System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to enter an event that occurred on the day using an input device; means for transmitting the entered data to a server; means for receiving and analyzing the entered data by natural language processing; means for extracting a positive aspect from the analysis result; means for generating a feedback message based on the extracted positive aspect; means for transmitting the generated feedback message to the input device; and means for displaying the feedback message to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, many users experience various events in their daily lives, but find it difficult to find the positive aspects of their day. In particular, when negative events leave a strong impression, they often overlook the positive events. As a result, users' sense of happiness and self-esteem decrease, and they may experience unnecessary stress. In response, there is a need for a method or system that allows users to recognize the positive aspects of their day and end their day on a positive note. [Means for solving the problem]

[0005] The present invention provides a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing. This system specifically includes the following means.

[0006] 1. A means for the user to input events that occurred that day using an input device.

[0007] 2. A means for transmitting the input data to a server.

[0008] 3. A means for receiving the input data and analyzing it using natural language processing.

[0009] 4. A means of extracting positive aspects from the results of said analysis.

[0010] 5. Means for generating a feedback message based on the extracted positive aspects.

[0011] 6. Means for transmitting the generated feedback message to the input device.

[0012] 7. Means for displaying said feedback message to the user.

[0013] This way, users can focus on the positive aspects at the end of the day, improving their happiness and self-esteem.

[0014] "User" refers to an individual who uses the system to input what happened that day and receive feedback.

[0015] An "input device" is a device that allows a user to input events that occurred that day in text form, and includes, for example, a smartphone or a personal computer.

[0016] "Server" refers to a central computer system for receiving user input data, analyzing it and generating feedback.

[0017] "Input data" refers to text data about events that occurred that day that is input by the user using an input device.

[0018] "Natural language processing" refers to the technology of analyzing input text data and understanding and interpreting its content, including sentiment analysis and extraction of positive aspects.

[0019] An "emotion score" is a numerical representation of an emotional evaluation, such as positive, negative, or neutral, based on the input text data.

[0020] "Positive aspects" refer to events or emotional elements in the input data that have a positive impact on the user.

[0021] "Feedback message" refers to a message generated by the server based on the analysis results that conveys positive aspects to the user.

[0022] The "means for displaying" refers to a function for visually conveying a feedback message to the user, and includes the screen or display of an input device.

[0023] "Means for analysis" refers to software and systems that use natural language processing to analyze input data and extract sentiment scores and positive aspects. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] 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.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] 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.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] 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.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing. An embodiment of this system will be described below.

[0046] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0047] The server analyzes the data received from the device. Natural language processing (NLP) technology is used for the analysis. At this time, the server performs sentiment analysis on the input text data and calculates sentiment scores such as positive, negative, and neutral. For example, NLTK (Natural Language Toolkit) can be used as an NLP tool.

[0048] The server extracts positive aspects from the text data based on the emotion score. Specifically, if the positive emotion score is high or the overall emotion score is positive, it adds this to the list as a positive aspect. For example, if someone inputs "Today was busy, but I was able to finish up some work. I met up with friends and had a great time," this text contains positive elements, so the extracted aspects would include "I had a good time with friends" and "I finished up some work."

[0049] The server then generates a feedback message based on the extracted positive aspects. The generated feedback message takes the form of, "I found some positive aspects of your day: Today's events gave me positive emotions. Overall, it seems like you had a great day!" This message helps boost the user's self-esteem and end the day on a positive note.

[0050] Finally, the server sends the generated feedback message to the device, which then displays it to the user, allowing the user to review the feedback message and notice the positive aspects of the day.

[0051] By implementing such a system, users can reaffirm the positive aspects at the end of the day, which is expected to improve their sense of happiness and self-esteem.

[0052] For example, if a user types, "Today, my presentation went well and I received positive feedback on my new project," the server parses this text and extracts "my presentation went well" and "I received positive feedback" as positive aspects. The generated feedback message is, "I found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user uses an input device to input the events that occurred that day in text form.

[0056] Step 2:

[0057] The device receives input text from the user.

[0058] Step 3:

[0059] The input text acquired by the terminal is sent to the server via a POST request.

[0060] Step 4:

[0061] The server stores the input text received from the terminal.

[0062] Step 5:

[0063] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, or neutral scores.

[0064] Step 6:

[0065] The server extracts positive aspects in the text based on the calculated sentiment score, for example, listing parts with a high positive sentiment score or an overall positive sentiment score.

[0066] Step 7:

[0067] The server generates a feedback message for the user based on the extracted positive aspects. The message lists the extracted positive aspects and emphasizes the positive aspects to the user.

[0068] Step 8:

[0069] The server sends the generated feedback message to the terminal.

[0070] Step 9:

[0071] The terminal displays the feedback message received from the server.

[0072] Step 10:

[0073] Users can review the feedback messages displayed on their device and feel positive by noticing the positive aspects of their day.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Conventional feedback systems have difficulty in analyzing users' emotions and providing appropriate feedback for everyday events. Furthermore, they lack a system that allows users to rediscover the positive aspects of their experiences. As a result, they are not effective in increasing users' happiness and self-esteem.

[0077] 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.

[0078] In this invention, the server includes means for analyzing text data by natural language processing and calculating an emotion score, means for extracting positive aspects based on the emotion score, and means for generating a feedback message based on the extracted positive aspects, thereby enabling the user to reaffirm the positive experiences of the day and improve their sense of happiness and self-esteem.

[0079] A "user" is someone who accesses the system and enters events that occur during the day.

[0080] An "input device" is hardware that a user uses to input events in text form, including, for example, a smartphone or personal computer.

[0081] A "terminal" is an intermediate device that transmits data from a user's input device to a server.

[0082] A "server" is a computer system responsible for receiving and analyzing user-entered data, and generating and sending feedback messages.

[0083] "Natural language processing" is the technology used by computers to understand, interpret, or generate human language.

[0084] An "emotion score" is a numerical value related to positive, negative, or neutral emotions calculated by analyzing text data.

[0085] "Positive aspects" refer to positive emotions and events contained in the text data.

[0086] A "feedback message" is a message generated and provided to a user based on the analyzed data and extracted positive aspects.

[0087] This system allows users to input events that occurred during the day, analyzes the data using natural language processing, extracts positive aspects, and generates feedback messages. This system is designed to increase users' sense of happiness and self-esteem.

[0088] First, the user uses an input device (such as a smartphone or personal computer) to enter the events that occurred that day in text form. The input form has text boxes in which the user can freely describe their experiences that day. For example, they might enter, "Today I went to a cafe with my friends and had a great time. Work went well, too."

[0089] Next, the terminal sends the text data entered by the user to the server as a POST request. At this time, the terminal uses the HTTP protocol and encodes the data as necessary. It is recommended to use SSL / TLS to ensure security when transmitting data.

[0090] The server analyzes the text data received from the device. This analysis uses natural language processing (NLP) technology. Specifically, the server uses NLTK (Natural Language Toolkit) to perform sentiment analysis of the text data and calculates a positive, negative, or neutral sentiment score for each sentence. For example, a positive score is calculated for the text "I went to a cafe with my friends today and had a great time."

[0091] Based on the emotion score, the server extracts positive aspects from the text data. It lists the parts with high positive emotion scores and identifies them as positive elements. For example, "I went to a cafe with my friends and had a good time" is extracted as a positive aspect.

[0092] The server then generates a feedback message based on the extracted positive aspects, such as "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0093] The server generates a feedback message and sends it to the device. The server handles any errors appropriately to ensure the message reaches the device. The device then displays the received feedback message to the user. For example, the feedback message could be displayed using a dialog box or notification bar in the application. The user would see a message like, "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0094] In this way, this system allows users to re-recognize the positive experiences of the day and increase their sense of happiness and self-esteem. For example, a user might input, "Today, my presentation went well, and I received positive feedback on my new project." In this case, the server analyzes this text, extracts the positive aspects, such as "my presentation went well" and "I received positive feedback," and provides them to the user as a feedback message.

[0095] An example of a prompt sentence to use is, "Please tell us how your day went. For example, 'I went to a cafe with my friends today and had a great time.'" This can be designed to make it easier for users to enter input.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] The user uses an input device to input the events that occurred that day in text format. Specifically, the user types "I went to a cafe with my friends today and had a great time" into a text box on a dedicated web form or application. The input text becomes the initial input data for the system.

[0099] Step 2:

[0100] The terminal sends the text data entered by the user to the server as a POST request. Specifically, the terminal properly encodes the text data and sends the data to the server using the HTTP protocol. The input is the text data entered by the user, and the output is the HTTP request sent to the server.

[0101] Step 3:

[0102] The server receives text data from the terminal. Specifically, the server receives an HTTP request, decodes the text data, and converts it into an internal data format. The input is the text data sent from the terminal, and the output is the data converted into a format that can be used for analysis.

[0103] Step 4:

[0104] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, the server uses NLP tools such as NLTK (Natural Language Toolkit) to calculate the sentiment score of the text data. The input is the text data converted into an internal data format, and the output is a sentiment score such as positive, negative, or neutral.

[0105] Step 5:

[0106] The server extracts positive aspects from text data based on the emotion score. Specifically, the server lists parts with high positive emotion scores and identifies them as positive elements. The input is the emotion score, and the output is the data from which the positive aspects have been extracted.

[0107] Step 6:

[0108] The server generates a feedback message based on the extracted positive aspects. Specifically, the server uses a template to generate a message such as "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the data from which the positive aspects were extracted, and the output is the generated feedback message.

[0109] Step 7:

[0110] The server sends the generated feedback message to the terminal. Specifically, the server sends the generated message to the terminal as an HTTP response. The input is the generated feedback message, and the output is the HTTP response.

[0111] Step 8:

[0112] The terminal displays the feedback message received from the server to the user. Specifically, the terminal analyzes the received message and displays it in a dialog box or notification bar within the application. The message displayed is "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the feedback message received as an HTTP response, and the output is the message displayed to the user.

[0113] (Application example 1)

[0114] 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."

[0115] There is a lack of mechanisms for effectively utilizing customer feedback in brick-and-mortar stores to improve service quality and motivate store clerks and managers. Specifically, it is necessary to quickly identify positive elements contained in the feedback and provide appropriate feedback to store clerks and managers. Conventional methods often involve analyzing feedback and generating feedback messages manually, resulting in low efficiency and a lack of real-time capabilities.

[0116] 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.

[0117] In this invention, the server includes: a means for a user to input events that occurred that day using an input device; a means for transmitting the input data to the server; a means for receiving the input data and analyzing it using natural language processing; a means for extracting positive aspects from the analysis results; a means for generating a feedback message based on the extracted positive aspects; a means for transmitting the generated feedback message to the input device; a means for displaying the feedback message to the user; a means for analyzing feedback input by customers in a physical store; and a means for providing positive aspects included in the feedback from customers to store staff or managers. This enables fast and effective analysis of feedback and provision of positive feedback in real time.

[0118] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone, tablet, personal computer, etc.

[0119] A "server" is a computer system for receiving and analyzing data sent from an input device.

[0120] "Natural language processing" is a technology that analyzes input text data, and mainly calculates emotional scores and extracts positive aspects.

[0121] The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0122] "Positive aspects" are positive elements extracted from text data analyzed using natural language processing.

[0123] "Feedback messages" are messages generated based on the extracted positive aspects and are provided to users, store staff, and managers.

[0124] A "physical store" is a store that exists in a physical location and provides services or products that customers can visit in person.

[0125] "Customers" refer to people who visit physical stores and use services and products.

[0126] A "store associate" is an employee who works in a physical store and provides services to customers.

[0127] A "manager" is a person in charge of operating and managing a physical store, and is responsible for instructing store staff and optimizing operations.

[0128] "Real-time" means that processing and data provision are carried out close to the moment an event occurs.

[0129] The present invention is a system that analyzes customer feedback using natural language processing technology to improve the quality of service and customer satisfaction in brick-and-mortar stores. This system is implemented in the following steps.

[0130] Users, or customers, use input devices such as smartphones or tablets to enter text about their experiences and impressions at the store that day. This feedback is sent from the smartphone or tablet to the server as a POST request. Input devices are generally called "input devices."

[0131] The server analyzes the received feedback data using natural language processing (NLP) tools (e.g., Google Cloud Natural Language API or NLTK). This analysis involves tokenizing the text data, tagging it, and calculating a sentiment score to extract positive aspects.

[0132] To calculate the emotion score, a technique is used to evaluate the content of text data with a positive, negative, or neutral score. For example, the TextBlob library can be used. This analysis evaluates the emotional content of the text data and extracts positive elements. The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0133] Based on the extracted positive aspects, the server automatically generates feedback messages that are provided to store staff and managers, such as "Today's feedback was positive, and our staff's friendliness and smooth ordering process were highly praised. Thank you, customer!"

[0134] This feedback message is sent to the user's smartphone or tablet and displayed in real time, allowing store associates and managers to receive prompt, positive feedback. "Real-time" means that processing and data provision occur close to the moment an event occurs.

[0135] For example, if a customer enters feedback such as "The staff were very kind and the ordering process went smoothly," the server analyzes this text and extracts "The staff were kind" and "The ordering process went smoothly" as positive aspects. The generated feedback message will be "Today's feedback rated the kindness of the staff and the smoothness of the ordering process highly. We appreciate your patronage!" and will be provided to store staff and managers.

[0136] A specific example of a prompt sentence is "Please analyze today's feedback. For example, 'The staff were very kind. Ordering went smoothly.'"

[0137] As described above, the present invention makes it possible to improve the quality of service in physical stores and increase the motivation of store staff and managers through the rapid and effective analysis of feedback and the provision of positive feedback in real time.

[0138] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0139] Step 1:

[0140] The user uses an input device such as a smartphone or tablet to input the events that occurred that day. An example of input data would be, "The staff were very kind. The ordering process went smoothly." This input data is sent from the device to the server as a POST request.

[0141] Step 2:

[0142] The server receives the feedback data sent from the device. The received data is text input data, and prepares it to be passed to a data analysis tool for natural language processing. The input of this step is the text data sent by the user, and the output is data ready for analysis.

[0143] Step 3:

[0144] The server uses a natural language processing tool (such as NLTK or Google Cloud Natural Language API) to analyze the received text data. Specifically, it tokenizes the text data, tags it, and calculates its sentiment score. The input for this step is the text data received in step 2, and the output is data containing the analysis results.

[0145] Step 4:

[0146] The server extracts positive aspects based on the sentiment score results. Specifically, it calculates a sentiment score for each sentence in the text, and if the score is positive, it adds the sentence to a list of positive aspects. The input of this step is the analysis result obtained in step 3, and the output is a list of positive aspects.

[0147] Step 5:

[0148] The server generates a feedback message based on the extracted positive aspects. For example, if the extracted positive aspects are "Friendly staff" and "Smooth ordering," the generated feedback message will be "Today's feedback rated the friendliness of the staff and the smooth ordering process highly. Thank you, customer!" The input of this step is the list of positive aspects obtained in step 4, and the output is the generated feedback message.

[0149] Step 6:

[0150] The server sends the generated feedback message to the device, preparing it for real-time display on the user's smartphone or tablet. The input to this step is the feedback message generated in step 5, and the output is the message sent to the device.

[0151] Step 7:

[0152] The user's device displays the feedback message received from the server. Specifically, the user, store clerk, or manager can check the feedback message and use it to improve service. The input of this step is the feedback message sent from the server in step 6, and the output is the message displayed on the user's device.

[0153] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing and an emotion engine. The following describes an embodiment of this system.

[0155] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0156] The server stores the data received from the device and first uses an emotion engine to recognize emotions in real time from the user's input text. The emotion engine can use not only text data, but also emotional data such as voice and images as needed. The emotional data recognized by the emotion engine influences analysis using natural language processing (NLP).

[0157] The server then uses natural language processing techniques to calculate a sentiment score for the input text. The NLP tool analyzes the text for positive, negative, and neutral sentiment scores. For example, the NLP tool can be NLTK (Natural Language Toolkit) or another sentiment analysis tool.

[0158] After the emotion engine and NLP analysis are complete, the server extracts positive aspects from the text based on the emotion score. It lists parts with high positive emotion scores and elements that indicate positive emotions. For example, for an input such as "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server extracts positive aspects such as "I finished work" and "I had a great time with friends."

[0159] Next, the server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished work, you had a good time with your friends, it was a great day!"

[0160] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0161] For example, if a user types, "Today, my presentation went well, and I received positive feedback on my new project," the server analyzes this text and extracts the positive aspects of "my presentation went well" and "I received positive feedback" based on emotion recognition by the emotion engine and emotion score calculation by NLP. The generated feedback message will be, "We found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, it was a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0162] The processing flow will be explained below.

[0163] Step 1:

[0164] The user uses an input device to input the events that occurred that day in text form.

[0165] Step 2:

[0166] The device receives input text from the user.

[0167] Step 3:

[0168] The input text acquired by the terminal is sent to the server via a POST request.

[0169] Step 4:

[0170] The server stores the input text received from the terminal.

[0171] Step 5:

[0172] The server uses an emotion engine to recognize emotions in real time from input text, analyzing not only the text but also auxiliary emotion data such as audio and images.

[0173] Step 6:

[0174] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, and neutral sentiment scores.

[0175] Step 7:

[0176] By combining the results of the sentiment engine with the sentiment score generated by NLP, the server extracts positive aspects from the text, particularly listing parts with a positive sentiment score and a positive overall score.

[0177] Step 8:

[0178] The server generates feedback messages based on the extracted positive aspects. The generated messages emphasize the positive aspects and enhance the user's self-esteem.

[0179] Step 9:

[0180] The server sends the generated feedback message to the terminal.

[0181] Step 10:

[0182] The device displays the feedback message received from the server to the user.

[0183] Step 11:

[0184] Users review the feedback messages displayed on their devices, notice the positive aspects of their day, and feel positive.

[0185] Example 2

[0186] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional systems only perform simple analysis of text data entered by users, without performing advanced analysis using emotion recognition or natural language processing. This makes it difficult to generate accurate feedback messages based on the user's emotions. The present invention aims to provide a system that combines an emotion engine and natural language processing technology to perform more accurate emotion recognition and text analysis and provide positive feedback to users.

[0188] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving the input data and storing it in a database, means for recognizing emotions using an emotion engine based on the stored data, and means for calculating an emotion score of the text through natural language processing using the recognized emotion data. This makes it possible to accurately analyze the emotions of the text input by the user, extract positive aspects, and provide appropriate feedback to the user.

[0189] A "user" is an entity that inputs data into the system and receives feedback.

[0190] An "input device" is an electronic device that a user uses to input text data, and examples include smartphones and personal computers.

[0191] "Server" means a central computer system that receives, stores, and analyzes data submitted by users.

[0192] A "database" is a storage system managed by a server for systematically storing data entered by users.

[0193] An "emotion engine" is software that analyzes and recognizes user emotions from text data.

[0194] "Natural Language Processing (NLP)" is a technology for analyzing text data and understanding and extracting meaning and emotion.

[0195] An "emotion score" is a numerical representation of the emotional state of text data, expressed as a positive, negative, or neutral score.

[0196] "Positive aspects" are parts or elements with high emotional scores, and refer to positive events or impressions for the user.

[0197] A "feedback message" is a message generated based on the extracted positive aspects, and has content that conveys to the user the positive aspects of the events of the day.

[0198] The present invention is a system that generates and presents positive feedback by having a user input events that occurred that day and analyzing the data using natural language processing (NLP) and an emotion engine. The following describes an embodiment of the present invention.

[0199] Hardware and Software Instructions

[0200] A smartphone or personal computer is used as an input device for users to enter data. Through this input device, users enter the events that occurred that day in text format. The entered data is sent to a server via the device. The server stores the received data in a database.

[0201] Next, the server launches an emotion engine (e.g., an emotion analysis tool such as TextBlob or VADER) to perform real-time emotion recognition on the stored text data. The emotion engine analyzes the text data and calculates an emotion score: positive, negative, or neutral.

[0202] Furthermore, the server uses natural language processing tools (such as NLTK or spaCy) to analyze detailed sentiment scores within the text. Based on the results of the sentiment score analysis, positive aspects are listed. For example, if a user enters "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server will extract the positive aspects of "finishing work" and "having a good time with friends" from this text.

[0203] The server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished some work, you had a good time with your friends, it was a great day!"

[0204] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0205] Examples of specific examples and prompts

[0206] As a concrete example, consider the case where a user enters, "Today, my presentation went well, and I received positive feedback on my new project." The server analyzes this text and extracts the positive aspects, "My presentation went well" and "I received positive feedback," based on emotion recognition by an emotion engine and emotion scores calculated using natural language processing (NLP tools). The generated feedback message is, "We found some positive aspects of your day: Your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0207] An example of a prompt statement can be written as follows:

[0208] "Analyze the following text, extract the positive aspects, and generate a feedback message: 'My presentation went well today, and I received positive feedback on my new project.'"

[0209] The above is a specific embodiment of the present invention. This system aims to improve the user's well-being by analyzing the data entered by the user in an advanced manner and providing positive feedback.

[0210] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0211] Step 1:

[0212] The user uses an input device (smartphone or personal computer) to input the events that occurred that day in text format. This is done by directly entering data into a text box on the screen of the input device. An example of input data is "I had an important meeting today, and I was able to complete it successfully." This input data becomes the raw data that is used in the subsequent analysis process.

[0213] Step 2:

[0214] The device sends the entered text data to the server as an HTTP POST request. The sending format is JSON, and the data sent is a JSON object with a "text" key. Specifically, the application on the device captures the user's input and makes a request to the configured server endpoint.

[0215] Step 3:

[0216] The server receives the POST request and saves the entered text data in a database. The database used could be, for example, MySQL or PostgreSQL. Specifically, the server parses the received JSON data and stores the data based on the appropriate schema. Once the input data has been saved, it can be referenced in a later analysis step.

[0217] Step 4:

[0218] The server performs emotion recognition on the stored text data using an emotion engine (such as TextBlob or VADER). The emotion engine analyzes words and phrases in the text and assigns each word a score: positive, negative, or neutral. Specifically, the emotion engine tokenizes the text and calculates its emotion value. It receives text data as input and generates an emotion score as output.

[0219] Step 5:

[0220] The server uses a natural language processing (NLP) tool (such as NLTK or spaCy) to calculate a detailed sentiment score for the text. The NLP tool then further analyzes the output of the sentiment engine to understand the overall sentiment trend of the text. Specifically, it performs grammatical and semantic analysis of the text and integrates the sentiment score. For example, it uses the NLTK sentiment analysis library to receive text data and sentiment scores as input and output the overall sentiment analysis results.

[0221] Step 6:

[0222] The server extracts positive aspects based on the results of the emotion score analysis. It lists the parts and elements with high positive emotion scores. Specifically, it filters based on the analysis results to extract positive elements. It receives the emotion score analysis results as input and generates a list of positive aspects as output. For example, a positive element such as "The meeting ended successfully" is extracted.

[0223] Step 7:

[0224] The server generates a feedback message based on the extracted positive aspects. The generation process creates a message that emphasizes the extracted positive aspects. Specifically, the extracted elements are embedded in a template. It receives a list of positive aspects as input and generates a feedback message as output. A message of the form "I found some positive aspects of your day: The meeting went well, it was a great day!" is generated.

[0225] Step 8:

[0226] The server sends the generated feedback message to the terminal. The message is sent as an HTTP response and is received by the terminal. Specifically, the generated feedback message is packaged in JSON format and sent as the body of the HTTP response. The generated feedback message is received as input and the HTTP response is sent as output.

[0227] Step 9:

[0228] The device displays the received feedback message to the user. Specifically, the feedback message is rendered appropriately on the device screen, for example, as a pop-up message or notification within the application. The device takes the received feedback message as input and displays it on the user's screen as output. The user can review the message and notice the positive aspects of their day.

[0229] (Application example 2)

[0230] 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."

[0231] Maintaining high motivation for employees, especially food delivery drivers, who perform their daily tasks is a difficult task. If employees have few opportunities to receive positive feedback about their work, their satisfaction with their work may decline. The present invention aims to provide a system that improves employee satisfaction and motivation by allowing employees to receive positive feedback after completing their work.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0233] In this invention, the server includes means for a user to input events that occurred that day using an input device, means for transmitting the input data to the server, and means for receiving the input data and analyzing it using natural language processing. This allows employees to reflect on their day's events after work and receive feedback in which the system extracts positive aspects, thereby increasing their satisfaction with their work and their motivation.

[0234] "User" refers to a person who uses this system.

[0235] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone or personal computer.

[0236] "Server" refers to a computer system that receives and analyzes data sent by users.

[0237] "Natural language processing" refers to the technology of analyzing text data and calculating emotional scores, etc., specifically using NLP tools.

[0238] "Emotion score" refers to a value that quantifies the emotion contained in text data.

[0239] "Positive aspects" refer to the positive elements contained in the text data.

[0240] "Feedback message" refers to a message generated based on the extracted positive aspects and provided to the user.

[0241] "Specific work" refers to the work the user is engaged in, primarily including the delivery work performed by food delivery drivers that day.

[0242] "Provided for the purpose of increasing motivation" means that the feedback message provided to the user is designed to increase the user's motivation.

[0243] In the system of the present invention, a user inputs events that occurred that day using an input device, and the server analyzes the input and generates and provides feedback messages.

[0244] Hardware and Software Configuration

[0245] A system for implementing the present invention uses the following hardware and software.

[0246] Hardware:

[0247] User input device: Smartphone (iOS or Android device)

[0248] Server: A computer system for processing and analyzing data.

[0249] software:

[0250] Smartphone app: Developed with React Native

[0251] Server application: Node.js + Express

[0252] Sentiment analysis engine: Amazon Comprehend or IBM Watson as natural language processing tools

[0253] NLP tools: spaCy or NLTK

[0254] Data processing and calculation

[0255] 1. Data Entry

[0256] The user starts the smartphone app and inputs the events that occurred that day. After entering the text, the user presses the submit button to send the input data.

[0257] 2. Data Transmission

[0258] Text data is sent from the smartphone to the server as a POST request.

[0259] 3. Data Reception and Analysis

[0260] The server receives a POST request using Node.js and Express to retrieve text data, which is then sent to a sentiment analysis engine to generate a sentiment score.

[0261] A sentiment analysis engine (e.g., Amazon Comprehend) calculates a sentiment score (positive, negative, or neutral) for text data and returns the result.

[0262] 4. Natural Language Processing

[0263] Based on the obtained sentiment score, the server uses NLP tools (e.g., spaCy) to extract positive aspects of the text data.

[0264] 5. Feedback Generation

[0265] The server automatically generates a feedback message based on the extracted positive aspects. The generated feedback message is created according to a template and emphasizes the positive elements.

[0266] 6. Sending and displaying results

[0267] The generated feedback message is sent back to the smartphone and displayed for the user to review.

[0268] Specific examples

[0269] As a concrete example, suppose the user enters the following text:

[0270] Prompt Sentence Examples

[0271] We received many thank you comments from our customers today. We had no trouble finding complex addresses and completed all orders on time.

[0272] When this text is entered into the system, the server performs the following process:

[0273] The sentiment analysis engine calculates a sentiment score for the text, with positive sentiment being given a higher rating.

[0274] Natural language processing tools extract positive aspects (e.g., customer appreciation, successfully locating a complex address, completing all orders on time).

[0275] Based on the extraction results, a feedback message like the one below is generated.

[0276] Feedback message example

[0277] Identify the positive aspects of your day: you received a thank you note from a customer, you had no trouble finding a complicated address, and you completed all the orders on time. What a great day!

[0278] This allows users to receive positive feedback on their work and improve their motivation.

[0279] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0280] Step 1:

[0281] The user enters the events that occurred that day into the smartphone's input device. The user launches the app and enters text into the input form. An example of input might be, "We received many words of thanks from customers today. We were able to find complex addresses without any problems, and we completed all orders on time."

[0282] Step 2:

[0283] The terminal sends the data entered by the user to the server. A POST request is used for sending, and the entered text data is passed to the server. The input is the data entered by the user, and the output is the text data sent to the server.

[0284] Step 3:

[0285] The server receives a POST request and retrieves text data. The server uses Node.js and Express to receive the sent data. The input is the text data sent from the terminal, and the output is the text data retrieved by the server.

[0286] Step 4:

[0287] The text data acquired by the server is sent to a sentiment analysis engine, which analyzes the sentiment score. Here, a sentiment analysis engine such as Amazon Comprehend or IBM Watson is used to calculate the sentiment score (positive, negative, neutral). The input is the text data acquired by the server, and the output is the sentiment score obtained from the sentiment analysis engine.

[0288] Step 5:

[0289] The server uses natural language processing tools to extract positive aspects based on the sentiment scores. Using spaCy or NLTK as the NLP tool, it analyzes and extracts positive elements in the text (e.g., words of gratitude, order completion, etc.). The input is text data with sentiment scores assigned, and the output is text data containing positive aspects.

[0290] Step 6:

[0291] The server generates a feedback message based on the extracted positive aspects. The generated message is designed to increase the user's motivation and emphasizes the positive aspects according to a template. The input is the text data from which the positive aspects have been extracted, and the output is the feedback message.

[0292] Step 7:

[0293] The server sends the generated feedback message back to the device. To send it, it uses a POST request to pass the generated feedback message to the smartphone. The input is the feedback message generated by the server, and the output is the sent feedback message.

[0294] Step 8:

[0295] The device displays the received feedback message to the user. The smartphone app displays the message so that the user can check it. The input is the feedback message sent from the server, and the output is the feedback message displayed on the user's smartphone.

[0296] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0297] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0298] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0299] [Second embodiment]

[0300] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0301] 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.

[0302] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0303] 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.

[0304] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0305] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0306] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0307] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0308] 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.

[0309] 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.

[0310] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0311] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0312] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing. An embodiment of this system will be described below.

[0313] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0314] The server analyzes the data received from the device. Natural language processing (NLP) technology is used for the analysis. At this time, the server performs sentiment analysis on the input text data and calculates sentiment scores such as positive, negative, and neutral. For example, NLTK (Natural Language Toolkit) can be used as an NLP tool.

[0315] The server extracts positive aspects from the text data based on the emotion score. Specifically, if the positive emotion score is high or the overall emotion score is positive, it adds this to the list as a positive aspect. For example, if someone inputs "Today was busy, but I was able to finish up some work. I met up with friends and had a great time," this text contains positive elements, so the extracted aspects would include "I had a good time with friends" and "I finished up some work."

[0316] The server then generates a feedback message based on the extracted positive aspects. The generated feedback message takes the form of, "I found some positive aspects of your day: Today's events gave me positive emotions. Overall, it seems like you had a great day!" This message helps boost the user's self-esteem and end the day on a positive note.

[0317] Finally, the server sends the generated feedback message to the device, which then displays it to the user, allowing the user to review the feedback message and notice the positive aspects of the day.

[0318] By implementing such a system, users can reaffirm the positive aspects at the end of the day, which is expected to improve their sense of happiness and self-esteem.

[0319] For example, if a user types, "Today, my presentation went well and I received positive feedback on my new project," the server parses this text and extracts "my presentation went well" and "I received positive feedback" as positive aspects. The generated feedback message is, "I found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0320] The processing flow will be explained below.

[0321] Step 1:

[0322] The user uses an input device to input the events that occurred that day in text form.

[0323] Step 2:

[0324] The device receives input text from the user.

[0325] Step 3:

[0326] The input text acquired by the terminal is sent to the server via a POST request.

[0327] Step 4:

[0328] The server stores the input text received from the terminal.

[0329] Step 5:

[0330] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, or neutral scores.

[0331] Step 6:

[0332] The server extracts positive aspects in the text based on the calculated sentiment score, for example, listing parts with a high positive sentiment score or an overall positive sentiment score.

[0333] Step 7:

[0334] The server generates a feedback message for the user based on the extracted positive aspects. The message lists the extracted positive aspects and emphasizes the positive aspects to the user.

[0335] Step 8:

[0336] The server sends the generated feedback message to the terminal.

[0337] Step 9:

[0338] The terminal displays the feedback message received from the server.

[0339] Step 10:

[0340] Users can review the feedback messages displayed on their device and feel positive by noticing the positive aspects of their day.

[0341] Example 1

[0342] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0343] Conventional feedback systems have difficulty in analyzing users' emotions and providing appropriate feedback for everyday events. Furthermore, they lack a system that allows users to rediscover the positive aspects of their experiences. As a result, they are not effective in increasing users' happiness and self-esteem.

[0344] 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.

[0345] In this invention, the server includes means for analyzing text data by natural language processing and calculating an emotion score, means for extracting positive aspects based on the emotion score, and means for generating a feedback message based on the extracted positive aspects, thereby enabling the user to reaffirm the positive experiences of the day and improve their sense of happiness and self-esteem.

[0346] A "user" is someone who accesses the system and enters events that occur during the day.

[0347] An "input device" is hardware that a user uses to input events in text form, including, for example, a smartphone or personal computer.

[0348] A "terminal" is an intermediate device that transmits data from a user's input device to a server.

[0349] A "server" is a computer system responsible for receiving and analyzing user-entered data, and generating and sending feedback messages.

[0350] "Natural language processing" is the technology used by computers to understand, interpret, or generate human language.

[0351] An "emotion score" is a numerical value related to positive, negative, or neutral emotions calculated by analyzing text data.

[0352] "Positive aspects" refer to positive emotions and events contained in the text data.

[0353] A "feedback message" is a message generated and provided to a user based on the analyzed data and extracted positive aspects.

[0354] This system allows users to input events that occurred during the day, analyzes the data using natural language processing, extracts positive aspects, and generates feedback messages. This system is designed to increase users' sense of happiness and self-esteem.

[0355] First, the user uses an input device (such as a smartphone or personal computer) to enter the events that occurred that day in text form. The input form has text boxes in which the user can freely describe their experiences that day. For example, they might enter, "Today I went to a cafe with my friends and had a great time. Work went well, too."

[0356] Next, the terminal sends the text data entered by the user to the server as a POST request. At this time, the terminal uses the HTTP protocol and encodes the data as necessary. It is recommended to use SSL / TLS to ensure security when transmitting data.

[0357] The server analyzes the text data received from the device. This analysis uses natural language processing (NLP) technology. Specifically, the server uses NLTK (Natural Language Toolkit) to perform sentiment analysis of the text data and calculates a positive, negative, or neutral sentiment score for each sentence. For example, a positive score is calculated for the text "I went to a cafe with my friends today and had a great time."

[0358] Based on the emotion score, the server extracts positive aspects from the text data. It lists the parts with high positive emotion scores and identifies them as positive elements. For example, "I went to a cafe with my friends and had a good time" is extracted as a positive aspect.

[0359] The server then generates a feedback message based on the extracted positive aspects, such as "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0360] The server generates a feedback message and sends it to the device. The server handles any errors appropriately to ensure the message reaches the device. The device then displays the received feedback message to the user. For example, the feedback message could be displayed using a dialog box or notification bar in the application. The user would see a message like, "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0361] In this way, this system allows users to re-recognize the positive experiences of the day and increase their sense of happiness and self-esteem. For example, a user might input, "Today, my presentation went well, and I received positive feedback on my new project." In this case, the server analyzes this text, extracts the positive aspects, such as "my presentation went well" and "I received positive feedback," and provides them to the user as a feedback message.

[0362] An example of a prompt sentence to use is, "Please tell us how your day went. For example, 'I went to a cafe with my friends today and had a great time.'" This can be designed to make it easier for users to enter input.

[0363] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] The user uses an input device to input the events that occurred that day in text format. Specifically, the user types "I went to a cafe with my friends today and had a great time" into a text box on a dedicated web form or application. The input text becomes the initial input data for the system.

[0366] Step 2:

[0367] The terminal sends the text data entered by the user to the server as a POST request. Specifically, the terminal properly encodes the text data and sends the data to the server using the HTTP protocol. The input is the text data entered by the user, and the output is the HTTP request sent to the server.

[0368] Step 3:

[0369] The server receives text data from the terminal. Specifically, the server receives an HTTP request, decodes the text data, and converts it into an internal data format. The input is the text data sent from the terminal, and the output is the data converted into a format that can be used for analysis.

[0370] Step 4:

[0371] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, the server uses NLP tools such as NLTK (Natural Language Toolkit) to calculate the sentiment score of the text data. The input is the text data converted into an internal data format, and the output is a sentiment score such as positive, negative, or neutral.

[0372] Step 5:

[0373] The server extracts positive aspects from text data based on the emotion score. Specifically, the server lists parts with high positive emotion scores and identifies them as positive elements. The input is the emotion score, and the output is the data from which the positive aspects have been extracted.

[0374] Step 6:

[0375] The server generates a feedback message based on the extracted positive aspects. Specifically, the server uses a template to generate a message such as "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the data from which the positive aspects were extracted, and the output is the generated feedback message.

[0376] Step 7:

[0377] The server sends the generated feedback message to the terminal. Specifically, the server sends the generated message to the terminal as an HTTP response. The input is the generated feedback message, and the output is the HTTP response.

[0378] Step 8:

[0379] The terminal displays the feedback message received from the server to the user. Specifically, the terminal analyzes the received message and displays it in a dialog box or notification bar within the application. The message displayed is "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the feedback message received as an HTTP response, and the output is the message displayed to the user.

[0380] (Application example 1)

[0381] 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."

[0382] There is a lack of mechanisms for effectively utilizing customer feedback in brick-and-mortar stores to improve service quality and motivate store clerks and managers. Specifically, it is necessary to quickly identify positive elements contained in the feedback and provide appropriate feedback to store clerks and managers. Conventional methods often involve analyzing feedback and generating feedback messages manually, resulting in low efficiency and a lack of real-time capabilities.

[0383] 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.

[0384] In this invention, the server includes: a means for a user to input events that occurred that day using an input device; a means for transmitting the input data to the server; a means for receiving the input data and analyzing it using natural language processing; a means for extracting positive aspects from the analysis results; a means for generating a feedback message based on the extracted positive aspects; a means for transmitting the generated feedback message to the input device; a means for displaying the feedback message to the user; a means for analyzing feedback input by customers in a physical store; and a means for providing positive aspects included in the feedback from customers to store staff or managers. This enables fast and effective analysis of feedback and provision of positive feedback in real time.

[0385] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone, tablet, personal computer, etc.

[0386] A "server" is a computer system for receiving and analyzing data sent from an input device.

[0387] "Natural language processing" is a technology that analyzes input text data, and mainly calculates emotional scores and extracts positive aspects.

[0388] The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0389] "Positive aspects" are positive elements extracted from text data analyzed using natural language processing.

[0390] "Feedback messages" are messages generated based on the extracted positive aspects and are provided to users, store staff, and managers.

[0391] A "physical store" is a store that exists in a physical location and provides services or products that customers can visit in person.

[0392] "Customers" refer to people who visit physical stores and use services and products.

[0393] A "store associate" is an employee who works in a physical store and provides services to customers.

[0394] A "manager" is a person in charge of operating and managing a physical store, and is responsible for instructing store staff and optimizing operations.

[0395] "Real-time" means that processing and data provision are carried out close to the moment an event occurs.

[0396] The present invention is a system that analyzes customer feedback using natural language processing technology to improve the quality of service and customer satisfaction in brick-and-mortar stores. This system is implemented in the following steps.

[0397] Users, or customers, use input devices such as smartphones or tablets to enter text about their experiences and impressions at the store that day. This feedback is sent from the smartphone or tablet to the server as a POST request. Input devices are generally called "input devices."

[0398] The server analyzes the received feedback data using natural language processing (NLP) tools (e.g., Google Cloud Natural Language API or NLTK). This analysis involves tokenizing the text data, tagging it, and calculating a sentiment score to extract positive aspects.

[0399] To calculate the emotion score, a technique is used to evaluate the content of text data with a positive, negative, or neutral score. For example, the TextBlob library can be used. This analysis evaluates the emotional content of the text data and extracts positive elements. The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0400] Based on the extracted positive aspects, the server automatically generates feedback messages that are provided to store staff and managers, such as "Today's feedback was positive, and our staff's friendliness and smooth ordering process were highly praised. Thank you, customer!"

[0401] This feedback message is sent to the user's smartphone or tablet and displayed in real time, allowing store associates and managers to receive prompt, positive feedback. "Real-time" means that processing and data provision occur close to the moment an event occurs.

[0402] For example, if a customer enters feedback such as "The staff were very kind and the ordering process went smoothly," the server analyzes this text and extracts "The staff were kind" and "The ordering process went smoothly" as positive aspects. The generated feedback message will be "Today's feedback rated the kindness of the staff and the smoothness of the ordering process highly. We appreciate your patronage!" and will be provided to store staff and managers.

[0403] A specific example of a prompt sentence is "Please analyze today's feedback. For example, 'The staff were very kind. Ordering went smoothly.'"

[0404] As described above, the present invention makes it possible to improve the quality of service in physical stores and increase the motivation of store staff and managers through the rapid and effective analysis of feedback and the provision of positive feedback in real time.

[0405] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0406] Step 1:

[0407] The user uses an input device such as a smartphone or tablet to input the events that occurred that day. An example of input data would be, "The staff were very kind. The ordering process went smoothly." This input data is sent from the device to the server as a POST request.

[0408] Step 2:

[0409] The server receives the feedback data sent from the device. The received data is text input data, and prepares it to be passed to a data analysis tool for natural language processing. The input of this step is the text data sent by the user, and the output is data ready for analysis.

[0410] Step 3:

[0411] The server uses a natural language processing tool (such as NLTK or Google Cloud Natural Language API) to analyze the received text data. Specifically, it tokenizes the text data, tags it, and calculates its sentiment score. The input for this step is the text data received in step 2, and the output is data containing the analysis results.

[0412] Step 4:

[0413] The server extracts positive aspects based on the sentiment score results. Specifically, it calculates a sentiment score for each sentence in the text, and if the score is positive, it adds the sentence to a list of positive aspects. The input of this step is the analysis result obtained in step 3, and the output is a list of positive aspects.

[0414] Step 5:

[0415] The server generates a feedback message based on the extracted positive aspects. For example, if the extracted positive aspects are "Friendly staff" and "Smooth ordering," the generated feedback message will be "Today's feedback rated the friendliness of the staff and the smooth ordering process highly. Thank you, customer!" The input of this step is the list of positive aspects obtained in step 4, and the output is the generated feedback message.

[0416] Step 6:

[0417] The server sends the generated feedback message to the device, preparing it for real-time display on the user's smartphone or tablet. The input to this step is the feedback message generated in step 5, and the output is the message sent to the device.

[0418] Step 7:

[0419] The user's device displays the feedback message received from the server. Specifically, the user, store clerk, or manager can check the feedback message and use it to improve service. The input of this step is the feedback message sent from the server in step 6, and the output is the message displayed on the user's device.

[0420] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0421] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing and an emotion engine. The following describes an embodiment of this system.

[0422] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0423] The server stores the data received from the device and first uses an emotion engine to recognize emotions in real time from the user's input text. The emotion engine can use not only text data, but also emotional data such as voice and images as needed. The emotional data recognized by the emotion engine influences analysis using natural language processing (NLP).

[0424] The server then uses natural language processing techniques to calculate a sentiment score for the input text. The NLP tool analyzes the text for positive, negative, and neutral sentiment scores. For example, the NLP tool can be NLTK (Natural Language Toolkit) or another sentiment analysis tool.

[0425] After the emotion engine and NLP analysis are complete, the server extracts positive aspects from the text based on the emotion score. It lists parts with high positive emotion scores and elements that indicate positive emotions. For example, for an input such as "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server extracts positive aspects such as "I finished work" and "I had a great time with friends."

[0426] Next, the server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished work, you had a good time with your friends, it was a great day!"

[0427] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0428] For example, if a user types, "Today, my presentation went well, and I received positive feedback on my new project," the server analyzes this text and extracts the positive aspects of "my presentation went well" and "I received positive feedback" based on emotion recognition by the emotion engine and emotion score calculation by NLP. The generated feedback message will be, "We found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, it was a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0429] The processing flow will be explained below.

[0430] Step 1:

[0431] The user uses an input device to input the events that occurred that day in text form.

[0432] Step 2:

[0433] The device receives input text from the user.

[0434] Step 3:

[0435] The input text acquired by the terminal is sent to the server via a POST request.

[0436] Step 4:

[0437] The server stores the input text received from the terminal.

[0438] Step 5:

[0439] The server uses an emotion engine to recognize emotions in real time from input text, analyzing not only the text but also auxiliary emotion data such as audio and images.

[0440] Step 6:

[0441] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, and neutral sentiment scores.

[0442] Step 7:

[0443] By combining the results of the sentiment engine with the sentiment score generated by NLP, the server extracts positive aspects from the text, particularly listing parts with a positive sentiment score and a positive overall score.

[0444] Step 8:

[0445] The server generates feedback messages based on the extracted positive aspects. The generated messages emphasize the positive aspects and enhance the user's self-esteem.

[0446] Step 9:

[0447] The server sends the generated feedback message to the terminal.

[0448] Step 10:

[0449] The device displays the feedback message received from the server to the user.

[0450] Step 11:

[0451] Users review the feedback messages displayed on their devices, notice the positive aspects of their day, and feel positive.

[0452] Example 2

[0453] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0454] Conventional systems only perform simple analysis of text data entered by users, without performing advanced analysis using emotion recognition or natural language processing. This makes it difficult to generate accurate feedback messages based on the user's emotions. The present invention aims to provide a system that combines an emotion engine and natural language processing technology to perform more accurate emotion recognition and text analysis and provide positive feedback to users.

[0455] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving the input data and storing it in a database, means for recognizing emotions using an emotion engine based on the stored data, and means for calculating an emotion score of the text through natural language processing using the recognized emotion data. This makes it possible to accurately analyze the emotions of the text input by the user, extract positive aspects, and provide appropriate feedback to the user.

[0456] A "user" is an entity that inputs data into the system and receives feedback.

[0457] An "input device" is an electronic device that a user uses to input text data, and examples include smartphones and personal computers.

[0458] "Server" means a central computer system that receives, stores, and analyzes data submitted by users.

[0459] A "database" is a storage system managed by a server for systematically storing data entered by users.

[0460] An "emotion engine" is software that analyzes and recognizes user emotions from text data.

[0461] "Natural Language Processing (NLP)" is a technology for analyzing text data and understanding and extracting meaning and emotion.

[0462] An "emotion score" is a numerical representation of the emotional state of text data, expressed as a positive, negative, or neutral score.

[0463] "Positive aspects" are parts or elements with high emotional scores, and refer to positive events or impressions for the user.

[0464] A "feedback message" is a message generated based on the extracted positive aspects, and has content that conveys to the user the positive aspects of the events of the day.

[0465] The present invention is a system that generates and presents positive feedback by having a user input events that occurred that day and analyzing the data using natural language processing (NLP) and an emotion engine. The following describes an embodiment of the present invention.

[0466] Hardware and Software Instructions

[0467] A smartphone or personal computer is used as an input device for users to enter data. Through this input device, users enter the events that occurred that day in text format. The entered data is sent to a server via the device. The server stores the received data in a database.

[0468] Next, the server launches an emotion engine (e.g., an emotion analysis tool such as TextBlob or VADER) to perform real-time emotion recognition on the stored text data. The emotion engine analyzes the text data and calculates an emotion score: positive, negative, or neutral.

[0469] Furthermore, the server uses natural language processing tools (such as NLTK or spaCy) to analyze detailed sentiment scores within the text. Based on the results of the sentiment score analysis, positive aspects are listed. For example, if a user enters "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server will extract the positive aspects of "finishing work" and "having a good time with friends" from this text.

[0470] The server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished some work, you had a good time with your friends, it was a great day!"

[0471] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0472] Examples of specific examples and prompts

[0473] As a concrete example, consider the case where a user enters, "Today, my presentation went well, and I received positive feedback on my new project." The server analyzes this text and extracts the positive aspects, "My presentation went well" and "I received positive feedback," based on emotion recognition by an emotion engine and emotion scores calculated using natural language processing (NLP tools). The generated feedback message is, "We found some positive aspects of your day: Your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0474] An example of a prompt statement can be written as follows:

[0475] "Analyze the following text, extract the positive aspects, and generate a feedback message: 'My presentation went well today, and I received positive feedback on my new project.'"

[0476] The above is a specific embodiment of the present invention. This system aims to improve the user's well-being by analyzing the data entered by the user in an advanced manner and providing positive feedback.

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

[0478] Step 1:

[0479] The user uses an input device (smartphone or personal computer) to input the events that occurred that day in text format. This is done by directly entering data into a text box on the screen of the input device. An example of input data is "I had an important meeting today, and I was able to complete it successfully." This input data becomes the raw data that is used in the subsequent analysis process.

[0480] Step 2:

[0481] The device sends the entered text data to the server as an HTTP POST request. The sending format is JSON, and the data sent is a JSON object with a "text" key. Specifically, the application on the device captures the user's input and makes a request to the configured server endpoint.

[0482] Step 3:

[0483] The server receives the POST request and saves the entered text data in a database. The database used could be, for example, MySQL or PostgreSQL. Specifically, the server parses the received JSON data and stores the data based on the appropriate schema. Once the input data has been saved, it can be referenced in a later analysis step.

[0484] Step 4:

[0485] The server performs emotion recognition on the stored text data using an emotion engine (such as TextBlob or VADER). The emotion engine analyzes words and phrases in the text and assigns each word a score: positive, negative, or neutral. Specifically, the emotion engine tokenizes the text and calculates its emotion value. It receives text data as input and generates an emotion score as output.

[0486] Step 5:

[0487] The server uses a natural language processing (NLP) tool (such as NLTK or spaCy) to calculate a detailed sentiment score for the text. The NLP tool then further analyzes the output of the sentiment engine to understand the overall sentiment trend of the text. Specifically, it performs grammatical and semantic analysis of the text and integrates the sentiment score. For example, it uses the NLTK sentiment analysis library to receive text data and sentiment scores as input and output the overall sentiment analysis results.

[0488] Step 6:

[0489] The server extracts positive aspects based on the results of the emotion score analysis. It lists the parts and elements with high positive emotion scores. Specifically, it filters based on the analysis results to extract positive elements. It receives the emotion score analysis results as input and generates a list of positive aspects as output. For example, a positive element such as "The meeting ended successfully" is extracted.

[0490] Step 7:

[0491] The server generates a feedback message based on the extracted positive aspects. The generation process creates a message that emphasizes the extracted positive aspects. Specifically, the extracted elements are embedded in a template. It receives a list of positive aspects as input and generates a feedback message as output. A message of the form "I found some positive aspects of your day: The meeting went well, it was a great day!" is generated.

[0492] Step 8:

[0493] The server sends the generated feedback message to the terminal. The message is sent as an HTTP response and is received by the terminal. Specifically, the generated feedback message is packaged in JSON format and sent as the body of the HTTP response. The generated feedback message is received as input and the HTTP response is sent as output.

[0494] Step 9:

[0495] The device displays the received feedback message to the user. Specifically, the feedback message is rendered appropriately on the device screen, for example, as a pop-up message or notification within the application. The device takes the received feedback message as input and displays it on the user's screen as output. The user can review the message and notice the positive aspects of their day.

[0496] (Application example 2)

[0497] 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."

[0498] Maintaining high motivation for employees, especially food delivery drivers, who perform their daily tasks is a difficult task. If employees have few opportunities to receive positive feedback about their work, their satisfaction with their work may decline. The present invention aims to provide a system that improves employee satisfaction and motivation by allowing employees to receive positive feedback after completing their work.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0500] In this invention, the server includes means for a user to input events that occurred that day using an input device, means for transmitting the input data to the server, and means for receiving the input data and analyzing it using natural language processing. This allows employees to reflect on their day's events after work and receive feedback in which the system extracts positive aspects, thereby increasing their satisfaction with their work and their motivation.

[0501] "User" refers to a person who uses this system.

[0502] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone or personal computer.

[0503] "Server" refers to a computer system that receives and analyzes data sent by users.

[0504] "Natural language processing" refers to the technology of analyzing text data and calculating emotional scores, etc., specifically using NLP tools.

[0505] "Emotion score" refers to a value that quantifies the emotion contained in text data.

[0506] "Positive aspects" refer to the positive elements contained in the text data.

[0507] "Feedback message" refers to a message generated based on the extracted positive aspects and provided to the user.

[0508] "Specific work" refers to the work the user is engaged in, primarily including the delivery work performed by food delivery drivers that day.

[0509] "Provided for the purpose of increasing motivation" means that the feedback message provided to the user is designed to increase the user's motivation.

[0510] In the system of the present invention, a user inputs events that occurred that day using an input device, and the server analyzes the input and generates and provides feedback messages.

[0511] Hardware and Software Configuration

[0512] A system for implementing the present invention uses the following hardware and software.

[0513] Hardware:

[0514] User input device: Smartphone (iOS or Android device)

[0515] Server: A computer system for processing and analyzing data.

[0516] software:

[0517] Smartphone app: Developed with React Native

[0518] Server application: Node.js + Express

[0519] Sentiment analysis engine: Amazon Comprehend or IBM Watson as natural language processing tools

[0520] NLP tools: spaCy or NLTK

[0521] Data processing and calculation

[0522] 1. Data Entry

[0523] The user starts the smartphone app and inputs the events that occurred that day. After entering the text, the user presses the submit button to send the input data.

[0524] 2. Data Transmission

[0525] Text data is sent from the smartphone to the server as a POST request.

[0526] 3. Data Reception and Analysis

[0527] The server receives a POST request using Node.js and Express to retrieve text data, which is then sent to a sentiment analysis engine to generate a sentiment score.

[0528] A sentiment analysis engine (e.g., Amazon Comprehend) calculates a sentiment score (positive, negative, or neutral) for text data and returns the result.

[0529] 4. Natural Language Processing

[0530] Based on the obtained sentiment score, the server uses NLP tools (e.g., spaCy) to extract positive aspects of the text data.

[0531] 5. Feedback Generation

[0532] The server automatically generates a feedback message based on the extracted positive aspects. The generated feedback message is created according to a template and emphasizes the positive elements.

[0533] 6. Sending and displaying results

[0534] The generated feedback message is sent back to the smartphone and displayed for the user to review.

[0535] Specific examples

[0536] As a concrete example, suppose the user enters the following text:

[0537] Prompt Sentence Examples

[0538] We received many thank you comments from our customers today. We had no trouble finding complex addresses and completed all orders on time.

[0539] When this text is entered into the system, the server performs the following process:

[0540] The sentiment analysis engine calculates a sentiment score for the text, with positive sentiment being given a higher rating.

[0541] Natural language processing tools extract positive aspects (e.g., customer appreciation, successfully locating a complex address, completing all orders on time).

[0542] Based on the extraction results, a feedback message like the one below is generated.

[0543] Feedback message example

[0544] Identify the positive aspects of your day: you received a thank you note from a customer, you had no trouble finding a complicated address, and you completed all the orders on time. What a great day!

[0545] This allows users to receive positive feedback on their work and improve their motivation.

[0546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0547] Step 1:

[0548] The user enters the events that occurred that day into the smartphone's input device. The user launches the app and enters text into the input form. An example of input might be, "We received many words of thanks from customers today. We were able to find complex addresses without any problems, and we completed all orders on time."

[0549] Step 2:

[0550] The terminal sends the data entered by the user to the server. A POST request is used for sending, and the entered text data is passed to the server. The input is the data entered by the user, and the output is the text data sent to the server.

[0551] Step 3:

[0552] The server receives a POST request and retrieves text data. The server uses Node.js and Express to receive the sent data. The input is the text data sent from the terminal, and the output is the text data retrieved by the server.

[0553] Step 4:

[0554] The text data acquired by the server is sent to a sentiment analysis engine, which analyzes the sentiment score. Here, a sentiment analysis engine such as Amazon Comprehend or IBM Watson is used to calculate the sentiment score (positive, negative, neutral). The input is the text data acquired by the server, and the output is the sentiment score obtained from the sentiment analysis engine.

[0555] Step 5:

[0556] The server uses natural language processing tools to extract positive aspects based on the sentiment scores. Using spaCy or NLTK as the NLP tool, it analyzes and extracts positive elements in the text (e.g., words of gratitude, order completion, etc.). The input is text data with sentiment scores assigned, and the output is text data containing positive aspects.

[0557] Step 6:

[0558] The server generates a feedback message based on the extracted positive aspects. The generated message is designed to increase the user's motivation and emphasizes the positive aspects according to a template. The input is the text data from which the positive aspects have been extracted, and the output is the feedback message.

[0559] Step 7:

[0560] The server sends the generated feedback message back to the device. To send it, it uses a POST request to pass the generated feedback message to the smartphone. The input is the feedback message generated by the server, and the output is the sent feedback message.

[0561] Step 8:

[0562] The device displays the received feedback message to the user. The smartphone app displays the message so that the user can check it. The input is the feedback message sent from the server, and the output is the feedback message displayed on the user's smartphone.

[0563] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0564] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0565] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0566] [Third embodiment]

[0567] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0568] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0569] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0570] 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.

[0571] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0572] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0573] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0574] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0575] 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.

[0576] 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.

[0577] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0578] 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."

[0579] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing. An embodiment of this system will be described below.

[0580] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0581] The server analyzes the data received from the device. Natural language processing (NLP) technology is used for the analysis. At this time, the server performs sentiment analysis on the input text data and calculates sentiment scores such as positive, negative, and neutral. For example, NLTK (Natural Language Toolkit) can be used as an NLP tool.

[0582] The server extracts positive aspects from the text data based on the emotion score. Specifically, if the positive emotion score is high or the overall emotion score is positive, it adds this to the list as a positive aspect. For example, if someone inputs "Today was busy, but I was able to finish up some work. I met up with friends and had a great time," this text contains positive elements, so the extracted aspects would include "I had a good time with friends" and "I finished up some work."

[0583] The server then generates a feedback message based on the extracted positive aspects. The generated feedback message takes the form of, "I found some positive aspects of your day: Today's events gave me positive emotions. Overall, it seems like you had a great day!" This message helps boost the user's self-esteem and end the day on a positive note.

[0584] Finally, the server sends the generated feedback message to the device, which then displays it to the user, allowing the user to review the feedback message and notice the positive aspects of the day.

[0585] By implementing such a system, users can reaffirm the positive aspects at the end of the day, which is expected to improve their sense of happiness and self-esteem.

[0586] For example, if a user types, "Today, my presentation went well and I received positive feedback on my new project," the server parses this text and extracts "my presentation went well" and "I received positive feedback" as positive aspects. The generated feedback message is, "I found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0587] The processing flow will be explained below.

[0588] Step 1:

[0589] The user uses an input device to input the events that occurred that day in text form.

[0590] Step 2:

[0591] The device receives input text from the user.

[0592] Step 3:

[0593] The input text acquired by the terminal is sent to the server via a POST request.

[0594] Step 4:

[0595] The server stores the input text received from the terminal.

[0596] Step 5:

[0597] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, or neutral scores.

[0598] Step 6:

[0599] The server extracts positive aspects in the text based on the calculated sentiment score, for example, listing parts with a high positive sentiment score or an overall positive sentiment score.

[0600] Step 7:

[0601] The server generates a feedback message for the user based on the extracted positive aspects. The message lists the extracted positive aspects and emphasizes the positive aspects to the user.

[0602] Step 8:

[0603] The server sends the generated feedback message to the terminal.

[0604] Step 9:

[0605] The terminal displays the feedback message received from the server.

[0606] Step 10:

[0607] Users can review the feedback messages displayed on their device and feel positive by noticing the positive aspects of their day.

[0608] Example 1

[0609] 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."

[0610] Conventional feedback systems have difficulty in analyzing users' emotions and providing appropriate feedback for everyday events. Furthermore, they lack a system that allows users to rediscover the positive aspects of their experiences. As a result, they are not effective in increasing users' happiness and self-esteem.

[0611] 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.

[0612] In this invention, the server includes means for analyzing text data by natural language processing and calculating an emotion score, means for extracting positive aspects based on the emotion score, and means for generating a feedback message based on the extracted positive aspects, thereby enabling the user to reaffirm the positive experiences of the day and improve their sense of happiness and self-esteem.

[0613] A "user" is someone who accesses the system and enters events that occur during the day.

[0614] An "input device" is hardware that a user uses to input events in text form, including, for example, a smartphone or personal computer.

[0615] A "terminal" is an intermediate device that transmits data from a user's input device to a server.

[0616] A "server" is a computer system responsible for receiving and analyzing user-entered data, and generating and sending feedback messages.

[0617] "Natural language processing" is the technology used by computers to understand, interpret, or generate human language.

[0618] An "emotion score" is a numerical value related to positive, negative, or neutral emotions calculated by analyzing text data.

[0619] "Positive aspects" refer to positive emotions and events contained in the text data.

[0620] A "feedback message" is a message generated and provided to a user based on the analyzed data and extracted positive aspects.

[0621] This system allows users to input events that occurred during the day, analyzes the data using natural language processing, extracts positive aspects, and generates feedback messages. This system is designed to increase users' sense of happiness and self-esteem.

[0622] First, the user uses an input device (such as a smartphone or personal computer) to enter the events that occurred that day in text form. The input form has text boxes in which the user can freely describe their experiences that day. For example, they might enter, "Today I went to a cafe with my friends and had a great time. Work went well, too."

[0623] Next, the terminal sends the text data entered by the user to the server as a POST request. At this time, the terminal uses the HTTP protocol and encodes the data as necessary. It is recommended to use SSL / TLS to ensure security when transmitting data.

[0624] The server analyzes the text data received from the device. This analysis uses natural language processing (NLP) technology. Specifically, the server uses NLTK (Natural Language Toolkit) to perform sentiment analysis of the text data and calculates a positive, negative, or neutral sentiment score for each sentence. For example, a positive score is calculated for the text "I went to a cafe with my friends today and had a great time."

[0625] Based on the emotion score, the server extracts positive aspects from the text data. It lists the parts with high positive emotion scores and identifies them as positive elements. For example, "I went to a cafe with my friends and had a good time" is extracted as a positive aspect.

[0626] The server then generates a feedback message based on the extracted positive aspects, such as "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0627] The server generates a feedback message and sends it to the device. The server handles any errors appropriately to ensure the message reaches the device. The device then displays the received feedback message to the user. For example, the feedback message could be displayed using a dialog box or notification bar in the application. The user would see a message like, "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0628] In this way, this system allows users to re-recognize the positive experiences of the day and increase their sense of happiness and self-esteem. For example, a user might input, "Today, my presentation went well, and I received positive feedback on my new project." In this case, the server analyzes this text, extracts the positive aspects, such as "my presentation went well" and "I received positive feedback," and provides them to the user as a feedback message.

[0629] An example of a prompt sentence to use is, "Please tell us how your day went. For example, 'I went to a cafe with my friends today and had a great time.'" This can be designed to make it easier for users to enter input.

[0630] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0631] Step 1:

[0632] The user uses an input device to input the events that occurred that day in text format. Specifically, the user types "I went to a cafe with my friends today and had a great time" into a text box on a dedicated web form or application. The input text becomes the initial input data for the system.

[0633] Step 2:

[0634] The terminal sends the text data entered by the user to the server as a POST request. Specifically, the terminal properly encodes the text data and sends the data to the server using the HTTP protocol. The input is the text data entered by the user, and the output is the HTTP request sent to the server.

[0635] Step 3:

[0636] The server receives text data from the terminal. Specifically, the server receives an HTTP request, decodes the text data, and converts it into an internal data format. The input is the text data sent from the terminal, and the output is the data converted into a format that can be used for analysis.

[0637] Step 4:

[0638] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, the server uses NLP tools such as NLTK (Natural Language Toolkit) to calculate the sentiment score of the text data. The input is the text data converted into an internal data format, and the output is a sentiment score such as positive, negative, or neutral.

[0639] Step 5:

[0640] The server extracts positive aspects from text data based on the emotion score. Specifically, the server lists parts with high positive emotion scores and identifies them as positive elements. The input is the emotion score, and the output is the data from which the positive aspects have been extracted.

[0641] Step 6:

[0642] The server generates a feedback message based on the extracted positive aspects. Specifically, the server uses a template to generate a message such as "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the data from which the positive aspects were extracted, and the output is the generated feedback message.

[0643] Step 7:

[0644] The server sends the generated feedback message to the terminal. Specifically, the server sends the generated message to the terminal as an HTTP response. The input is the generated feedback message, and the output is the HTTP response.

[0645] Step 8:

[0646] The terminal displays the feedback message received from the server to the user. Specifically, the terminal analyzes the received message and displays it in a dialog box or notification bar within the application. The message displayed is "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the feedback message received as an HTTP response, and the output is the message displayed to the user.

[0647] (Application example 1)

[0648] 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."

[0649] There is a lack of mechanisms for effectively utilizing customer feedback in brick-and-mortar stores to improve service quality and motivate store clerks and managers. Specifically, it is necessary to quickly identify positive elements contained in the feedback and provide appropriate feedback to store clerks and managers. Conventional methods often involve analyzing feedback and generating feedback messages manually, resulting in low efficiency and a lack of real-time capabilities.

[0650] 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.

[0651] In this invention, the server includes: a means for a user to input events that occurred that day using an input device; a means for transmitting the input data to the server; a means for receiving the input data and analyzing it using natural language processing; a means for extracting positive aspects from the analysis results; a means for generating a feedback message based on the extracted positive aspects; a means for transmitting the generated feedback message to the input device; a means for displaying the feedback message to the user; a means for analyzing feedback input by customers in a physical store; and a means for providing positive aspects included in the feedback from customers to store staff or managers. This enables fast and effective analysis of feedback and provision of positive feedback in real time.

[0652] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone, tablet, personal computer, etc.

[0653] A "server" is a computer system for receiving and analyzing data sent from an input device.

[0654] "Natural language processing" is a technology that analyzes input text data, and mainly calculates emotional scores and extracts positive aspects.

[0655] The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0656] "Positive aspects" are positive elements extracted from text data analyzed using natural language processing.

[0657] "Feedback messages" are messages generated based on the extracted positive aspects and are provided to users, store staff, and managers.

[0658] A "physical store" is a store that exists in a physical location and provides services or products that customers can visit in person.

[0659] "Customers" refer to people who visit physical stores and use services and products.

[0660] A "store associate" is an employee who works in a physical store and provides services to customers.

[0661] A "manager" is a person in charge of operating and managing a physical store, and is responsible for instructing store staff and optimizing operations.

[0662] "Real-time" means that processing and data provision are carried out close to the moment an event occurs.

[0663] The present invention is a system that analyzes customer feedback using natural language processing technology to improve the quality of service and customer satisfaction in brick-and-mortar stores. This system is implemented in the following steps.

[0664] Users, or customers, use input devices such as smartphones or tablets to enter text about their experiences and impressions at the store that day. This feedback is sent from the smartphone or tablet to the server as a POST request. Input devices are generally called "input devices."

[0665] The server analyzes the received feedback data using natural language processing (NLP) tools (e.g., Google Cloud Natural Language API or NLTK). This analysis involves tokenizing the text data, tagging it, and calculating a sentiment score to extract positive aspects.

[0666] To calculate the emotion score, a technique is used to evaluate the content of text data with a positive, negative, or neutral score. For example, the TextBlob library can be used. This analysis evaluates the emotional content of the text data and extracts positive elements. The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0667] Based on the extracted positive aspects, the server automatically generates feedback messages that are provided to store staff and managers, such as "Today's feedback was positive, and our staff's friendliness and smooth ordering process were highly praised. Thank you, customer!"

[0668] This feedback message is sent to the user's smartphone or tablet and displayed in real time, allowing store associates and managers to receive prompt, positive feedback. "Real-time" means that processing and data provision occur close to the moment an event occurs.

[0669] For example, if a customer enters feedback such as "The staff were very kind and the ordering process went smoothly," the server analyzes this text and extracts "The staff were kind" and "The ordering process went smoothly" as positive aspects. The generated feedback message will be "Today's feedback rated the kindness of the staff and the smoothness of the ordering process highly. We appreciate your patronage!" and will be provided to store staff and managers.

[0670] A specific example of a prompt sentence is "Please analyze today's feedback. For example, 'The staff were very kind. Ordering went smoothly.'"

[0671] As described above, the present invention makes it possible to improve the quality of service in physical stores and increase the motivation of store staff and managers through the rapid and effective analysis of feedback and the provision of positive feedback in real time.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Step 1:

[0674] The user uses an input device such as a smartphone or tablet to input the events that occurred that day. An example of input data would be, "The staff were very kind. The ordering process went smoothly." This input data is sent from the device to the server as a POST request.

[0675] Step 2:

[0676] The server receives the feedback data sent from the device. The received data is text input data, and prepares it to be passed to a data analysis tool for natural language processing. The input of this step is the text data sent by the user, and the output is data ready for analysis.

[0677] Step 3:

[0678] The server uses a natural language processing tool (such as NLTK or Google Cloud Natural Language API) to analyze the received text data. Specifically, it tokenizes the text data, tags it, and calculates its sentiment score. The input for this step is the text data received in step 2, and the output is data containing the analysis results.

[0679] Step 4:

[0680] The server extracts positive aspects based on the sentiment score results. Specifically, it calculates a sentiment score for each sentence in the text, and if the score is positive, it adds the sentence to a list of positive aspects. The input of this step is the analysis result obtained in step 3, and the output is a list of positive aspects.

[0681] Step 5:

[0682] The server generates a feedback message based on the extracted positive aspects. For example, if the extracted positive aspects are "Friendly staff" and "Smooth ordering," the generated feedback message will be "Today's feedback rated the friendliness of the staff and the smooth ordering process highly. Thank you, customer!" The input of this step is the list of positive aspects obtained in step 4, and the output is the generated feedback message.

[0683] Step 6:

[0684] The server sends the generated feedback message to the device, preparing it for real-time display on the user's smartphone or tablet. The input to this step is the feedback message generated in step 5, and the output is the message sent to the device.

[0685] Step 7:

[0686] The user's device displays the feedback message received from the server. Specifically, the user, store clerk, or manager can check the feedback message and use it to improve service. The input of this step is the feedback message sent from the server in step 6, and the output is the message displayed on the user's device.

[0687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0688] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing and an emotion engine. The following describes an embodiment of this system.

[0689] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0690] The server stores the data received from the device and first uses an emotion engine to recognize emotions in real time from the user's input text. The emotion engine can use not only text data, but also emotional data such as voice and images as needed. The emotional data recognized by the emotion engine influences analysis using natural language processing (NLP).

[0691] The server then uses natural language processing techniques to calculate a sentiment score for the input text. The NLP tool analyzes the text for positive, negative, and neutral sentiment scores. For example, the NLP tool can be NLTK (Natural Language Toolkit) or another sentiment analysis tool.

[0692] After the emotion engine and NLP analysis are complete, the server extracts positive aspects from the text based on the emotion score. It lists parts with high positive emotion scores and elements that indicate positive emotions. For example, for an input such as "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server extracts positive aspects such as "I finished work" and "I had a great time with friends."

[0693] Next, the server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished work, you had a good time with your friends, it was a great day!"

[0694] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0695] For example, if a user types, "Today, my presentation went well, and I received positive feedback on my new project," the server analyzes this text and extracts the positive aspects of "my presentation went well" and "I received positive feedback" based on emotion recognition by the emotion engine and emotion score calculation by NLP. The generated feedback message will be, "We found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, it was a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user uses an input device to input the events that occurred that day in text form.

[0699] Step 2:

[0700] The device receives input text from the user.

[0701] Step 3:

[0702] The input text acquired by the terminal is sent to the server via a POST request.

[0703] Step 4:

[0704] The server stores the input text received from the terminal.

[0705] Step 5:

[0706] The server uses an emotion engine to recognize emotions in real time from input text, analyzing not only the text but also auxiliary emotion data such as audio and images.

[0707] Step 6:

[0708] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, and neutral sentiment scores.

[0709] Step 7:

[0710] By combining the results of the sentiment engine with the sentiment score generated by NLP, the server extracts positive aspects from the text, particularly listing parts with a positive sentiment score and a positive overall score.

[0711] Step 8:

[0712] The server generates feedback messages based on the extracted positive aspects. The generated messages emphasize the positive aspects and enhance the user's self-esteem.

[0713] Step 9:

[0714] The server sends the generated feedback message to the terminal.

[0715] Step 10:

[0716] The device displays the feedback message received from the server to the user.

[0717] Step 11:

[0718] Users review the feedback messages displayed on their devices, notice the positive aspects of their day, and feel positive.

[0719] Example 2

[0720] 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."

[0721] Conventional systems only perform simple analysis of text data entered by users, without performing advanced analysis using emotion recognition or natural language processing. This makes it difficult to generate accurate feedback messages based on the user's emotions. The present invention aims to provide a system that combines an emotion engine and natural language processing technology to perform more accurate emotion recognition and text analysis and provide positive feedback to users.

[0722] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving the input data and storing it in a database, means for recognizing emotions using an emotion engine based on the stored data, and means for calculating an emotion score of the text through natural language processing using the recognized emotion data. This makes it possible to accurately analyze the emotions of the text input by the user, extract positive aspects, and provide appropriate feedback to the user.

[0723] A "user" is an entity that inputs data into the system and receives feedback.

[0724] An "input device" is an electronic device that a user uses to input text data, and examples include smartphones and personal computers.

[0725] "Server" means a central computer system that receives, stores, and analyzes data submitted by users.

[0726] A "database" is a storage system managed by a server for systematically storing data entered by users.

[0727] An "emotion engine" is software that analyzes and recognizes user emotions from text data.

[0728] "Natural Language Processing (NLP)" is a technology for analyzing text data and understanding and extracting meaning and emotion.

[0729] An "emotion score" is a numerical representation of the emotional state of text data, expressed as a positive, negative, or neutral score.

[0730] "Positive aspects" are parts or elements with high emotional scores, and refer to positive events or impressions for the user.

[0731] A "feedback message" is a message generated based on the extracted positive aspects, and has content that conveys to the user the positive aspects of the events of the day.

[0732] The present invention is a system that generates and presents positive feedback by having a user input events that occurred that day and analyzing the data using natural language processing (NLP) and an emotion engine. The following describes an embodiment of the present invention.

[0733] Hardware and Software Instructions

[0734] A smartphone or personal computer is used as an input device for users to enter data. Through this input device, users enter the events that occurred that day in text format. The entered data is sent to a server via the device. The server stores the received data in a database.

[0735] Next, the server launches an emotion engine (e.g., an emotion analysis tool such as TextBlob or VADER) to perform real-time emotion recognition on the stored text data. The emotion engine analyzes the text data and calculates an emotion score: positive, negative, or neutral.

[0736] Furthermore, the server uses natural language processing tools (such as NLTK or spaCy) to analyze detailed sentiment scores within the text. Based on the results of the sentiment score analysis, positive aspects are listed. For example, if a user enters "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server will extract the positive aspects of "finishing work" and "having a good time with friends" from this text.

[0737] The server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished some work, you had a good time with your friends, it was a great day!"

[0738] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0739] Examples of specific examples and prompts

[0740] As a concrete example, consider the case where a user enters, "Today, my presentation went well, and I received positive feedback on my new project." The server analyzes this text and extracts the positive aspects, "My presentation went well" and "I received positive feedback," based on emotion recognition by an emotion engine and emotion scores calculated using natural language processing (NLP tools). The generated feedback message is, "We found some positive aspects of your day: Your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0741] An example of a prompt statement can be written as follows:

[0742] "Analyze the following text, extract the positive aspects, and generate a feedback message: 'My presentation went well today, and I received positive feedback on my new project.'"

[0743] The above is a specific embodiment of the present invention. This system aims to improve the user's well-being by analyzing the data entered by the user in an advanced manner and providing positive feedback.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1:

[0746] The user uses an input device (smartphone or personal computer) to input the events that occurred that day in text format. This is done by directly entering data into a text box on the screen of the input device. An example of input data is "I had an important meeting today, and I was able to complete it successfully." This input data becomes the raw data that is used in the subsequent analysis process.

[0747] Step 2:

[0748] The device sends the entered text data to the server as an HTTP POST request. The sending format is JSON, and the data sent is a JSON object with a "text" key. Specifically, the application on the device captures the user's input and makes a request to the configured server endpoint.

[0749] Step 3:

[0750] The server receives the POST request and saves the entered text data in a database. The database used could be, for example, MySQL or PostgreSQL. Specifically, the server parses the received JSON data and stores the data based on the appropriate schema. Once the input data has been saved, it can be referenced in a later analysis step.

[0751] Step 4:

[0752] The server performs emotion recognition on the stored text data using an emotion engine (such as TextBlob or VADER). The emotion engine analyzes words and phrases in the text and assigns each word a score: positive, negative, or neutral. Specifically, the emotion engine tokenizes the text and calculates its emotion value. It receives text data as input and generates an emotion score as output.

[0753] Step 5:

[0754] The server uses a natural language processing (NLP) tool (such as NLTK or spaCy) to calculate a detailed sentiment score for the text. The NLP tool then further analyzes the output of the sentiment engine to understand the overall sentiment trend of the text. Specifically, it performs grammatical and semantic analysis of the text and integrates the sentiment score. For example, it uses the NLTK sentiment analysis library to receive text data and sentiment scores as input and output the overall sentiment analysis results.

[0755] Step 6:

[0756] The server extracts positive aspects based on the results of the emotion score analysis. It lists the parts and elements with high positive emotion scores. Specifically, it filters based on the analysis results to extract positive elements. It receives the emotion score analysis results as input and generates a list of positive aspects as output. For example, a positive element such as "The meeting ended successfully" is extracted.

[0757] Step 7:

[0758] The server generates a feedback message based on the extracted positive aspects. The generation process creates a message that emphasizes the extracted positive aspects. Specifically, the extracted elements are embedded in a template. It receives a list of positive aspects as input and generates a feedback message as output. A message of the form "I found some positive aspects of your day: The meeting went well, it was a great day!" is generated.

[0759] Step 8:

[0760] The server sends the generated feedback message to the terminal. The message is sent as an HTTP response and is received by the terminal. Specifically, the generated feedback message is packaged in JSON format and sent as the body of the HTTP response. The generated feedback message is received as input and the HTTP response is sent as output.

[0761] Step 9:

[0762] The device displays the received feedback message to the user. Specifically, the feedback message is rendered appropriately on the device screen, for example, as a pop-up message or notification within the application. The device takes the received feedback message as input and displays it on the user's screen as output. The user can review the message and notice the positive aspects of their day.

[0763] (Application example 2)

[0764] 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."

[0765] Maintaining high motivation for employees, especially food delivery drivers, who perform their daily tasks is a difficult task. If employees have few opportunities to receive positive feedback about their work, their satisfaction with their work may decline. The present invention aims to provide a system that improves employee satisfaction and motivation by allowing employees to receive positive feedback after completing their work.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0767] In this invention, the server includes means for a user to input events that occurred that day using an input device, means for transmitting the input data to the server, and means for receiving the input data and analyzing it using natural language processing. This allows employees to reflect on their day's events after work and receive feedback in which the system extracts positive aspects, thereby increasing their satisfaction with their work and their motivation.

[0768] "User" refers to a person who uses this system.

[0769] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone or personal computer.

[0770] "Server" refers to a computer system that receives and analyzes data sent by users.

[0771] "Natural language processing" refers to the technology of analyzing text data and calculating emotional scores, etc., specifically using NLP tools.

[0772] "Emotion score" refers to a value that quantifies the emotion contained in text data.

[0773] "Positive aspects" refer to the positive elements contained in the text data.

[0774] "Feedback message" refers to a message generated based on the extracted positive aspects and provided to the user.

[0775] "Specific work" refers to the work the user is engaged in, primarily including the delivery work performed by food delivery drivers that day.

[0776] "Provided for the purpose of increasing motivation" means that the feedback message provided to the user is designed to increase the user's motivation.

[0777] In the system of the present invention, a user inputs events that occurred that day using an input device, and the server analyzes the input and generates and provides feedback messages.

[0778] Hardware and Software Configuration

[0779] A system for implementing the present invention uses the following hardware and software.

[0780] Hardware:

[0781] User input device: Smartphone (iOS or Android device)

[0782] Server: A computer system for processing and analyzing data.

[0783] software:

[0784] Smartphone app: Developed with React Native

[0785] Server application: Node.js + Express

[0786] Sentiment analysis engine: Amazon Comprehend or IBM Watson as natural language processing tools

[0787] NLP tools: spaCy or NLTK

[0788] Data processing and calculation

[0789] 1. Data Entry

[0790] The user starts the smartphone app and inputs the events that occurred that day. After entering the text, the user presses the submit button to send the input data.

[0791] 2. Data Transmission

[0792] Text data is sent from the smartphone to the server as a POST request.

[0793] 3. Data Reception and Analysis

[0794] The server receives a POST request using Node.js and Express to retrieve text data, which is then sent to a sentiment analysis engine to generate a sentiment score.

[0795] A sentiment analysis engine (e.g., Amazon Comprehend) calculates a sentiment score (positive, negative, or neutral) for text data and returns the result.

[0796] 4. Natural Language Processing

[0797] Based on the obtained sentiment score, the server uses NLP tools (e.g., spaCy) to extract positive aspects of the text data.

[0798] 5. Feedback Generation

[0799] The server automatically generates a feedback message based on the extracted positive aspects. The generated feedback message is created according to a template and emphasizes the positive elements.

[0800] 6. Sending and displaying results

[0801] The generated feedback message is sent back to the smartphone and displayed for the user to review.

[0802] Specific examples

[0803] As a concrete example, suppose the user enters the following text:

[0804] Prompt Sentence Examples

[0805] We received many thank you comments from our customers today. We had no trouble finding complex addresses and completed all orders on time.

[0806] When this text is entered into the system, the server performs the following process:

[0807] The sentiment analysis engine calculates a sentiment score for the text, with positive sentiment being given a higher rating.

[0808] Natural language processing tools extract positive aspects (e.g., customer appreciation, successfully locating a complex address, completing all orders on time).

[0809] Based on the extraction results, a feedback message like the one below is generated.

[0810] Feedback message example

[0811] Identify the positive aspects of your day: you received a thank you note from a customer, you had no trouble finding a complicated address, and you completed all the orders on time. What a great day!

[0812] This allows users to receive positive feedback on their work and improve their motivation.

[0813] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0814] Step 1:

[0815] The user enters the events that occurred that day into the smartphone's input device. The user launches the app and enters text into the input form. An example of input might be, "We received many words of thanks from customers today. We were able to find complex addresses without any problems, and we completed all orders on time."

[0816] Step 2:

[0817] The terminal sends the data entered by the user to the server. A POST request is used for sending, and the entered text data is passed to the server. The input is the data entered by the user, and the output is the text data sent to the server.

[0818] Step 3:

[0819] The server receives a POST request and retrieves text data. The server uses Node.js and Express to receive the sent data. The input is the text data sent from the terminal, and the output is the text data retrieved by the server.

[0820] Step 4:

[0821] The text data acquired by the server is sent to a sentiment analysis engine, which analyzes the sentiment score. Here, a sentiment analysis engine such as Amazon Comprehend or IBM Watson is used to calculate the sentiment score (positive, negative, neutral). The input is the text data acquired by the server, and the output is the sentiment score obtained from the sentiment analysis engine.

[0822] Step 5:

[0823] The server uses natural language processing tools to extract positive aspects based on the sentiment scores. Using spaCy or NLTK as the NLP tool, it analyzes and extracts positive elements in the text (e.g., words of gratitude, order completion, etc.). The input is text data with sentiment scores assigned, and the output is text data containing positive aspects.

[0824] Step 6:

[0825] The server generates a feedback message based on the extracted positive aspects. The generated message is designed to increase the user's motivation and emphasizes the positive aspects according to a template. The input is the text data from which the positive aspects have been extracted, and the output is the feedback message.

[0826] Step 7:

[0827] The server sends the generated feedback message back to the device. To send it, it uses a POST request to pass the generated feedback message to the smartphone. The input is the feedback message generated by the server, and the output is the sent feedback message.

[0828] Step 8:

[0829] The device displays the received feedback message to the user. The smartphone app displays the message so that the user can check it. The input is the feedback message sent from the server, and the output is the feedback message displayed on the user's smartphone.

[0830] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0831] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] 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.

[0833] [Fourth embodiment]

[0834] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0835] 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.

[0836] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0837] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0838] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0839] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0840] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0841] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0842] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0843] 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.

[0844] 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.

[0845] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0846] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0847] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing. An embodiment of this system will be described below.

[0848] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0849] The server analyzes the data received from the device. Natural language processing (NLP) technology is used for the analysis. At this time, the server performs sentiment analysis on the input text data and calculates sentiment scores such as positive, negative, and neutral. For example, NLTK (Natural Language Toolkit) can be used as an NLP tool.

[0850] The server extracts positive aspects from the text data based on the emotion score. Specifically, if the positive emotion score is high or the overall emotion score is positive, it adds this to the list as a positive aspect. For example, if someone inputs "Today was busy, but I was able to finish up some work. I met up with friends and had a great time," this text contains positive elements, so the extracted aspects would include "I had a good time with friends" and "I finished up some work."

[0851] The server then generates a feedback message based on the extracted positive aspects. The generated feedback message takes the form of, "I found some positive aspects of your day: Today's events gave me positive emotions. Overall, it seems like you had a great day!" This message helps boost the user's self-esteem and end the day on a positive note.

[0852] Finally, the server sends the generated feedback message to the device, which then displays it to the user, allowing the user to review the feedback message and notice the positive aspects of the day.

[0853] By implementing such a system, users can reaffirm the positive aspects at the end of the day, which is expected to improve their sense of happiness and self-esteem.

[0854] For example, if a user types, "Today, my presentation went well and I received positive feedback on my new project," the server parses this text and extracts "my presentation went well" and "I received positive feedback" as positive aspects. The generated feedback message is, "I found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] The user uses an input device to input the events that occurred that day in text form.

[0858] Step 2:

[0859] The device receives input text from the user.

[0860] Step 3:

[0861] The input text acquired by the terminal is sent to the server via a POST request.

[0862] Step 4:

[0863] The server stores the input text received from the terminal.

[0864] Step 5:

[0865] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, or neutral scores.

[0866] Step 6:

[0867] The server extracts positive aspects in the text based on the calculated sentiment score, for example, listing parts with a high positive sentiment score or an overall positive sentiment score.

[0868] Step 7:

[0869] The server generates a feedback message for the user based on the extracted positive aspects. The message lists the extracted positive aspects and emphasizes the positive aspects to the user.

[0870] Step 8:

[0871] The server sends the generated feedback message to the terminal.

[0872] Step 9:

[0873] The terminal displays the feedback message received from the server.

[0874] Step 10:

[0875] Users can review the feedback messages displayed on their device and feel positive by noticing the positive aspects of their day.

[0876] Example 1

[0877] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0878] Conventional feedback systems have difficulty in analyzing users' emotions and providing appropriate feedback for everyday events. Furthermore, they lack a system that allows users to rediscover the positive aspects of their experiences. As a result, they are not effective in increasing users' happiness and self-esteem.

[0879] 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.

[0880] In this invention, the server includes means for analyzing text data by natural language processing and calculating an emotion score, means for extracting positive aspects based on the emotion score, and means for generating a feedback message based on the extracted positive aspects, thereby enabling the user to reaffirm the positive experiences of the day and improve their sense of happiness and self-esteem.

[0881] A "user" is someone who accesses the system and enters events that occur during the day.

[0882] An "input device" is hardware that a user uses to input events in text form, including, for example, a smartphone or personal computer.

[0883] A "terminal" is an intermediate device that transmits data from a user's input device to a server.

[0884] A "server" is a computer system responsible for receiving and analyzing user-entered data, and generating and sending feedback messages.

[0885] "Natural language processing" is the technology used by computers to understand, interpret, or generate human language.

[0886] An "emotion score" is a numerical value related to positive, negative, or neutral emotions calculated by analyzing text data.

[0887] "Positive aspects" refer to positive emotions and events contained in the text data.

[0888] A "feedback message" is a message generated and provided to a user based on the analyzed data and extracted positive aspects.

[0889] This system allows users to input events that occurred during the day, analyzes the data using natural language processing, extracts positive aspects, and generates feedback messages. This system is designed to increase users' sense of happiness and self-esteem.

[0890] First, the user uses an input device (such as a smartphone or personal computer) to enter the events that occurred that day in text form. The input form has text boxes in which the user can freely describe their experiences that day. For example, they might enter, "Today I went to a cafe with my friends and had a great time. Work went well, too."

[0891] Next, the terminal sends the text data entered by the user to the server as a POST request. At this time, the terminal uses the HTTP protocol and encodes the data as necessary. It is recommended to use SSL / TLS to ensure security when transmitting data.

[0892] The server analyzes the text data received from the device. This analysis uses natural language processing (NLP) technology. Specifically, the server uses NLTK (Natural Language Toolkit) to perform sentiment analysis of the text data and calculates a positive, negative, or neutral sentiment score for each sentence. For example, a positive score is calculated for the text "I went to a cafe with my friends today and had a great time."

[0893] Based on the emotion score, the server extracts positive aspects from the text data. It lists the parts with high positive emotion scores and identifies them as positive elements. For example, "I went to a cafe with my friends and had a good time" is extracted as a positive aspect.

[0894] The server then generates a feedback message based on the extracted positive aspects, such as "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0895] The server generates a feedback message and sends it to the device. The server handles any errors appropriately to ensure the message reaches the device. The device then displays the received feedback message to the user. For example, the feedback message could be displayed using a dialog box or notification bar in the application. The user would see a message like, "I found some positive aspects of your day: You had a great time with your friends. What a great day!"

[0896] In this way, this system allows users to re-recognize the positive experiences of the day and increase their sense of happiness and self-esteem. For example, a user might input, "Today, my presentation went well, and I received positive feedback on my new project." In this case, the server analyzes this text, extracts the positive aspects, such as "my presentation went well" and "I received positive feedback," and provides them to the user as a feedback message.

[0897] An example of a prompt sentence to use is, "Please tell us how your day went. For example, 'I went to a cafe with my friends today and had a great time.'" This can be designed to make it easier for users to enter input.

[0898] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0899] Step 1:

[0900] The user uses an input device to input the events that occurred that day in text format. Specifically, the user types "I went to a cafe with my friends today and had a great time" into a text box on a dedicated web form or application. The input text becomes the initial input data for the system.

[0901] Step 2:

[0902] The terminal sends the text data entered by the user to the server as a POST request. Specifically, the terminal properly encodes the text data and sends the data to the server using the HTTP protocol. The input is the text data entered by the user, and the output is the HTTP request sent to the server.

[0903] Step 3:

[0904] The server receives text data from the terminal. Specifically, the server receives an HTTP request, decodes the text data, and converts it into an internal data format. The input is the text data sent from the terminal, and the output is the data converted into a format that can be used for analysis.

[0905] Step 4:

[0906] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, the server uses NLP tools such as NLTK (Natural Language Toolkit) to calculate the sentiment score of the text data. The input is the text data converted into an internal data format, and the output is a sentiment score such as positive, negative, or neutral.

[0907] Step 5:

[0908] The server extracts positive aspects from text data based on the emotion score. Specifically, the server lists parts with high positive emotion scores and identifies them as positive elements. The input is the emotion score, and the output is the data from which the positive aspects have been extracted.

[0909] Step 6:

[0910] The server generates a feedback message based on the extracted positive aspects. Specifically, the server uses a template to generate a message such as "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the data from which the positive aspects were extracted, and the output is the generated feedback message.

[0911] Step 7:

[0912] The server sends the generated feedback message to the terminal. Specifically, the server sends the generated message to the terminal as an HTTP response. The input is the generated feedback message, and the output is the HTTP response.

[0913] Step 8:

[0914] The terminal displays the feedback message received from the server to the user. Specifically, the terminal analyzes the received message and displays it in a dialog box or notification bar within the application. The message displayed is "I found some positive aspects of your day: You had a great time with your friends. It was a great day!" The input is the feedback message received as an HTTP response, and the output is the message displayed to the user.

[0915] (Application example 1)

[0916] 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."

[0917] There is a lack of mechanisms for effectively utilizing customer feedback in brick-and-mortar stores to improve service quality and motivate store clerks and managers. Specifically, it is necessary to quickly identify positive elements contained in the feedback and provide appropriate feedback to store clerks and managers. Conventional methods often involve analyzing feedback and generating feedback messages manually, resulting in low efficiency and a lack of real-time capabilities.

[0918] 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.

[0919] In this invention, the server includes: a means for a user to input events that occurred that day using an input device; a means for transmitting the input data to the server; a means for receiving the input data and analyzing it using natural language processing; a means for extracting positive aspects from the analysis results; a means for generating a feedback message based on the extracted positive aspects; a means for transmitting the generated feedback message to the input device; a means for displaying the feedback message to the user; a means for analyzing feedback input by customers in a physical store; and a means for providing positive aspects included in the feedback from customers to store staff or managers. This enables fast and effective analysis of feedback and provision of positive feedback in real time.

[0920] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone, tablet, personal computer, etc.

[0921] A "server" is a computer system for receiving and analyzing data sent from an input device.

[0922] "Natural language processing" is a technology that analyzes input text data, and mainly calculates emotional scores and extracts positive aspects.

[0923] The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0924] "Positive aspects" are positive elements extracted from text data analyzed using natural language processing.

[0925] "Feedback messages" are messages generated based on the extracted positive aspects and are provided to users, store staff, and managers.

[0926] A "physical store" is a store that exists in a physical location and provides services or products that customers can visit in person.

[0927] "Customers" refer to people who visit physical stores and use services and products.

[0928] A "store associate" is an employee who works in a physical store and provides services to customers.

[0929] A "manager" is a person in charge of operating and managing a physical store, and is responsible for instructing store staff and optimizing operations.

[0930] "Real-time" means that processing and data provision are carried out close to the moment an event occurs.

[0931] The present invention is a system that analyzes customer feedback using natural language processing technology to improve the quality of service and customer satisfaction in brick-and-mortar stores. This system is implemented in the following steps.

[0932] Users, or customers, use input devices such as smartphones or tablets to enter text about their experiences and impressions at the store that day. This feedback is sent from the smartphone or tablet to the server as a POST request. Input devices are generally called "input devices."

[0933] The server analyzes the received feedback data using natural language processing (NLP) tools (e.g., Google Cloud Natural Language API or NLTK). This analysis involves tokenizing the text data, tagging it, and calculating a sentiment score to extract positive aspects.

[0934] To calculate the emotion score, a technique is used to evaluate the content of text data with a positive, negative, or neutral score. For example, the TextBlob library can be used. This analysis evaluates the emotional content of the text data and extracts positive elements. The "emotion score" evaluates the emotional content of the input text with a positive, negative, or neutral score.

[0935] Based on the extracted positive aspects, the server automatically generates feedback messages that are provided to store staff and managers, such as "Today's feedback was positive, and our staff's friendliness and smooth ordering process were highly praised. Thank you, customer!"

[0936] This feedback message is sent to the user's smartphone or tablet and displayed in real time, allowing store associates and managers to receive prompt, positive feedback. "Real-time" means that processing and data provision occur close to the moment an event occurs.

[0937] For example, if a customer enters feedback such as "The staff were very kind and the ordering process went smoothly," the server analyzes this text and extracts "The staff were kind" and "The ordering process went smoothly" as positive aspects. The generated feedback message will be "Today's feedback rated the kindness of the staff and the smoothness of the ordering process highly. We appreciate your patronage!" and will be provided to store staff and managers.

[0938] A specific example of a prompt sentence is "Please analyze today's feedback. For example, 'The staff were very kind. Ordering went smoothly.'"

[0939] As described above, the present invention makes it possible to improve the quality of service in physical stores and increase the motivation of store staff and managers through the rapid and effective analysis of feedback and the provision of positive feedback in real time.

[0940] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0941] Step 1:

[0942] The user uses an input device such as a smartphone or tablet to input the events that occurred that day. An example of input data would be, "The staff were very kind. The ordering process went smoothly." This input data is sent from the device to the server as a POST request.

[0943] Step 2:

[0944] The server receives the feedback data sent from the device. The received data is text input data, and prepares it to be passed to a data analysis tool for natural language processing. The input of this step is the text data sent by the user, and the output is data ready for analysis.

[0945] Step 3:

[0946] The server uses a natural language processing tool (such as NLTK or Google Cloud Natural Language API) to analyze the received text data. Specifically, it tokenizes the text data, tags it, and calculates its sentiment score. The input for this step is the text data received in step 2, and the output is data containing the analysis results.

[0947] Step 4:

[0948] The server extracts positive aspects based on the sentiment score results. Specifically, it calculates a sentiment score for each sentence in the text, and if the score is positive, it adds the sentence to a list of positive aspects. The input of this step is the analysis result obtained in step 3, and the output is a list of positive aspects.

[0949] Step 5:

[0950] The server generates a feedback message based on the extracted positive aspects. For example, if the extracted positive aspects are "Friendly staff" and "Smooth ordering," the generated feedback message will be "Today's feedback rated the friendliness of the staff and the smooth ordering process highly. Thank you, customer!" The input of this step is the list of positive aspects obtained in step 4, and the output is the generated feedback message.

[0951] Step 6:

[0952] The server sends the generated feedback message to the device, preparing it for real-time display on the user's smartphone or tablet. The input to this step is the feedback message generated in step 5, and the output is the message sent to the device.

[0953] Step 7:

[0954] The user's device displays the feedback message received from the server. Specifically, the user, store clerk, or manager can check the feedback message and use it to improve service. The input of this step is the feedback message sent from the server in step 6, and the output is the message displayed on the user's device.

[0955] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0956] The present invention is a system in which a user inputs events that occurred that day and the data is analyzed using natural language processing and an emotion engine. The following describes an embodiment of this system.

[0957] The user uses an input device (such as a smartphone or personal computer) to input the events that occurred that day in text format. The input data is sent to the server via the device. Specifically, the device sends the user's input text to the server as a POST request.

[0958] The server stores the data received from the device and first uses an emotion engine to recognize emotions in real time from the user's input text. The emotion engine can use not only text data, but also emotional data such as voice and images as needed. The emotional data recognized by the emotion engine influences analysis using natural language processing (NLP).

[0959] The server then uses natural language processing techniques to calculate a sentiment score for the input text. The NLP tool analyzes the text for positive, negative, and neutral sentiment scores. For example, the NLP tool can be NLTK (Natural Language Toolkit) or another sentiment analysis tool.

[0960] After the emotion engine and NLP analysis are complete, the server extracts positive aspects from the text based on the emotion score. It lists parts with high positive emotion scores and elements that indicate positive emotions. For example, for an input such as "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server extracts positive aspects such as "I finished work" and "I had a great time with friends."

[0961] Next, the server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished work, you had a good time with your friends, it was a great day!"

[0962] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[0963] For example, if a user types, "Today, my presentation went well, and I received positive feedback on my new project," the server analyzes this text and extracts the positive aspects of "my presentation went well" and "I received positive feedback" based on emotion recognition by the emotion engine and emotion score calculation by NLP. The generated feedback message will be, "We found some positive aspects of your day: your presentation went well, you received positive feedback on your new project, it was a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[0964] The processing flow will be explained below.

[0965] Step 1:

[0966] The user uses an input device to input the events that occurred that day in text form.

[0967] Step 2:

[0968] The device receives input text from the user.

[0969] Step 3:

[0970] The input text acquired by the terminal is sent to the server via a POST request.

[0971] Step 4:

[0972] The server stores the input text received from the terminal.

[0973] Step 5:

[0974] The server uses an emotion engine to recognize emotions in real time from input text, analyzing not only the text but also auxiliary emotion data such as audio and images.

[0975] Step 6:

[0976] The server uses natural language processing (NLP) tools to calculate a sentiment score for the input text. Specifically, the NLP tools analyze the text for positive, negative, and neutral sentiment scores.

[0977] Step 7:

[0978] By combining the results of the sentiment engine with the sentiment score generated by NLP, the server extracts positive aspects from the text, particularly listing parts with a positive sentiment score and a positive overall score.

[0979] Step 8:

[0980] The server generates feedback messages based on the extracted positive aspects. The generated messages emphasize the positive aspects and enhance the user's self-esteem.

[0981] Step 9:

[0982] The server sends the generated feedback message to the terminal.

[0983] Step 10:

[0984] The device displays the feedback message received from the server to the user.

[0985] Step 11:

[0986] Users review the feedback messages displayed on their devices, notice the positive aspects of their day, and feel positive.

[0987] Example 2

[0988] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0989] Conventional systems only perform simple analysis of text data entered by users, without performing advanced analysis using emotion recognition or natural language processing. This makes it difficult to generate accurate feedback messages based on the user's emotions. The present invention aims to provide a system that combines an emotion engine and natural language processing technology to perform more accurate emotion recognition and text analysis and provide positive feedback to users.

[0990] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving the input data and storing it in a database, means for recognizing emotions using an emotion engine based on the stored data, and means for calculating an emotion score of the text through natural language processing using the recognized emotion data. This makes it possible to accurately analyze the emotions of the text input by the user, extract positive aspects, and provide appropriate feedback to the user.

[0991] A "user" is an entity that inputs data into the system and receives feedback.

[0992] An "input device" is an electronic device that a user uses to input text data, and examples include smartphones and personal computers.

[0993] "Server" means a central computer system that receives, stores, and analyzes data submitted by users.

[0994] A "database" is a storage system managed by a server for systematically storing data entered by users.

[0995] An "emotion engine" is software that analyzes and recognizes user emotions from text data.

[0996] "Natural Language Processing (NLP)" is a technology for analyzing text data and understanding and extracting meaning and emotion.

[0997] An "emotion score" is a numerical representation of the emotional state of text data, expressed as a positive, negative, or neutral score.

[0998] "Positive aspects" are parts or elements with high emotional scores, and refer to positive events or impressions for the user.

[0999] A "feedback message" is a message generated based on the extracted positive aspects, and has content that conveys to the user the positive aspects of the events of the day.

[1000] The present invention is a system that generates and presents positive feedback by having a user input events that occurred that day and analyzing the data using natural language processing (NLP) and an emotion engine. The following describes an embodiment of the present invention.

[1001] Hardware and Software Instructions

[1002] A smartphone or personal computer is used as an input device for users to enter data. Through this input device, users enter the events that occurred that day in text format. The entered data is sent to a server via the device. The server stores the received data in a database.

[1003] Next, the server launches an emotion engine (e.g., an emotion analysis tool such as TextBlob or VADER) to perform real-time emotion recognition on the stored text data. The emotion engine analyzes the text data and calculates an emotion score: positive, negative, or neutral.

[1004] Furthermore, the server uses natural language processing tools (such as NLTK or spaCy) to analyze detailed sentiment scores within the text. Based on the results of the sentiment score analysis, positive aspects are listed. For example, if a user enters "Today was busy, but I was able to finish work. I also met up with friends and had a great time," the server will extract the positive aspects of "finishing work" and "having a good time with friends" from this text.

[1005] The server generates a feedback message based on the extracted positive aspects. The generated feedback message lists the extracted positive aspects and emphasizes the positive aspects to the user. For example, it could be something like, "I found some positive aspects of your day: you finished some work, you had a good time with your friends, it was a great day!"

[1006] Finally, the server sends the generated feedback message to the device, which then displays it to the user. The user can check the feedback message displayed on the device, notice the positive aspects of the day, and end the day on a positive note.

[1007] Examples of specific examples and prompts

[1008] As a concrete example, consider the case where a user enters, "Today, my presentation went well, and I received positive feedback on my new project." The server analyzes this text and extracts the positive aspects, "My presentation went well" and "I received positive feedback," based on emotion recognition by an emotion engine and emotion scores calculated using natural language processing (NLP tools). The generated feedback message is, "We found some positive aspects of your day: Your presentation went well, you received positive feedback on your new project, what a great day!" This allows the user to reaffirm their success and end their day on a positive note.

[1009] An example of a prompt statement can be written as follows:

[1010] "Analyze the following text, extract the positive aspects, and generate a feedback message: 'My presentation went well today, and I received positive feedback on my new project.'"

[1011] The above is a specific embodiment of the present invention. This system aims to improve the user's well-being by analyzing the data entered by the user in an advanced manner and providing positive feedback.

[1012] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1013] Step 1:

[1014] The user uses an input device (smartphone or personal computer) to input the events that occurred that day in text format. This is done by directly entering data into a text box on the screen of the input device. An example of input data is "I had an important meeting today, and I was able to complete it successfully." This input data becomes the raw data that is used in the subsequent analysis process.

[1015] Step 2:

[1016] The device sends the entered text data to the server as an HTTP POST request. The sending format is JSON, and the data sent is a JSON object with a "text" key. Specifically, the application on the device captures the user's input and makes a request to the configured server endpoint.

[1017] Step 3:

[1018] The server receives the POST request and saves the entered text data in a database. The database used could be, for example, MySQL or PostgreSQL. Specifically, the server parses the received JSON data and stores the data based on the appropriate schema. Once the input data has been saved, it can be referenced in a later analysis step.

[1019] Step 4:

[1020] The server performs emotion recognition on the stored text data using an emotion engine (such as TextBlob or VADER). The emotion engine analyzes words and phrases in the text and assigns each word a score: positive, negative, or neutral. Specifically, the emotion engine tokenizes the text and calculates its emotion value. It receives text data as input and generates an emotion score as output.

[1021] Step 5:

[1022] The server uses a natural language processing (NLP) tool (such as NLTK or spaCy) to calculate a detailed sentiment score for the text. The NLP tool then further analyzes the output of the sentiment engine to understand the overall sentiment trend of the text. Specifically, it performs grammatical and semantic analysis of the text and integrates the sentiment score. For example, it uses the NLTK sentiment analysis library to receive text data and sentiment scores as input and output the overall sentiment analysis results.

[1023] Step 6:

[1024] The server extracts positive aspects based on the results of the emotion score analysis. It lists the parts and elements with high positive emotion scores. Specifically, it filters based on the analysis results to extract positive elements. It receives the emotion score analysis results as input and generates a list of positive aspects as output. For example, a positive element such as "The meeting ended successfully" is extracted.

[1025] Step 7:

[1026] The server generates a feedback message based on the extracted positive aspects. The generation process creates a message that emphasizes the extracted positive aspects. Specifically, the extracted elements are embedded in a template. It receives a list of positive aspects as input and generates a feedback message as output. A message of the form "I found some positive aspects of your day: The meeting went well, it was a great day!" is generated.

[1027] Step 8:

[1028] The server sends the generated feedback message to the terminal. The message is sent as an HTTP response and is received by the terminal. Specifically, the generated feedback message is packaged in JSON format and sent as the body of the HTTP response. The generated feedback message is received as input and the HTTP response is sent as output.

[1029] Step 9:

[1030] The device displays the received feedback message to the user. Specifically, the feedback message is rendered appropriately on the device screen, for example, as a pop-up message or notification within the application. The device takes the received feedback message as input and displays it on the user's screen as output. The user can review the message and notice the positive aspects of their day.

[1031] (Application example 2)

[1032] 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."

[1033] Maintaining high motivation for employees, especially food delivery drivers, who perform their daily tasks is a difficult task. If employees have few opportunities to receive positive feedback about their work, their satisfaction with their work may decline. The present invention aims to provide a system that improves employee satisfaction and motivation by allowing employees to receive positive feedback after completing their work.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1035] In this invention, the server includes means for a user to input events that occurred that day using an input device, means for transmitting the input data to the server, and means for receiving the input data and analyzing it using natural language processing. This allows employees to reflect on their day's events after work and receive feedback in which the system extracts positive aspects, thereby increasing their satisfaction with their work and their motivation.

[1036] "User" refers to a person who uses this system.

[1037] An "input device" is a device that allows a user to input events that occurred that day, and specifically includes a smartphone or personal computer.

[1038] "Server" refers to a computer system that receives and analyzes data sent by users.

[1039] "Natural language processing" refers to the technology of analyzing text data and calculating emotional scores, etc., specifically using NLP tools.

[1040] "Emotion score" refers to a value that quantifies the emotion contained in text data.

[1041] "Positive aspects" refer to the positive elements contained in the text data.

[1042] "Feedback message" refers to a message generated based on the extracted positive aspects and provided to the user.

[1043] "Specific work" refers to the work the user is engaged in, primarily including the delivery work performed by food delivery drivers that day.

[1044] "Provided for the purpose of increasing motivation" means that the feedback message provided to the user is designed to increase the user's motivation.

[1045] In the system of the present invention, a user inputs events that occurred that day using an input device, and the server analyzes the input and generates and provides feedback messages.

[1046] Hardware and Software Configuration

[1047] A system for implementing the present invention uses the following hardware and software.

[1048] Hardware:

[1049] User input device: Smartphone (iOS or Android device)

[1050] Server: A computer system for processing and analyzing data.

[1051] software:

[1052] Smartphone app: Developed with React Native

[1053] Server application: Node.js + Express

[1054] Sentiment analysis engine: Amazon Comprehend or IBM Watson as natural language processing tools

[1055] NLP tools: spaCy or NLTK

[1056] Data processing and calculation

[1057] 1. Data Entry

[1058] The user starts the smartphone app and inputs the events that occurred that day. After entering the text, the user presses the submit button to send the input data.

[1059] 2. Data Transmission

[1060] Text data is sent from the smartphone to the server as a POST request.

[1061] 3. Data Reception and Analysis

[1062] The server receives a POST request using Node.js and Express to retrieve text data, which is then sent to a sentiment analysis engine to generate a sentiment score.

[1063] A sentiment analysis engine (e.g., Amazon Comprehend) calculates a sentiment score (positive, negative, or neutral) for text data and returns the result.

[1064] 4. Natural Language Processing

[1065] Based on the obtained sentiment score, the server uses NLP tools (e.g., spaCy) to extract positive aspects of the text data.

[1066] 5. Feedback Generation

[1067] The server automatically generates a feedback message based on the extracted positive aspects. The generated feedback message is created according to a template and emphasizes the positive elements.

[1068] 6. Sending and displaying results

[1069] The generated feedback message is sent back to the smartphone and displayed for the user to review.

[1070] Specific examples

[1071] As a concrete example, suppose the user enters the following text:

[1072] Prompt Sentence Examples

[1073] We received many thank you comments from our customers today. We had no trouble finding complex addresses and completed all orders on time.

[1074] When this text is entered into the system, the server performs the following process:

[1075] The sentiment analysis engine calculates a sentiment score for the text, with positive sentiment being given a higher rating.

[1076] Natural language processing tools extract positive aspects (e.g., customer appreciation, successfully locating a complex address, completing all orders on time).

[1077] Based on the extraction results, a feedback message like the one below is generated.

[1078] Feedback message example

[1079] Identify the positive aspects of your day: you received a thank you note from a customer, you had no trouble finding a complicated address, and you completed all the orders on time. What a great day!

[1080] This allows users to receive positive feedback on their work and improve their motivation.

[1081] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1082] Step 1:

[1083] The user enters the events that occurred that day into the smartphone's input device. The user launches the app and enters text into the input form. An example of input might be, "We received many words of thanks from customers today. We were able to find complex addresses without any problems, and we completed all orders on time."

[1084] Step 2:

[1085] The terminal sends the data entered by the user to the server. A POST request is used for sending, and the entered text data is passed to the server. The input is the data entered by the user, and the output is the text data sent to the server.

[1086] Step 3:

[1087] The server receives a POST request and retrieves text data. The server uses Node.js and Express to receive the sent data. The input is the text data sent from the terminal, and the output is the text data retrieved by the server.

[1088] Step 4:

[1089] The text data acquired by the server is sent to a sentiment analysis engine, which analyzes the sentiment score. Here, a sentiment analysis engine such as Amazon Comprehend or IBM Watson is used to calculate the sentiment score (positive, negative, neutral). The input is the text data acquired by the server, and the output is the sentiment score obtained from the sentiment analysis engine.

[1090] Step 5:

[1091] The server uses natural language processing tools to extract positive aspects based on the sentiment scores. Using spaCy or NLTK as the NLP tool, it analyzes and extracts positive elements in the text (e.g., words of gratitude, order completion, etc.). The input is text data with sentiment scores assigned, and the output is text data containing positive aspects.

[1092] Step 6:

[1093] The server generates a feedback message based on the extracted positive aspects. The generated message is designed to increase the user's motivation and emphasizes the positive aspects according to a template. The input is the text data from which the positive aspects have been extracted, and the output is the feedback message.

[1094] Step 7:

[1095] The server sends the generated feedback message back to the device. To send it, it uses a POST request to pass the generated feedback message to the smartphone. The input is the feedback message generated by the server, and the output is the sent feedback message.

[1096] Step 8:

[1097] The device displays the received feedback message to the user. The smartphone app displays the message so that the user can check it. The input is the feedback message sent from the server, and the output is the feedback message displayed on the user's smartphone.

[1098] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1100] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1101] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1102] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1103] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1104] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1105] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1106] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1107] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1108] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1109] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1110] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1111] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1112] 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.

[1113] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1114] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1115] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1116] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1117] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1118] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1119] The following is further disclosed regarding the above embodiment.

[1120] (Claim 1)

[1121] a means for a user to input events that occurred that day using an input device;

[1122] means for transmitting the input data to a server;

[1123] means for receiving the input data and analyzing it by natural language processing;

[1124] A means for extracting positive aspects from the analysis results;

[1125] means for generating a feedback message based on the extracted positive aspects;

[1126] means for transmitting the generated feedback message to the input device;

[1127] means for displaying said feedback message to a user;

[1128] A system including:

[1129] (Claim 2)

[1130] 10. The system of claim 1, wherein the means for analyzing using natural language processing calculates a sentiment score for the text.

[1131] (Claim 3)

[1132] 2. The system of claim 1, wherein the means for extracting positive aspects adds positive aspects to a list if the sentiment score is positive.

[1133] "Example 1"

[1134] (Claim 1)

[1135] A means for a user to input events that occurred that day in text form using an input device;

[1136] means for transmitting the input data to a server via a terminal;

[1137] means for receiving the input data, analyzing it by natural language processing, and calculating an emotion score;

[1138] means for extracting positive aspects based on the emotion scores;

[1139] means for generating a feedback message based on the extracted positive aspects;

[1140] means for transmitting the generated feedback message to the input device;

[1141] means for displaying said feedback message to a user;

[1142] A system including:

[1143] (Claim 2)

[1144] 2. The system of claim 1, wherein the means for analyzing by natural language processing calculates an emotion score for the text data.

[1145] (Claim 3)

[1146] 2. The system of claim 1, wherein the means for extracting positive aspects adds positive aspects to a list if the sentiment score is positive.

[1147] "Application Example 1"

[1148] (Claim 1)

[1149] a means for a user to input events that occurred that day using an input device;

[1150] means for transmitting the input data to a server;

[1151] means for receiving the input data and analyzing it by natural language processing;

[1152] A means for extracting positive aspects from the analysis results;

[1153] means for generating a feedback message based on the extracted positive aspects;

[1154] means for transmitting the generated feedback message to the input device;

[1155] means for displaying said feedback message to a user;

[1156] A means for analyzing feedback input by customers at a physical store;

[1157] a means for providing positive aspects of said customer feedback to store staff and managers;

[1158] A system including:

[1159] (Claim 2)

[1160] 2. The system of claim 1, wherein the means for analyzing using natural language processing calculates a sentiment score for the text and extracts positive aspects based on the customer feedback.

[1161] (Claim 3)

[1162] The system of claim 1, wherein the means for extracting positive aspects includes means for adding positive aspects to a list when the sentiment score is positive and providing the list to staff at the physical store in real time.

[1163] "Example 2: Combining Emotion Engines"

[1164] (Claim 1)

[1165] a means for a user to input events that occurred that day using an input device;

[1166] means for transmitting the input data to a server;

[1167] means for receiving the input data and storing it in a database;

[1168] means for recognizing emotions using an emotion engine based on the stored data;

[1169] means for calculating an emotion score for the text by natural language processing using the recognized emotion data;

[1170] means for extracting positive aspects from the calculated sentiment scores;

[1171] means for generating a feedback message based on the extracted positive aspects;

[1172] means for transmitting the generated feedback message to the input device;

[1173] means for displaying said feedback message to a user;

[1174] A system including:

[1175] (Claim 2)

[1176] The system of claim 1, wherein the means for analyzing by natural language processing calculates the emotion score using a plurality of emotion analysis tools.

[1177] (Claim 3)

[1178] The system according to claim 1, wherein the means for extracting positive aspects lists parts or elements with positive emotional scores.

[1179] "Application example 2 when combining emotion engines"

[1180] (Claim 1)

[1181] a means for a user to input events that occurred that day using an input device;

[1182] means for transmitting the input data to a server;

[1183] means for receiving the input data and analyzing it by natural language processing;

[1184] A means for extracting positive aspects from the analysis results;

[1185] means for generating a feedback message based on the extracted positive aspects;

[1186] means for transmitting the generated feedback message to the input device;

[1187] means for displaying said feedback message to a user;

[1188] A means of providing feedback messages aimed at improving motivation for specific tasks;

[1189] A system including:

[1190] (Claim 2)

[1191] 10. The system of claim 1, wherein the means for analyzing using natural language processing calculates a sentiment score for the text.

[1192] (Claim 3)

[1193] 2. The system of claim 1, wherein the means for extracting positive aspects adds positive aspects to a list if the sentiment score is positive. [Explanation of symbols]

[1194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input events that occurred that day using an input device; means for transmitting the input data to a server; means for receiving the input data and analyzing it by natural language processing; A means for extracting positive aspects from the analysis results; means for generating a feedback message based on the extracted positive aspects; means for transmitting the generated feedback message to the input device; means for displaying said feedback message to a user; A system including:

2. The system of claim 1 , wherein the means for analyzing using natural language processing calculates a sentiment score for the text.

3. The system of claim 1 , wherein the means for extracting positive aspects adds a positive aspect to a list if the sentiment score is positive.

Images