, time management data, and location information data stored in an information processing device. As a means of data transmission, the acquired information is sent to an external computer. As a data analysis method, the acquired information is analyzed on an external computer, and a life record is created in natural language. Means for generating life records, A means for transmitting the recorded data generated by the aforementioned life record generation means to an information processing device and notifying the user, A system that includes means of analyzing local information data, generating suggestions to enrich users' lives, and notifying them of these suggestions.

Description

Technical Field

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] There is a problem that creating a diary takes time and effort, and it is difficult for many people to continue doing so. Also, in the case of creating a diary manually, it is difficult to record all daily events and used services in detail. Furthermore, managing shopping histories individually also becomes an additional burden, and there is a risk of missing opportunities for re-purchases. There is a need for a method that solves the above problems, allows users to create a diary without burden, and at the same time improves the convenience of life.

Means for Solving the Problems

[0006] A "data collection means" is a mechanism for efficiently acquiring image data, schedule data, and location data stored on a mobile information terminal.

[0007] "Data transmission means" refers to the communication means necessary to transmit data acquired from a terminal to an external server.

[0008] A "data analysis system" is a mechanism that analyzes data received by a server and uses the resulting information to create a diary in natural language.

[0009] The "diary generation method" is a process that uses analysis results to represent the user's activities and events in text and automatically generates them as a diary.

[0010] "Generated diary data" refers to natural language information compiled by data analysis tools to record the user's life.

[0011] "Shopping history data" refers to information about the products and services that a user has purchased, and is used to suggest repurchases.

[0012] "Repurchase suggestions" are a form of information provision that uses a user's purchase history to inform them of products and services they are likely to need again, thereby encouraging them to make repeat purchases.

[0013] "Communication methods" refer to technologies used to exchange data between mobile information terminals and servers, or between servers and users. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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 disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that automatically generates a diary using a mobile information terminal that a user uses on a daily basis. This system significantly reduces the effort required from the user by linking the functions of data collection, transmission, analysis, diary generation, and notification.

[0036] When a user initializes their diary generation settings, the device automatically collects data such as images, schedules, and location information according to those settings. The device then sends this data to the server using a secure communication protocol.

[0037] The server analyzes the received data and uses machine learning algorithms and natural language processing techniques to automatically generate a diary that reflects the user's activities for the day. This process involves object recognition from images, event organization from schedules, and behavior tracking using GPS information.

[0038] For example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the server will organize these activities into a sequential sentence and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at △△ Cafe. Afterwards, I went shopping at □□ Store."

[0039] The generated diary data is sent to the device, which then sends a push notification to the user stating, "Today's diary is complete." The user can then open the app to view the details and edit or save the content as needed.

[0040] Furthermore, the system also utilizes purchase history data. The server analyzes the user's past purchase history, predicts when similar products will be needed, and suggests repurchases. For example, for regularly consumed items, a notification might be sent saying, "Your shampoo will run out in 10 days. We recommend repurchasing it."

[0041] By implementing the present invention in this way, users can use the diary function without any burden, and their daily lives can be made more convenient through suggestions for repurchases.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device checks user settings and prepares to collect data for diary generation. Here, the user is asked to select the type of data to be used for diary generation (images, schedule, location information). Based on these settings, the device starts the data collection process at the designated time.

[0045] Step 2:

[0046] At a set time, the device collects photos from the smartphone's image folder, retrieves event information from the calendar app, and extracts location history from GPS data. This allows it to collect data related to the day's activities.

[0047] Step 3:

[0048] The device sends the collected data to the server using encryption technology while considering privacy. The device uses SSL / TLS protocols and other secure methods for data transfer via the internet connection.

[0049] Step 4:

[0050] The server analyzes the received data. Using image analysis algorithms, it identifies people, places, and objects within the images, and combines this with schedule data and geographical information to gain a detailed understanding of the user's activities.

[0051] Step 5:

[0052] The server automatically generates a diary using a natural language generation algorithm based on the analyzed information. For example, based on data indicating that the user visited a shopping mall in the morning and spent the afternoon at a cafe, it would create a diary entry such as, "I visited △△ Shopping Mall in the morning and relaxed at □□ Cafe in the afternoon."

[0053] Step 6:

[0054] The server generates diary data and sends it to the device. The device receives this data and notifies the user via push notification that "Today's diary is complete," allowing the user to review and edit it.

[0055] Step 7:

[0056] The server analyzes past purchase history data and suggests products that the user is likely to repurchase. For example, for products that are regularly purchased, the server generates a notification such as, "The stock of △△ that you purchased last time is running low. We recommend repurchasing it," and sends this notification to the user via their device.

[0057] (Example 1)

[0058] 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."

[0059] For users of mobile devices, there is a need to reduce the effort required to record and manage daily activities, as well as to alleviate the burden of managing and purchasing daily consumables. However, traditional methods require individual operations for data collection, analysis, and diary creation, which poses a significant burden. Furthermore, the lack of mechanisms to promptly notify users of the need for repurchase has led to problems with proper management of consumables.

[0060] 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.

[0061] In this invention, the server includes means for acquiring photo data, schedule data, and location information stored in a portable information processing device; means for transmitting the acquired information to an external processing device; means for analyzing the acquired information in the external processing device and creating human data using machine learning algorithms and natural language processing technology; and means for analyzing past purchase history and generating repurchase suggestions. As a result, users can record and analyze their daily lives in detail, automatically generate a diary, and further improve the convenience of their lives through appropriate management of consumables and repurchase suggestions.

[0062] A "portable information processing device" is a computer device that is portable and capable of collecting, processing, and communicating information.

[0063] An "external processing unit" is a computer system that receives data transmitted from a portable information processing unit and performs processing such as analysis.

[0064] "Photo data" refers to image information captured using a camera device, which visually records the user's activities.

[0065] "Schedule data" refers to information about events and tasks related to a specific date and time, stored in a schedule management application or similar system.

[0066] "Location information" refers to data indicating geographical coordinates or locations, acquired by a portable information processing device.

[0067] A "machine learning algorithm" is a computational method for automatically learning patterns and regularities from data.

[0068] "Natural language processing technology" refers to the technology used to process and generate human language using computers, and involves the analysis and generation of text data.

[0069] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0070] A "repurchase suggestion" is a suggestion that predicts when users should repurchase necessary items based on their past purchase history and informs them accordingly.

[0071] The information processing system of the present invention consists of a portable information processing device owned by the user and an external processing device that interacts with it via a network. This system automatically collects data related to the user's daily activities, such as images, schedules, and location information, generates a diary using machine learning algorithms and natural language processing technology, and further improves the convenience of daily life by analyzing the user's purchase history.

[0072] Portable information processing devices, such as smartphones and tablet computers, fulfill this role. These devices collect photos taken using their camera functions, schedule information obtained from scheduler and calendar apps, and location information obtained through GPS functions. This information is transmitted to an external processing device via a secure communication protocol such as HTTPS.

[0073] The external processing unit, or server, is responsible for analyzing the received data. This analysis uses machine learning algorithms such as TENSORFLOW® to recognize objects and locations within images, and employs NLP (Neuro-Language Programming) technology to transcribe the scheduled data into natural language. It also understands the user's actions and schedules and generates a diary based on this information. The generated diary is sent to a portable information processing device and notified to the user.

[0074] As a concrete example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the system will automatically record these activities and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at a cafe. Afterwards, I enjoyed shopping."

[0075] Furthermore, the server analyzes the user's purchase history and uses machine learning models to predict future repurchases of products the user will need. For example, regarding shampoo, which is consumed regularly, a notification such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated, sent to a portable information device, and suggested to the user.

[0076] An example of a prompt message would be: "Collect the data necessary for the user to record their daily activities and generate a diary in natural language. Include an example of a day where the user visited a park on a holiday, met up with friends at a cafe, and enjoyed shopping." This would allow the user to easily record and manage their daily life through the system's functions.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user performs the initial setup for diary generation on a portable information device. This includes granting permission to take photos, access calendar and schedule data, and use location services. Once the setup is complete, the device is ready to automatically collect this data at appropriate times. The input is user settings information, and the output is a specification of the data that can be collected.

[0080] Step 2:

[0081] The device collects information using the camera, GPS, and calendar app according to user settings. For example, the device automatically takes photos at a set time each day and saves them to its internal storage. Location information and schedule data are also acquired periodically. Inputs include the user's daily activities and their timing, while outputs include image data, schedule data, and location information.

[0082] Step 3:

[0083] The device sends collected image data, schedule information, and location information to the server using HTTPS. This communication is secure and designed to protect the privacy of user data. Input consists of various types of data stored on the device, and output consists of securely encrypted data packets.

[0084] Step 4:

[0085] The server analyzes the received data and uses image recognition technology to identify objects and landscapes within the photograph. Simultaneously, it uses machine learning algorithms to analyze location information and understand the user's movement trajectory. It utilizes NLP technology to generate natural language text from the schedule. The input is encrypted data packets, and the output is analyzed text data.

[0086] Step 5:

[0087] The server uses the analyzed data to pass prompt sentences to the generative AI model, which then converts the day's activities into a natural language diary. This diary is created in detail based on the places the user visited and the people they met. The input is the text data generated in step 4, and the output is the text of the completed diary.

[0088] Step 6:

[0089] The server sends the generated diary data to the terminal, which then notifies the user that "Today's diary is complete." Furthermore, it analyzes the user's past purchase history and provides suggestions for predicting repurchase timing. For example, a suggestion such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated and sent to the terminal. The inputs are diary data and purchase history, and the output is notification data for the user.

[0090] (Application Example 1)

[0091] 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."

[0092] Traditional lifestyle management systems had several drawbacks: they required users to meticulously record their activities, which was time-consuming, and they lacked concrete suggestions for improving quality of life. As a result, the busyness of daily life made it difficult for users to reflect on their activities or take concrete actions to improve their lives.

[0093] 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.

[0094] In this invention, the server includes means for acquiring image data, time management data, and location data stored in an information processing device; means for transmitting the acquired information to an external computer; means for analyzing the acquired information on the external computer and creating a lifestyle record in natural language; and means for analyzing regional information data and generating and notifying the user of suggestions to enrich their life. This makes it possible for the user to create a daily lifestyle record without any effort and to receive personalized lifestyle improvement suggestions.

[0095] An "information processing device" is a portable device used for collecting, transmitting, and receiving data.

[0096] "Image data" refers to data of still images and videos captured by an information processing device.

[0097] "Time management data" refers to digital calendar data that records the user's schedule and past activity history.

[0098] "Location information data" refers to data indicating the location of an information processing device, including geographical coordinates.

[0099] An "external computer" is a computer server used to analyze and process data transmitted from an information processing device.

[0100] "Natural language" refers to the language of ordinary humans, and includes texts that are mechanically generated by algorithms.

[0101] A "daily life record" is a document that summarizes the user's daily activities in written form.

[0102] "Local information data" refers to digital data that includes information about stores and events within a geographical area.

[0103] "Notification" refers to sending a message to a user's device to inform them of information.

[0104] "Suggestions" refer to information and advice provided based on analysis results with the aim of improving the user's life.

[0105] This invention provides a system that automatically collects and analyzes data generated by a user in their daily life and generates a lifestyle record, by utilizing a portable information processing device and an external computer. The information processing device acquires the user's time management data, image data, and location data in their daily life. For example, it collects GPS data using the smartphone's location information service and image data using the camera function. This data is transmitted from the information processing device to the external computer via a secure communication protocol such as HTTPS.

[0106] External computers process the received data using cloud-based computing platforms. Machine learning algorithms are executed using Amazon SageMaker and Google® Cloud AI Platform to analyze the user's daily activities, and life records are generated using natural language processing technologies such as Google BERT and OpenAI® GPT-3®. This process makes it possible to build a diary based on information such as where the user visited, what events they attended, and what was depicted in their photographs.

[0107] The generated lifestyle record data is sent to an information processing device and delivered to the user via push notifications. Furthermore, suggestions for enriching daily life are made through the analysis of local information data. This includes information on local events and new store openings, and notifications are sent using Firebase Cloud Messaging and other tools.

[0108] For example, if a user visits a local market on the weekend, a record such as "Today I visited a local market and enjoyed a variety of goods" is generated from GPS and image data. Then, information about local food fair events is notified, and suggestions such as "We have an event we recommend for next weekend" are sent. An example of a prompt message would be, "Based on your past event data, we suggest activities for your next holiday. Are there any recommended places in the city that you might enjoy?"

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The information processing device collects data based on the user's daily activities. This data includes GPS data from location services, time management data from a calendar app, and image data from a camera. The input is raw data from these sensors, and the output is information organized according to the time axis. Data from the sensors is acquired periodically and temporarily stored within the device.

[0112] Step 2:

[0113] The information processing device transmits the data collected in step 1 to an external computer. During this process, encrypted communication protocols such as HTTPS are used to ensure the secure transfer of data. The input is the organized data stored on the terminal, and the output is encrypted data awaiting transmission. The information processing device transmits data to the server periodically, or according to user settings.

[0114] Step 3:

[0115] The server receives data via an external computer and begins analyzing it. It uses machine learning models to extract meaningful information from the data. For example, it performs object recognition from image data and extracts event information from time-managed data. The input is the received encrypted data, and the output is high-level feature information obtained through the analysis. This process preprocesses the data for natural language processing.

[0116] Step 4:

[0117] The server uses natural language processing technology to generate a life record from the feature information extracted in step 3. Important activities and events are described in sentence form. A generative AI model is used to generate the record according to the prompt. The input is high-level feature information, and the output is a life record in the form of specific sentences. This generation process aims to produce grammatically accurate and easy-to-read sentences.

[0118] Step 5:

[0119] The server sends the generated lifestyle records to the information processing device. Furthermore, it analyzes local information and adds useful suggestions for the user. This includes information on local events and notifications of new store openings. The input is the generated lifestyle records and local information data, and the output is notification information with suggestions added to the lifestyle records. The information processing device receives this notification and sends a push notification to the user.

[0120] Step 6:

[0121] Users receive notifications on the information processing device and view their life records. They can modify the records and review suggestions as needed. The input is the notification data displayed on the information processing device, and the output is the record data after user review and editing. This step incorporates user feedback to improve the accuracy of future suggestions.

[0122] 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.

[0123] This invention is an automated diary generation system that utilizes the user's mobile device to combine image data, schedule information, and location information for the day with an emotion engine. The aim of this system is to provide a more personalized diary by recognizing emotions from images taken by the user and accumulated text data, and reflecting them in the analysis process.

[0124] When a user activates the system by operating the device, the device collects images, schedules, and location information on a daily basis. This includes data from the past 24 hours. In addition, the device utilizes an emotion engine to recognize emotions from the facial expressions of people in images and the text written on them. For example, a photo with a smile and a comment like "I had fun!" would be recognized as a positive emotion.

[0125] The data collected by the device is sent to the server via encrypted communication. On the server side, the user's behavioral history and emotional data are combined and analyzed, and a diary is generated based on this. Here, a natural language generation algorithm creates the diary by taking into account visited locations obtained from location information, activity details from the schedule, and emotional data. This diary is written based on emotions, for example, "Today I had a fun time at a cafe I visited with a friend."

[0126] Furthermore, the emotion engine analyzes the user's emotional tendencies when generating daily journal entries. This allows it to record the user's long-term emotional patterns and provide feedback aimed at improving their quality of life. For example, users experiencing significant emotional ups and downs may be offered relaxation suggestions.

[0127] The generated diary data is sent back to the device and presented to the user via notification. The user can view the diary details on the device and edit or save its contents. In addition, the server analyzes the user's shopping history and notifies them of products that may be suitable based on their emotional changes, further supporting their daily life.

[0128] In this way, the present invention, which combines an emotion engine, enhances the automatic diary function and significantly improves convenience for the user.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device checks user settings and prepares to collect image data, schedule data, and location data. This includes specifying which apps and folders to retrieve data from.

[0132] Step 2:

[0133] The device collects photos from its image folder, reads schedule data, and obtains location information. It also passes this data to an emotion engine to recognize emotions based on facial expressions and text within the images. For example, a smiling photo might be interpreted as a positive emotion, while a gloomy expression might be interpreted as a negative emotion.

[0134] Step 3:

[0135] The collected data is encrypted to protect privacy before being sent to the server. The device uses the SSL / TLS protocol for data transmission to ensure secure data transfer.

[0136] Step 4:

[0137] The server analyzes the received data. Here, machine learning algorithms are used to integrate image, schedule, and location information to prepare input data for diary generation. The output of the emotion engine is also incorporated into this analysis process.

[0138] Step 5:

[0139] The server automatically generates a diary using a natural language generation algorithm. Based on the analyzed sentiment information, it creates sentences that reflect the emotional aspects of the user's experience. For example, "There were a lot of fun photos today. It seems you really enjoyed your time at △△."

[0140] Step 6:

[0141] The generated diary data is sent from the server to the terminal. The terminal receives it and notifies the user that "Today's diary is complete."

[0142] Step 7:

[0143] The server re-evaluates the user's shopping history based on sentiment analysis data and suggests products and services that match the user's mood and emotions. This information is then sent back to the terminal, and the user receives a suggestion such as, "It would be good to repurchase products related to □□. Please consider it."

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional automated diary generation systems have struggled to provide personalized content that fully reflects the user's emotions. Furthermore, data security and the provision of real-time feedback have been inadequate. This highlights the need for valuable information provision that takes into account the individual experiences and emotions of each user.

[0147] 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.

[0148] In this invention, the server includes emotion recognition means, data analysis means, and document generation means. This makes it possible to generate personalized diary data that utilizes the emotions experienced by the user.

[0149] A "digital device" is a portable electronic device used by a user on a daily basis, and has the function of storing and acquiring image data, schedule data, and location data.

[0150] "Still image data" refers to image information that records visual information and captures a specific moment.

[0151] "Schedule management data" refers to data used to record and manage a user's daily activities and schedules.

[0152] "Location data" refers to information that indicates a geographical location and is used to determine where a user was at a specific time period.

[0153] "Emotion recognition means" refers to algorithms and software functions that extract a person's emotions from image or text data.

[0154] An "external computing device" is an external computer system that can receive and process data transmitted from a user's digital device.

[0155] "Data analysis methods" refer to the process of analyzing collected and recognized data and generating conclusions or outputs based on the results.

[0156] A "natural language document" is text that is generated by a machine but written in a language format that humans can understand.

[0157] A "document generation means" is a technical system that creates documents in natural language based on the results of data analysis.

[0158] A "user" is an individual or organization that utilizes the functions of this system and receives services based on their own information.

[0159] This invention is a system that automatically generates personalized diaries based on the user's individual experiences. Specifically, it provides a process for collecting various data using the user's digital devices (e.g., smartphones and tablets), analyzing that data, and creating a diary.

[0160] The device carried by the user first collects image data, schedule management data, and location data. This is done using the device's built-in camera, GPS function, and scheduling application. Next, the device uses emotion recognition technology to determine emotions from facial expressions and text in the images. This process utilizes image analysis algorithms and text mining techniques. For example, if a photo taken with family members shows smiles, a positive emotion will be recognized.

[0161] The collected and recognized data is encrypted and sent to an external server. The server receives this data and analyzes it using data analysis tools. The analyzed data is then used with a generative AI model to generate a document in natural language, i.e., a diary. An example of a prompt used in this process is: "Places visited today: Park, Emotion: Positive, Photo: Smiling. Based on this, please create the user's diary."

[0162] The generated diary is sent back to the device and the user is notified. The user can read this diary and reflect on their emotions and activities. The server can also provide feedback to the user based on past emotional data. Based on this feedback, suggestions are made to improve the user's quality of life.

[0163] In this way, the system of the present invention, which combines emotion recognition technology and data analysis technology, provides convenience and value to the user.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The terminal uses the camera function of the digital device to collect still image data, user schedule management data, and location information data. Inputs include images, schedules, and location information stored on the digital device. The data collection forms a user activity history. The output is the collected dataset.

[0167] Step 2:

[0168] The device applies emotion recognition technology to analyze facial expressions from input image data using an image analysis algorithm to identify the user's emotions. It also performs emotion analysis on text within schedule management data using text mining. As part of data processing, it analyzes the facial expression data and text data extracted from the images to generate recognized emotion information as output.

[0169] Step 3:

[0170] The terminal transmits the data collected and recognized in steps 1 and 2 to an external server using an encryption protocol. The input consists of the dataset and sentiment information obtained in steps 1 and 2. The data is securely delivered to the server while maintaining data confidentiality during the encryption process. The output is encrypted data.

[0171] Step 4:

[0172] The server receives data sent from the terminal and analyzes its contents using data analysis tools. The input is encrypted data. In the analysis process, a generative AI model is used to create prompt sentences based on location and sentiment information, and then a natural language document is generated based on these prompts. An example of such a prompt sentence is, "Places visited today: Park, Sentiment: Positive, Photo: Smiling." The output is the generated diary document.

[0173] Step 5:

[0174] The diary generated on the server is sent back to the device. This allows the user to reflect on the events of their day, along with their emotions. The input is the diary document generated on the server, and the output is the diary data notified to the user. The user can view the diary on their device and edit or save it as needed.

[0175] (Application Example 2)

[0176] 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".

[0177] In modern society, it is important for individual users to easily summarize their daily information and reflect on their emotions and activities. However, manually creating a diary is time-consuming and laborious, making it difficult to maintain consistently. Furthermore, the technology for generating diaries that reflect individual emotions is still immature, and there is a challenge in that it does not fully meet the need for more personalized records.

[0178] 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.

[0179] In this invention, the server includes means for acquiring image information, schedule information, and location information stored in an information device; means for transmitting the acquired information to an external processing device; and means for analyzing emotions from the acquired image information and text information and modifying the creation of a natural language document based on that analysis. This makes it possible to generate a natural language document that reflects the individual activities and emotions of the user.

[0180] An "information device" is a device that manages and stores image information, schedule information, and location information, such as a mobile information terminal or a home robot used by a user.

[0181] An "external processing device" is a device that functions as a cloud or server, receiving and analyzing acquired information.

[0182] "Emotion recognition means" refers to technologies and algorithms for analyzing emotions from acquired image and text information.

[0183] "Natural language document creation" is the process of generating documents using natural language processing technology based on analyzed data.

[0184] "Diary generation means" refers to a function or process that creates a document based on analyzed information and transmits it to an information device.

[0185] To implement this invention, first, a portable information terminal or a home robot that functions as an information device is required. These devices collect image information, schedule information, and location information in daily life. The information device analyzes the emotions contained in the image information using emotion recognition technology such as the Google Cloud Vision API.

[0186] The various data collected by the information device is transmitted to an external processing unit, i.e., the cloud or a server, via an encrypted protocol. The server analyzes the acquired information using cloud computing services such as AWS® Lambda. The analyzed data is then used with natural language generation algorithms such as OpenAI GPT to create a customized document for each user, i.e., a diary.

[0187] The generated document is sent back to the information device and the user is notified. The user can review this notification and edit and save the diary entries. Through long-term use, the server learns the user's emotional tendencies and provides feedback to improve their quality of life. This feedback includes suggestions for relaxation methods and advice on purchasing products.

[0188] As a concrete example, using images and schedules of a user's time spent with family on a holiday, it can generate a diary entry that reflects their emotions, such as, "Today I visited the park with my family and had a fun picnic." An example of a prompt for the generation AI model could be, "Generate a diary entry for the following event. It should include an image of family members smiling, and the event name is 'Picnic'."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The device collects image information, schedule information, and location information from daily life. This information includes data saved by the user on the device, images taken in real time, and location information obtained using GPS. The collected information becomes input for the next processing step.

[0192] Step 2:

[0193] The device uses the Google Cloud Vision API to recognize emotions from collected image information. It analyzes a person's facial expressions from the input image and generates emotion data such as positive or negative. The output of this process is the analyzed emotion data.

[0194] Step 3:

[0195] The device encrypts image information, emotion data, schedule information, and location information and transmits it to an external processing unit. The external processing unit is cloud-based and receives the information securely using security protocols. The output is an encrypted data package.

[0196] Step 4:

[0197] The server uses AWS Lambda to analyze the received data. After verifying the integrity of the input data, it processes it by combining it with user activity history and sentiment data. This process outputs the results of the data analysis.

[0198] Step 5:

[0199] The server uses natural language generation algorithms such as OpenAI GPT to create diary entries. Based on the input analysis results, it generates a diary in natural language that reflects the user's daily life. The output of this process is the generated diary document.

[0200] Step 6:

[0201] The generated diary document is sent back to the device and the user is notified. The user can check the notification and view, edit, and save the generated diary. The output presented to the user is the diary content displayed on the device.

[0202] Step 7:

[0203] The server learns users' emotional tendencies over the long term and provides feedback. Based on the accumulated data, it analyzes user emotional patterns and generates feedback such as improvement suggestions and product recommendations. The output consists of detected emotional tendencies and corresponding action suggestions.

[0204] 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.

[0205] Data generation model 58 is a type of 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is a system that automatically generates a diary using a mobile information terminal that a user uses on a daily basis. This system significantly reduces the effort required from the user by linking the functions of data collection, transmission, analysis, diary generation, and notification.

[0221] When a user initializes their diary generation settings, the device automatically collects data such as images, schedules, and location information according to those settings. The device then sends this data to the server using a secure communication protocol.

[0222] The server analyzes the received data and uses machine learning algorithms and natural language processing techniques to automatically generate a diary that reflects the user's activities for the day. This process involves object recognition from images, event organization from schedules, and behavior tracking using GPS information.

[0223] For example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the server will organize these activities into a sequential sentence and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at △△ Cafe. Afterwards, I went shopping at □□ Store."

[0224] The generated diary data is sent to the device, which then sends a push notification to the user stating, "Today's diary is complete." The user can then open the app to view the details and edit or save the content as needed.

[0225] Furthermore, the system also utilizes purchase history data. The server analyzes the user's past purchase history, predicts when similar products will be needed, and suggests repurchases. For example, for regularly consumed items, a notification might be sent saying, "Your shampoo will run out in 10 days. We recommend repurchasing it."

[0226] By implementing the present invention in this way, users can use the diary function without any burden, and their daily lives can be made more convenient through suggestions for repurchases.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The device checks user settings and prepares to collect data for diary generation. Here, the user is asked to select the type of data to be used for diary generation (images, schedule, location information). Based on these settings, the device starts the data collection process at the designated time.

[0230] Step 2:

[0231] At a set time, the device collects photos from the smartphone's image folder, retrieves event information from the calendar app, and extracts location history from GPS data. This allows it to collect data related to the day's activities.

[0232] Step 3:

[0233] The device sends the collected data to the server using encryption technology while considering privacy. The device uses SSL / TLS protocols and other secure methods for data transfer via the internet connection.

[0234] Step 4:

[0235] The server analyzes the received data. Using image analysis algorithms, it identifies people, places, and objects within the images, and combines this with schedule data and geographical information to gain a detailed understanding of the user's activities.

[0236] Step 5:

[0237] The server automatically generates a diary using a natural language generation algorithm based on the analyzed information. For example, based on data indicating that the user visited a shopping mall in the morning and spent the afternoon at a cafe, it would create a diary entry such as, "I visited △△ Shopping Mall in the morning and relaxed at □□ Cafe in the afternoon."

[0238] Step 6:

[0239] The server generates diary data and sends it to the device. The device receives this data and notifies the user via push notification that "Today's diary is complete," allowing the user to review and edit it.

[0240] Step 7:

[0241] The server analyzes past purchase history data and suggests products that the user is likely to repurchase. For example, for products that are regularly purchased, the server generates a notification such as, "The stock of △△ that you purchased last time is running low. We recommend repurchasing it," and sends this notification to the user via their device.

[0242] (Example 1)

[0243] 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."

[0244] For users of mobile devices, there is a need to reduce the effort required to record and manage daily activities, as well as to alleviate the burden of managing and purchasing daily consumables. However, traditional methods require individual operations for data collection, analysis, and diary creation, which poses a significant burden. Furthermore, the lack of mechanisms to promptly notify users of the need for repurchase has led to problems with proper management of consumables.

[0245] 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.

[0246] In this invention, the server includes means for acquiring photo data, schedule data, and location information stored in a portable information processing device; means for transmitting the acquired information to an external processing device; means for analyzing the acquired information in the external processing device and creating human data using machine learning algorithms and natural language processing technology; and means for analyzing past purchase history and generating repurchase suggestions. As a result, users can record and analyze their daily lives in detail, automatically generate a diary, and further improve the convenience of their lives through appropriate management of consumables and repurchase suggestions.

[0247] A "portable information processing device" is a computer device that is portable and capable of collecting, processing, and communicating information.

[0248] An "external processing unit" is a computer system that receives data transmitted from a portable information processing unit and performs processing such as analysis.

[0249] "Photo data" refers to image information captured using a camera device, which visually records the user's activities.

[0250] "Schedule data" refers to information about events and tasks related to a specific date and time, stored in a schedule management application or similar system.

[0251] "Location information" refers to data indicating geographical coordinates or locations, acquired by a portable information processing device.

[0252] A "machine learning algorithm" is a computational method for automatically learning patterns and regularities from data.

[0253] "Natural language processing technology" refers to the technology used to process and generate human language using computers, and involves the analysis and generation of text data.

[0254] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0255] A "repurchase suggestion" is a suggestion that predicts when users should repurchase necessary items based on their past purchase history and informs them accordingly.

[0256] The information processing system of the present invention consists of a portable information processing device owned by the user and an external processing device that interacts with it via a network. This system automatically collects data related to the user's daily activities, such as images, schedules, and location information, generates a diary using machine learning algorithms and natural language processing technology, and further improves the convenience of daily life by analyzing the user's purchase history.

[0257] Portable information processing devices, such as smartphones and tablet computers, fulfill this role. These devices collect photos taken using their camera functions, schedule information obtained from scheduler and calendar apps, and location information obtained through GPS functions. This information is transmitted to an external processing device via a secure communication protocol such as HTTPS.

[0258] The external processing unit, or server, is responsible for analyzing the received data. This analysis uses machine learning algorithms such as TensorFlow to recognize objects and locations within images, and employs NLP (Neuro-Language Programming) techniques to transcribe the scheduled data into natural language. It also understands the user's actions and schedule, and generates a diary based on this information. The generated diary is sent to a portable information processing device and notified to the user.

[0259] As a concrete example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the system will automatically record these activities and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at a cafe. Afterwards, I enjoyed shopping."

[0260] Furthermore, the server analyzes the user's purchase history and uses machine learning models to predict future repurchases of products the user will need. For example, regarding shampoo, which is consumed regularly, a notification such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated, sent to a portable information device, and suggested to the user.

[0261] An example of a prompt message would be: "Collect the data necessary for the user to record their daily activities and generate a diary in natural language. Include an example of a day where the user visited a park on a holiday, met up with friends at a cafe, and enjoyed shopping." This would allow the user to easily record and manage their daily life through the system's functions.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user performs the initial setup for diary generation on a portable information device. This includes granting permission to take photos, access calendar and schedule data, and use location services. Once the setup is complete, the device is ready to automatically collect this data at appropriate times. The input is user settings information, and the output is a specification of the data that can be collected.

[0265] Step 2:

[0266] The device collects information using the camera, GPS, and calendar app according to user settings. For example, the device automatically takes photos at a set time each day and saves them to its internal storage. Location information and schedule data are also acquired periodically. Inputs include the user's daily activities and their timing, while outputs include image data, schedule data, and location information.

[0267] Step 3:

[0268] The device sends collected image data, schedule information, and location information to the server using HTTPS. This communication is secure and designed to protect the privacy of user data. Input consists of various types of data stored on the device, and output consists of securely encrypted data packets.

[0269] Step 4:

[0270] The server analyzes the received data and uses image recognition technology to identify objects and landscapes within the photograph. Simultaneously, it uses machine learning algorithms to analyze location information and understand the user's movement trajectory. It utilizes NLP technology to generate natural language text from the schedule. The input is encrypted data packets, and the output is analyzed text data.

[0271] Step 5:

[0272] The server uses the analyzed data to pass prompt sentences to the generative AI model, which then converts the day's activities into a natural language diary. This diary is created in detail based on the places the user visited and the people they met. The input is the text data generated in step 4, and the output is the text of the completed diary.

[0273] Step 6:

[0274] The server sends the generated diary data to the terminal, which then notifies the user that "Today's diary is complete." Furthermore, it analyzes the user's past purchase history and provides suggestions for predicting repurchase timing. For example, a suggestion such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated and sent to the terminal. The inputs are diary data and purchase history, and the output is notification data for the user.

[0275] (Application Example 1)

[0276] 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."

[0277] Traditional lifestyle management systems had several drawbacks: they required users to meticulously record their activities, which was time-consuming, and they lacked concrete suggestions for improving quality of life. As a result, the busyness of daily life made it difficult for users to reflect on their activities or take concrete actions to improve their lives.

[0278] 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.

[0279] In this invention, the server includes means for acquiring image data, time management data, and position information data stored in an information processing device, means for transmitting the acquired information to an external computer, means for analyzing the information acquired by the external computer and creating a life record in natural language, and means for analyzing regional information data, generating and notifying a proposal for enriching the user's life. As a result, the user can create daily life records without much effort and can further receive individual life improvement proposals.

[0280] The "information processing device" is a portable device for collecting, transmitting, and receiving data.

[0281] The "image data" refers to data of still images or videos taken by an information processing device.

[0282] The "time management data" is data of a digital calendar that records the user's schedule and past activity history.

[0283] The "position information data" is data indicating the position of an information processing device and includes geographical coordinates.

[0284] The "external computer" is a computer server for analyzing and processing data transmitted from an information processing device.

[0285] The "natural language" refers to a general human language and includes texts mechanically generated by an algorithm.

[0286] The "life record" is a document that summarizes the user's daily activities in text form.

[0287] The "regional information data" is digital data including information on stores and events in a geographical area.

[0288] The "notification" refers to the transmission of a message for informing the user's device of information.

[0289] "Suggestions" refer to information and advice provided based on analysis results with the aim of improving the user's life.

[0290] This invention provides a system that automatically collects and analyzes data generated by a user in their daily life and generates a lifestyle record, by utilizing a portable information processing device and an external computer. The information processing device acquires the user's time management data, image data, and location data in their daily life. For example, it collects GPS data using the smartphone's location information service and image data using the camera function. This data is transmitted from the information processing device to the external computer via a secure communication protocol such as HTTPS.

[0291] External computers process the received data using cloud-based computing platforms. Machine learning algorithms are executed using Amazon SageMaker and Google Cloud AI Platform to analyze the user's daily activities, and natural language processing technologies such as Google BERT and OpenAI GPT-3 are used to generate life records. This process makes it possible to build a diary based on information such as where the user visited, what events they attended, and what was depicted in their photographs.

[0292] The generated lifestyle record data is sent to an information processing device and delivered to the user via push notifications. Furthermore, suggestions for enriching daily life are made through the analysis of local information data. This includes information on local events and new store openings, and notifications are sent using Firebase Cloud Messaging and other tools.

[0293] For example, if a user visits a local market on the weekend, a record such as "Today I visited a local market and enjoyed a variety of goods" is generated from GPS and image data. Then, information about local food fair events is notified, and suggestions such as "We have an event we recommend for next weekend" are sent. An example of a prompt message would be, "Based on your past event data, we suggest activities for your next holiday. Are there any recommended places in the city that you might enjoy?"

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The information processing device collects data based on the user's daily activities. This data includes GPS data from location services, time management data from a calendar app, and image data from a camera. The input is raw data from these sensors, and the output is information organized according to the time axis. Data from the sensors is acquired periodically and temporarily stored within the device.

[0297] Step 2:

[0298] The information processing device transmits the data collected in step 1 to an external computer. During this process, encrypted communication protocols such as HTTPS are used to ensure the secure transfer of data. The input is the organized data stored on the terminal, and the output is encrypted data awaiting transmission. The information processing device transmits data to the server periodically, or according to user settings.

[0299] Step 3:

[0300] The server receives data via an external computer and begins analyzing it. It uses machine learning models to extract meaningful information from the data. For example, it performs object recognition from image data and extracts event information from time-managed data. The input is the received encrypted data, and the output is high-level feature information obtained through the analysis. This process preprocesses the data for natural language processing.

[0301] Step 4:

[0302] The server uses natural language processing technology to generate a life record from the feature information extracted in step 3. Important activities and events are described in sentence form. A generative AI model is used to generate the record according to the prompt. The input is high-level feature information, and the output is a life record in the form of specific sentences. This generation process aims to produce grammatically accurate and easy-to-read sentences.

[0303] Step 5:

[0304] The server sends the generated lifestyle records to the information processing device. Furthermore, it analyzes local information and adds useful suggestions for the user. This includes information on local events and notifications of new store openings. The input is the generated lifestyle records and local information data, and the output is notification information with suggestions added to the lifestyle records. The information processing device receives this notification and sends a push notification to the user.

[0305] Step 6:

[0306] Users receive notifications on the information processing device and view their life records. They can modify the records and review suggestions as needed. The input is the notification data displayed on the information processing device, and the output is the record data after user review and editing. This step incorporates user feedback to improve the accuracy of future suggestions.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0308] The present invention is an automatic diary generation system that utilizes the user's portable information terminal and combines an emotion engine in addition to the image data, schedule information, and location information for that day. In this system, the purpose is to provide a more personalized diary by recognizing emotions from the images taken by the user and the accumulated text data and reflecting it in the analysis process.

[0309] When the user operates the terminal to start the system, the terminal collects images, schedules, and location information for each date. This includes data for the past 24 hours. In addition, the terminal utilizes an emotion engine to recognize emotions from the expressions of the people shown in the images and the written text. For example, when there is a photo of a smiling face or a comment such as "It was fun!", it is recognized as a positive emotion.

[0310] The data collected by the terminal is transmitted to the server through encrypted communication. On the server side, the user's behavior history and emotion data are combined and analyzed, and a diary is generated based on this. Here, the natural language generation algorithm creates a diary considering any of the visited locations obtained from the location information, the activity content from the schedule, and the emotion data. This diary is written based on emotions, for example, "Today, I had a great time at the café I visited with my friends."

[0311] Furthermore, the emotion engine analyzes the user's emotion tendency when generating daily diaries. This makes it possible to record the user's long-term emotion pattern and provide feedback aimed at improving the quality of life. For example, relaxation suggestions are given to users with large fluctuations in emotions.

[0312] The generated diary data is sent back to the device and presented to the user via notification. The user can view the diary details on the device and edit or save its contents. In addition, the server analyzes the user's shopping history and notifies them of products that may be suitable based on their emotional changes, further supporting their daily life.

[0313] In this way, the present invention, which combines an emotion engine, enhances the automatic diary function and significantly improves convenience for the user.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The device checks user settings and prepares to collect image data, schedule data, and location data. This includes specifying which apps and folders to retrieve data from.

[0317] Step 2:

[0318] The device collects photos from its image folder, reads schedule data, and obtains location information. It also passes this data to an emotion engine to recognize emotions based on facial expressions and text within the images. For example, a smiling photo might be interpreted as a positive emotion, while a gloomy expression might be interpreted as a negative emotion.

[0319] Step 3:

[0320] The collected data is encrypted to protect privacy before being sent to the server. The device uses the SSL / TLS protocol for data transmission to ensure secure data transfer.

[0321] Step 4:

[0322] The server analyzes the received data. Here, machine learning algorithms are used to integrate image, schedule, and location information to prepare input data for diary generation. The output of the emotion engine is also incorporated into this analysis process.

[0323] Step 5:

[0324] The server automatically generates a diary using a natural language generation algorithm. Based on the analyzed sentiment information, it creates sentences that reflect the emotional aspects of the user's experience. For example, "There were a lot of fun photos today. It seems you really enjoyed your time at △△."

[0325] Step 6:

[0326] The generated diary data is sent from the server to the terminal. The terminal receives it and notifies the user that "Today's diary is complete."

[0327] Step 7:

[0328] The server re-evaluates the user's shopping history based on sentiment analysis data and suggests products and services that match the user's mood and emotions. This information is then sent back to the terminal, and the user receives a suggestion such as, "It would be good to repurchase products related to □□. Please consider it."

[0329] (Example 2)

[0330] 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".

[0331] Conventional automated diary generation systems have struggled to provide personalized content that fully reflects the user's emotions. Furthermore, data security and the provision of real-time feedback have been inadequate. This highlights the need for valuable information provision that takes into account the individual experiences and emotions of each user.

[0332] 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.

[0333] In this invention, the server includes emotion recognition means, data analysis means, and document generation means. This makes it possible to generate personalized diary data that utilizes the emotions experienced by the user.

[0334] A "digital device" is a portable electronic device used by a user on a daily basis, and has the function of storing and acquiring image data, schedule data, and location data.

[0335] "Still image data" refers to image information that records visual information and captures a specific moment.

[0336] "Schedule management data" refers to data used to record and manage a user's daily activities and schedules.

[0337] "Location data" refers to information that indicates a geographical location and is used to determine where a user was at a specific time period.

[0338] "Emotion recognition means" refers to algorithms and software functions that extract a person's emotions from image or text data.

[0339] An "external computing device" is an external computer system that can receive and process data transmitted from a user's digital device.

[0340] "Data analysis methods" refer to the process of analyzing collected and recognized data and generating conclusions or outputs based on the results.

[0341] A "natural language document" is text that is generated by a machine but written in a language format that humans can understand.

[0342] A "document generation means" is a technical system that creates documents in natural language based on the results of data analysis.

[0343] A "user" is an individual or organization that utilizes the functions of this system and receives services based on their own information.

[0344] This invention is a system that automatically generates personalized diaries based on the user's individual experiences. Specifically, it provides a process for collecting various data using the user's digital devices (e.g., smartphones and tablets), analyzing that data, and creating a diary.

[0345] The device carried by the user first collects image data, schedule management data, and location data. This is done using the device's built-in camera, GPS function, and scheduling application. Next, the device uses emotion recognition technology to determine emotions from facial expressions and text in the images. This process utilizes image analysis algorithms and text mining techniques. For example, if a photo taken with family members shows smiles, a positive emotion will be recognized.

[0346] The collected and recognized data is encrypted and sent to an external server. The server receives this data and analyzes it using data analysis tools. The analyzed data is then used with a generative AI model to generate a document in natural language, i.e., a diary. An example of a prompt used in this process is: "Places visited today: Park, Emotion: Positive, Photo: Smiling. Based on this, please create the user's diary."

[0347] The generated diary is sent back to the device and the user is notified. The user can read this diary and reflect on their emotions and activities. The server can also provide feedback to the user based on past emotional data. Based on this feedback, suggestions are made to improve the user's quality of life.

[0348] In this way, the system of the present invention, which combines emotion recognition technology and data analysis technology, provides convenience and value to the user.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The terminal uses the camera function of the digital device to collect still image data, user schedule management data, and location information data. Inputs include images, schedules, and location information stored on the digital device. The data collection forms a user activity history. The output is the collected dataset.

[0352] Step 2:

[0353] The device applies emotion recognition technology to analyze facial expressions from input image data using an image analysis algorithm to identify the user's emotions. It also performs emotion analysis on text within schedule management data using text mining. As part of data processing, it analyzes the facial expression data and text data extracted from the images to generate recognized emotion information as output.

[0354] Step 3:

[0355] The terminal transmits the data collected and recognized in steps 1 and 2 to an external server using an encryption protocol. The input consists of the dataset and sentiment information obtained in steps 1 and 2. The data is securely delivered to the server while maintaining data confidentiality during the encryption process. The output is encrypted data.

[0356] Step 4:

[0357] The server receives data sent from the terminal and analyzes its contents using data analysis tools. The input is encrypted data. In the analysis process, a generative AI model is used to create prompt sentences based on location and sentiment information, and then a natural language document is generated based on these prompts. An example of such a prompt sentence is, "Places visited today: Park, Sentiment: Positive, Photo: Smiling." The output is the generated diary document.

[0358] Step 5:

[0359] The diary generated on the server is sent back to the device. This allows the user to reflect on the events of their day, along with their emotions. The input is the diary document generated on the server, and the output is the diary data notified to the user. The user can view the diary on their device and edit or save it as needed.

[0360] (Application Example 2)

[0361] 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."

[0362] In modern society, it is important for individual users to easily summarize their daily information and reflect on their emotions and activities. However, manually creating a diary is time-consuming and laborious, making it difficult to maintain consistently. Furthermore, the technology for generating diaries that reflect individual emotions is still immature, and there is a challenge in that it does not fully meet the need for more personalized records.

[0363] 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.

[0364] In this invention, the server includes means for acquiring image information, schedule information, and location information stored in an information device; means for transmitting the acquired information to an external processing device; and means for analyzing emotions from the acquired image information and text information and modifying the creation of a natural language document based on that analysis. This makes it possible to generate a natural language document that reflects the individual activities and emotions of the user.

[0365] An "information device" is a device that manages and stores image information, schedule information, and location information, such as a mobile information terminal or a home robot used by a user.

[0366] An "external processing device" is a device that functions as a cloud or server, receiving and analyzing acquired information.

[0367] "Emotion recognition means" refers to technologies and algorithms for analyzing emotions from acquired image and text information.

[0368] "Natural language document creation" is the process of generating documents using natural language processing technology based on analyzed data.

[0369] "Diary generation means" refers to a function or process that creates a document based on analyzed information and transmits it to an information device.

[0370] To implement this invention, first, a portable information terminal or a home robot that functions as an information device is required. These devices collect image information, schedule information, and location information in daily life. The information device analyzes the emotions contained in the image information using emotion recognition technology such as the Google Cloud Vision API.

[0371] The various data collected by the information device is transmitted to an external processing unit, i.e., the cloud or a server, via an encrypted protocol. The server uses cloud computing services such as AWS Lambda to analyze the acquired information. The analyzed data is then used with natural language generation algorithms such as OpenAI GPT to create a customized document for each user, i.e., a diary.

[0372] The generated document is sent back to the information device and the user is notified. The user can review this notification and edit and save the diary entries. Through long-term use, the server learns the user's emotional tendencies and provides feedback to improve their quality of life. This feedback includes suggestions for relaxation methods and advice on purchasing products.

[0373] As a concrete example, using images and schedules of a user's time spent with family on a holiday, it can generate a diary entry that reflects their emotions, such as, "Today I visited the park with my family and had a fun picnic." An example of a prompt for the generation AI model could be, "Generate a diary entry for the following event. It should include an image of family members smiling, and the event name is 'Picnic'."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The device collects image information, schedule information, and location information from daily life. This information includes data saved by the user on the device, images taken in real time, and location information obtained using GPS. The collected information becomes input for the next processing step.

[0377] Step 2:

[0378] The device uses the Google Cloud Vision API to recognize emotions from collected image information. It analyzes a person's facial expressions from the input image and generates emotion data such as positive or negative. The output of this process is the analyzed emotion data.

[0379] Step 3:

[0380] The device encrypts image information, emotion data, schedule information, and location information and transmits it to an external processing unit. The external processing unit is cloud-based and receives the information securely using security protocols. The output is an encrypted data package.

[0381] Step 4:

[0382] The server uses AWS Lambda to analyze the received data. After verifying the integrity of the input data, it processes it by combining it with user activity history and sentiment data. This process outputs the results of the data analysis.

[0383] Step 5:

[0384] The server uses natural language generation algorithms such as OpenAI GPT to create diary entries. Based on the input analysis results, it generates a diary in natural language that reflects the user's daily life. The output of this process is the generated diary document.

[0385] Step 6:

[0386] The generated diary document is sent back to the device and the user is notified. The user can check the notification and view, edit, and save the generated diary. The output presented to the user is the diary content displayed on the device.

[0387] Step 7:

[0388] The server learns users' emotional tendencies over the long term and provides feedback. Based on the accumulated data, it analyzes user emotional patterns and generates feedback such as improvement suggestions and product recommendations. The output consists of detected emotional tendencies and corresponding action suggestions.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] This invention is a system that automatically generates a diary using a mobile information terminal that a user uses on a daily basis. This system significantly reduces the effort required from the user by linking the functions of data collection, transmission, analysis, diary generation, and notification.

[0406] When a user initializes their diary generation settings, the device automatically collects data such as images, schedules, and location information according to those settings. The device then sends this data to the server using a secure communication protocol.

[0407] The server analyzes the received data and uses machine learning algorithms and natural language processing techniques to automatically generate a diary that reflects the user's activities for the day. This process involves object recognition from images, event organization from schedules, and behavior tracking using GPS information.

[0408] For example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the server will organize these activities into a sequential sentence and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at △△ Cafe. Afterwards, I went shopping at □□ Store."

[0409] The generated diary data is sent to the device, which then sends a push notification to the user stating, "Today's diary is complete." The user can then open the app to view the details and edit or save the content as needed.

[0410] Furthermore, the system also utilizes purchase history data. The server analyzes the user's past purchase history, predicts when similar products will be needed, and suggests repurchases. For example, for regularly consumed items, a notification might be sent saying, "Your shampoo will run out in 10 days. We recommend repurchasing it."

[0411] By implementing the present invention in this way, users can use the diary function without any burden, and their daily lives can be made more convenient through suggestions for repurchases.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The device checks user settings and prepares to collect data for diary generation. Here, the user is asked to select the type of data to be used for diary generation (images, schedule, location information). Based on these settings, the device starts the data collection process at the designated time.

[0415] Step 2:

[0416] At a set time, the device collects photos from the smartphone's image folder, retrieves event information from the calendar app, and extracts location history from GPS data. This allows it to collect data related to the day's activities.

[0417] Step 3:

[0418] The device sends the collected data to the server using encryption technology while considering privacy. The device uses SSL / TLS protocols and other secure methods for data transfer via the internet connection.

[0419] Step 4:

[0420] The server analyzes the received data. Using image analysis algorithms, it identifies people, places, and objects within the images, and combines this with schedule data and geographical information to gain a detailed understanding of the user's activities.

[0421] Step 5:

[0422] The server automatically generates a diary using a natural language generation algorithm based on the analyzed information. For example, based on data indicating that the user visited a shopping mall in the morning and spent the afternoon at a cafe, it would create a diary entry such as, "I visited △△ Shopping Mall in the morning and relaxed at □□ Cafe in the afternoon."

[0423] Step 6:

[0424] The server generates diary data and sends it to the device. The device receives this data and notifies the user via push notification that "Today's diary is complete," allowing the user to review and edit it.

[0425] Step 7:

[0426] The server analyzes past purchase history data and suggests products that the user is likely to repurchase. For example, for products that are regularly purchased, the server generates a notification such as, "The stock of △△ that you purchased last time is running low. We recommend repurchasing it," and sends this notification to the user via their device.

[0427] (Example 1)

[0428] 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."

[0429] For users of mobile devices, there is a need to reduce the effort required to record and manage daily activities, as well as to alleviate the burden of managing and purchasing daily consumables. However, traditional methods require individual operations for data collection, analysis, and diary creation, which poses a significant burden. Furthermore, the lack of mechanisms to promptly notify users of the need for repurchase has led to problems with proper management of consumables.

[0430] 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.

[0431] In this invention, the server includes means for acquiring photo data, schedule data, and location information stored in a portable information processing device; means for transmitting the acquired information to an external processing device; means for analyzing the acquired information in the external processing device and creating human data using machine learning algorithms and natural language processing technology; and means for analyzing past purchase history and generating repurchase suggestions. As a result, users can record and analyze their daily lives in detail, automatically generate a diary, and further improve the convenience of their lives through appropriate management of consumables and repurchase suggestions.

[0432] A "portable information processing device" is a computer device that is portable and capable of collecting, processing, and communicating information.

[0433] An "external processing unit" is a computer system that receives data transmitted from a portable information processing unit and performs processing such as analysis.

[0434] "Photo data" refers to image information captured using a camera device, which visually records the user's activities.

[0435] "Schedule data" refers to information about events and tasks related to a specific date and time, stored in a schedule management application or similar system.

[0436] "Location information" refers to data indicating geographical coordinates or locations, acquired by a portable information processing device.

[0437] A "machine learning algorithm" is a computational method for automatically learning patterns and regularities from data.

[0438] "Natural language processing technology" refers to the technology used to process and generate human language using computers, and involves the analysis and generation of text data.

[0439] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0440] A "repurchase suggestion" is a suggestion that predicts when users should repurchase necessary items based on their past purchase history and informs them accordingly.

[0441] The information processing system of the present invention consists of a portable information processing device owned by the user and an external processing device that interacts with it via a network. This system automatically collects data related to the user's daily activities, such as images, schedules, and location information, generates a diary using machine learning algorithms and natural language processing technology, and further improves the convenience of daily life by analyzing the user's purchase history.

[0442] Portable information processing devices, such as smartphones and tablet computers, fulfill this role. These devices collect photos taken using their camera functions, schedule information obtained from scheduler and calendar apps, and location information obtained through GPS functions. This information is transmitted to an external processing device via a secure communication protocol such as HTTPS.

[0443] The external processing unit, or server, is responsible for analyzing the received data. This analysis uses machine learning algorithms such as TensorFlow to recognize objects and locations within images, and employs NLP (Neuro-Language Programming) techniques to transcribe the scheduled data into natural language. It also understands the user's actions and schedule, and generates a diary based on this information. The generated diary is sent to a portable information processing device and notified to the user.

[0444] As a concrete example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the system will automatically record these activities and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at a cafe. Afterwards, I enjoyed shopping."

[0445] Furthermore, the server analyzes the user's purchase history and uses machine learning models to predict future repurchases of products the user will need. For example, regarding shampoo, which is consumed regularly, a notification such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated, sent to a portable information device, and suggested to the user.

[0446] An example of a prompt message would be: "Collect the data necessary for the user to record their daily activities and generate a diary in natural language. Include an example of a day where the user visited a park on a holiday, met up with friends at a cafe, and enjoyed shopping." This would allow the user to easily record and manage their daily life through the system's functions.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The user performs the initial setup for diary generation on a portable information device. This includes granting permission to take photos, access calendar and schedule data, and use location services. Once the setup is complete, the device is ready to automatically collect this data at appropriate times. The input is user settings information, and the output is a specification of the data that can be collected.

[0450] Step 2:

[0451] The device collects information using the camera, GPS, and calendar app according to user settings. For example, the device automatically takes photos at a set time each day and saves them to its internal storage. Location information and schedule data are also acquired periodically. Inputs include the user's daily activities and their timing, while outputs include image data, schedule data, and location information.

[0452] Step 3:

[0453] The device sends collected image data, schedule information, and location information to the server using HTTPS. This communication is secure and designed to protect the privacy of user data. Input consists of various types of data stored on the device, and output consists of securely encrypted data packets.

[0454] Step 4:

[0455] The server analyzes the received data and uses image recognition technology to identify objects and landscapes within the photograph. Simultaneously, it uses machine learning algorithms to analyze location information and understand the user's movement trajectory. It utilizes NLP technology to generate natural language text from the schedule. The input is encrypted data packets, and the output is analyzed text data.

[0456] Step 5:

[0457] The server uses the analyzed data to pass prompt sentences to the generative AI model, which then converts the day's activities into a natural language diary. This diary is created in detail based on the places the user visited and the people they met. The input is the text data generated in step 4, and the output is the text of the completed diary.

[0458] Step 6:

[0459] The server sends the generated diary data to the terminal, which then notifies the user that "Today's diary is complete." Furthermore, it analyzes the user's past purchase history and provides suggestions for predicting repurchase timing. For example, a suggestion such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated and sent to the terminal. The inputs are diary data and purchase history, and the output is notification data for the user.

[0460] (Application Example 1)

[0461] 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."

[0462] Traditional lifestyle management systems had several drawbacks: they required users to meticulously record their activities, which was time-consuming, and they lacked concrete suggestions for improving quality of life. As a result, the busyness of daily life made it difficult for users to reflect on their activities or take concrete actions to improve their lives.

[0463] 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.

[0464] In this invention, the server includes means for acquiring image data, time management data, and location data stored in an information processing device; means for transmitting the acquired information to an external computer; means for analyzing the acquired information on the external computer and creating a lifestyle record in natural language; and means for analyzing regional information data and generating and notifying the user of suggestions to enrich their life. This makes it possible for the user to create a daily lifestyle record without any effort and to receive personalized lifestyle improvement suggestions.

[0465] An "information processing device" is a portable device used for collecting, transmitting, and receiving data.

[0466] "Image data" refers to data of still images and videos captured by an information processing device.

[0467] "Time management data" refers to digital calendar data that records the user's schedule and past activity history.

[0468] "Location information data" refers to data indicating the location of an information processing device, including geographical coordinates.

[0469] An "external computer" is a computer server used to analyze and process data transmitted from an information processing device.

[0470] "Natural language" refers to the language of ordinary humans, and includes texts that are mechanically generated by algorithms.

[0471] A "daily life record" is a document that summarizes the user's daily activities in written form.

[0472] "Local information data" refers to digital data that includes information about stores and events within a geographical area.

[0473] "Notification" refers to sending a message to a user's device to inform them of information.

[0474] "Suggestions" refer to information and advice provided based on analysis results with the aim of improving the user's life.

[0475] This invention provides a system that automatically collects and analyzes data generated by a user in their daily life and generates a lifestyle record, by utilizing a portable information processing device and an external computer. The information processing device acquires the user's time management data, image data, and location data in their daily life. For example, it collects GPS data using the smartphone's location information service and image data using the camera function. This data is transmitted from the information processing device to the external computer via a secure communication protocol such as HTTPS.

[0476] External computers process the received data using cloud-based computing platforms. Machine learning algorithms are executed using Amazon SageMaker and Google Cloud AI Platform to analyze the user's daily activities, and natural language processing technologies such as Google BERT and OpenAI GPT-3 are used to generate life records. This process makes it possible to build a diary based on information such as where the user visited, what events they attended, and what was depicted in their photographs.

[0477] The generated lifestyle record data is sent to an information processing device and delivered to the user via push notifications. Furthermore, suggestions for enriching daily life are made through the analysis of local information data. This includes information on local events and new store openings, and notifications are sent using Firebase Cloud Messaging and other tools.

[0478] For example, if a user visits a local market on the weekend, a record such as "Today I visited a local market and enjoyed a variety of goods" is generated from GPS and image data. Then, information about local food fair events is notified, and suggestions such as "We have an event we recommend for next weekend" are sent. An example of a prompt message would be, "Based on your past event data, we suggest activities for your next holiday. Are there any recommended places in the city that you might enjoy?"

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The information processing device collects data based on the user's daily activities. This data includes GPS data from location services, time management data from a calendar app, and image data from a camera. The input is raw data from these sensors, and the output is information organized according to the time axis. Data from the sensors is acquired periodically and temporarily stored within the device.

[0482] Step 2:

[0483] The information processing device transmits the data collected in step 1 to an external computer. During this process, encrypted communication protocols such as HTTPS are used to ensure the secure transfer of data. The input is the organized data stored on the terminal, and the output is encrypted data awaiting transmission. The information processing device transmits data to the server periodically, or according to user settings.

[0484] Step 3:

[0485] The server receives data via an external computer and begins analyzing it. It uses machine learning models to extract meaningful information from the data. For example, it performs object recognition from image data and extracts event information from time-managed data. The input is the received encrypted data, and the output is high-level feature information obtained through the analysis. This process preprocesses the data for natural language processing.

[0486] Step 4:

[0487] The server uses natural language processing technology to generate a life record from the feature information extracted in step 3. Important activities and events are described in sentence form. A generative AI model is used to generate the record according to the prompt. The input is high-level feature information, and the output is a life record in the form of specific sentences. This generation process aims to produce grammatically accurate and easy-to-read sentences.

[0488] Step 5:

[0489] The server sends the generated lifestyle records to the information processing device. Furthermore, it analyzes local information and adds useful suggestions for the user. This includes information on local events and notifications of new store openings. The input is the generated lifestyle records and local information data, and the output is notification information with suggestions added to the lifestyle records. The information processing device receives this notification and sends a push notification to the user.

[0490] Step 6:

[0491] Users receive notifications on the information processing device and view their life records. They can modify the records and review suggestions as needed. The input is the notification data displayed on the information processing device, and the output is the record data after user review and editing. This step incorporates user feedback to improve the accuracy of future suggestions.

[0492] 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.

[0493] This invention is an automated diary generation system that utilizes the user's mobile device to combine image data, schedule information, and location information for the day with an emotion engine. The aim of this system is to provide a more personalized diary by recognizing emotions from images taken by the user and accumulated text data, and reflecting them in the analysis process.

[0494] When a user activates the system by operating the device, the device collects images, schedules, and location information on a daily basis. This includes data from the past 24 hours. In addition, the device utilizes an emotion engine to recognize emotions from the facial expressions of people in images and the text written on them. For example, a photo with a smile and a comment like "I had fun!" would be recognized as a positive emotion.

[0495] The data collected by the device is sent to the server via encrypted communication. On the server side, the user's behavioral history and emotional data are combined and analyzed, and a diary is generated based on this. Here, a natural language generation algorithm creates the diary by taking into account visited locations obtained from location information, activity details from the schedule, and emotional data. This diary is written based on emotions, for example, "Today I had a fun time at a cafe I visited with a friend."

[0496] Furthermore, the emotion engine analyzes the user's emotional tendencies when generating daily journal entries. This allows it to record the user's long-term emotional patterns and provide feedback aimed at improving their quality of life. For example, users experiencing significant emotional ups and downs may be offered relaxation suggestions.

[0497] The generated diary data is sent back to the device and presented to the user via notification. The user can view the diary details on the device and edit or save its contents. In addition, the server analyzes the user's shopping history and notifies them of products that may be suitable based on their emotional changes, further supporting their daily life.

[0498] In this way, the present invention, which combines an emotion engine, enhances the automatic diary function and significantly improves convenience for the user.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The device checks user settings and prepares to collect image data, schedule data, and location data. This includes specifying which apps and folders to retrieve data from.

[0502] Step 2:

[0503] The device collects photos from its image folder, reads schedule data, and obtains location information. It also passes this data to an emotion engine to recognize emotions based on facial expressions and text within the images. For example, a smiling photo might be interpreted as a positive emotion, while a gloomy expression might be interpreted as a negative emotion.

[0504] Step 3:

[0505] The collected data is encrypted to protect privacy before being sent to the server. The device uses the SSL / TLS protocol for data transmission to ensure secure data transfer.

[0506] Step 4:

[0507] The server analyzes the received data. Here, machine learning algorithms are used to integrate image, schedule, and location information to prepare input data for diary generation. The output of the emotion engine is also incorporated into this analysis process.

[0508] Step 5:

[0509] The server automatically generates a diary using a natural language generation algorithm. Based on the analyzed sentiment information, it creates sentences that reflect the emotional aspects of the user's experience. For example, "There were a lot of fun photos today. It seems you really enjoyed your time at △△."

[0510] Step 6:

[0511] The generated diary data is sent from the server to the terminal. The terminal receives it and notifies the user that "Today's diary is complete."

[0512] Step 7:

[0513] The server re-evaluates the user's shopping history based on sentiment analysis data and suggests products and services that match the user's mood and emotions. This information is then sent back to the terminal, and the user receives a suggestion such as, "It would be good to repurchase products related to □□. Please consider it."

[0514] (Example 2)

[0515] 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."

[0516] Conventional automated diary generation systems have struggled to provide personalized content that fully reflects the user's emotions. Furthermore, data security and the provision of real-time feedback have been inadequate. This highlights the need for valuable information provision that takes into account the individual experiences and emotions of each user.

[0517] 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.

[0518] In this invention, the server includes emotion recognition means, data analysis means, and document generation means. This makes it possible to generate personalized diary data that utilizes the emotions experienced by the user.

[0519] A "digital device" is a portable electronic device used by a user on a daily basis, and has the function of storing and acquiring image data, schedule data, and location data.

[0520] "Still image data" refers to image information that records visual information and captures a specific moment.

[0521] "Schedule management data" refers to data used to record and manage a user's daily activities and schedules.

[0522] "Location data" refers to information that indicates a geographical location and is used to determine where a user was at a specific time period.

[0523] "Emotion recognition means" refers to algorithms and software functions that extract a person's emotions from image or text data.

[0524] An "external computing device" is an external computer system that can receive and process data transmitted from a user's digital device.

[0525] "Data analysis methods" refer to the process of analyzing collected and recognized data and generating conclusions or outputs based on the results.

[0526] A "natural language document" is text that is generated by a machine but written in a language format that humans can understand.

[0527] A "document generation means" is a technical system that creates documents in natural language based on the results of data analysis.

[0528] A "user" is an individual or organization that utilizes the functions of this system and receives services based on their own information.

[0529] This invention is a system that automatically generates personalized diaries based on the user's individual experiences. Specifically, it provides a process for collecting various data using the user's digital devices (e.g., smartphones and tablets), analyzing that data, and creating a diary.

[0530] The device carried by the user first collects image data, schedule management data, and location data. This is done using the device's built-in camera, GPS function, and scheduling application. Next, the device uses emotion recognition technology to determine emotions from facial expressions and text in the images. This process utilizes image analysis algorithms and text mining techniques. For example, if a photo taken with family members shows smiles, a positive emotion will be recognized.

[0531] The collected and recognized data is encrypted and sent to an external server. The server receives this data and analyzes it using data analysis tools. The analyzed data is then used with a generative AI model to generate a document in natural language, i.e., a diary. An example of a prompt used in this process is: "Places visited today: Park, Emotion: Positive, Photo: Smiling. Based on this, please create the user's diary."

[0532] The generated diary is sent back to the device and the user is notified. The user can read this diary and reflect on their emotions and activities. The server can also provide feedback to the user based on past emotional data. Based on this feedback, suggestions are made to improve the user's quality of life.

[0533] In this way, the system of the present invention, which combines emotion recognition technology and data analysis technology, provides convenience and value to the user.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The terminal uses the camera function of the digital device to collect still image data, user schedule management data, and location information data. Inputs include images, schedules, and location information stored on the digital device. The data collection forms a user activity history. The output is the collected dataset.

[0537] Step 2:

[0538] The device applies emotion recognition technology to analyze facial expressions from input image data using an image analysis algorithm to identify the user's emotions. It also performs emotion analysis on text within schedule management data using text mining. As part of data processing, it analyzes the facial expression data and text data extracted from the images to generate recognized emotion information as output.

[0539] Step 3:

[0540] The terminal transmits the data collected and recognized in steps 1 and 2 to an external server using an encryption protocol. The input consists of the dataset and sentiment information obtained in steps 1 and 2. The data is securely delivered to the server while maintaining data confidentiality during the encryption process. The output is encrypted data.

[0541] Step 4:

[0542] The server receives data sent from the terminal and analyzes its contents using data analysis tools. The input is encrypted data. In the analysis process, a generative AI model is used to create prompt sentences based on location and sentiment information, and then a natural language document is generated based on these prompts. An example of such a prompt sentence is, "Places visited today: Park, Sentiment: Positive, Photo: Smiling." The output is the generated diary document.

[0543] Step 5:

[0544] The diary generated on the server is sent back to the device. This allows the user to reflect on the events of their day, along with their emotions. The input is the diary document generated on the server, and the output is the diary data notified to the user. The user can view the diary on their device and edit or save it as needed.

[0545] (Application Example 2)

[0546] 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."

[0547] In modern society, it is important for individual users to easily summarize their daily information and reflect on their emotions and activities. However, manually creating a diary is time-consuming and laborious, making it difficult to maintain consistently. Furthermore, the technology for generating diaries that reflect individual emotions is still immature, and there is a challenge in that it does not fully meet the need for more personalized records.

[0548] 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.

[0549] In this invention, the server includes means for acquiring image information, schedule information, and location information stored in an information device; means for transmitting the acquired information to an external processing device; and means for analyzing emotions from the acquired image information and text information and modifying the creation of a natural language document based on that analysis. This makes it possible to generate a natural language document that reflects the individual activities and emotions of the user.

[0550] An "information device" is a device that manages and stores image information, schedule information, and location information, such as a mobile information terminal or a home robot used by a user.

[0551] An "external processing device" is a device that functions as a cloud or server, receiving and analyzing acquired information.

[0552] "Emotion recognition means" refers to technologies and algorithms for analyzing emotions from acquired image and text information.

[0553] "Natural language document creation" is the process of generating documents using natural language processing technology based on analyzed data.

[0554] "Diary generation means" refers to a function or process that creates a document based on analyzed information and transmits it to an information device.

[0555] To implement this invention, first, a portable information terminal or a home robot that functions as an information device is required. These devices collect image information, schedule information, and location information in daily life. The information device analyzes the emotions contained in the image information using emotion recognition technology such as the Google Cloud Vision API.

[0556] The various data collected by the information device is transmitted to an external processing unit, i.e., the cloud or a server, via an encrypted protocol. The server uses cloud computing services such as AWS Lambda to analyze the acquired information. The analyzed data is then used with natural language generation algorithms such as OpenAI GPT to create a customized document for each user, i.e., a diary.

[0557] The generated document is sent back to the information device and the user is notified. The user can review this notification and edit and save the diary entries. Through long-term use, the server learns the user's emotional tendencies and provides feedback to improve their quality of life. This feedback includes suggestions for relaxation methods and advice on purchasing products.

[0558] As a concrete example, using images and schedules of a user's time spent with family on a holiday, it can generate a diary entry that reflects their emotions, such as, "Today I visited the park with my family and had a fun picnic." An example of a prompt for the generation AI model could be, "Generate a diary entry for the following event. It should include an image of family members smiling, and the event name is 'Picnic'."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The device collects image information, schedule information, and location information from daily life. This information includes data saved by the user on the device, images taken in real time, and location information obtained using GPS. The collected information becomes input for the next processing step.

[0562] Step 2:

[0563] The device uses the Google Cloud Vision API to recognize emotions from collected image information. It analyzes a person's facial expressions from the input image and generates emotion data such as positive or negative. The output of this process is the analyzed emotion data.

[0564] Step 3:

[0565] The device encrypts image information, emotion data, schedule information, and location information and transmits it to an external processing unit. The external processing unit is cloud-based and receives the information securely using security protocols. The output is an encrypted data package.

[0566] Step 4:

[0567] The server uses AWS Lambda to analyze the received data. After verifying the integrity of the input data, it processes it by combining it with user activity history and sentiment data. This process outputs the results of the data analysis.

[0568] Step 5:

[0569] The server uses natural language generation algorithms such as OpenAI GPT to create diary entries. Based on the input analysis results, it generates a diary in natural language that reflects the user's daily life. The output of this process is the generated diary document.

[0570] Step 6:

[0571] The generated diary document is sent back to the device and the user is notified. The user can check the notification and view, edit, and save the generated diary. The output presented to the user is the diary content displayed on the device.

[0572] Step 7:

[0573] The server learns users' emotional tendencies over the long term and provides feedback. Based on the accumulated data, it analyzes user emotional patterns and generates feedback such as improvement suggestions and product recommendations. The output consists of detected emotional tendencies and corresponding action suggestions.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] This invention is a system that automatically generates a diary using a mobile information terminal that a user uses on a daily basis. This system significantly reduces the effort required from the user by linking the functions of data collection, transmission, analysis, diary generation, and notification.

[0592] When a user initializes their diary generation settings, the device automatically collects data such as images, schedules, and location information according to those settings. The device then sends this data to the server using a secure communication protocol.

[0593] The server analyzes the received data and uses machine learning algorithms and natural language processing techniques to automatically generate a diary that reflects the user's activities for the day. This process involves object recognition from images, event organization from schedules, and behavior tracking using GPS information.

[0594] For example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the server will organize these activities into a sequential sentence and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at △△ Cafe. Afterwards, I went shopping at □□ Store."

[0595] The generated diary data is sent to the device, which then sends a push notification to the user stating, "Today's diary is complete." The user can then open the app to view the details and edit or save the content as needed.

[0596] Furthermore, the system also utilizes purchase history data. The server analyzes the user's past purchase history, predicts when similar products will be needed, and suggests repurchases. For example, for regularly consumed items, a notification might be sent saying, "Your shampoo will run out in 10 days. We recommend repurchasing it."

[0597] By implementing the present invention in this way, users can use the diary function without any burden, and their daily lives can be made more convenient through suggestions for repurchases.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The device checks user settings and prepares to collect data for diary generation. Here, the user is asked to select the type of data to be used for diary generation (images, schedule, location information). Based on these settings, the device starts the data collection process at the designated time.

[0601] Step 2:

[0602] At a set time, the device collects photos from the smartphone's image folder, retrieves event information from the calendar app, and extracts location history from GPS data. This allows it to collect data related to the day's activities.

[0603] Step 3:

[0604] The device sends the collected data to the server using encryption technology while considering privacy. The device uses SSL / TLS protocols and other secure methods for data transfer via the internet connection.

[0605] Step 4:

[0606] The server analyzes the received data. Using image analysis algorithms, it identifies people, places, and objects within the images, and combines this with schedule data and geographical information to gain a detailed understanding of the user's activities.

[0607] Step 5:

[0608] The server automatically generates a diary using a natural language generation algorithm based on the analyzed information. For example, based on data indicating that the user visited a shopping mall in the morning and spent the afternoon at a cafe, it would create a diary entry such as, "I visited △△ Shopping Mall in the morning and relaxed at □□ Cafe in the afternoon."

[0609] Step 6:

[0610] The server generates diary data and sends it to the device. The device receives this data and notifies the user via push notification that "Today's diary is complete," allowing the user to review and edit it.

[0611] Step 7:

[0612] The server analyzes past purchase history data and suggests products that the user is likely to repurchase. For example, for products that are regularly purchased, the server generates a notification such as, "The stock of △△ that you purchased last time is running low. We recommend repurchasing it," and sends this notification to the user via their device.

[0613] (Example 1)

[0614] 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".

[0615] For users of mobile devices, there is a need to reduce the effort required to record and manage daily activities, as well as to alleviate the burden of managing and purchasing daily consumables. However, traditional methods require individual operations for data collection, analysis, and diary creation, which poses a significant burden. Furthermore, the lack of mechanisms to promptly notify users of the need for repurchase has led to problems with proper management of consumables.

[0616] 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.

[0617] In this invention, the server includes means for acquiring photo data, schedule data, and location information stored in a portable information processing device; means for transmitting the acquired information to an external processing device; means for analyzing the acquired information in the external processing device and creating human data using machine learning algorithms and natural language processing technology; and means for analyzing past purchase history and generating repurchase suggestions. As a result, users can record and analyze their daily lives in detail, automatically generate a diary, and further improve the convenience of their lives through appropriate management of consumables and repurchase suggestions.

[0618] A "portable information processing device" is a computer device that is portable and capable of collecting, processing, and communicating information.

[0619] An "external processing unit" is a computer system that receives data transmitted from a portable information processing unit and performs processing such as analysis.

[0620] "Photo data" refers to image information captured using a camera device, which visually records the user's activities.

[0621] "Schedule data" refers to information about events and tasks related to a specific date and time, stored in a schedule management application or similar system.

[0622] "Location information" refers to data indicating geographical coordinates or locations, acquired by a portable information processing device.

[0623] A "machine learning algorithm" is a computational method for automatically learning patterns and regularities from data.

[0624] "Natural language processing technology" refers to the technology used to process and generate human language using computers, and involves the analysis and generation of text data.

[0625] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0626] A "repurchase suggestion" is a suggestion that predicts when users should repurchase necessary items based on their past purchase history and informs them accordingly.

[0627] The information processing system of the present invention consists of a portable information processing device owned by the user and an external processing device that interacts with it via a network. This system automatically collects data related to the user's daily activities, such as images, schedules, and location information, generates a diary using machine learning algorithms and natural language processing technology, and further improves the convenience of daily life by analyzing the user's purchase history.

[0628] Portable information processing devices, such as smartphones and tablet computers, fulfill this role. These devices collect photos taken using their camera functions, schedule information obtained from scheduler and calendar apps, and location information obtained through GPS functions. This information is transmitted to an external processing device via a secure communication protocol such as HTTPS.

[0629] The external processing unit, or server, is responsible for analyzing the received data. This analysis uses machine learning algorithms such as TensorFlow to recognize objects and locations within images, and employs NLP (Neuro-Language Programming) techniques to transcribe the scheduled data into natural language. It also understands the user's actions and schedule, and generates a diary based on this information. The generated diary is sent to a portable information processing device and notified to the user.

[0630] As a concrete example, if a user visits a park on a holiday, meets a friend at a cafe, and then goes shopping, the system will automatically record these activities and generate a diary entry such as, "Today I enjoyed a walk in the park, then met up with a friend at a cafe. Afterwards, I enjoyed shopping."

[0631] Furthermore, the server analyzes the user's purchase history and uses machine learning models to predict future repurchases of products the user will need. For example, regarding shampoo, which is consumed regularly, a notification such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated, sent to a portable information device, and suggested to the user.

[0632] An example of a prompt message would be: "Collect the data necessary for the user to record their daily activities and generate a diary in natural language. Include an example of a day where the user visited a park on a holiday, met up with friends at a cafe, and enjoyed shopping." This would allow the user to easily record and manage their daily life through the system's functions.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The user performs the initial setup for diary generation on a portable information device. This includes granting permission to take photos, access calendar and schedule data, and use location services. Once the setup is complete, the device is ready to automatically collect this data at appropriate times. The input is user settings information, and the output is a specification of the data that can be collected.

[0636] Step 2:

[0637] The device collects information using the camera, GPS, and calendar app according to user settings. For example, the device automatically takes photos at a set time each day and saves them to its internal storage. Location information and schedule data are also acquired periodically. Inputs include the user's daily activities and their timing, while outputs include image data, schedule data, and location information.

[0638] Step 3:

[0639] The device sends collected image data, schedule information, and location information to the server using HTTPS. This communication is secure and designed to protect the privacy of user data. Input consists of various types of data stored on the device, and output consists of securely encrypted data packets.

[0640] Step 4:

[0641] The server analyzes the received data and uses image recognition technology to identify objects and landscapes within the photograph. Simultaneously, it uses machine learning algorithms to analyze location information and understand the user's movement trajectory. It utilizes NLP technology to generate natural language text from the schedule. The input is encrypted data packets, and the output is analyzed text data.

[0642] Step 5:

[0643] The server uses the analyzed data to pass prompt sentences to the generative AI model, which then converts the day's activities into a natural language diary. This diary is created in detail based on the places the user visited and the people they met. The input is the text data generated in step 4, and the output is the text of the completed diary.

[0644] Step 6:

[0645] The server sends the generated diary data to the terminal, which then notifies the user that "Today's diary is complete." Furthermore, it analyzes the user's past purchase history and provides suggestions for predicting repurchase timing. For example, a suggestion such as "Your shampoo will run out in 10 days. We recommend repurchasing it" is generated and sent to the terminal. The inputs are diary data and purchase history, and the output is notification data for the user.

[0646] (Application Example 1)

[0647] 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".

[0648] Traditional lifestyle management systems had several drawbacks: they required users to meticulously record their activities, which was time-consuming, and they lacked concrete suggestions for improving quality of life. As a result, the busyness of daily life made it difficult for users to reflect on their activities or take concrete actions to improve their lives.

[0649] 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.

[0650] In this invention, the server includes means for acquiring image data, time management data, and location data stored in an information processing device; means for transmitting the acquired information to an external computer; means for analyzing the acquired information on the external computer and creating a lifestyle record in natural language; and means for analyzing regional information data and generating and notifying the user of suggestions to enrich their life. This makes it possible for the user to create a daily lifestyle record without any effort and to receive personalized lifestyle improvement suggestions.

[0651] An "information processing device" is a portable device used for collecting, transmitting, and receiving data.

[0652] "Image data" refers to data of still images and videos captured by an information processing device.

[0653] "Time management data" refers to digital calendar data that records the user's schedule and past activity history.

[0654] "Location information data" refers to data indicating the location of an information processing device, including geographical coordinates.

[0655] An "external computer" is a computer server used to analyze and process data transmitted from an information processing device.

[0656] "Natural language" refers to the language of ordinary humans, and includes texts that are mechanically generated by algorithms.

[0657] A "daily life record" is a document that summarizes the user's daily activities in written form.

[0658] "Local information data" refers to digital data that includes information about stores and events within a geographical area.

[0659] "Notification" refers to sending a message to a user's device to inform them of information.

[0660] "Suggestions" refer to information and advice provided based on analysis results with the aim of improving the user's life.

[0661] This invention provides a system that automatically collects and analyzes data generated by a user in their daily life and generates a lifestyle record, by utilizing a portable information processing device and an external computer. The information processing device acquires the user's time management data, image data, and location data in their daily life. For example, it collects GPS data using the smartphone's location information service and image data using the camera function. This data is transmitted from the information processing device to the external computer via a secure communication protocol such as HTTPS.

[0662] External computers process the received data using cloud-based computing platforms. Machine learning algorithms are executed using Amazon SageMaker and Google Cloud AI Platform to analyze the user's daily activities, and natural language processing technologies such as Google BERT and OpenAI GPT-3 are used to generate life records. This process makes it possible to build a diary based on information such as where the user visited, what events they attended, and what was depicted in their photographs.

[0663] The generated lifestyle record data is sent to an information processing device and delivered to the user via push notifications. Furthermore, suggestions for enriching daily life are made through the analysis of local information data. This includes information on local events and new store openings, and notifications are sent using Firebase Cloud Messaging and other tools.

[0664] For example, if a user visits a local market on the weekend, a record such as "Today I visited a local market and enjoyed a variety of goods" is generated from GPS and image data. Then, information about local food fair events is notified, and suggestions such as "We have an event we recommend for next weekend" are sent. An example of a prompt message would be, "Based on your past event data, we suggest activities for your next holiday. Are there any recommended places in the city that you might enjoy?"

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The information processing device collects data based on the user's daily activities. This data includes GPS data from location services, time management data from a calendar app, and image data from a camera. The input is raw data from these sensors, and the output is information organized according to the time axis. Data from the sensors is acquired periodically and temporarily stored within the device.

[0668] Step 2:

[0669] The information processing device transmits the data collected in step 1 to an external computer. During this process, encrypted communication protocols such as HTTPS are used to ensure the secure transfer of data. The input is the organized data stored on the terminal, and the output is encrypted data awaiting transmission. The information processing device transmits data to the server periodically, or according to user settings.

[0670] Step 3:

[0671] The server receives data via an external computer and begins analyzing it. It uses machine learning models to extract meaningful information from the data. For example, it performs object recognition from image data and extracts event information from time-managed data. The input is the received encrypted data, and the output is high-level feature information obtained through the analysis. This process preprocesses the data for natural language processing.

[0672] Step 4:

[0673] The server uses natural language processing technology to generate a life record from the feature information extracted in step 3. Important activities and events are described in sentence form. A generative AI model is used to generate the record according to the prompt. The input is high-level feature information, and the output is a life record in the form of specific sentences. This generation process aims to produce grammatically accurate and easy-to-read sentences.

[0674] Step 5:

[0675] The server sends the generated lifestyle records to the information processing device. Furthermore, it analyzes local information and adds useful suggestions for the user. This includes information on local events and notifications of new store openings. The input is the generated lifestyle records and local information data, and the output is notification information with suggestions added to the lifestyle records. The information processing device receives this notification and sends a push notification to the user.

[0676] Step 6:

[0677] Users receive notifications on the information processing device and view their life records. They can modify the records and review suggestions as needed. The input is the notification data displayed on the information processing device, and the output is the record data after user review and editing. This step incorporates user feedback to improve the accuracy of future suggestions.

[0678] 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.

[0679] This invention is an automated diary generation system that utilizes the user's mobile device to combine image data, schedule information, and location information for the day with an emotion engine. The aim of this system is to provide a more personalized diary by recognizing emotions from images taken by the user and accumulated text data, and reflecting them in the analysis process.

[0680] When a user activates the system by operating the device, the device collects images, schedules, and location information on a daily basis. This includes data from the past 24 hours. In addition, the device utilizes an emotion engine to recognize emotions from the facial expressions of people in images and the text written on them. For example, a photo with a smile and a comment like "I had fun!" would be recognized as a positive emotion.

[0681] The data collected by the device is sent to the server via encrypted communication. On the server side, the user's behavioral history and emotional data are combined and analyzed, and a diary is generated based on this. Here, a natural language generation algorithm creates the diary by taking into account visited locations obtained from location information, activity details from the schedule, and emotional data. This diary is written based on emotions, for example, "Today I had a fun time at a cafe I visited with a friend."

[0682] Furthermore, the emotion engine analyzes the user's emotional tendencies when generating daily journal entries. This allows it to record the user's long-term emotional patterns and provide feedback aimed at improving their quality of life. For example, users experiencing significant emotional ups and downs may be offered relaxation suggestions.

[0683] The generated diary data is sent back to the device and presented to the user via notification. The user can view the diary details on the device and edit or save its contents. In addition, the server analyzes the user's shopping history and notifies them of products that may be suitable based on their emotional changes, further supporting their daily life.

[0684] In this way, the present invention, which combines an emotion engine, enhances the automatic diary function and significantly improves convenience for the user.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The device checks user settings and prepares to collect image data, schedule data, and location data. This includes specifying which apps and folders to retrieve data from.

[0688] Step 2:

[0689] The device collects photos from its image folder, reads schedule data, and obtains location information. It also passes this data to an emotion engine to recognize emotions based on facial expressions and text within the images. For example, a smiling photo might be interpreted as a positive emotion, while a gloomy expression might be interpreted as a negative emotion.

[0690] Step 3:

[0691] The collected data is encrypted to protect privacy before being sent to the server. The device uses the SSL / TLS protocol for data transmission to ensure secure data transfer.

[0692] Step 4:

[0693] The server analyzes the received data. Here, machine learning algorithms are used to integrate image, schedule, and location information to prepare input data for diary generation. The output of the emotion engine is also incorporated into this analysis process.

[0694] Step 5:

[0695] The server automatically generates a diary using a natural language generation algorithm. Based on the analyzed sentiment information, it creates sentences that reflect the emotional aspects of the user's experience. For example, "There were a lot of fun photos today. It seems you really enjoyed your time at △△."

[0696] Step 6:

[0697] The generated diary data is sent from the server to the terminal. The terminal receives it and notifies the user that "Today's diary is complete."

[0698] Step 7:

[0699] The server re-evaluates the user's shopping history based on sentiment analysis data and suggests products and services that match the user's mood and emotions. This information is then sent back to the terminal, and the user receives a suggestion such as, "It would be good to repurchase products related to □□. Please consider it."

[0700] (Example 2)

[0701] 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".

[0702] Conventional automated diary generation systems have struggled to provide personalized content that fully reflects the user's emotions. Furthermore, data security and the provision of real-time feedback have been inadequate. This highlights the need for valuable information provision that takes into account the individual experiences and emotions of each user.

[0703] 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.

[0704] In this invention, the server includes emotion recognition means, data analysis means, and document generation means. This makes it possible to generate personalized diary data that utilizes the emotions experienced by the user.

[0705] A "digital device" is a portable electronic device used by a user on a daily basis, and has the function of storing and acquiring image data, schedule data, and location data.

[0706] "Still image data" refers to image information that records visual information and captures a specific moment.

[0707] "Schedule management data" refers to data used to record and manage a user's daily activities and schedules.

[0708] "Location data" refers to information that indicates a geographical location and is used to determine where a user was at a specific time period.

[0709] "Emotion recognition means" refers to algorithms and software functions that extract a person's emotions from image or text data.

[0710] An "external computing device" is an external computer system that can receive and process data transmitted from a user's digital device.

[0711] "Data analysis methods" refer to the process of analyzing collected and recognized data and generating conclusions or outputs based on the results.

[0712] A "natural language document" is text that is generated by a machine but written in a language format that humans can understand.

[0713] A "document generation means" is a technical system that creates documents in natural language based on the results of data analysis.

[0714] A "user" is an individual or organization that utilizes the functions of this system and receives services based on their own information.

[0715] This invention is a system that automatically generates personalized diaries based on the user's individual experiences. Specifically, it provides a process for collecting various data using the user's digital devices (e.g., smartphones and tablets), analyzing that data, and creating a diary.

[0716] The device carried by the user first collects image data, schedule management data, and location data. This is done using the device's built-in camera, GPS function, and scheduling application. Next, the device uses emotion recognition technology to determine emotions from facial expressions and text in the images. This process utilizes image analysis algorithms and text mining techniques. For example, if a photo taken with family members shows smiles, a positive emotion will be recognized.

[0717] The collected and recognized data is encrypted and sent to an external server. The server receives this data and analyzes it using data analysis tools. The analyzed data is then used with a generative AI model to generate a document in natural language, i.e., a diary. An example of a prompt used in this process is: "Places visited today: Park, Emotion: Positive, Photo: Smiling. Based on this, please create the user's diary."

[0718] The generated diary is sent back to the device and the user is notified. The user can read this diary and reflect on their emotions and activities. The server can also provide feedback to the user based on past emotional data. Based on this feedback, suggestions are made to improve the user's quality of life.

[0719] In this way, the system of the present invention, which combines emotion recognition technology and data analysis technology, provides convenience and value to the user.

[0720] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0721] Step 1:

[0722] The terminal uses the camera function of the digital device to collect still image data, user schedule management data, and location information data. Inputs include images, schedules, and location information stored on the digital device. The data collection forms a user activity history. The output is the collected dataset.

[0723] Step 2:

[0724] The device applies emotion recognition technology to analyze facial expressions from input image data using an image analysis algorithm to identify the user's emotions. It also performs emotion analysis on text within schedule management data using text mining. As part of data processing, it analyzes the facial expression data and text data extracted from the images to generate recognized emotion information as output.

[0725] Step 3:

[0726] The terminal transmits the data collected and recognized in steps 1 and 2 to an external server using an encryption protocol. The input consists of the dataset and sentiment information obtained in steps 1 and 2. The data is securely delivered to the server while maintaining data confidentiality during the encryption process. The output is encrypted data.

[0727] Step 4:

[0728] The server receives data sent from the terminal and analyzes its contents using data analysis tools. The input is encrypted data. In the analysis process, a generative AI model is used to create prompt sentences based on location and sentiment information, and then a natural language document is generated based on these prompts. An example of such a prompt sentence is, "Places visited today: Park, Sentiment: Positive, Photo: Smiling." The output is the generated diary document.

[0729] Step 5:

[0730] The diary generated on the server is sent back to the device. This allows the user to reflect on the events of their day, along with their emotions. The input is the diary document generated on the server, and the output is the diary data notified to the user. The user can view the diary on their device and edit or save it as needed.

[0731] (Application Example 2)

[0732] 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".

[0733] In modern society, it is important for individual users to easily summarize their daily information and reflect on their emotions and activities. However, manually creating a diary is time-consuming and laborious, making it difficult to maintain consistently. Furthermore, the technology for generating diaries that reflect individual emotions is still immature, and there is a challenge in that it does not fully meet the need for more personalized records.

[0734] 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.

[0735] In this invention, the server includes means for acquiring image information, schedule information, and location information stored in an information device; means for transmitting the acquired information to an external processing device; and means for analyzing emotions from the acquired image information and text information and modifying the creation of a natural language document based on that analysis. This makes it possible to generate a natural language document that reflects the individual activities and emotions of the user.

[0736] An "information device" is a device that manages and stores image information, schedule information, and location information, such as a mobile information terminal or a home robot used by a user.

[0737] An "external processing device" is a device that functions as a cloud or server, receiving and analyzing acquired information.

[0738] "Emotion recognition means" refers to technologies and algorithms for analyzing emotions from acquired image and text information.

[0739] "Natural language document creation" is the process of generating documents using natural language processing technology based on analyzed data.

[0740] "Diary generation means" refers to a function or process that creates a document based on analyzed information and transmits it to an information device.

[0741] To implement this invention, first, a portable information terminal or a home robot that functions as an information device is required. These devices collect image information, schedule information, and location information in daily life. The information device analyzes the emotions contained in the image information using emotion recognition technology such as the Google Cloud Vision API.

[0742] The various data collected by the information device is transmitted to an external processing unit, i.e., the cloud or a server, via an encrypted protocol. The server uses cloud computing services such as AWS Lambda to analyze the acquired information. The analyzed data is then used with natural language generation algorithms such as OpenAI GPT to create a customized document for each user, i.e., a diary.

[0743] The generated document is sent back to the information device and the user is notified. The user can review this notification and edit and save the diary entries. Through long-term use, the server learns the user's emotional tendencies and provides feedback to improve their quality of life. This feedback includes suggestions for relaxation methods and advice on purchasing products.

[0744] As a concrete example, using images and schedules of a user's time spent with family on a holiday, it can generate a diary entry that reflects their emotions, such as, "Today I visited the park with my family and had a fun picnic." An example of a prompt for the generation AI model could be, "Generate a diary entry for the following event. It should include an image of family members smiling, and the event name is 'Picnic'."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The device collects image information, schedule information, and location information from daily life. This information includes data saved by the user on the device, images taken in real time, and location information obtained using GPS. The collected information becomes input for the next processing step.

[0748] Step 2:

[0749] The device uses the Google Cloud Vision API to recognize emotions from collected image information. It analyzes a person's facial expressions from the input image and generates emotion data such as positive or negative. The output of this process is the analyzed emotion data.

[0750] Step 3:

[0751] The device encrypts image information, emotion data, schedule information, and location information and transmits it to an external processing unit. The external processing unit is cloud-based and receives the information securely using security protocols. The output is an encrypted data package.

[0752] Step 4:

[0753] The server uses AWS Lambda to analyze the received data. After verifying the integrity of the input data, it processes it by combining it with user activity history and sentiment data. This process outputs the results of the data analysis.

[0754] Step 5:

[0755] The server uses natural language generation algorithms such as OpenAI GPT to create diary entries. Based on the input analysis results, it generates a diary in natural language that reflects the user's daily life. The output of this process is the generated diary document.

[0756] Step 6:

[0757] The generated diary document is sent back to the device and the user is notified. The user can check the notification and view, edit, and save the generated diary. The output presented to the user is the diary content displayed on the device.

[0758] Step 7:

[0759] The server learns users' emotional tendencies over the long term and provides feedback. Based on the accumulated data, it analyzes user emotional patterns and generates feedback such as improvement suggestions and product recommendations. The output consists of detected emotional tendencies and corresponding action suggestions.

[0760] 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.

[0761] 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.

[0762] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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."

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] As a means of data collection, image data, schedule data, and location data stored on mobile devices are acquired.

[0784] As a means of data transmission, the acquired data is sent to an external server.

[0785] As a data analysis method, the acquired data is analyzed on an external server, and a diary is created using natural language.

[0786] Diary generation method,

[0787] A system including means for transmitting diary data generated by the diary generation means to a mobile information terminal and notifying the user.

[0788] (Claim 2)

[0789] The system according to claim 1, which analyzes shopping history data, generates repurchase suggestions, and notifies the user via communication means.

[0790] (Claim 3)

[0791] The system according to claim 1, which automatically generates and notifies users of generated diary data based on a time set by the user.

[0792] "Example 1"

[0793] (Claim 1)

[0794] A means for acquiring photo data, schedule data, and location information stored in a portable information processing device,

[0795] A means for transmitting the acquired information to an external processing device,

[0796] An external processing device provides a means for analyzing acquired information and creating humanities using machine learning algorithms and natural language processing techniques,

[0797] A means for transmitting document data generated by the aforementioned humanities generation means to a portable information processing device and notifying the user,

[0798] A means of analyzing past purchase history and generating suggestions for repurchases,

[0799] ...

[0800] An information processing system that includes this.

[0801] (Claim 2)

[0802] The information processing system according to claim 1, which utilizes a machine learning model to predict the timing of the next purchase based on past purchase history.

[0803] (Claim 3)

[0804] The information processing system according to claim 1, which automatically generates and notifies the user of generated document data at a time specified by the user.

[0805] "Application Example 1"

[0806] (Claim 1)

[0807] As a means of data collection, it acquires image data, time management data, and location information data stored in an information processing device.

[0808] As a means of data transmission, the acquired information is sent to an external computer.

[0809] As a data analysis method, the acquired information is analyzed on an external computer, and a life record is created in natural language.

[0810] Means for generating life records,

[0811] A means for transmitting the recorded data generated by the aforementioned life record generation means to an information processing device and notifying the user,

[0812] A system that includes means of analyzing local information data, generating suggestions to enrich users' lives, and notifying them of these suggestions.

[0813] (Claim 2)

[0814] The system according to claim 1, which analyzes purchase history data, generates repurchase suggestions, and notifies the user via communication means.

[0815] (Claim 3)

[0816] The system according to claim 1, which automatically generates and notifies the user of the generated record data based on a time set by the user.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] The data collection means includes means for acquiring still image data, schedule management data, and location information data stored in a digital device.

[0820] As a means of emotion recognition, a means of recognizing emotions from acquired image and text data,

[0821] As a data transmission means, means for transmitting acquired and recognized data to an external computing device,

[0822] As a data analysis means, an external computing device is used to analyze the acquired and recognized data and generate a document in natural language.

[0823] Document generation means and

[0824] A means for transmitting the document data generated by the document generation means to a digital device and notifying the user,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, which analyzes purchase history data, generates repurchase suggestions, and notifies the user via communication means.

[0828] (Claim 3)

[0829] The system according to claim 1, which automatically generates and notifies the user of generated document data based on a time set by the user.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] The data collection means includes means for acquiring image information, schedule information, and location information stored in an information device.

[0833] As a means of data transmission, there is a means for transmitting acquired information to an external processing device,

[0834] As a data analysis means, an external processing device is used to analyze acquired information and generate a document in natural language.

[0835] As a means of emotion recognition, it involves analyzing emotions from acquired image and text information and modifying natural language document creation based on that analysis.

[0836] Document generation means and

[0837] A system including means for transmitting document information generated by the document generation means to an information device and notifying a user.

[0838] (Claim 2)

[0839] The system according to claim 1, which analyzes product transaction history data, generates repurchase suggestions, and notifies the user via communication means.

[0840] (Claim 3)

[0841] The system according to claim 1, which automatically generates and notifies the user of generated document information based on a time set by the user. [Explanation of Symbols]

[0842] 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. As a means of data collection, it acquires image data, time management data, and location information data stored in an information processing device. As a means of data transmission, the acquired information is sent to an external computer. As a data analysis method, the acquired information is analyzed on an external computer, and a life record is created in natural language. Means for generating life records, A means for transmitting the recorded data generated by the aforementioned life record generation means to an information processing device and notifying the user, A system that includes means of analyzing local information data, generating suggestions to enrich users' lives, and notifying them of these suggestions.

2. The system according to claim 1, which analyzes purchase history data, generates repurchase suggestions, and notifies the user via communication means.

3. The system according to claim 1, which automatically generates and notifies the user of the generated record data based on a time set by the user.

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