System

Abstract

To propose the optimal content on the basis of a user's listening habit and interest.SOLUTION: A system of the present disclosure includes: means that acquires input information including a user's mood on the day and items in which the user has an interest; means that uses a generative model, and a prompt sentence instructing creating, from the acquired input information, a play list consisting of a plurality of contents in a database that stores video contents, to create a play list consisting of the plurality of contents in the database; and means that provides the created play list to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, videos such as movies and TV programs are provided through streaming playback. In some cases, a playlist is provided to a user to allow the user to select videos to be streamed. However, conventional video streaming playlist generation systems have a problem in that they cannot suggest optimal content based on a user's viewing habits and interests. [Means for solving the problem]

[0005] The present disclosure provides a system that can generate more personalized playlists by obtaining information about a user's mood and interests for the day.

[0006] The system according to the first aspect of the present disclosure includes a data acquisition unit that acquires input information including a user's mood for the day and matters of interest to the user, a playlist generation unit that uses a playlist generation model to generate a playlist consisting of a plurality of contents in a database in which contents including movies and TV programs are stored, from the acquired input information, and an output unit that provides the generated playlist to the user. Note that "matters of interest to the user" refers to information related to topics or themes in which the user is interested.

[0007] A system according to a second aspect of the present disclosure is the system according to the first aspect described above, in which the input information further includes profile information including at least the age and sex of the user. Note that "profile information" refers to basic personal information such as the age and sex of the user.

[0008] A system according to a third aspect of the present disclosure is the system according to the first or second aspect described above, in which the data acquisition unit further acquires schedule information of the user, and the playlist generation unit creates a playlist including content of a length corresponding to a rest time or workout time included in the schedule information from the acquired input information and the schedule information. Note that "schedule information" refers to information regarding the user's plan or schedule for a specific day or a specific period of time.

[0009] A system according to a fourth aspect of the present disclosure is a system according to any one of the first to third aspects described above, further including an emotion recognition unit that recognizes the emotion of the user using an emotion engine. [Brief description of the drawings]

[0010] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] 4 is a sequence diagram showing a process flow of the data processing system according to the first embodiment. FIG. [Figure 12] 11 is a sequence diagram showing a process flow of the data processing system in application example 1. FIG. [Figure 13] FIG. 11 is a sequence diagram showing the flow of processing of the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 11 is a sequence diagram showing the flow of processing in the data processing system in application example 2 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] 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.

[0012] First, the terms used in the following description will be explained.

[0013] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0014] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0015] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0016] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0017] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0018] [First embodiment]

[0019] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0020] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0021] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0022] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0023] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0024] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0025] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0026] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0027] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0028] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0029] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0030] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0031] The first embodiment of the present disclosure comprises the following elements.

[0032] 1. Data acquisition unit: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0033] 2. Playlist generation unit: The server has a means for identifying optimal content from a database including movies and TV programs based on information acquired from a user, adding the identified content to a list, and generating an optimal playlist. A playlist generation model (data generation model) can be used to generate the playlist. The database may be one or more databases prepared in advance.

[0034] 3. Output unit: The server includes a means for transmitting the generated playlist to a terminal and proposing it to a user.

[0035] The data acquisition unit may further include a means for acquiring and processing schedule information of a user. The playlist generation unit may include a means for identifying rest periods and workout periods in the schedule based on input information such as information on the mood and interests of the day and profile information provided by the user and the schedule information when the data acquisition unit acquires the schedule information, identifying content that can be viewed during the period, and creating a playlist including the identified content.

[0036] The above is the first embodiment of the present disclosure, which makes it possible to generate and propose an optimal playlist based on the user's mood of the day and the matters he or she is interested in. It is also possible to propose content that matches the user's schedule.

[0037] The process flow will be explained below.

[0038] Step 1: The server receives and processes input information from the user regarding profile information (e.g., age and gender), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0039] Step 2: The server identifies the most suitable content based on the user's input information from a database that contains pre-prepared content including movies and TV shows. Specifically, the server searches the content in the database and selects the appropriate content based on the user's profile information, mood of the day, and interests.

[0040] Step 3: The server adds the identified content to the list and generates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist.

[0041] Step 4: The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and allows the user to view the proposed playlist on the application.

[0042] Furthermore, if the data acquisition unit is capable of acquiring the user's schedule information, the following processing can be executed.

[0043] Step 11: The server receives and processes profile information (e.g., age and sex), input information on the user's mood for the day, and the user's interests, and the user's schedule information from the user. For example, the user inputs the schedule information into an application on the terminal, and the terminal transmits the input schedule information to the server, where the server receives it and processes it internally.

[0044] Step 12: The server identifies content of a length that can be viewed during the user's break time or workout time based on the user's schedule information. Specifically, the server analyzes the user's schedule information, identifies break times or workout times in the schedule, and selects content of a length suitable for viewing during the break time, etc.

[0045] Step 13: The server adds the identified content to the list and creates a playlist associated with the schedule. Specifically, the server saves the identified content as a playlist and organizes it as a playlist associated with the schedule. Step 14: The server transmits the playlist associated with the created schedule to the terminal and proposes it to the user. Specifically, the server transmits the playlist associated with the schedule to the terminal and allows the user to view the proposed playlist on the application.

[0046] The above is the flow of the program's processing, in which the server and terminal work together to process the user's information and schedule, and create and propose optimal playlists.

[0047] Example 1 Next, a description will be given of Example 1. In the following description, the data processing device 12 is referred to as a "server" and the smart device 14 is referred to as a "terminal."

[0048] Conventional content recommendation systems lacked the flexibility to provide optimal content based on the user's mood and interests, making it difficult to effectively identify the content the user wanted. In addition, they were unable to recommend content according to the user's schedule, making efficient time management difficult. Furthermore, they lacked technology for secure communication and high-precision playlist generation.

[0049] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0050] In this invention, the server includes: a means for acquiring input information including the user's mood of the day and the user's interests; a means for generating a playlist consisting of a plurality of contents in a database in which video contents are stored from the acquired input information using a playlist generation model; a means for providing the generated playlist to the user; a means for securely receiving and storing the input information provided by the user using an encrypted communication protocol; a means for using a general-purpose query language to search and acquire video contents from the database; a means for generating a playlist by utilizing a machine learning library; and a means for transmitting the generated playlist to the user's terminal using a notification service. This enables the generation of a highly accurate playlist according to the user's mood and interests, and safe and rapid content suggestions. In addition, it is also possible to realize the recommendation of appropriate content according to the user's schedule.

[0051] "Input information including the user's mood of the day and the user's interests" refers to data that reflects the user's emotions and interests of the day and is input into the system.

[0052] The term "data acquisition means" refers to a device or software having a function for receiving input information and schedule information provided by a user.

[0053] "Playlist generation model" refers to a machine learning algorithm or statistical model for generating a playlist composed of appropriate content based on user input information.

[0054] The term "playlist generation means" refers to a device or software having a function for selecting optimal content from input information acquired using a playlist generation model and generating a playlist.

[0055] A "database storing video content" refers to a data storage system for accumulating and managing viewing media such as movies and television programs.

[0056] A "playlist" refers to a list of multiple pieces of content selected based on a specific theme or criteria.

[0057] "Output means" refers to a device or software having a function for presenting the generated playlist on a user's terminal or other device.

[0058] An "encrypted communications protocol" refers to a communications method for protecting the confidentiality and integrity of information during the data transmission and reception process.

[0059] "General-purpose query language" refers to a standardized language used to search and retrieve information from a database, including, for example, SQL.

[0060] A "machine learning library" refers to a software tool that learns from data, recognizes patterns, and makes predictions or classifications.

[0061] "Notification Service" means an Internet service that has the ability to transmit information to a User's Device in real time.

[0062] To implement the present invention, a system including the following components is required.

[0063] Data Acquisition Method The server obtains the user's mood for the day, interests, and profile information (age and gender). This information is entered by the user using a web browser or a dedicated application. For example, if the user enters "I want to relax today," the server provides information about the user's mood for the day. The server also obtains the user's schedule information through the Google (registered trademark) Calendar API, etc.

[0064] Database Access The server searches for and retrieves content from a database that stores video content using a general-purpose query language (e.g., SQL), and retrieves information on appropriate movies and TV programs from the database based on the information that the user wants to relax.

[0065] Playlist Generation Method The server uses a playlist generation model to generate an optimal playlist based on the acquired input information and the content information in the database. At this time, the server uses a Python machine learning library (e.g., TENSORFLOW (registered trademark)) to generate a playlist that best suits the user's preferences. The generation AI model uses past user data and movie rating data to improve the accuracy of the playlist.

[0066] Playlist provision method The server provides the generated playlist to the user's device by sending a push notification to the user's smartphone or computer using a notification service such as Firebase Cloud Messaging. The user receives the notification, displays the playlist in a dedicated app or web browser, and can enjoy the content.

[0067] Encrypted communication methods The server securely receives and stores the input information provided by the user using an encryption protocol (e.g. SSL / TLS), thereby protecting the user's personal information.

[0068] Examples Suppose a user inputs "I want to watch an uplifting movie today." The server receives "uplifting movies" as the mood of the day and also applies the profile information that the user is a man in his 30s. Next, the server searches for "uplifting movies" from the video content database and retrieves a list. In this case, the server selects "action movie A" or "comedy movie B" by taking into account the playback time and genre. The playlist generated using the machine learning model is sent to the user's smartphone via push notification, and the user opens the app to watch the suggested movies.

[0069] Examples of prompt statements An example of a prompt sentence for generating a playlist based on today's mood is "Please suggest movies that are good for a man in his 30s who wants to relax."

[0070] In this way, the present invention is a system that makes highly accurate content recommendations based on a user's mood and interests, improving the user experience, while also achieving safe information management and efficient time management.

[0071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0072] Step 1: The user uses the device to input the user's mood and interests for the day, as well as profile information. For example, the user provides information such as "I want to relax today," "Age: 30," and "Gender: Male" through a specific web form or smartphone app. This information becomes the input data for making recommendations based on the user's mood and interests for the day.

[0073] Step 2: The server securely receives the input information using an encrypted communication protocol (e.g., SSL / TLS) and stores it in a database. By storing the input information such as "I want to relax," "age," and "gender," it will be used in subsequent processing.

[0074] Step 3: The server obtains the user's schedule information using the Google (registered trademark) Calendar API or the like. The schedule information includes the user's break times, workout times, etc. The obtained information is used to recommend appropriate content.

[0075] Step 4: The server searches the database for video content based on the acquired input information and schedule information. A general-purpose query language (e.g., SQL) is used to retrieve movies and TV shows that meet the criteria from the database. Specifically, a search query for "relaxing movies" is sent to the database, and the corresponding video content is retrieved.

[0076] Step 5: The server uses the playlist generation model to generate an optimal playlist for the user from the acquired video content list. Using a Python machine learning library (e.g., TensorFlow), a highly accurate playlist is generated by referencing past user data and movie rating data. The input data "relax" is linked to a list of "relaxing movies," and the optimal movie is selected.

[0077] Step 6: The server sends the generated playlist to the user's device using a notification service (e.g., Firebase Cloud Messaging). The playlist arrives as a push notification on the user's smartphone or computer. When the user clicks on the notification, the playlist is displayed in the app or in a web browser.

[0078] Step 7: The device displays the received playlist and suggests it to the user. For example, the suggestions are displayed in the "Today's Recommendations" section of a dedicated app on the user's smartphone, and the user can watch movies or TV shows in the list.

[0079] This trend will enable safe and rapid provision of optimal content tailored to a user's mood and schedule.

[0080] (Application example 1) Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] Conventional content delivery systems do not fully utilize the function of generating optimal playlists taking into account the user's mood and interests of the day. They also lack the function of suggesting content for an appropriate time period based on the user's schedule. This makes it difficult for users to enjoy the optimal content at the optimal time.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0083] In this invention, the server includes a data acquisition means for acquiring input information including the user's mood and interests for the day, a playlist generation means for generating a playlist consisting of a plurality of contents in a database storing contents including movies and TV programs from the acquired input information using a playlist generation model, and a means for transmitting the generated playlist to the user's terminal and displaying it, thereby making it possible to provide optimal content in a timely manner according to the user's mood, interests, and schedule.

[0084] "User's mood of the day" refers to the psychological state or emotions felt by the user on a particular day.

[0085] "Interests" refers to particular topics or content that capture a user's interest or focus.

[0086] The term "data acquisition means" refers to a method or device for acquiring input information from a user.

[0087] "Playlist generation model" refers to an algorithm or machine learning model that identifies optimal content and generates lists based on collected input information.

[0088] "Playlist generator" refers to a method or apparatus for creating a playlist using a playlist generation model.

[0089] A "database" refers to a collection of information that stores various content such as movies and television programs.

[0090] "Output means" refers to a method or device for providing the generated playlist to a user.

[0091] "Terminal" refers to a device that a user directly operates to view content.

[0092] "Schedule information" refers to information related to time management such as a user's planned activities and timetable.

[0093] A "break time" refers to a period of time during which a user takes a break from activity to rest.

[0094] "Time engaged in exercise" refers to time during which a user is engaged in physical activity or exercise.

[0095] To implement this invention, a user terminal, a server, and a communication environment for exchanging data between them are required. Specifically, the invention is realized in the following procedure.

[0096] First, the server obtains profile information and schedule information, such as the user's mood, interests, age, and gender, from the user's device. This input information can be input by the user through a device such as a smartphone or smart glasses. The schedule information also includes the times when the user works, takes a break, and exercises on a particular day.

[0097] Next, the server uses a playlist generation model to generate an optimal playlist based on the input information. This playlist generation model uses a generative AI model such as ChatGPT (registered trademark) to select content (movies, TV shows, music, podcasts, etc.) that matches the user's mood and interests. At this time, various contents are stored in the database in advance.

[0098] The generated playlist is then sent from the server to the user's device, where the user can browse the suggested playlist via their smartphone or smart glasses and enjoy the optimal content at the right time.

[0099] As a concrete example, suppose that a 30-year-old male user inputs that he is in the mood to relax and is interested in comedies and science fiction, and that his working hours that day are from 9:00 a.m. to 5:00 p.m. and his lunch break is from 12:00 p.m. to 12:30 p.m. In this case, based on the user's input information and schedule information, the server generates a playlist by selecting short comedy programs that can be enjoyed during the lunch break and science fiction movies to relax after work, and transmits the playlist to the user's terminal.

[0100] Example of a prompt to be input to a generative AI model: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0101] As described above, the present invention is capable of providing optimal content in a timely manner according to a user's mood, interests, and schedule, thereby improving the user's entertainment experience.

[0102] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0103] Step 1: The user enters profile information such as that day's mood, interests, age, and gender through the terminal. The user enters the information using the interface of a smartphone or smart glasses, and this information is sent directly to the server.

[0104] Input: Profile information such as mood, interests, age, and gender

[0105] Output: User information sent to the server

[0106] Step 2: The server receives and stores the user's input information.

[0107] Input: Profile information submitted by the user

[0108] Output: User profile information stored in the server database.

[0109] Step 3: The user inputs schedule information using the terminal and transmits it to the server, such as the start time, end time, and break time of work.

[0110] Input: User's schedule information

[0111] Output: Schedule information sent to the server

[0112] Step 4: The server stores the received schedule information and associates it with the user's profile information.

[0113] Input: Schedule information submitted by the user

[0114] Output: Schedule information and profile information associations stored in the server database

[0115] Step 5: The server generates a playlist using the playlist generation model based on the user information and schedule information, and sends a prompt to the generation AI model to obtain a list of content.

[0116] Input: User profile information, schedule information

[0117] Output: Generated playlist

[0118] Specific prompt examples: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0119] Step 6: The server sends the generated playlist to the user's device, which contains content that matches the user's mood, interests, and time of day.

[0120] Input: Generated playlist

[0121] Output: The playlist sent to the user's device

[0122] Step 7: The user browses the provided playlist using the device and selects the content to watch. The user starts watching from the suggested playlist via their smartphone or smart glasses.

[0123] Input: A user-provided playlist

[0124] Output: User begins watching the content selected by the user

[0125] These steps allow users to enjoy content that is best suited to their mood, interests, and schedule.

[0126] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0127] A modification of the first embodiment of the present disclosure comprises the following elements.

[0128] 1. Data acquisition means: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0129] 2. Means for combining emotion engines: The server is provided with means for combining emotion engines. Specifically, the server has an emotion recognition unit for recognizing the emotion of the user. The ring recognition unit incorporates an emotion engine for recognizing the emotion of the user into the system.

[0130] 3. Playlist generation means: The server is equipped with a means for identifying optimal content from a database including movies and TV shows based on information obtained from the user and information obtained from the emotion engine, adding the identified content to a list, and generating an optimal playlist.

[0131] 4. Playlist suggesting means: The server has a means for transmitting the created playlist to the terminal and suggesting it to the user.

[0132] The above is a modified example of the first embodiment, which makes it possible to generate an optimal playlist based on input information such as the user's mood for the day and matters of interest, as well as emotional information obtained from the emotion engine, and to suggest content that matches the user's schedule.

[0133] The process flow will be explained below.

[0134] Step 1: Getting information from the user

[0135] The server receives and processes input information from the user regarding profile information (e.g., age and sex), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0136] Step 2: Combining emotion engines

[0137] The server combines an emotion engine. Specifically, an emotion engine for recognizing the user's emotion is incorporated into the system. The emotion engine can also be used to obtain information required for generating a playlist, such as the user's mood for the day.

[0138] Step 3: Identify content

[0139] The server identifies the most suitable content from a database containing movies and TV programs prepared in advance, based on the input information from the user and the emotion information obtained from the emotion engine. Specifically, the server searches the content in the database and selects the appropriate content by comprehensively considering the input information such as the user's profile information, mood of the day, or interests, and the emotion information.

[0140] Step 4: Create a playlist

[0141] The server adds the identified content to the list and creates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist. The above-mentioned processes in steps 3 and 4 can be realized using a playlist generation model.

[0142] Step 5: Suggest a playlist

[0143] The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and enables the user to view the proposed playlist on the application.

[0144] The above is the flow of the program processing, in which the server and the terminal work together to process the user's information, and create and propose an optimal playlist that combines the emotion engine. Note that the schedule information mentioned above may also be acquired as the data to be acquired. The control in that case has already been described above, so a detailed explanation of it will be omitted here.

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

[0146] In conventional systems, the means to provide optimal content according to the user's mood and interests were limited, making it difficult to process data based on the user's emotions. In addition, playlists were not created according to the user's profile information or schedule, making it impossible to provide optimal content to individual users.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0148] In this invention, the server includes an information acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion from the input information using an emotion engine, a playlist generation means for generating a playlist made up of a plurality of contents in a database in which contents including movies and TV programs are stored, from the recognized emotion information and the acquired input information using a playlist generation model, and an information provision means for providing the generated playlist to the user. This makes it possible to provide optimal content based on the user's mood and interests.

[0149] The "information acquisition means" is a means for receiving and processing input information including the user's mood and interests for the day.

[0150] The "emotion recognition means" refers to a means for analyzing and recognizing a user's emotions from the user's input information obtained from the information acquisition means, using an emotion engine.

[0151] The "playlist generating means" is a means for generating an optimal playlist by combining a plurality of contents in the database based on the recognized emotion information and the acquired input information.

[0152] The "information providing means" is a means for transmitting the generated playlist to the user's terminal and suggesting it to the user.

[0153] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from user input information.

[0154] A "database" is a collection of information in which content such as movies and television programs are stored.

[0155] The purpose of this system is to generate and provide an optimal playlist based on the user's mood of the day and the things he or she is interested in. A specific embodiment of this system will be described below.

[0156] System Configuration

[0157] Hardware and Software

[0158] Server: Acts as the central processing unit that receives and processes input information. Contains the emotion engine, database, and playlist generation model.

[0159] Terminal: A device on which a user provides input information. Examples include a smartphone or tablet.

[0160] Emotion engine: Software for analyzing and recognizing emotions from user input information. Uses natural language processing (NLP) technology.

[0161] Database: A source where content such as movies and TV shows are stored.

[0162] Playlist generation model: An algorithm that selects the optimal content based on emotion recognition results and user input information, and generates a playlist.

[0163] System Operation Details

[0164] Data Acquisition The user uses the terminal to input profile information such as the mood of the day, interests, age, gender, etc. For example, the user can input "I want to relax today," and the terminal will send this information to the server.

[0165] emotion recognition After receiving the input information, the server uses the emotion engine to analyze the information. For example, if the input information is "I want to relax," the emotion engine will recognize the emotion "relax."

[0166] Playlist Creation The server searches the database for relevant content based on the recognized emotion information. For example, it finds movies and music that correspond to the emotion of "relaxation." It selects these contents and generates a playlist. The playlist generation model does this.

[0167] Providing playlists The server sends the generated playlist to the user's device. The device displays the received playlist to the user and provides viewing and playback options. For example, the user can check the presented playlist on their smartphone, select a relaxing movie, and play it.

[0168] Examples of concrete examples and prompts

[0169] Examples A user uses a smartphone app and inputs, "I want to relieve stress today." The server receives this information and uses an emotion engine to recognize the emotion "relieve stress." The server then searches the database for music and movies with a relaxing effect and generates a playlist. The server then sends this playlist to the user's smartphone, where the user can view and watch the music and movies using the app.

[0170] Examples of prompt statements

[0171] Users input their mood and interests for the day

[0172] Recognize user emotions using an AI emotion engine

[0173] Identify suitable content from a database of movies and TV shows

[0174] Generate optimal playlists and suggest them to users

[0175] This prompt can be fed into a generative AI model to help the system understand a sequence of actions that will provide the optimal content based on the user's mood and interests.

[0176] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0177] Step 1: The user uses the terminal to input the day's mood, interests, and profile information (age, gender).

[0178] Specific operation: A user opens the app on their smartphone and enters, "I want to relax today." This input information is sent from the device to the server.

[0179] Input: User mood, interests, and profile information.

[0180] Output: Sending input information to the server.

[0181] Step 2: The server receives the input information sent from the terminal.

[0182] Specific operation: The server receives information such as "I want to relax," "Age: 30," and "Gender: Male," and stores it in a database.

[0183] Input: Input information from the terminal.

[0184] Output: The saved input information.

[0185] Step 3: The server analyzes the received input information using the emotion recognition means and recognizes the user's emotion.

[0186] Specific behavior: The emotion engine recognizes the emotion "relax" from the input "I want to relax."

[0187] Input: The saved input information.

[0188] Output: Recognized emotion information.

[0189] Step 4: Based on the recognized emotion information, the server uses a playlist generation means to search for related content from the database and generate a playlist.

[0190] Specific behavior: Based on the emotion information of "relaxation," search for movies and music with a relaxing effect from the database and generate a playlist. For example, add a movie and relaxing music called "Your Name." to the list.

[0191] Input: Recognized emotion information, stored input information.

[0192] Output: The generated playlist.

[0193] Step 5: The server sends the generated playlist to the user's terminal.

[0194] Specific operation: The server generates a playlist and sends it to the user's smartphone, where it is displayed to the user through the app.

[0195] Input: Generierung playlist.

[0196] Output: Send the playlist to a device.

[0197] Step 6: The device displays the received playlist to the user and provides options for viewing and playing.

[0198] Specific behavior: The user browses the playlist in the app, selects "Your Name." and plays it.

[0199] Input: The playlist sent by the server.

[0200] Output: The playlist and playback options presented to the user.

[0201] (Application example 2) Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0202] Conventional content delivery systems generate playlists based only on simple input information from the user, making it difficult to suggest optimal content that matches the user's emotions or mood of the day. In addition, systems that use face recognition or emotion recognition have not been fully utilized, and delays in providing content that matches the user's real-time emotions have been an issue.

[0203] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a data acquisition means for acquiring input information including the user's mood of the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion, a playlist generation means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and TV programs are stored, from the acquired input information and the emotion information obtained by the emotion recognition means, using a playlist generation model, an output means for providing the generated playlist to the user, and a means for detecting the user's emotion by face recognition using a camera. This makes it possible to generate a playlist of optimal content based on the user's mood of the day and real-time emotions.

[0204] The "data acquisition means" is a means for acquiring input information including the user's mood for the day and matters of interest to the user.

[0205] An "emotion recognition means" is a means for recognizing the user's emotions.

[0206] The "playlist generation means" refers to means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and television programs are stored, from acquired input information and emotion information obtained by the emotion recognition means, using a playlist generation model.

[0207] The "output means" is a means for providing the generated playlist to the user.

[0208] The "means for detecting a user's emotions through facial recognition using a camera" refers to a means for recognizing a user's face using a camera and detecting emotions from the facial expressions.

[0209] The present invention is a system for providing an optimal content playlist according to a user's mood, interests, and emotional state on that day.

[0210] The server includes a data acquisition unit that acquires input information including the user's mood and interests. The data acquisition unit collects the input information from the user through an application installed on a terminal such as a smartphone or a personal computer.

[0211] The server also includes an emotion recognition means for recognizing the emotion of the user. This emotion recognition is achieved by capturing the image of the user's face using the camera of the terminal and analyzing the facial expressions. Specific emotion recognition software is used for emotion recognition. This software processes the facial expression data and determines the emotion based on the data.

[0212] The playlist generation means generates an optimal playlist using a playlist generation model based on the input information based on the acquired mood and interests and the emotion information obtained by the emotion recognition means. This model utilizes a database that stores content such as movies and TV programs, and selects content that is suitable for the user's information.

[0213] The generated playlist is provided to the user via an output means, which displays the list or notifies the user through a smartphone application, for example.

[0214] For example, if a user indicates that they are looking to feel relaxed and the camera detects a "calm" emotion, the system will generate a playlist of relaxing movies and TV shows that can be enjoyed at leisure, which will then be suggested to the user through the application.

[0215] An example of a prompt required for a generative AI model is:

[0216] "Today I want to relax. I have been characterized as feeling calm. Please suggest a playlist of movies and TV shows suitable for relaxation. Age is 30, Gender is female."

[0217] With this invention, users can easily receive optimal content playlists based on their mood of the day and real-time emotions.

[0218] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0219] Step 1: The user uses the device to input their mood, interests, and profile information for the day. The input data is then sent from the device to the server.

[0220] Input: Profile information such as the user’s mood, interests, age, and gender.

[0221] Output: The entered information is saved on the server.

[0222] Specific operation: When a user enters information into a form displayed on the application screen and presses the "Submit" button, the information is sent from the terminal to the server as an HTTP request.

[0223] Step 2: The server receives and stores the input information sent from the terminal.

[0224] Input: Your mood, interests, and profile information sent from your device.

[0225] Output: User input information stored on the server.

[0226] Specific operation: The received data is stored in a database and used for further processing.

[0227] Step 3: The user uses the device's camera to perform face recognition, and the emotion recognition means analyzes the user's facial expressions.

[0228] Input: A user's facial expression captured by the device's camera.

[0229] Output: Parsed emotion data (e.g. calm, happy, sad).

[0230] How it works: The user follows the instructions to turn on the camera and point their face at it. The emotion recognition software analyzes their facial expressions and generates emotion data.

[0231] Step 4: The emotion data generated by the emotion recognition means is transmitted to the server.

[0232] Input: Parsed emotion data.

[0233] Output: Emotion data sent to the server.

[0234] Specific operation: The results of analyzing the user's facial expressions are sent from the device to the server. This data is also processed as an HTTP request.

[0235] Step 5: The server receives the input information and the emotion data, and generates an optimal content playlist using a playlist generating means.

[0236] Input: User mood, interests, profile information, emotional data.

[0237] Output: A user-optimized content playlist.

[0238] Specific operation: The server uses the acquired information and emotion data to generate a playlist using a generative AI model. It selects the most suitable content from the database and composes the playlist.

[0239] Step 6: The generated playlist is sent to the terminal and provided to the user.

[0240] Input: The generated content playlist.

[0241] Output: The playlist that is served to the user.

[0242] Specific operation: The server sends the generated playlist to the terminal. The user receives the playlist through the application and starts watching.

[0243] This enables the generation and provision of an optimal content playlist based on the user's mood of the day and real-time emotions.

[0244] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0245] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the 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">) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0246] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0247] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0248] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0249] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0250] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0251] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0252] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0253] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0254] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0255] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0257] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0258] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0259] The second embodiment of the present disclosure comprises the following elements:

[0260] 1. Data acquisition unit: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0261] 2. Playlist generation unit: The server has a means for identifying optimal content from a database including movies and TV programs based on input information acquired from a user, adding the identified content to a list, and generating an optimal playlist. A playlist generation model (data generation model) can be used to generate the playlist. The database may be one or more databases prepared in advance.

[0262] 3. Output unit: The server includes a means for transmitting the generated playlist to a terminal and proposing it to a user.

[0263] The above is the second embodiment, which makes it possible to generate an optimal playlist based on the user's mood and interests on that day and propose it to the user.

[0264] The process flow will be explained below.

[0265] Step 1: The server receives and processes input information from the user regarding profile information (e.g., age and gender), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0266] Step 2: The server identifies the most suitable content based on the user's input information from a database that contains pre-prepared content including movies and TV shows. Specifically, the server searches the content in the database and selects the appropriate content based on the user's profile information, mood of the day, and interests.

[0267] Step 3: The server adds the identified content to the list and generates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist.

[0268] Step 4: The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and allows the user to view the proposed playlist on the application.

[0269] Furthermore, if the data acquisition unit is capable of acquiring the user's schedule information, the following processing can be executed.

[0270] Step 11: The server receives and processes profile information (e.g., age and sex), input information on the user's mood for the day, and the user's interests, and the user's schedule information from the user. The user inputs the schedule information into an application on the terminal, and the terminal transmits the input schedule information to the server, where the server receives it and processes it internally.

[0271] Step 12: The server identifies content of a length that can be viewed during the user's break time or workout time based on the user's schedule information. Specifically, the server analyzes the user's schedule information, identifies break times or workout times in the schedule, and selects content of a length suitable for viewing during the break time, etc.

[0272] Step 13: The server adds the identified content to the list and creates a playlist associated with the schedule. Specifically, the server saves the identified content as a playlist and organizes it as a playlist associated with the schedule. Step 14: The server transmits the playlist associated with the created schedule to the terminal and proposes it to the user. Specifically, the server transmits the playlist associated with the schedule to the terminal and allows the user to view the proposed playlist on the application.

[0273] The above is the flow of the program processing, in which the server and the terminal work together to process the user's information and create and propose an optimal playlist. Note that the schedule information mentioned above may also be acquired as the data to be acquired. The control in that case has already been described above, so a detailed explanation will be omitted here.

[0274] Example 1 Next, a description will be given of Example 1. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0275] Conventional content recommendation systems lacked the flexibility to provide optimal content based on the user's mood and interests, making it difficult to effectively identify the content the user wanted. In addition, they were unable to recommend content according to the user's schedule, making efficient time management difficult. Furthermore, they lacked technology for secure communication and high-precision playlist generation.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0277] In this invention, the server includes: a means for acquiring input information including the user's mood of the day and the user's interests; a means for generating a playlist consisting of a plurality of contents in a database in which video contents are stored from the acquired input information using a playlist generation model; a means for providing the generated playlist to the user; a means for securely receiving and storing the input information provided by the user using an encrypted communication protocol; a means for using a general-purpose query language to search and acquire video contents from the database; a means for generating a playlist by utilizing a machine learning library; and a means for transmitting the generated playlist to the user's terminal using a notification service. This enables the generation of a highly accurate playlist according to the user's mood and interests, and safe and rapid content suggestions. In addition, it is also possible to realize the recommendation of appropriate content according to the user's schedule.

[0278] "Input information including the user's mood of the day and the user's interests" refers to data that reflects the user's emotions and interests of the day and is input into the system.

[0279] The term "data acquisition means" refers to a device or software having a function for receiving input information and schedule information provided by a user.

[0280] "Playlist generation model" refers to a machine learning algorithm or statistical model for generating a playlist composed of appropriate content based on user input information.

[0281] The term "playlist generation means" refers to a device or software having a function for selecting optimal content from input information acquired using a playlist generation model and generating a playlist.

[0282] A "database storing video content" refers to a data storage system for accumulating and managing viewing media such as movies and television programs.

[0283] A "playlist" refers to a list of multiple pieces of content selected based on a specific theme or criteria.

[0284] "Output means" refers to a device or software having a function for presenting the generated playlist on a user's terminal or other device.

[0285] An "encrypted communications protocol" refers to a communications method for protecting the confidentiality and integrity of information during the data transmission and reception process.

[0286] "General-purpose query language" refers to a standardized language used to search and retrieve information from a database, including, for example, SQL.

[0287] A "machine learning library" refers to a software tool that learns from data, recognizes patterns, and makes predictions or classifications.

[0288] "Notification Service" means an Internet service that has the ability to transmit information to a User's Device in real time.

[0289] To implement the present invention, a system including the following components is required.

[0290] Data Acquisition Method The server obtains the user's mood for the day, interests, and profile information (age and gender). This information is entered by the user using a web browser or a dedicated application. For example, if the user enters "I want to relax today," the server provides information about the user's mood for the day. The server also obtains the user's schedule information through the Google (registered trademark) Calendar API, etc.

[0291] Database Access The server searches for and retrieves content from a database that stores video content using a general-purpose query language (e.g., SQL), and retrieves information on appropriate movies and TV programs from the database based on the information that the user wants to relax.

[0292] Playlist Generation Method The server uses a playlist generation model to generate an optimal playlist based on the acquired input information and the content information in the database. At this time, the server uses a Python machine learning library (e.g. TensorFlow) to generate a playlist that best suits the user's preferences. The generative AI model uses past user data and movie rating data to improve the accuracy of the playlist.

[0293] Playlist provision method The server provides the generated playlist to the user's device by sending a push notification to the user's smartphone or computer using a notification service such as Firebase Cloud Messaging. The user receives the notification, displays the playlist in a dedicated app or web browser, and can enjoy the content.

[0294] Encrypted communication methods The server securely receives and stores the input information provided by the user using an encryption protocol (e.g. SSL / TLS), thereby protecting the user's personal information.

[0295] Examples Suppose a user inputs "I want to watch an uplifting movie today." The server receives "uplifting movies" as the mood of the day and also applies the profile information that the user is a man in his 30s. Next, the server searches for "uplifting movies" from the video content database and retrieves a list. In this case, the server selects "action movie A" or "comedy movie B" by taking into account the playback time and genre. The playlist generated using the machine learning model is sent to the user's smartphone via push notification, and the user opens the app to watch the suggested movies.

[0296] Examples of prompt statements

[0297] An example of a prompt sentence for generating a playlist based on today's mood is "Please suggest movies that are good for a man in his 30s who wants to relax."

[0298] In this way, the present invention is a system that makes highly accurate content recommendations based on a user's mood and interests, improving the user experience, while also achieving safe information management and efficient time management.

[0299] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0300] Step 1: The user uses the device to input the user's mood and interests for the day, as well as profile information. For example, the user provides information such as "I want to relax today," "Age: 30," and "Gender: Male" through a specific web form or smartphone app. This information becomes the input data for making recommendations based on the user's mood and interests for the day.

[0301] Step 2: The server securely receives the input information using an encrypted communication protocol (e.g., SSL / TLS) and stores it in a database. By storing the input information such as "I want to relax," "age," and "gender," it will be used in subsequent processing.

[0302] Step 3: The server obtains the user's schedule information using the Google (registered trademark) Calendar API or the like. The schedule information includes the user's break times, workout times, etc. The obtained information is used to recommend appropriate content.

[0303] Step 4: The server searches the database for video content based on the acquired input information and schedule information. A general-purpose query language (e.g., SQL) is used to retrieve movies and TV shows that meet the criteria from the database. Specifically, a search query for "relaxing movies" is sent to the database, and the corresponding video content is retrieved.

[0304] Step 5: The server uses the playlist generation model to generate an optimal playlist for the user from the acquired video content list. Using a Python machine learning library (e.g., TensorFlow), a highly accurate playlist is generated by referencing past user data and movie rating data. The input data "relax" is linked to a list of "relaxing movies," and the optimal movie is selected.

[0305] Step 6: The server sends the generated playlist to the user's device using a notification service (e.g., Firebase Cloud Messaging). The playlist arrives as a push notification on the user's smartphone or computer. When the user clicks on the notification, the playlist is displayed in the app or in a web browser.

[0306] Step 7: The device displays the received playlist and suggests it to the user. For example, the suggestions are displayed in the "Today's Recommendations" section of a dedicated app on the user's smartphone, and the user can watch movies or TV shows in the list.

[0307] This trend will enable safe and rapid provision of optimal content tailored to a user's mood and schedule.

[0308] (Application example 1) Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0309] Conventional content delivery systems do not fully utilize the function of generating optimal playlists taking into account the user's mood and interests of the day. They also lack the function of suggesting content for an appropriate time period based on the user's schedule. This makes it difficult for users to enjoy the optimal content at the optimal time.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0311] In this invention, the server includes a data acquisition means for acquiring input information including the user's mood and interests for the day, a playlist generation means for generating a playlist consisting of a plurality of contents in a database storing contents including movies and TV programs from the acquired input information using a playlist generation model, and a means for transmitting the generated playlist to the user's terminal and displaying it, thereby making it possible to provide optimal content in a timely manner according to the user's mood, interests, and schedule.

[0312] "User's mood of the day" refers to the psychological state or emotions felt by the user on a particular day.

[0313] "Interests" refers to particular topics or content that capture a user's interest or focus.

[0314] The term "data acquisition means" refers to a method or device for acquiring input information from a user.

[0315] "Playlist generation model" refers to an algorithm or machine learning model that identifies optimal content and generates lists based on collected input information.

[0316] "Playlist generator" refers to a method or apparatus for creating a playlist using a playlist generation model.

[0317] A "database" refers to a collection of information that stores various content such as movies and television programs.

[0318] "Output means" refers to a method or device for providing the generated playlist to a user.

[0319] "Terminal" refers to a device that a user directly operates to view content.

[0320] "Schedule information" refers to information related to time management such as a user's planned activities and timetable.

[0321] A "break time" refers to a period of time during which a user takes a break from activity to rest.

[0322] "Time engaged in exercise" refers to time during which a user is engaged in physical activity or exercise.

[0323] To implement this invention, a user terminal, a server, and a communication environment for exchanging data between them are required. Specifically, the invention is realized in the following procedure.

[0324] First, the server obtains profile information and schedule information, such as the user's mood, interests, age, and gender, from the user's device. This input information can be input by the user through a device such as a smartphone or smart glasses. The schedule information also includes the times when the user works, takes a break, and exercises on a particular day.

[0325] Next, the server uses the input information to generate an optimal playlist using a playlist generation model. This playlist generation model uses a generative AI model such as ChatGPT to select content (movies, TV shows, music, podcasts, etc.) that matches the user's mood and interests. At this time, various contents are pre-stored in the database.

[0326] The generated playlist is then sent from the server to the user's device, where the user can browse the suggested playlist via their smartphone or smart glasses and enjoy the optimal content at the right time.

[0327] As a concrete example, suppose that a 30-year-old male user inputs that he is in the mood to relax and is interested in comedies and science fiction, and that his working hours that day are from 9:00 a.m. to 5:00 p.m. and his lunch break is from 12:00 p.m. to 12:30 p.m. In this case, based on the user's input information and schedule information, the server generates a playlist by selecting short comedy programs that can be enjoyed during the lunch break and science fiction movies to relax after work, and transmits the playlist to the user's terminal.

[0328] Example of a prompt to be input to a generative AI model: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0329] As described above, the present invention is capable of providing optimal content in a timely manner according to a user's mood, interests, and schedule, thereby improving the user's entertainment experience.

[0330] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0331] Step 1: The user enters profile information such as that day's mood, interests, age, and gender through the terminal. The user enters the information using the interface of a smartphone or smart glasses, and this information is sent directly to the server.

[0332] Input: Profile information such as mood, interests, age, and gender

[0333] Output: User information sent to the server

[0334] Step 2: The server receives and stores the user's input information.

[0335] Input: Profile information submitted by the user

[0336] Output: User profile information stored in the server database.

[0337] Step 3: The user inputs schedule information using the terminal and transmits it to the server, such as the start time, end time, and break time of work.

[0338] Input: User's schedule information

[0339] Output: Schedule information sent to the server

[0340] Step 4: The server stores the received schedule information and associates it with the user's profile information.

[0341] Input: Schedule information submitted by the user

[0342] Output: Schedule information and profile information associations stored in the server database

[0343] Step 5: The server generates a playlist using the playlist generation model based on the user information and schedule information, and sends a prompt to the generation AI model to obtain a list of content.

[0344] Input: User profile information, schedule information

[0345] Output: Generated playlist

[0346] Example of a specific prompt: "Generate a playlist of comedy and science fiction songs for a laid-back user based on the following user information: age 30, male, profile as relaxed, interests as comedy and science fiction."

[0347] Step 6: The server sends the generated playlist to the user's device, which contains content that matches the user's mood, interests, and time of day.

[0348] Input: Generated playlist

[0349] Output: The playlist sent to the user's device

[0350] Step 7: The user browses the provided playlist using the device and selects the content to watch. The user starts watching from the suggested playlist via their smartphone or smart glasses.

[0351] Input: A user-provided playlist

[0352] Output: User begins watching the content selected by the user

[0353] These steps allow users to enjoy content that is best suited to their mood, interests, and schedule.

[0354] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0355] A variation of the second embodiment included in the present disclosure includes the following elements.

[0356] 1. Data acquisition means: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0357] 2. Means for combining emotion engines: The server is provided with means for combining emotion engines. Specifically, the server has an emotion recognition unit for recognizing the emotion of the user. The ring recognition unit incorporates an emotion engine for recognizing the emotion of the user into the system.

[0358] 3. Playlist generation means: The server is equipped with a means for identifying optimal content from a database including movies and TV shows based on information obtained from the user and information obtained from the emotion engine, adding the identified content to a list, and generating an optimal playlist.

[0359] 4. Playlist suggesting means: The server has a means for transmitting the created playlist to the terminal and suggesting it to the user.

[0360] The above is a modified example of the second embodiment of the present disclosure, which makes it possible to generate an optimal playlist based on input information such as the user's profile information, mood of the day, and matters of interest, as well as emotional information obtained from the emotion engine, and to suggest content that matches the user's schedule.

[0361] The process flow will be explained below.

[0362] Step 1: Getting information from the user

[0363] The server receives and processes input information from the user regarding profile information (e.g., age and sex), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0364] Step 2: Combining emotion engines

[0365] The server combines an emotion engine. Specifically, an emotion engine for recognizing the user's emotion is incorporated into the system. The emotion engine can also be used to obtain information required for generating a playlist, such as the user's mood for the day.

[0366] Step 3: Identify content

[0367] The server identifies the most suitable content from a database containing pre-prepared movies and TV programs based on the input information from the user and the emotion information obtained from the emotion engine. Specifically, the server searches the content in the database and selects the appropriate content by comprehensively considering the input information such as the user's profile information, mood or interests of the day, and the emotion information.

[0368] Step 4: Create a playlist

[0369] The server adds the identified content to the list and creates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist. The above-mentioned processes in steps 3 and 4 can be realized using a playlist generation model.

[0370] Step 5: Suggest a playlist

[0371] The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and enables the user to view the proposed playlist on the application.

[0372] The above is the flow of the program processing, in which the server and the terminal work together to process the user's information, and create and propose an optimal playlist that combines the emotion engine. Note that the schedule information mentioned above may also be acquired as the data to be acquired. The control in that case has already been described above, so a detailed explanation of it will be omitted here.

[0373] Example 2 Next, a description will be given of Example 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0374] In conventional systems, the means to provide optimal content according to the user's mood and interests were limited, making it difficult to process data based on the user's emotions. In addition, playlists were not created according to the user's profile information or schedule, making it impossible to provide optimal content to individual users.

[0375] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0376] In this invention, the server includes an information acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion from the input information using an emotion engine, a playlist generation means for generating a playlist made up of a plurality of contents in a database in which contents including movies and TV programs are stored, from the recognized emotion information and the acquired input information using a playlist generation model, and an information provision means for providing the generated playlist to the user. This makes it possible to provide optimal content based on the user's mood and interests.

[0377] The "information acquisition means" is a means for receiving and processing input information including the user's mood and interests for the day.

[0378] The "emotion recognition means" refers to a means for analyzing and recognizing a user's emotions from the user's input information obtained from the information acquisition means, using an emotion engine.

[0379] The "playlist generating means" is a means for generating an optimal playlist by combining a plurality of contents in the database based on the recognized emotion information and the acquired input information.

[0380] The "information providing means" is a means for transmitting the generated playlist to the user's terminal and suggesting it to the user.

[0381] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from user input information.

[0382] A "database" is a collection of information in which content such as movies and television programs are stored.

[0383] The purpose of this system is to generate and provide an optimal playlist based on the user's mood of the day and the things he or she is interested in. A specific embodiment of this system will be described below.

[0384] System Configuration

[0385] Hardware and Software

[0386] Server: Acts as the central processing unit that receives and processes input information. Contains the emotion engine, database, and playlist generation model.

[0387] Terminal: A device on which a user provides input information. Examples include a smartphone or tablet.

[0388] Emotion engine: Software for analyzing and recognizing emotions from user input information. Uses natural language processing (NLP) technology.

[0389] Database: A source where content such as movies and TV shows are stored.

[0390] Playlist generation model: An algorithm that selects the optimal content based on emotion recognition results and user input information, and generates a playlist.

[0391] System Operation Details

[0392] Data Acquisition The user uses the terminal to input profile information such as the mood of the day, interests, age, gender, etc. For example, the user can input "I want to relax today," and the terminal will send this information to the server.

[0393] emotion recognition After receiving the input information, the server uses the emotion engine to analyze the information. For example, if the input information is "I want to relax," the emotion engine will recognize the emotion "relax."

[0394] Playlist Creation The server searches the database for relevant content based on the recognized emotion information. For example, it finds movies and music that correspond to the emotion of "relaxation." It selects these contents and generates a playlist. The playlist generation model does this.

[0395] Providing playlists The server sends the generated playlist to the user's device. The device displays the received playlist to the user and provides viewing and playback options. For example, the user can check the presented playlist on their smartphone, select a relaxing movie, and play it.

[0396] Examples of concrete examples and prompts

[0397] Examples A user uses a smartphone app and inputs, "I want to relieve stress today." The server receives this information and uses an emotion engine to recognize the emotion "relieve stress." The server then searches the database for music and movies with a relaxing effect and generates a playlist. The server then sends this playlist to the user's smartphone, where the user can view and watch the music and movies using the app.

[0398] Examples of prompt statements

[0399] Users input their mood and interests for the day

[0400] Recognize user emotions using an AI emotion engine

[0401] Identify suitable content from a database of movies and TV shows

[0402] Generate optimal playlists and suggest them to users

[0403] This prompt can be fed into a generative AI model to help the system understand a sequence of actions that will provide the optimal content based on the user's mood and interests.

[0404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0405] Step 1: The user uses the terminal to input the day's mood, interests, and profile information (age, gender).

[0406] Specific operation: A user opens the app on their smartphone and enters, "I want to relax today." This input information is sent from the device to the server.

[0407] Input: User mood, interests, and profile information.

[0408] Output: Sending input information to the server.

[0409] Step 2: The server receives the input information sent from the terminal.

[0410] Specific operation: The server receives information such as "I want to relax," "Age: 30," and "Gender: Male," and stores it in a database.

[0411] Input: Input information from the terminal.

[0412] Output: The saved input information.

[0413] Step 3: The server analyzes the received input information using the emotion recognition means and recognizes the user's emotion.

[0414] Specific behavior: The emotion engine recognizes the emotion "relax" from the input "I want to relax."

[0415] Input: The saved input information.

[0416] Output: Recognized emotion information.

[0417] Step 4: Based on the recognized emotion information, the server uses a playlist generation means to search for related content from the database and generate a playlist.

[0418] Specific behavior: Based on the emotion information of "relaxation," search for movies and music with a relaxing effect from the database and generate a playlist. For example, add a movie and relaxing music called "Your Name." to the list.

[0419] Input: Recognized emotion information, stored input information.

[0420] Output: The generated playlist.

[0421] Step 5: The server sends the generated playlist to the user's terminal.

[0422] Specific operation: The server generates a playlist and sends it to the user's smartphone, where it is displayed to the user through the app.

[0423] Input:Generierung playlist.

[0424] Output: Send the playlist to a device.

[0425] Step 6: The device displays the received playlist to the user and provides options for viewing and playing.

[0426] Specific behavior: The user browses the playlist in the app, selects "Your Name." and plays it.

[0427] Input: The playlist sent by the server.

[0428] Output: The playlist and playback options presented to the user.

[0429] (Application example 2) Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0430] Conventional content delivery systems generate playlists based only on simple input information from the user, making it difficult to suggest optimal content that matches the user's emotions or mood of the day. In addition, systems that use face recognition or emotion recognition have not been fully utilized, and delays in providing content that matches the user's real-time emotions have been an issue.

[0431] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a data acquisition means for acquiring input information including the user's mood of the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion, a playlist generation means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and TV programs are stored, from the acquired input information and the emotion information obtained by the emotion recognition means, using a playlist generation model, an output means for providing the generated playlist to the user, and a means for detecting the user's emotion by face recognition using a camera. This makes it possible to generate a playlist of optimal content based on the user's mood of the day and real-time emotions.

[0432] The "data acquisition means" is a means for acquiring input information including the user's mood for the day and matters of interest to the user.

[0433] An "emotion recognition means" is a means for recognizing the user's emotions.

[0434] The "playlist generation means" refers to means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and television programs are stored, from acquired input information and emotion information obtained by the emotion recognition means, using a playlist generation model.

[0435] The "output means" is a means for providing the generated playlist to the user.

[0436] The "means for detecting a user's emotions through facial recognition using a camera" refers to a means for recognizing a user's face using a camera and detecting emotions from the facial expressions.

[0437] The present invention is a system for providing an optimal content playlist according to a user's mood, interests, and emotional state on that day.

[0438] The server includes a data acquisition unit that acquires input information including the user's mood and interests. The data acquisition unit collects the input information from the user through an application installed on a terminal such as a smartphone or a personal computer.

[0439] The server also includes an emotion recognition means for recognizing the emotion of the user. This emotion recognition is achieved by capturing the image of the user's face using the camera of the terminal and analyzing the facial expressions. Specific emotion recognition software is used for emotion recognition. This software processes the facial expression data and determines the emotion based on the data.

[0440] The playlist generation means generates an optimal playlist using a playlist generation model based on the input information based on the acquired mood and interests and the emotion information obtained by the emotion recognition means. This model utilizes a database that stores content such as movies and TV programs, and selects content that is suitable for the user's information.

[0441] The generated playlist is provided to the user via an output means, which displays the list or notifies the user through a smartphone application, for example.

[0442] For example, if a user indicates that they are looking to feel relaxed and the camera detects a "calm" emotion, the system will generate a playlist of relaxing movies and TV shows that can be enjoyed at leisure, which will then be suggested to the user through the application.

[0443] An example of a prompt required for a generative AI model is:

[0444] "Today I want to relax. I have been characterized as feeling calm. Please suggest a playlist of movies and TV shows suitable for relaxation. Age is 30, Gender is female."

[0445] With this invention, users can easily receive optimal content playlists based on their mood of the day and real-time emotions.

[0446] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0447] Step 1: The user uses the device to input their mood, interests, and profile information for the day. The input data is then sent from the device to the server.

[0448] Input: Profile information such as the user’s mood, interests, age, and gender.

[0449] Output: The entered information is saved on the server.

[0450] Specific operation: When a user enters information into a form displayed on the application screen and presses the "Submit" button, the information is sent from the terminal to the server as an HTTP request.

[0451] Step 2: The server receives and stores the input information sent from the terminal.

[0452] Input: Your mood, interests, and profile information sent from your device.

[0453] Output: User input information stored on the server.

[0454] Specific operation: The received data is stored in a database and used for further processing.

[0455] Step 3: The user uses the device's camera to perform face recognition, and the emotion recognition means analyzes the user's facial expressions.

[0456] Input: A user's facial expression captured by the device's camera.

[0457] Output: Parsed emotion data (e.g. calm, happy, sad).

[0458] How it works: The user follows the instructions to turn on the camera and point their face at it. The emotion recognition software analyzes their facial expressions and generates emotion data.

[0459] Step 4: The emotion data generated by the emotion recognition means is transmitted to the server.

[0460] Input: Parsed emotion data.

[0461] Output: Emotion data sent to the server.

[0462] Specific operation: The results of analyzing the user's facial expressions are sent from the device to the server. This data is also processed as an HTTP request.

[0463] Step 5: The server receives the input information and the emotion data, and generates an optimal content playlist using a playlist generating means.

[0464] Input: User mood, interests, profile information, emotional data.

[0465] Output: A user-optimized content playlist.

[0466] Specific operation: The server uses the acquired information and emotion data to generate a playlist using a generative AI model. It selects the most suitable content from the database and composes the playlist.

[0467] Step 6: The generated playlist is sent to the terminal and provided to the user.

[0468] Input: The generated content playlist.

[0469] Output: The playlist that is served to the user.

[0470] Specific operation: The server sends the generated playlist to the terminal. The user receives the playlist through the application and starts watching.

[0471] This enables the generation and provision of an optimal content playlist based on the user's mood of the day and real-time emotions.

[0472] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0473] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0474] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0475] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0476] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0477] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0478] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0479] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0480] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0481] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0482] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0483] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0484] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0485] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0486] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server", and the headset type terminal 314 will be referred to as the "terminal".

[0487] The third embodiment of the present disclosure comprises the following elements:

[0488] 1. Data acquisition unit: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0489] 2. Playlist generation unit: The server has a means for identifying optimal content from a database including movies and TV programs based on input information acquired from a user, adding the identified content to a list, and generating an optimal playlist. A playlist generation model (data generation model) can be used to generate the playlist. The database may be one or more databases prepared in advance.

[0490] 3. Output unit: The server includes a means for transmitting the generated playlist to a terminal and proposing it to a user.

[0491] The data acquisition unit may further include a means for acquiring and processing schedule information of a user. The playlist generation unit may include a means for identifying rest periods and workout periods in the schedule based on input information such as information on the mood and interests of the day and profile information provided by the user and the schedule information when the data acquisition unit acquires the schedule information, identifying content that can be viewed during the period, and creating a playlist including the identified content.

[0492] The above is the third embodiment of the present disclosure, which makes it possible to generate an optimal playlist based on the user's mood and interests on that day, and to suggest content that matches the user's schedule.

[0493] The process flow will be explained below.

[0494] Step 1: The server receives and processes input information from the user regarding profile information (e.g., age and gender), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0495] Step 2: The server identifies the most suitable content based on the input information from the user from a database that contains pre-prepared content including movies and TV shows. Specifically, the server searches the content in the database and selects the appropriate content based on the input information such as the user's profile information, mood of the day, and interests.

[0496] Step 3: The server adds the identified content to the list and generates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist.

[0497] Step 4: The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and allows the user to view the proposed playlist on the application.

[0498] Furthermore, if the data acquisition unit is capable of acquiring the user's schedule information, the following processing can be executed.

[0499] Step 11: The server receives and processes profile information (e.g., age and sex), input information on the user's mood for the day, and the user's interests, and the user's schedule information from the user. The user inputs the schedule information into an application on the terminal, and the terminal transmits the input schedule information to the server, where the server receives it and processes it internally.

[0500] Step 12: The server identifies content of a length that can be viewed during the user's break time or workout time based on the user's schedule information. Specifically, the server analyzes the user's schedule information, identifies break times or workout times in the schedule, and selects content of a length suitable for viewing during the break time, etc.

[0501] Step 13: The server adds the identified content to a list and creates a playlist associated with the schedule. Specifically, the server saves the identified content as a playlist and organizes it as a playlist associated with the schedule.

[0502] Step 14: The server transmits a playlist associated with the created schedule to the terminal and proposes it to the user. Specifically, the server transmits a playlist associated with the schedule to the terminal and allows the user to view the proposed playlist on the application.

[0503] The above is the flow of the program's processing, in which the server and terminal work together to process the user's input information and schedule, and create and propose optimal playlists.

[0504] Example 1 Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0505] Conventional content recommendation systems lacked the flexibility to provide optimal content based on the user's mood and interests, making it difficult to effectively identify the content the user wanted. In addition, they were unable to recommend content according to the user's schedule, making efficient time management difficult. Furthermore, they lacked technology for secure communication and high-precision playlist generation.

[0506] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0507] In this invention, the server includes: a means for acquiring input information including the user's mood of the day and the user's interests; a means for generating a playlist consisting of a plurality of contents in a database in which video contents are stored from the acquired input information using a playlist generation model; a means for providing the generated playlist to the user; a means for securely receiving and storing the input information provided by the user using an encrypted communication protocol; a means for using a general-purpose query language to search and acquire video contents from the database; a means for generating a playlist by utilizing a machine learning library; and a means for transmitting the generated playlist to the user's terminal using a notification service. This enables the generation of a highly accurate playlist according to the user's mood and interests, and safe and rapid content suggestions. In addition, it is also possible to realize the recommendation of appropriate content according to the user's schedule.

[0508] "Input information including the user's mood of the day and the user's interests" refers to data that reflects the user's emotions and interests of the day and is input into the system.

[0509] The term "data acquisition means" refers to a device or software having a function for receiving input information and schedule information provided by a user.

[0510] "Playlist generation model" refers to a machine learning algorithm or statistical model for generating a playlist composed of appropriate content based on user input information.

[0511] The term "playlist generation means" refers to a device or software having a function for selecting optimal content from input information acquired using a playlist generation model and generating a playlist.

[0512] A "database storing video content" refers to a data storage system for accumulating and managing viewing media such as movies and television programs.

[0513] A "playlist" refers to a list of multiple pieces of content selected based on a specific theme or criteria.

[0514] "Output means" refers to a device or software having a function for presenting the generated playlist on a user's terminal or other device.

[0515] An "encrypted communications protocol" refers to a communications method for protecting the confidentiality and integrity of information during the data transmission and reception process.

[0516] "General-purpose query language" refers to a standardized language used to search and retrieve information from a database, including, for example, SQL.

[0517] A "machine learning library" refers to a software tool that learns from data, recognizes patterns, and makes predictions or classifications.

[0518] "Notification Service" means an Internet service that has the ability to transmit information to a User's Device in real time.

[0519] To implement the present invention, a system including the following components is required.

[0520] Data Acquisition Method The server obtains the user's mood for the day, interests, and profile information (age and gender). This information is entered by the user using a web browser or a dedicated application. For example, if the user enters "I want to relax today," the server provides information about the user's mood for the day. The server also obtains the user's schedule information through the Google (registered trademark) Calendar API, etc.

[0521] Database Access The server searches for and retrieves content from a database that stores video content using a general-purpose query language (e.g., SQL), and retrieves information on appropriate movies and TV programs from the database based on the information that the user wants to relax.

[0522] Playlist Generation Method The server uses a playlist generation model to generate an optimal playlist based on the acquired input information and the content information in the database. At this time, the server uses a Python machine learning library (e.g. TensorFlow) to generate a playlist that best suits the user's preferences. The generative AI model uses past user data and movie rating data to improve the accuracy of the playlist.

[0523] Playlist provision method The server provides the generated playlist to the user's device by sending a push notification to the user's smartphone or computer using a notification service such as Firebase Cloud Messaging. The user receives the notification, displays the playlist in a dedicated app or web browser, and can enjoy the content.

[0524] Encrypted communication methods The server securely receives and stores the input information provided by the user using an encryption protocol (e.g. SSL / TLS), thereby protecting the user's personal information.

[0525] Examples Suppose a user inputs "I want to watch an uplifting movie today." The server receives "uplifting movies" as the mood of the day and also applies the profile information that the user is a man in his 30s. Next, the server searches for "uplifting movies" from the video content database and retrieves a list. In this case, the server selects "action movie A" or "comedy movie B" by taking into account the playback time and genre. The playlist generated using the machine learning model is sent to the user's smartphone via push notification, and the user opens the app to watch the suggested movies.

[0526] Examples of prompt statements An example of a prompt sentence for generating a playlist based on today's mood is "Please suggest movies that are good for a man in his 30s who wants to relax."

[0527] In this way, the present invention is a system that makes highly accurate content recommendations based on a user's mood and interests, improving the user experience, while also achieving safe information management and efficient time management.

[0528] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0529] Step 1: The user uses the device to input the user's mood and interests for the day, as well as profile information. For example, the user provides information such as "I want to relax today," "Age: 30," and "Gender: Male" through a specific web form or smartphone app. This information becomes the input data for making recommendations based on the user's mood and interests for the day.

[0530] Step 2: The server securely receives the input information using an encrypted communication protocol (e.g., SSL / TLS) and stores it in a database. By storing the input information such as "I want to relax," "age," and "gender," it will be used in subsequent processing.

[0531] Step 3: The server obtains the user's schedule information using the Google (registered trademark) Calendar API or the like. The schedule information includes the user's break times, workout times, etc. The obtained information is used to recommend appropriate content.

[0532] Step 4: The server searches the database for video content based on the acquired input information and schedule information. A general-purpose query language (e.g., SQL) is used to retrieve movies and TV shows that meet the criteria from the database. Specifically, a search query for "relaxing movies" is sent to the database, and the corresponding video content is retrieved.

[0533] Step 5: The server uses the playlist generation model to generate an optimal playlist for the user from the acquired video content list. Using a Python machine learning library (e.g., TensorFlow), a highly accurate playlist is generated by referencing past user data and movie rating data. The input data "relax" is linked to a list of "relaxing movies," and the optimal movie is selected.

[0534] Step 6: The server sends the generated playlist to the user's device using a notification service (e.g., Firebase Cloud Messaging). The playlist arrives as a push notification on the user's smartphone or computer. When the user clicks on the notification, the playlist is displayed in the app or in a web browser.

[0535] Step 7: The device displays the received playlist and suggests it to the user. For example, the suggestions are displayed in the "Today's Recommendations" section of a dedicated app on the user's smartphone, and the user can watch movies or TV shows in the list.

[0536] This trend will enable safe and rapid provision of optimal content tailored to a user's mood and schedule.

[0537] (Application example 1) Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0538] Conventional content delivery systems do not fully utilize the function of generating optimal playlists taking into account the user's mood and interests of the day. They also lack the function of suggesting content for an appropriate time period based on the user's schedule. This makes it difficult for users to enjoy the optimal content at the optimal time.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0540] In this invention, the server includes a data acquisition means for acquiring input information including the user's mood and interests for the day, a playlist generation means for generating a playlist consisting of a plurality of contents in a database storing contents including movies and TV programs from the acquired input information using a playlist generation model, and a means for transmitting the generated playlist to the user's terminal and displaying it, thereby making it possible to provide optimal content in a timely manner according to the user's mood, interests, and schedule.

[0541] "User's mood of the day" refers to the psychological state or emotions felt by the user on a particular day.

[0542] "Interests" refers to particular topics or content that capture a user's interest or focus.

[0543] The term "data acquisition means" refers to a method or device for acquiring input information from a user.

[0544] "Playlist generation model" refers to an algorithm or machine learning model that identifies optimal content and generates lists based on collected input information.

[0545] "Playlist generator" refers to a method or apparatus for creating a playlist using a playlist generation model.

[0546] A "database" refers to a collection of information that stores various content such as movies and television programs.

[0547] "Output means" refers to a method or device for providing the generated playlist to a user.

[0548] "Terminal" refers to a device that a user directly operates to view content.

[0549] "Schedule information" refers to information related to time management such as a user's planned activities and timetable.

[0550] A "break time" refers to a period of time during which a user takes a break from activity to rest.

[0551] "Time in exercise" refers to time during which a user is engaged in physical activity or exercise.

[0552] To implement this invention, a user terminal, a server, and a communication environment for exchanging data between them are required. Specifically, the invention is realized in the following procedure.

[0553] First, the server obtains profile information and schedule information, such as the user's mood, interests, age, and gender, from the user's device. This input information can be input by the user through a device such as a smartphone or smart glasses. The schedule information also includes the times when the user works, takes a break, and exercises on a particular day.

[0554] Next, the server uses the input information to generate an optimal playlist using a playlist generation model. This playlist generation model uses a generative AI model such as ChatGPT to select content (movies, TV shows, music, podcasts, etc.) that matches the user's mood and interests. At this time, various contents are pre-stored in the database.

[0555] The generated playlist is then sent from the server to the user's device, where the user can browse the suggested playlist via their smartphone or smart glasses and enjoy the optimal content at the right time.

[0556] As a concrete example, suppose that a 30-year-old male user inputs that he is in the mood to relax and is interested in comedies and science fiction, and that his working hours that day are from 9:00 a.m. to 5:00 p.m. and his lunch break is from 12:00 p.m. to 12:30 p.m. In this case, based on the user's input information and schedule information, the server generates a playlist by selecting short comedy programs that can be enjoyed during the lunch break and science fiction movies to relax after work, and transmits the playlist to the user's terminal.

[0557] Example of a prompt to be input to a generative AI model: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0558] As described above, the present invention is capable of providing optimal content in a timely manner according to a user's mood, interests, and schedule, thereby improving the user's entertainment experience.

[0559] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0560] Step 1: The user enters profile information such as that day's mood, interests, age, and gender through the terminal. The user enters the information using the interface of a smartphone or smart glasses, and this information is sent directly to the server.

[0561] Input: Profile information such as mood, interests, age, and gender

[0562] Output: User information sent to the server

[0563] Step 2: The server receives and stores the user's input information.

[0564] Input: Profile information submitted by the user

[0565] Output: User profile information stored in the server database.

[0566] Step 3: The user inputs schedule information using the terminal and transmits it to the server, such as the start time, end time, and break time of work.

[0567] Input: User's schedule information

[0568] Output: Schedule information sent to the server

[0569] Step 4: The server stores the received schedule information and associates it with the user's profile information.

[0570] Input: Schedule information submitted by the user

[0571] Output: Schedule information and profile information associations stored in the server database

[0572] Step 5: The server generates a playlist using the playlist generation model based on the user information and schedule information, and sends a prompt to the generation AI model to obtain a list of content.

[0573] Input: User profile information, schedule information

[0574] Output: Generated playlist

[0575] Specific prompt examples: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0576] Step 6: The server sends the generated playlist to the user's device, which contains content that matches the user's mood, interests, and time of day.

[0577] Input: Generated playlist

[0578] Output: The playlist sent to the user's device

[0579] Step 7: The user browses the provided playlist using the device and selects the content to watch. The user starts watching from the suggested playlist via their smartphone or smart glasses.

[0580] Input: A user-provided playlist

[0581] Output: User begins watching the content selected by the user

[0582] These steps allow users to enjoy content that is best suited to their mood, interests, and schedule.

[0583] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0584] A modified example of the third embodiment of the present disclosure comprises the following elements.

[0585] 1. Data acquisition means: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0586] 2. Means for combining emotion engines: The server is provided with means for combining emotion engines. Specifically, the server has an emotion recognition unit for recognizing the emotion of the user. The ring recognition unit incorporates an emotion engine for recognizing the emotion of the user into the system.

[0587] 3. Playlist generation means: The server is equipped with a means for identifying optimal content from a database including movies and TV shows based on information obtained from the user and information obtained from the emotion engine, adding the identified content to a list, and generating an optimal playlist.

[0588] 4. Playlist suggesting means: The server has a means for transmitting the created playlist to the terminal and suggesting it to the user.

[0589] The above is a modified example of the third embodiment of the present disclosure, which makes it possible to generate an optimal playlist based on emotion information obtained from the emotion engine in addition to input information such as the user's mood and interests for that day, and to suggest content that matches the user's schedule.

[0590] The process flow will be explained below.

[0591] Step 1: Getting information from the user

[0592] The server receives and processes input information from the user regarding profile information (e.g., age and sex), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0593] Step 2: Combining emotion engines

[0594] The server combines an emotion engine. Specifically, an emotion engine for recognizing the user's emotion is incorporated into the system. The emotion engine can also be used to obtain information required for generating a playlist, such as the user's mood for the day.

[0595] Step 3: Identify content

[0596] The server identifies the most suitable content from a database containing pre-prepared movies and TV programs based on the input information from the user and the emotion information obtained from the emotion engine. Specifically, the server searches the content in the database and selects the appropriate content by comprehensively considering the input information such as the user's profile information, mood and interests of the day, and the emotion information.

[0597] Step 4: Create a playlist

[0598] The server adds the identified content to the list and creates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist. The above-mentioned processes in steps 3 and 4 can be realized using a playlist generation model.

[0599] Step 5: Suggest a playlist

[0600] The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and enables the user to view the proposed playlist on the application.

[0601] The above is the flow of the program processing, in which the server and the terminal work together to process the information input by the user, and create and propose an optimal playlist that combines emotion engines. Note that the schedule information mentioned above may also be acquired as the data to be acquired. The control in that case has already been described above, so a detailed explanation of it will be omitted here.

[0602] Example 2 Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0603] In conventional systems, the means to provide optimal content according to the user's mood and interests were limited, making it difficult to process data based on the user's emotions. In addition, playlists were not created according to the user's profile information or schedule, making it impossible to provide optimal content to individual users.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0605] In this invention, the server includes an information acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion from the input information using an emotion engine, a playlist generation means for generating a playlist made up of a plurality of contents in a database in which contents including movies and TV programs are stored, from the recognized emotion information and the acquired input information using a playlist generation model, and an information provision means for providing the generated playlist to the user. This makes it possible to provide optimal content based on the user's mood and interests.

[0606] The "information acquisition means" is a means for receiving and processing input information including the user's mood and interests for the day.

[0607] The "emotion recognition means" refers to a means for analyzing and recognizing a user's emotions from the user's input information obtained from the information acquisition means, using an emotion engine.

[0608] The "playlist generating means" is a means for generating an optimal playlist by combining a plurality of contents in the database based on the recognized emotion information and the acquired input information.

[0609] The "information providing means" is a means for transmitting the generated playlist to the user's terminal and suggesting it to the user.

[0610] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from user input information.

[0611] A "database" is a collection of information in which content such as movies and television programs are stored.

[0612] The purpose of this system is to generate and provide an optimal playlist based on the user's mood of the day and the things he or she is interested in. A specific embodiment of this system will be described below.

[0613] System Configuration

[0614] Hardware and Software

[0615] Server: Acts as the central processing unit that receives and processes input information. Contains the emotion engine, database, and playlist generation model.

[0616] Terminal: A device on which a user provides input information. Examples include a smartphone or tablet.

[0617] Emotion engine: Software for analyzing and recognizing emotions from user input information. Uses natural language processing (NLP) technology.

[0618] Database: A source where content such as movies and TV shows are stored.

[0619] Playlist generation model: An algorithm that selects the optimal content based on emotion recognition results and user input information, and generates a playlist.

[0620] System Operation Details

[0621] Data Acquisition The user uses the terminal to input profile information such as the mood of the day, interests, age, gender, etc. For example, the user can input "I want to relax today," and the terminal will send this information to the server.

[0622] emotion recognition After receiving the input information, the server uses the emotion engine to analyze the information. For example, if the input information is "I want to relax," the emotion engine will recognize the emotion "relax."

[0623] Playlist Creation The server searches the database for relevant content based on the recognized emotion information. For example, it finds movies and music that correspond to the emotion of "relaxation." It selects these contents and generates a playlist. The playlist generation model does this.

[0624] Providing playlists The server sends the generated playlist to the user's device. The device displays the received playlist to the user and provides viewing and playback options. For example, the user can check the presented playlist on their smartphone, select a relaxing movie, and play it.

[0625] Examples of concrete examples and prompts

[0626] Examples A user uses a smartphone app and inputs, "I want to relieve stress today." The server receives this information and uses an emotion engine to recognize the emotion "relieve stress." The server then searches the database for music and movies with a relaxing effect and generates a playlist. The server then sends this playlist to the user's smartphone, where the user can view and watch the music and movies using the app.

[0627] Examples of prompt statements

[0628] Users input their mood and interests for the day

[0629] Recognize user emotions using an AI emotion engine

[0630] Identify suitable content from a database of movies and TV shows

[0631] Generate optimal playlists and suggest them to users

[0632] This prompt can be fed into a generative AI model to help the system understand a sequence of actions that will provide the optimal content based on the user's mood and interests.

[0633] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0634] Step 1: The user uses the terminal to input the day's mood, interests, and profile information (age, gender).

[0635] Specific operation: A user opens the app on their smartphone and enters, "I want to relax today." This input information is sent from the device to the server.

[0636] Input: User mood, interests, and profile information.

[0637] Output: Sending input information to the server.

[0638] Step 2: The server receives the input information sent from the terminal.

[0639] Specific operation: The server receives information such as "I want to relax," "Age: 30," and "Gender: Male," and stores it in a database.

[0640] Input: Input information from the terminal.

[0641] Output: The saved input information.

[0642] Step 3: The server analyzes the received input information using the emotion recognition means and recognizes the user's emotion.

[0643] Specific behavior: The emotion engine recognizes the emotion "relax" from the input "I want to relax."

[0644] Input: The saved input information.

[0645] Output: Recognized emotion information.

[0646] Step 4: Based on the recognized emotion information, the server uses a playlist generation means to search for related content from the database and generate a playlist.

[0647] Specific behavior: Based on the emotion information of "relaxation," search for movies and music with a relaxing effect from the database and generate a playlist. For example, add a movie and relaxing music called "Your Name." to the list.

[0648] Input: Recognized emotion information, stored input information.

[0649] Output: The generated playlist.

[0650] Step 5: The server sends the generated playlist to the user's terminal.

[0651] Specific operation: The server generates a playlist and sends it to the user's smartphone, where it is displayed to the user through the app.

[0652] Input:Generierung playlist.

[0653] Output: Send the playlist to a device.

[0654] Step 6: The device displays the received playlist to the user and provides options for viewing and playing.

[0655] Specific behavior: The user browses the playlist in the app, selects "Your Name." and plays it.

[0656] Input: The playlist sent by the server.

[0657] Output: The playlist and playback options presented to the user.

[0658] (Application example 2) Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[0659] Conventional content delivery systems generate playlists based only on simple input information from the user, making it difficult to suggest optimal content that matches the user's emotions or mood of the day. In addition, systems that use face recognition or emotion recognition have not been fully utilized, and delays in providing content that matches the user's real-time emotions have been an issue.

[0660] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a data acquisition means for acquiring input information including the user's mood of the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion, a playlist generation means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and TV programs are stored, from the acquired input information and the emotion information obtained by the emotion recognition means, using a playlist generation model, an output means for providing the generated playlist to the user, and a means for detecting the user's emotion by face recognition using a camera. This makes it possible to generate a playlist of optimal content based on the user's mood of the day and real-time emotions.

[0661] The "data acquisition means" is a means for acquiring input information including the user's mood for the day and matters of interest to the user.

[0662] An "emotion recognition means" is a means for recognizing the user's emotions.

[0663] The "playlist generation means" refers to means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and television programs are stored, from acquired input information and emotion information obtained by the emotion recognition means, using a playlist generation model.

[0664] The "output means" is a means for providing the generated playlist to the user.

[0665] The "means for detecting a user's emotions through facial recognition using a camera" refers to a means for recognizing a user's face using a camera and detecting emotions from the facial expressions.

[0666] The present invention is a system for providing an optimal content playlist according to a user's mood, interests, and emotional state on that day.

[0667] The server includes a data acquisition unit that acquires input information including the user's mood and interests. The data acquisition unit collects the input information from the user through an application installed on a terminal such as a smartphone or a personal computer.

[0668] The server also includes an emotion recognition means for recognizing the emotion of the user. This emotion recognition is achieved by capturing the image of the user's face using the camera of the terminal and analyzing the facial expressions. Specific emotion recognition software is used for emotion recognition. This software processes the facial expression data and determines the emotion based on the data.

[0669] The playlist generation means generates an optimal playlist using a playlist generation model based on the input information based on the acquired mood and interests and the emotion information obtained by the emotion recognition means. This model utilizes a database that stores content such as movies and TV programs, and selects content that is suitable for the user's information.

[0670] The generated playlist is provided to the user via an output means, which displays the list or notifies the user through a smartphone application, for example.

[0671] For example, if a user indicates that they are looking to feel relaxed and the camera detects a "calm" emotion, the system will generate a playlist of relaxing movies and TV shows that can be enjoyed at leisure, which will then be suggested to the user through the application.

[0672] An example of a prompt required for a generative AI model is:

[0673] "Today I want to relax. I have been characterized as feeling calm. Please suggest a playlist of movies and TV shows suitable for relaxation. Age is 30, Gender is female."

[0674] With this invention, users can easily receive optimal content playlists based on their mood of the day and real-time emotions.

[0675] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0676] Step 1: The user uses the device to input their mood, interests, and profile information for the day. The input data is then sent from the device to the server.

[0677] Input: Profile information such as the user’s mood, interests, age, and gender.

[0678] Output: The entered information is saved on the server.

[0679] Specific operation: When a user enters information into a form displayed on the application screen and presses the "Submit" button, the information is sent from the terminal to the server as an HTTP request.

[0680] Step 2: The server receives and stores the input information sent from the terminal.

[0681] Input: Your mood, interests, and profile information sent from your device.

[0682] Output: User input information stored on the server.

[0683] Specific operation: The received data is stored in a database and used for further processing.

[0684] Step 3: The user uses the device's camera to perform face recognition, and the emotion recognition means analyzes the user's facial expressions.

[0685] Input: A user's facial expression captured by the device's camera.

[0686] Output: Parsed emotion data (e.g. calm, happy, sad).

[0687] How it works: The user follows the instructions to turn on the camera and point their face at it. The emotion recognition software analyzes their facial expressions and generates emotion data.

[0688] Step 4: The emotion data generated by the emotion recognition means is transmitted to the server.

[0689] Input: Parsed emotion data.

[0690] Output: Emotion data sent to the server.

[0691] Specific operation: The results of analyzing the user's facial expressions are sent from the device to the server. This data is also processed as an HTTP request.

[0692] Step 5: The server receives the input information and the emotion data, and generates an optimal content playlist using a playlist generating means.

[0693] Input: User mood, interests, profile information, emotional data.

[0694] Output: A user-optimized content playlist.

[0695] Specific operation: The server uses the acquired information and emotion data to generate a playlist using a generative AI model. It selects the most suitable content from the database and composes the playlist.

[0696] Step 6: The generated playlist is sent to the terminal and provided to the user.

[0697] Input: The generated content playlist.

[0698] Output: The playlist that is served to the user.

[0699] Specific operation: The server sends the generated playlist to the terminal. The user receives the playlist through the application and starts watching.

[0700] This enables the generation and provision of an optimal content playlist based on the user's mood of the day and real-time emotions.

[0701] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0702] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0703] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0704] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0705] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0706] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0707] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0708] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0709] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0710] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0711] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0712] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0713] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0714] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0715] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0716] Next, a description will be given of the specific processing by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[0717] The fourth embodiment of the present disclosure comprises the following elements.

[0718] 1. Data acquisition unit: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server also has a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0719] It is okay to do so.

[0720] 2. Playlist generation unit: The server has a means for identifying optimal content from a database including movies and TV programs based on input information acquired from a user, adding the identified content to a list, and generating an optimal playlist. A playlist generation model (data generation model) can be used to generate the playlist. The database may be one or more databases prepared in advance.

[0721] 3. Output unit: The server includes a means for transmitting the generated playlist to a terminal and proposing it to a user.

[0722] The data acquisition unit may further include a means for acquiring and processing schedule information of a user. The playlist generation unit may include a means for, when the schedule information is acquired by the data acquisition unit, identifying rest periods and workout periods in the schedule based on input information such as information on the mood and interests of the day provided by the user and profile information and the schedule information, identifying content that can be viewed during the period, and creating a playlist including the identified content.

[0723] The above is the fourth embodiment of the present disclosure, which makes it possible to generate an optimal playlist based on the user's mood and interests on that day, and to suggest content that matches the user's schedule.

[0724] The process flow will be explained below.

[0725] Step 1: The server receives and processes input information from the user regarding profile information (e.g., age and gender), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0726] Step 2: The server identifies the most suitable content based on the user's input information from a database that contains pre-prepared content including movies and TV shows. Specifically, the server searches the content in the database and selects the appropriate content based on the user's profile information, mood of the day, and interests.

[0727] Step 3: The server adds the identified content to the list and generates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist.

[0728] Step 4: The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and allows the user to view the proposed playlist on the application.

[0729] Furthermore, if the data acquisition unit is capable of acquiring the user's schedule information, the following processing can be executed.

[0730] Step 11: The server receives and processes profile information (e.g., age and sex), input information on the user's mood for the day, and the user's interests, and the user's schedule information from the user. The user inputs the schedule information into an application on the terminal, and the terminal transmits the input schedule information to the server, where the server receives it and processes it internally.

[0731] Step 12: The server identifies content of a length that can be viewed during the user's break time or workout time based on the user's schedule information. Specifically, the server analyzes the user's schedule information, identifies break times or workout times in the schedule, and selects content of a length suitable for viewing during the break time, etc.

[0732] Step 13: The server adds the identified content to a list and creates a playlist associated with the schedule. Specifically, the server saves the identified content as a playlist and organizes it as a playlist associated with the schedule.

[0733] Step 14: The server transmits a playlist associated with the created schedule to the terminal and proposes it to the user. Specifically, the server transmits a playlist associated with the schedule to the terminal and allows the user to view the proposed playlist on the application.

[0734] The above is the flow of the program's processing, in which the server and terminal work together to process the user's information and schedule, and create and propose optimal playlists.

[0735] Example 1 Next, a description will be given of Example 1. In the following description, the data processing device 12 is referred to as a "server" and the robot 414 is referred to as a "terminal."

[0736] Conventional content recommendation systems lacked the flexibility to provide optimal content based on the user's mood and interests, making it difficult to effectively identify the content the user wanted. In addition, they were unable to recommend content according to the user's schedule, making efficient time management difficult. Furthermore, they lacked technology for secure communication and high-precision playlist generation.

[0737] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0738] In this invention, the server includes: a means for acquiring input information including the user's mood of the day and the user's interests; a means for generating a playlist consisting of a plurality of contents in a database in which video contents are stored from the acquired input information using a playlist generation model; a means for providing the generated playlist to the user; a means for securely receiving and storing the input information provided by the user using an encrypted communication protocol; a means for using a general-purpose query language to search and acquire video contents from the database; a means for generating a playlist by utilizing a machine learning library; and a means for transmitting the generated playlist to the user's terminal using a notification service. This enables the generation of a highly accurate playlist according to the user's mood and interests, and safe and rapid content suggestions. In addition, it is also possible to realize the recommendation of appropriate content according to the user's schedule.

[0739] "Input information including the user's mood of the day and the user's interests" refers to data that reflects the user's emotions and interests of the day and is input into the system.

[0740] The term "data acquisition means" refers to a device or software having a function for receiving input information and schedule information provided by a user.

[0741] "Playlist generation model" refers to a machine learning algorithm or statistical model for generating a playlist composed of appropriate content based on user input information.

[0742] The term "playlist generation means" refers to a device or software having a function for selecting optimal content from input information acquired using a playlist generation model and generating a playlist.

[0743] A "database storing video content" refers to a data storage system for accumulating and managing viewing media such as movies and television programs.

[0744] A "playlist" refers to a list of multiple pieces of content selected based on a specific theme or criteria.

[0745] "Output means" refers to a device or software having a function for presenting the generated playlist on a user's terminal or other device.

[0746] An "encrypted communications protocol" refers to a communications method for protecting the confidentiality and integrity of information during the data transmission and reception process.

[0747] "General-purpose query language" refers to a standardized language used to search and retrieve information from a database, including, for example, SQL.

[0748] A "machine learning library" refers to a software tool that learns from data, recognizes patterns, and makes predictions or classifications.

[0749] "Notification Service" means an Internet service that has the ability to transmit information to a User's Device in real time.

[0750] To implement the present invention, a system including the following components is required.

[0751] Data Acquisition Method The server obtains the user's mood for the day, interests, and profile information (age and gender). This information is entered by the user using a web browser or a dedicated application. For example, if the user enters "I want to relax today," the server provides information about the user's mood for the day. The server also obtains the user's schedule information through the Google (registered trademark) Calendar API, etc.

[0752] Database Access The server searches for and retrieves content from a database that stores video content using a general-purpose query language (e.g., SQL), and retrieves information on appropriate movies and TV programs from the database based on the information that the user wants to relax.

[0753] Playlist Generation Method The server uses a playlist generation model to generate an optimal playlist based on the acquired input information and the content information in the database. At this time, the server uses a Python machine learning library (e.g. TensorFlow) to generate a playlist that best suits the user's preferences. The generative AI model uses past user data and movie rating data to improve the accuracy of the playlist.

[0754] Playlist provision method The server provides the generated playlist to the user's device by sending a push notification to the user's smartphone or computer using a notification service such as Firebase Cloud Messaging. The user receives the notification, displays the playlist in a dedicated app or web browser, and can enjoy the content.

[0755] Encrypted communication methods The server securely receives and stores the input information provided by the user using an encryption protocol (e.g. SSL / TLS), thereby protecting the user's personal information.

[0756] Examples Suppose a user inputs "I want to watch an uplifting movie today." The server receives "uplifting movies" as the mood of the day and also applies the profile information that the user is a man in his 30s. Next, the server searches for "uplifting movies" from the video content database and retrieves a list. In this case, the server selects "action movie A" or "comedy movie B" by taking into account the playback time and genre. The playlist generated using the machine learning model is sent to the user's smartphone via push notification, and the user opens the app to watch the suggested movies.

[0757] Examples of prompt statements An example of a prompt sentence for generating a playlist based on today's mood is "Please suggest movies that are good for a man in his 30s who wants to relax."

[0758] In this way, the present invention is a system that makes highly accurate content recommendations based on a user's mood and interests, improving the user experience, while also achieving safe information management and efficient time management.

[0759] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0760] Step 1: The user uses the device to input the user's mood and interests for the day, as well as profile information. For example, the user provides information such as "I want to relax today," "Age: 30," and "Gender: Male" through a specific web form or smartphone app. This information becomes the input data for making recommendations based on the user's mood and interests for the day.

[0761] Step 2: The server securely receives the input information using an encrypted communication protocol (e.g., SSL / TLS) and stores it in a database. By storing the input information such as "I want to relax," "age," and "gender," it will be used in subsequent processing.

[0762] Step 3: The server obtains the user's schedule information using the Google (registered trademark) Calendar API or the like. The schedule information includes the user's break times, workout times, etc. The obtained information is used to recommend appropriate content.

[0763] Step 4: The server searches the database for video content based on the acquired input information and schedule information. A general-purpose query language (e.g., SQL) is used to retrieve movies and TV shows that meet the criteria from the database. Specifically, a search query for "relaxing movies" is sent to the database, and the corresponding video content is retrieved.

[0764] Step 5: The server uses the playlist generation model to generate an optimal playlist for the user from the acquired video content list. Using a Python machine learning library (e.g., TensorFlow), a highly accurate playlist is generated by referencing past user data and movie rating data. The input data "relax" is linked to a list of "relaxing movies," and the optimal movie is selected.

[0765] Step 6: The server sends the generated playlist to the user's device using a notification service (e.g., Firebase Cloud Messaging). The playlist arrives as a push notification on the user's smartphone or computer. When the user clicks on the notification, the playlist is displayed in the app or in a web browser.

[0766] Step 7: The device displays the received playlist and suggests it to the user. For example, the suggestions are displayed in the "Today's Recommendations" section of a dedicated app on the user's smartphone, and the user can watch movies or TV shows in the list.

[0767] This trend will enable safe and rapid provision of optimal content tailored to a user's mood and schedule.

[0768] (Application example 1) Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0769] Conventional content delivery systems do not fully utilize the function of generating optimal playlists taking into account the user's mood and interests of the day. They also lack the function of suggesting content for an appropriate time period based on the user's schedule. This makes it difficult for users to enjoy the optimal content at the optimal time.

[0770] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0771] In this invention, the server includes a data acquisition means for acquiring input information including the user's mood and interests for the day, a playlist generation means for generating a playlist consisting of a plurality of contents in a database storing contents including movies and TV programs from the acquired input information using a playlist generation model, and a means for transmitting the generated playlist to the user's terminal and displaying it, thereby making it possible to provide optimal content in a timely manner according to the user's mood, interests, and schedule.

[0772] "User's mood of the day" refers to the psychological state or emotions felt by the user on a particular day.

[0773] "Interests" refers to particular topics or content that capture a user's interest or focus.

[0774] The term "data acquisition means" refers to a method or device for acquiring input information from a user.

[0775] "Playlist generation model" refers to an algorithm or machine learning model that identifies optimal content and generates lists based on collected input information.

[0776] "Playlist generator" refers to a method or apparatus for creating a playlist using a playlist generation model.

[0777] A "database" refers to a collection of information that stores various content such as movies and television programs.

[0778] "Output means" refers to a method or device for providing the generated playlist to a user.

[0779] "Terminal" refers to a device that a user directly operates to view content.

[0780] "Schedule information" refers to information related to time management such as a user's planned activities and timetable.

[0781] A "break time" refers to a period of time during which a user takes a break from activity to rest.

[0782] "Time in exercise" refers to time during which a user is engaged in physical activity or exercise.

[0783] To implement this invention, a user terminal, a server, and a communication environment for exchanging data between them are required. Specifically, the invention is realized in the following procedure.

[0784] First, the server obtains profile information and schedule information, such as the user's mood, interests, age, and gender, from the user's device. This input information can be input by the user through a device such as a smartphone or smart glasses. The schedule information also includes the times when the user works, takes a break, and exercises on a particular day.

[0785] Next, the server uses the input information to generate an optimal playlist using a playlist generation model. This playlist generation model uses a generative AI model such as ChatGPT to select content (movies, TV shows, music, podcasts, etc.) that matches the user's mood and interests. At this time, various contents are pre-stored in the database.

[0786] The generated playlist is then sent from the server to the user's device, where the user can browse the suggested playlist via their smartphone or smart glasses and enjoy the optimal content at the right time.

[0787] As a concrete example, suppose that a 30-year-old male user inputs that he is in the mood to relax and is interested in comedies and science fiction, and that his working hours that day are from 9:00 a.m. to 5:00 p.m. and his lunch break is from 12:00 p.m. to 12:30 p.m. In this case, based on the user's input information and schedule information, the server generates a playlist by selecting short comedy programs that can be enjoyed during the lunch break and science fiction movies to relax after work, and transmits the playlist to the user's terminal.

[0788] Example of a prompt to be input to a generative AI model: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0789] As described above, the present invention is capable of providing optimal content in a timely manner according to a user's mood, interests, and schedule, thereby improving the user's entertainment experience.

[0790] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0791] Step 1: The user enters profile information such as that day's mood, interests, age, and gender through the terminal. The user enters the information using the interface of a smartphone or smart glasses, and this information is sent directly to the server.

[0792] Input: Profile information such as mood, interests, age, and gender

[0793] Output: User information sent to the server

[0794] Step 2: The server receives and stores the user's input information.

[0795] Input: Profile information submitted by the user

[0796] Output: User profile information stored in the server database.

[0797] Step 3: The user inputs schedule information using the terminal and transmits it to the server, such as the start time, end time, and break time of work.

[0798] Input: User's schedule information

[0799] Output: Schedule information sent to the server

[0800] Step 4: The server stores the received schedule information and associates it with the user's profile information.

[0801] Input: Schedule information submitted by the user

[0802] Output: Schedule information and profile information associations stored in the server database

[0803] Step 5: The server generates a playlist using the playlist generation model based on the user information and schedule information, and sends a prompt to the generation AI model to obtain a list of content.

[0804] Input: User profile information, schedule information

[0805] Output: Generated playlist

[0806] Specific prompt examples: "Generate a playlist of comedy and science fiction songs for a laid back atmosphere, based on the following user information: age 30, male, profile as Relaxed, interests as Comedy and Science Fiction."

[0807] Step 6: The server sends the generated playlist to the user's device, which contains content that matches the user's mood, interests, and time of day.

[0808] Input: Generated playlist

[0809] Output: The playlist sent to the user's device

[0810] Step 7: The user browses the provided playlist using the device and selects the content to watch. The user starts watching from the suggested playlist via their smartphone or smart glasses.

[0811] Input: A user-provided playlist

[0812] Output: User begins watching the content selected by the user

[0813] These steps allow users to enjoy content that is best suited to their mood, interests, and schedule.

[0814] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0815] A modification of the fourth embodiment of the present disclosure comprises the following elements.

[0816] 1. Data acquisition means: The server has a means for receiving and processing input information provided by the user regarding the user's mood and interests for the day. The server may also have a means for receiving and processing profile information (age and gender) provided by the user as input information.

[0817] 2. Means for combining emotion engines: The server is provided with means for combining emotion engines. Specifically, the server has an emotion recognition unit for recognizing the emotion of the user. The ring recognition unit incorporates an emotion engine for recognizing the emotion of the user into the system.

[0818] 3. Playlist generation means: The server is equipped with a means for identifying optimal content from a database including movies and TV shows based on information obtained from the user and information obtained from the emotion engine, adding the identified content to a list, and generating an optimal playlist.

[0819] 4. Playlist suggesting means: The server has a means for transmitting the created playlist to the terminal and suggesting it to the user.

[0820] The above is a modified example of the fourth embodiment of the present disclosure, which makes it possible to generate an optimal playlist based on emotion information obtained from the emotion engine in addition to input information including the user's mood and interests for that day, and to suggest content that matches the user's schedule.

[0821] The process flow will be explained below.

[0822] Step 1: Getting information from the user

[0823] The server receives and processes input information from the user regarding profile information (e.g., age and sex), the user's mood for the day, and the user's interests. Specifically, the user inputs the profile information into the application, and the server receives and processes it internally. The user also inputs the user's mood for the day and the user's interests into the application, and the server receives and processes it internally.

[0824] Step 2: Combining emotion engines

[0825] The server combines an emotion engine. Specifically, an emotion engine for recognizing the user's emotion is incorporated into the system. The emotion engine can also be used to obtain information required for generating a playlist, such as the user's mood for the day.

[0826] Step 3: Identify content

[0827] The server identifies the most suitable content from a database containing pre-prepared movies and TV programs based on the input information from the user and the emotion information obtained from the emotion engine. Specifically, the server searches the content in the database and selects the appropriate content by comprehensively considering the user's profile information, mood of the day, interests, and emotion information.

[0828] Step 4: Create a playlist

[0829] The server adds the identified content to the list and creates an optimal playlist. Specifically, the server saves the identified content as a playlist and arranges it in an optimal order to create the playlist. The above-mentioned processes in steps 3 and 4 can be realized using a playlist generation model.

[0830] Step 5: Suggest a playlist

[0831] The server transmits the created playlist to the terminal and proposes it to the user. Specifically, the server transmits the playlist to the terminal and enables the user to view the proposed playlist on the application.

[0832] The above is the flow of the program processing, in which the server and the terminal work together to process the user's information, and create and propose an optimal playlist that combines the emotion engine. Note that the schedule information mentioned above may also be acquired as the data to be acquired. The control in that case has already been described above, so a detailed explanation of it will be omitted here.

[0833] Example 2 Next, a description will be given of Example 2. In the following description, the data processing device 12 is referred to as a "server" and the robot 414 is referred to as a "terminal."

[0834] In conventional systems, the means to provide optimal content according to the user's mood and interests were limited, making it difficult to process data based on the user's emotions. In addition, playlists were not created according to the user's profile information or schedule, making it impossible to provide optimal content to individual users.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0836] In this invention, the server includes an information acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion from the input information using an emotion engine, a playlist generation means for generating a playlist made up of a plurality of contents in a database in which contents including movies and TV programs are stored, from the recognized emotion information and the acquired input information using a playlist generation model, and an information provision means for providing the generated playlist to the user. This makes it possible to provide optimal content based on the user's mood and interests.

[0837] The "information acquisition means" is a means for receiving and processing input information including the user's mood and interests for the day.

[0838] The "emotion recognition means" refers to a means for analyzing and recognizing a user's emotions from the user's input information obtained from the information acquisition means, using an emotion engine.

[0839] The "playlist generating means" is a means for generating an optimal playlist by combining a plurality of contents in the database based on the recognized emotion information and the acquired input information.

[0840] The "information providing means" is a means for transmitting the generated playlist to the user's terminal and suggesting it to the user.

[0841] An "emotion engine" is software or algorithms for analyzing and recognizing emotions from user input information.

[0842] A "database" is a collection of information in which content such as movies and television programs are stored.

[0843] The purpose of this system is to generate and provide an optimal playlist based on the user's mood of the day and the things he or she is interested in. A specific embodiment of this system will be described below.

[0844] System Configuration

[0845] Hardware and Software

[0846] Server: Acts as the central processing unit that receives and processes input information. Contains the emotion engine, database, and playlist generation model.

[0847] Terminal: A device on which a user provides input information. Examples include a smartphone or tablet.

[0848] Emotion engine: Software for analyzing and recognizing emotions from user input information. Uses natural language processing (NLP) technology.

[0849] Database: A source where content such as movies and TV shows are stored.

[0850] Playlist generation model: An algorithm that selects the optimal content based on emotion recognition results and user input information, and generates a playlist.

[0851] System Operation Details

[0852] Data Acquisition The user uses the terminal to input profile information such as the mood of the day, interests, age, gender, etc. For example, the user can input "I want to relax today," and the terminal will send this information to the server.

[0853] emotion recognition After receiving the input information, the server uses the emotion engine to analyze the information. For example, if the input information is "I want to relax," the emotion engine will recognize the emotion "relax."

[0854] Playlist Creation The server searches the database for relevant content based on the recognized emotion information. For example, it finds movies and music that correspond to the emotion of "relaxation." It selects these contents and generates a playlist. The playlist generation model does this.

[0855] Providing playlists The server sends the generated playlist to the user's device. The device displays the received playlist to the user and provides viewing and playback options. For example, the user can check the presented playlist on their smartphone, select a relaxing movie, and play it.

[0856] Examples of concrete examples and prompts

[0857] Examples A user uses a smartphone app and inputs, "I want to relieve stress today." The server receives this information and uses an emotion engine to recognize the emotion "relieve stress." The server then searches the database for music and movies with a relaxing effect and generates a playlist. The server then sends this playlist to the user's smartphone, where the user can view and watch the music and movies using the app.

[0858] Examples of prompt statements

[0859] Users input their mood and interests for the day

[0860] Recognize user emotions using an AI emotion engine

[0861] Identify suitable content from a database of movies and TV shows

[0862] Generate optimal playlists and suggest them to users

[0863] This prompt can be fed into a generative AI model to help the system understand a sequence of actions that will provide the optimal content based on the user's mood and interests.

[0864] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0865] Step 1: The user uses the terminal to input the day's mood, interests, and profile information (age, gender).

[0866] Specific operation: A user opens the app on their smartphone and enters, "I want to relax today." This input information is sent from the device to the server.

[0867] Input: User mood, interests, and profile information.

[0868] Output: Sending input information to the server.

[0869] Step 2: The server receives the input information sent from the terminal.

[0870] Specific operation: The server receives information such as "I want to relax," "Age: 30," and "Gender: Male," and stores it in a database.

[0871] Input: Input information from the terminal.

[0872] Output: The saved input information.

[0873] Step 3: The server analyzes the received input information using the emotion recognition means and recognizes the user's emotion.

[0874] Specific behavior: The emotion engine recognizes the emotion "relax" from the input "I want to relax."

[0875] Input: The saved input information.

[0876] Output: Recognized emotion information.

[0877] Step 4: Based on the recognized emotion information, the server uses a playlist generation means to search for related content from the database and generate a playlist.

[0878] Specific behavior: Based on the emotion information of "relaxation," search for movies and music with a relaxing effect from the database and generate a playlist. For example, add a movie and relaxing music called "Your Name." to the list.

[0879] Input: Recognized emotion information, stored input information.

[0880] Output: The generated playlist.

[0881] Step 5: The server sends the generated playlist to the user's terminal.

[0882] Specific operation: The server generates a playlist and sends it to the user's smartphone, where it is displayed to the user through the app.

[0883] Input:Generierung playlist.

[0884] Output: Send the playlist to a device.

[0885] Step 6: The device displays the received playlist to the user and provides options for viewing and playing.

[0886] Specific behavior: The user browses the playlist in the app, selects "Your Name." and plays it.

[0887] Input: The playlist sent by the server.

[0888] Output: The playlist and playback options presented to the user.

[0889] (Application example 2) Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[0890] Conventional content delivery systems generate playlists based only on simple input information from the user, making it difficult to suggest optimal content that matches the user's emotions or mood of the day. In addition, systems that use face recognition or emotion recognition have not been fully utilized, and delays in providing content that matches the user's real-time emotions have been an issue.

[0891] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means. In this invention, the server includes a data acquisition means for acquiring input information including the user's mood of the day and matters of interest to the user, an emotion recognition means for recognizing the user's emotion, a playlist generation means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and TV programs are stored, from the acquired input information and the emotion information obtained by the emotion recognition means, using a playlist generation model, an output means for providing the generated playlist to the user, and a means for detecting the user's emotion by face recognition using a camera. This makes it possible to generate a playlist of optimal content based on the user's mood of the day and real-time emotions.

[0892] The "data acquisition means" is a means for acquiring input information including the user's mood for the day and matters of interest to the user.

[0893] An "emotion recognition means" is a means for recognizing the user's emotions.

[0894] The "playlist generation means" refers to means for generating a playlist consisting of a plurality of contents in a database in which contents including movies and television programs are stored, from acquired input information and emotion information obtained by the emotion recognition means, using a playlist generation model.

[0895] The "output means" is a means for providing the generated playlist to the user.

[0896] The "means for detecting a user's emotions through facial recognition using a camera" refers to a means for recognizing a user's face using a camera and detecting emotions from the facial expressions.

[0897] The present invention is a system for providing an optimal content playlist according to a user's mood, interests, and emotional state on that day.

[0898] The server includes a data acquisition unit that acquires input information including the user's mood and interests. The data acquisition unit collects the input information from the user through an application installed on a terminal such as a smartphone or a personal computer.

[0899] The server also includes an emotion recognition means for recognizing the emotion of the user. This emotion recognition is achieved by capturing the image of the user's face using the camera of the terminal and analyzing the facial expressions. Specific emotion recognition software is used for emotion recognition. This software processes the facial expression data and determines the emotion based on the data.

[0900] The playlist generation means generates an optimal playlist using a playlist generation model based on the input information based on the acquired mood and interests and the emotion information obtained by the emotion recognition means. This model utilizes a database that stores content such as movies and TV programs, and selects content that is suitable for the user's information.

[0901] The generated playlist is provided to the user via an output means, which displays the list or notifies the user through a smartphone application, for example.

[0902] For example, if a user indicates that they are looking to feel relaxed and the camera detects a "calm" emotion, the system will generate a playlist of relaxing movies and TV shows that can be enjoyed at leisure, which will then be suggested to the user through the application.

[0903] An example of a prompt required for a generative AI model is:

[0904] "Today I want to relax. I have been characterized as feeling calm. Please suggest a playlist of movies and TV shows suitable for relaxation. Age is 30, Gender is female."

[0905] With this invention, users can easily receive optimal content playlists based on their mood of the day and real-time emotions.

[0906] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0907] Step 1: The user uses the device to input their mood, interests, and profile information for the day. The input data is then sent from the device to the server.

[0908] Input: Profile information such as the user’s mood, interests, age, and gender.

[0909] Output: The entered information is saved on the server.

[0910] Specific operation: When a user enters information into a form displayed on the application screen and presses the "Submit" button, the information is sent from the terminal to the server as an HTTP request.

[0911] Step 2: The server receives and stores the input information sent from the terminal.

[0912] Input: Your mood, interests, and profile information sent from your device.

[0913] Output: User input information stored on the server.

[0914] Specific operation: The received data is stored in a database and used for further processing.

[0915] Step 3: The user uses the device's camera to perform face recognition, and the emotion recognition means analyzes the user's facial expressions.

[0916] Input: A user's facial expression captured by the device's camera.

[0917] Output: Parsed emotion data (e.g. calm, happy, sad).

[0918] How it works: The user follows the instructions to turn on the camera and point their face at it. The emotion recognition software analyzes their facial expressions and generates emotion data.

[0919] Step 4: The emotion data generated by the emotion recognition means is transmitted to the server.

[0920] Input: Parsed emotion data.

[0921] Output: Emotion data sent to the server.

[0922] Specific operation: The results of analyzing the user's facial expressions are sent from the device to the server. This data is also processed as an HTTP request.

[0923] Step 5: The server receives the input information and the emotion data, and generates an optimal content playlist using a playlist generating means.

[0924] Input: User mood, interests, profile information, emotional data.

[0925] Output: A user-optimized content playlist.

[0926] Specific operation: The server uses the acquired information and emotion data to generate a playlist using a generative AI model. It selects the most suitable content from the database and composes the playlist.

[0927] Step 6: The generated playlist is sent to the terminal and provided to the user.

[0928] Input: The generated content playlist.

[0929] Output: The playlist that is served to the user.

[0930] Specific operation: The server sends the generated playlist to the terminal. The user receives the playlist through the application and starts watching.

[0931] This enables the generation and provision of an optimal content playlist based on the user's mood of the day and real-time emotions.

[0932] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0933] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0934] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[0935] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0936] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0937] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0938] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0939] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0940] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0941] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0942] Although the system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, the system according to the present disclosure is not necessarily implemented in a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program that runs on a personal computer, or an application that runs on a smartphone or the like. The method according to the present disclosure may be provided to a user in the form of SaaS (Software as a Service).

[0943] In the above embodiment, an example is given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to input data.

[0944] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0945] In addition, 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 upon request from the data processing device 12.

[0946] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0947] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0948] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0949] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0950] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0951] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0952] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or standard was specifically and individually indicated to be incorporated by reference.

[0953] The following is further disclosed regarding the above embodiment.

[0954] (First aspect) A data acquisition unit that acquires input information including a user's mood for the day and matters of interest to the user; a playlist generation unit that uses a playlist generation model to generate a playlist consisting of a plurality of contents in a database that stores contents including movies and TV programs, from the acquired input information; and an output unit that provides the generated playlist to the user. system.

[0955] (Second aspect) The input information further includes profile information including at least the age and gender of the user. The system according to the first aspect.

[0956] (Third aspect) The data acquisition unit further acquires schedule information of the user; the playlist generation unit creates a playlist including content having a length corresponding to a rest period or a workout period included in the schedule information, based on the acquired input information and the schedule information; 2. The system according to the first or second aspect.

[0957] (Fourth aspect) An emotion recognition unit that recognizes the emotion of the user using an emotion engine. A system according to any one of the first to third aspects.

[0958] (Fifth aspect) The emotion engine analyzes information such as the user's facial expressions, voice, and text to recognize the user's emotions. The system according to the fourth aspect.

[0959] (Sixth aspect) The emotion engine classifies user emotions into categories such as positive, negative, and neutral. 10. The system according to the fourth or fifth aspect.

[0960] (Seventh aspect) The playlist generation unit includes a means for identifying optimal content based on the user's emotion information obtained from the emotion engine. A system according to any one of the fourth to fifth aspects.

[0961] (Eighth aspect) the playlist generation unit comprehensively considers the user's emotion information obtained from the emotion engine and the input information, and identifies optimal content; A system according to any one of the fourth to seventh aspects.

[0962] (Ninth aspect) In the system according to claim 8, the playlist generating unit generates a personalized playlist according to the user's tastes and preferences based on the user's emotion information obtained from the emotion engine. A system according to any one of the fourth to eighth aspects.

[0963] (Tenth aspect) The playlist generator selects the genre, theme, mood, etc. of the content according to the user's emotions and creates a playlist based on them. A system according to any one of the fourth to ninth aspects.

[0964] "Example 1"

[0965] (Claim 1) means for obtaining input information including a user's mood for the day and the user's interests; a means for generating a playlist consisting of a plurality of contents in a database in which video contents are stored, from the acquired input information by using a playlist generation model; means for providing the generated playlist to the user; A means for securely receiving and storing input information provided by a user using an encrypted communication protocol; a means for using a general purpose query language to search and retrieve video content from a database; A means to generate playlists using machine learning libraries; means for transmitting the generated playlist to the user's terminal using a notification service; A system including:

[0966] (Claim 2) The system of claim 1 , wherein the input information further comprises profile information including at least an age and a gender of the user.

[0967] (Claim 3) The system of claim 1, wherein the data acquisition means further acquires schedule information of the user, and the playlist generation means creates a playlist from the acquired input information and the schedule information, including content whose length corresponds to the rest time or exercise time included in the schedule information.

[0968] "Application example 1"

[0969] (Claim 1) A data acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user; a playlist generation means for generating a playlist from the acquired input information using a playlist generation model, the playlist being made up of a plurality of contents in a database in which contents including movies and TV programs are stored; an output means for providing the generated playlist to the user; A means for transmitting the generated playlist to a user's terminal and displaying it; A system including:

[0970] (Claim 2) The system of claim 1 , wherein the input information further comprises profile information including at least an age and a gender of the user.

[0971] (Claim 3) The data acquisition means further acquires schedule information of the user; 2. The system according to claim 1, wherein the playlist generation means creates a playlist including content whose length corresponds to a rest period or exercise period included in the schedule information, based on the acquired input information and the schedule information.

[0972] "Example 2 of combining emotion engines"

[0973] (Claim 1) An information acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user; an emotion recognition means for recognizing an emotion of a user from the input information using an emotion engine; a playlist generation means for generating a playlist from a plurality of contents in a database storing contents including movies and TV programs, based on the recognized emotion information and the acquired input information, using a playlist generation model; an information providing means for providing the generated playlist to the user; A system including:

[0974] (Claim 2) The input information further includes profile information including at least the age and gender of the user. 2. The system of claim 1.

[0975] (Claim 3) The information acquisition means further acquires schedule information of the user; the playlist generating means generates a playlist including contents having a length corresponding to a rest period or an exercise period included in the schedule information, based on the acquired input information and the schedule information; 2. The system of claim 1.

[0976] "Application example 2 when combining emotion engines"

[0977] (Claim 1) A data acquisition means for acquiring input information including a user's mood for the day and matters of interest to the user; An emotion recognition means for recognizing an emotion of a user; a playlist generation means for generating a playlist made up of a plurality of contents in a database storing contents including movies and TV programs, based on the acquired input information and the emotion information obtained by the emotion recognition means, using a playlist generation model; an output means for providing the generated playlist to the user; A means for detecting a user's emotions through facial recognition using a camera; A system including:

[0978] (Claim 2) The system of claim 1 , wherein the input information further includes profile information including at least an age and a gender of the user.

[0979] (Claim 3) The data acquisition means further acquires schedule information of the user; the playlist generation means creates a playlist including contents having a length corresponding to a rest time or a workout time included in the schedule information, based on the acquired input information, the acquired emotion information, and the schedule information; 2. The system of claim 1. [Explanation of symbols]

[0980] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining input information including a user's mood for the day and the user's interests; a means for generating a playlist made up of a plurality of contents in a database in which video contents are stored, using a prompt sentence for instructing to generate a playlist made up of a plurality of contents in the database from the acquired input information and a generative model; and means for providing the generated playlist to the user. system.

2. A means for securely receiving and storing input information provided by a user using an encrypted communication protocol; a means for using a general purpose query language to search and retrieve video content from a database; A means to generate playlists using machine learning libraries; and means for transmitting the generated playlist to the user's terminal using a notification service. The system of claim 1 .

3. The input information further includes profile information including at least the age and gender of the user. The system of claim 1 .

4. The means for acquiring input information further acquires schedule information of the user; the playlist generating means generates a playlist including content having a length corresponding to a rest time or a workout time included in the schedule information, using the generative model and a prompt sentence instructing to generate a playlist including content having a length corresponding to a rest time or a workout time included in the schedule information from the acquired input information and the schedule information; The system of claim 1 .

5. means for recognizing a user's emotion from the input information using an emotion engine; the playlist generating means generates a playlist made up of a plurality of contents in the database in which the video content is stored, using the generative model and a prompt sentence instructing to generate a playlist made up of a plurality of contents in the database in which the video content is stored, based on the acquired input information and the recognized emotion information of the user; The system of claim 1 .

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