system

Abstract

The system according to this embodiment aims to enable users to input orders by voice. [Solution] The system according to the embodiment comprises a reception unit, a conversion unit, and a processing unit. The reception unit receives the user's order by voice. The conversion unit recognizes the voice input by the reception unit and converts it into text. The processing unit processes the order details based on the text data converted by the conversion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the ordering process in a restaurant is limited to touch operations and voice ordering is not possible.

[0005] The system according to the embodiment aims to enable a user to input an order by voice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a conversion unit, and a processing unit. The reception unit receives an order input by a user by voice. The conversion unit recognizes the voice input by the reception unit and converts it into text. The processing unit processes the order content based on the text data converted by the conversion unit.

Effects of the Invention

[0007] The system according to this embodiment can enable users to input orders by voice. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The SmartOrderAI system according to an embodiment of the present invention is a system that adds a voice ordering function using AI to tablets and vending machines placed in restaurants. With the SmartOrderAI system, the user voice-inputs their order into the tablet or vending machine. The AI ​​recognizes the voice and converts it into text. Based on the converted text, the order is processed. This mechanism makes it easy for elderly people and visually impaired people who have difficulty operating touch panels to place orders. In addition, anyone can easily place an order regardless of the different screen designs and operating methods of each store. Furthermore, because the AI ​​responds in a natural conversational manner, order errors are reduced, making the job of store staff easier. For example, the user voice-inputs an order such as "I want X of XX" into the tablet or vending machine. In this case, the user only needs to specify the specific menu item and quantity. For example, the user voice-inputs an order such as "I want 2 hamburgers." This information is input into the AI. Next, the AI ​​recognizes the input voice and converts it into text. The AI ​​uses speech recognition technology to convert the user's voice into text data. For example, the voice "I want 2 hamburgers" is converted into text. This text data is processed as the order details. Furthermore, the AI ​​processes orders based on text data. The AI ​​analyzes the text data and confirms the order details. For example, it analyzes the text data "I want two hamburgers" and confirms the order for two hamburgers. At this time, the AI ​​can also translate the menu language in real time based on the order details. This system makes it easy for elderly people and visually impaired people who have difficulty operating touch panels to place orders. Users can easily enter orders by voice without having to perform complex operations. Also, anyone can easily place an order regardless of the different screen designs and operation methods at each store. In addition, because the AI ​​responds in a natural conversational manner, order errors are reduced and the work of staff is made easier. The AI ​​generates natural responses that match the user's voice and confirms the order details. For example, it generates a response such as "Two hamburgers, right?" This reduces order errors and alleviates the burden on staff. The AI ​​can also identify age group and gender from the voice and use this for marketing purposes.For example, it's possible to understand the sales performance of targeted products for specific age groups or genders and utilize this information in marketing strategies. In this way, SmartOrderAI adds voice ordering functionality to restaurant tablets and vending machines, making it easy for anyone to order. Furthermore, because the AI ​​responds in a natural conversational manner, order errors are reduced, making the job easier for staff. It can also be used for marketing, contributing to increased store sales. Thus, the SmartOrderAI system adds voice ordering functionality to restaurant tablets and vending machines, making it easy for anyone to order.

[0029] The SmartOrderAI system according to this embodiment comprises a reception unit, a conversion unit, and a processing unit. The reception unit receives orders from the user by voice. Users can use voice commands or natural language to enter orders by voice. For example, a user can enter an order such as "I want two hamburgers" by voice. The conversion unit recognizes the voice input by the reception unit and converts it into text. The conversion unit converts the user's voice into text data using, for example, a speech recognition algorithm. For example, the conversion unit can convert the voice "I want two hamburgers" into text. The processing unit processes the order based on the text data converted by the conversion unit. The processing unit processes the order according to, for example, an order confirmation method and processing procedure. For example, the processing unit can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers. Thus, the SmartOrderAI system according to this embodiment makes it easy for users to place orders by voice input, converting them to text, and processing them. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without using AI. For example, the reception unit can input the user's voice into the AI, which can then perform speech recognition. Some or all of the above-described processes in the conversion unit may be performed using the AI, or not. For example, the conversion unit can input the user's voice into the AI, which can then convert it into text. Some or all of the above-described processes in the processing unit may be performed using the AI, or not. For example, the processing unit can input text data into the AI, which can then process the order details.

[0030] The reception desk allows users to enter orders by voice. Users can use voice commands or natural language to enter orders. Specifically, users can voice orders such as "I want two hamburgers" into their smartphone or a dedicated device. The reception desk can use a microphone with noise-canceling capabilities to capture these voice inputs with high accuracy. Furthermore, the reception desk captures the user's voice in real time and saves it as audio data. This audio data is stored in the reception desk's temporary memory and sent to the next processing step. The reception desk provides a user-friendly interface to smoothly accept user voice input. For example, it can provide a visual indicator to show when voice input has started and audio feedback to show when voice input is complete. This allows users to confirm whether their order has been correctly received. Additionally, the reception desk can support multiple languages, accommodating users who speak different languages. For example, it can accept voice input in multiple languages ​​such as English, Japanese, and Spanish. This allows the reception desk to cater to a wider user base and improve convenience.

[0031] The conversion unit recognizes the voice input from the reception unit and converts it into text. The conversion unit uses, for example, a speech recognition algorithm to convert the user's voice into text data. Specifically, the conversion unit uses a speech recognition engine to analyze the voice data and identify phonemes and words. The speech recognition engine is trained using deep learning technology and has high recognition accuracy. For example, the conversion unit can convert the voice "I want two hamburgers" into text. When converting voice data into text data, the conversion unit considers context and grammar to generate accurate text. For example, it considers context to generate accurate text so that the voice input "I want two hamburgers" is not misrecognized as "I want two hamburgers." Furthermore, the conversion unit can learn the user's pronunciation and speaking characteristics to improve the accuracy of speech recognition. For example, if the user has a specific accent or dialect, it can learn these characteristics to improve recognition accuracy. The conversion unit generates the speech recognition results in real time and sends them to the next processing step. This allows the user to enter orders smoothly and have them processed quickly.

[0032] The processing unit processes the order based on the text data converted by the conversion unit. The processing unit processes the order according to the order confirmation method and processing procedure, for example. Specifically, the processing unit analyzes the text data to identify the order details. For example, it can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers. After confirming the order details, the processing unit generates an order confirmation message and presents it to the user. For example, it generates a confirmation message such as "Your order for two hamburgers has been accepted" and displays it on the user's device. Furthermore, the processing unit saves the order details to a database and manages the order history. This allows the user to refer to their past order history and makes reordering easier. After confirming the order details, the processing unit executes the order according to the order processing procedure. For example, it sends the order details to the kitchen or delivery department to arrange cooking and delivery. The processing unit can monitor the progress of the order in real time and notify the user. For example, it can notify the user when the order is being cooked or delivered, allowing them to keep track of the order's progress. This enables the processing unit to process the user's order quickly and accurately, providing a smooth ordering experience.

[0033] The processing unit can translate the menu language in real time based on the order content. The processing unit translates the order content in real time based on, for example, the translation algorithm used and the accuracy of the translation. For example, the processing unit can translate the order content from English to Japanese. It can also translate the order content from Japanese to English. Furthermore, the processing unit can translate the order content into multiple languages. For example, the processing unit can translate the order content into English, Japanese, French, etc. This makes it possible to order across language barriers by translating the menu language in real time based on the order content. Some or all of the above processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the order content into AI, and the AI ​​can translate it in real time.

[0034] The processing unit can generate natural responses based on the user's voice and confirm the order details. The processing unit generates natural responses based, for example, on the dialogue generation algorithm used and the criteria for evaluating the naturalness of the response. For example, the processing unit can generate a response such as "Two hamburgers, correct?" to the user's voice. The processing unit can also generate a response such as "Is that all for your order?" Furthermore, the processing unit can generate a response such as "Would you like something to drink?" By generating natural responses, ordering errors are reduced and user convenience is improved. Some or all of the above processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the user's voice into an AI, which can then generate a natural response.

[0035] The processing unit can identify age group and gender from voice and utilize this information for marketing purposes. For example, the processing unit identifies age group and gender based on methods for analyzing voice features and the accuracy of the identification. For instance, the processing unit can identify age group from a user's voice and understand the sales performance of targeted products for a specific age group. It can also identify gender from a user's voice and understand the sales performance of targeted products for a specific gender. Furthermore, the processing unit can formulate marketing strategies based on age group and gender. For example, the processing unit can propose effective marketing strategies for specific age groups and genders. This allows for understanding the sales performance of targeted products by identifying age group and gender, and utilizing this information in marketing strategies. Some or all of the above processing in the processing unit may be performed using AI, or without AI. For example, the processing unit can input the user's voice into an AI, which can then identify age group and gender.

[0036] The reception desk can receive voice input specifying specific menu items and quantities. The reception desk inputs voice commands specifying specific menu items and quantities based on, for example, the format and recognition accuracy of the voice command. For example, the reception desk can receive voice input from the user such as, "I want two hamburgers." The reception desk can also receive voice input from the user such as, "I want one cola." Furthermore, the reception desk can also receive voice input from the user such as, "I want three salads." This makes ordering easy by allowing users to input voice commands specifying specific menu items and quantities. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's voice into the AI, which can recognize specific menu items and quantities.

[0037] The conversion unit can convert the user's voice into text data using speech recognition technology. The conversion unit converts the user's voice into text data using specific speech recognition technologies, such as deep learning-based speech recognition or HMM-based speech recognition. For example, the conversion unit can convert the user's voice into text data with high accuracy using deep learning-based speech recognition technology. The conversion unit can also convert the user's voice into text data using HMM-based speech recognition technology. Furthermore, the conversion unit can combine speech recognition technologies to convert the user's voice into text data. For example, the conversion unit can convert the user's voice into text data using speech recognition technology that combines deep learning and HMM. This allows for accurate conversion of the user's voice into text data using speech recognition technology. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's voice into AI, which can then perform speech recognition.

[0038] The reception desk can analyze a user's past order history and select the optimal reception method. The reception desk analyzes a user's past order history based on data mining techniques and analytical algorithms, for example. For example, the reception desk can prioritize displaying menus that the user has frequently ordered in the past. The reception desk can also prioritize suggesting ordering methods (voice, touch, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest menus that the user will use at a particular time of day based on their past order history. In this way, by analyzing past order history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past order history into AI, which can then select the optimal reception method.

[0039] The reception desk can filter orders based on the user's current situation and areas of interest. For example, the reception desk can identify the user's current situation and areas of interest based on methods for collecting real-time data and identifying areas of interest. For instance, if the user is health-conscious, the reception desk can prioritize displaying low-calorie or organic menus. It can also prioritize displaying allergen-free menus if the user has specific allergies. Furthermore, if the user is participating in a particular event, the reception desk can prioritize displaying menus related to that event. This allows for the provision of more appropriate menus by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into an AI, which can then perform the filtering.

[0040] The reception desk can prioritize accepting orders that are highly relevant to the user, taking into account the user's geographical location information. The reception desk considers the user's geographical location information based on, for example, the method of acquiring location information and the criteria for evaluating relevance. For example, if the user is in a specific region, the reception desk can prioritize displaying local specialties from that region. Similarly, if the user is in a tourist destination, the reception desk can prioritize displaying menus related to that tourist destination. Furthermore, if the user is at a specific event venue, the reception desk can prioritize displaying menus related to that event. This allows the reception desk to prioritize accepting orders that are highly relevant to the user by considering their geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into an AI, which can then prioritize accepting highly relevant orders.

[0041] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. The reception desk analyzes a user's social media activity based on, for example, data collection methods and analysis algorithms. For example, if the reception desk mentions a particular menu item on social media, it can prioritize displaying that menu item. Similarly, if the reception desk participates in a particular event on social media, it can prioritize displaying menu items related to that event. Furthermore, if the reception desk indicates a particular health-conscious trend on social media, it can prioritize displaying health-conscious menu items. This allows for the priority acceptance of orders relevant to the user by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's social media activity into an AI, which can then accept relevant orders.

[0042] The conversion unit can adjust the level of detail in the conversion based on the importance of the order during speech recognition. For example, the conversion unit adjusts the level of detail based on criteria for evaluating the importance of the order and methods for adjusting the level of detail. For example, if the order is important, the conversion unit can perform detailed speech recognition and accurate text conversion. If the order is general, the conversion unit can perform normal speech recognition and standard text conversion. Furthermore, if the order is simple, the conversion unit can perform simplified speech recognition and rapid text conversion. By adjusting the level of detail in the conversion based on the importance of the order, more appropriate speech recognition becomes possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the importance of the order into the AI, and the AI ​​can adjust the level of detail in the conversion.

[0043] The conversion unit can apply different recognition algorithms depending on the order category during speech recognition. For example, the conversion unit applies different recognition algorithms based on the classification method of the order category and the type of algorithm to be applied. For example, in the case of a food order, the conversion unit can apply a speech recognition algorithm specialized for food. In the case of a beverage order, the conversion unit can also apply a speech recognition algorithm specialized for beverages. Furthermore, in the case of a dessert order, the conversion unit can also apply a speech recognition algorithm specialized for desserts. By applying different recognition algorithms depending on the order category, more accurate speech recognition becomes possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the order category into the AI, and the AI ​​can apply an appropriate recognition algorithm.

[0044] The conversion unit can determine the recognition priority based on the order submission time during speech recognition. The conversion unit determines the recognition priority based, for example, on evaluation criteria and methods for determining priority based on the submission time. For example, if an order is submitted during peak hours, the conversion unit can set a high recognition priority to enable a quick response. The conversion unit can also set a normal recognition priority if the order is submitted during normal hours. Furthermore, if an order is submitted during off-peak hours, the conversion unit can set a low recognition priority to prioritize other orders. This allows for speech recognition in a more appropriate order by determining the recognition priority based on the order submission time. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the order submission time into the AI, and the AI ​​can determine the recognition priority.

[0045] The conversion unit can adjust the recognition order based on the relevance of orders during speech recognition. The conversion unit adjusts the recognition order based, for example, on criteria for evaluating order relevance and methods for adjusting the order. For example, the conversion unit can prioritize the recognition order if an order is related to a specific campaign. It can also set the recognition order to normal if an order is related to a general menu item. Furthermore, the conversion unit can prioritize the recognition order if an order is related to a specific event. This allows for more appropriate speech recognition by adjusting the recognition order based on order relevance. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the relevance of orders into the AI, which can then adjust the recognition order.

[0046] The processing unit can analyze the user's past order history and select the optimal processing method when processing an order. For example, the processing unit can analyze the user's past order history based on past order history analysis methods and optimization algorithms. For example, the processing unit can prioritize processing menus that the user has frequently ordered in the past. The processing unit can also prioritize processing orders using methods that the user has used in the past (voice, touch, etc.). Furthermore, the processing unit can predict and process menus that the user will use at a specific time of day based on their past order history. In this way, by analyzing past order history, the processing unit can provide the user with the most optimal processing method. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past order history into AI, and the AI ​​can select the optimal processing method.

[0047] The processing unit can customize the processing methods based on the user's current situation when processing an order. For example, the processing unit evaluates the user's current situation based on evaluation criteria and customization methods. For example, if the user is health-conscious, the processing unit can prioritize low-calorie or organic menus. It can also prioritize allergen-free menus if the user has specific allergies. Furthermore, if the user is participating in a specific event, the processing unit can prioritize menus related to that event. This allows for more appropriate order processing by customizing the processing methods based on the user's situation. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's current situation into the AI, which can then customize the processing methods.

[0048] The processing unit can select the optimal processing method when processing an order, taking into account the user's geographical location. The processing unit considers the user's geographical location based, for example, on the method of acquiring location information and the criteria for evaluating relevance. For example, if the user is in a specific region, the processing unit can prioritize processing local specialties from that region. Similarly, if the user is in a tourist destination, the processing unit can prioritize processing menus related to that tourist destination. Furthermore, if the user is at a specific event venue, the processing unit can prioritize processing menus related to that event. This allows the processing unit to provide the user with the most optimal processing method by considering geographical location. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location into an AI, which can then select the optimal processing method.

[0049] The processing unit can analyze a user's social media activity and suggest processing methods when processing an order. For example, the processing unit analyzes a user's social media activity based on data collection methods and analysis algorithms. For example, if a user mentions a particular menu item on social media, the processing unit can prioritize processing that menu item. Similarly, if a user participates in a particular event on social media, the processing unit can prioritize processing menu items related to that event. Furthermore, if a user indicates a particular health-conscious trend on social media, the processing unit can prioritize processing health-conscious menu items. This allows for the prioritization of orders relevant to a user by analyzing their social media activity. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI, which can then suggest processing methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze a user's past order history and select the most suitable ordering method. For example, it can prioritize displaying menu items that the user has frequently ordered in the past. It can also prioritize suggesting ordering methods (voice, touch, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest menu items that the user will use at a particular time of day based on their past order history. In this way, by analyzing past order history, the reception desk can provide the user with the most suitable ordering method.

[0052] The ordering system can filter orders based on the user's current situation and areas of interest. For example, if a user is health-conscious, the system can prioritize displaying low-calorie or organic menu items. It can also prioritize displaying allergen-free menu items if the user has specific allergies. Furthermore, if a user is attending a particular event, the system can prioritize displaying menu items related to that event. This allows for more appropriate menu options to be provided by filtering based on the user's situation and areas of interest.

[0053] The reception desk can prioritize orders that are highly relevant to the user by considering their geographical location. For example, if the reception desk is in a specific region, it can prioritize displaying local specialties from that region. Similarly, if the reception desk is in a tourist area, it can prioritize displaying menus related to that tourist destination. Furthermore, if the reception desk is at a specific event venue, it can prioritize displaying menus related to that event. In this way, by considering geographical location, the reception desk can prioritize orders that are highly relevant to the user.

[0054] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. For example, if the reception desk mentions a particular menu item on social media, it can prioritize displaying that menu item. Similarly, if the reception desk is participating in a particular event on social media, it can prioritize displaying menu items related to that event. Furthermore, if the reception desk indicates a specific health-conscious trend on social media, it can prioritize displaying health-conscious menu items. This allows the system to prioritize orders relevant to the user by analyzing their social media activity.

[0055] The conversion unit can adjust the level of detail in the conversion based on the importance of the order during speech recognition. For example, for important orders, the conversion unit can perform detailed speech recognition and accurate text conversion. For general orders, the conversion unit can perform normal speech recognition and standard text conversion. Furthermore, for simple orders, the conversion unit can perform simplified speech recognition and rapid text conversion. By adjusting the level of detail in the conversion based on the importance of the order, more appropriate speech recognition becomes possible.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk allows users to enter their orders by voice. Users can enter their orders using voice commands or natural language. For example, they can enter an order by voice such as "I want two hamburgers." Step 2: The conversion unit recognizes the voice input by the reception unit and converts it into text. The conversion unit uses a speech recognition algorithm to convert the user's voice into text data. For example, it can convert the voice saying "I want two hamburgers" into text. Step 3: The processing unit processes the order details based on the text data converted by the conversion unit. The processing unit processes the order details according to the order confirmation method and processing procedure. For example, it can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers.

[0058] (Example of form 2) The SmartOrderAI system according to an embodiment of the present invention is a system that adds a voice ordering function using AI to tablets and vending machines placed in restaurants. With the SmartOrderAI system, the user voice-inputs their order into the tablet or vending machine. The AI ​​recognizes the voice and converts it into text. Based on the converted text, the order is processed. This mechanism makes it easy for elderly people and visually impaired people who have difficulty operating touch panels to place orders. In addition, anyone can easily place an order regardless of the different screen designs and operating methods of each store. Furthermore, because the AI ​​responds in a natural conversational manner, order errors are reduced, making the job of store staff easier. For example, the user voice-inputs an order such as "I want X of XX" into the tablet or vending machine. In this case, the user only needs to specify the specific menu item and quantity. For example, the user voice-inputs an order such as "I want 2 hamburgers." This information is input into the AI. Next, the AI ​​recognizes the input voice and converts it into text. The AI ​​uses speech recognition technology to convert the user's voice into text data. For example, the voice "I want 2 hamburgers" is converted into text. This text data is processed as the order details. Furthermore, the AI ​​processes orders based on text data. The AI ​​analyzes the text data and confirms the order details. For example, it analyzes the text data "I want two hamburgers" and confirms the order for two hamburgers. At this time, the AI ​​can also translate the menu language in real time based on the order details. This system makes it easy for elderly people and visually impaired people who have difficulty operating touch panels to place orders. Users can easily enter orders by voice without having to perform complex operations. Also, anyone can easily place an order regardless of the different screen designs and operation methods at each store. In addition, because the AI ​​responds in a natural conversational manner, order errors are reduced and the work of staff is made easier. The AI ​​generates natural responses that match the user's voice and confirms the order details. For example, it generates a response such as "Two hamburgers, right?" This reduces order errors and alleviates the burden on staff. The AI ​​can also identify age group and gender from the voice and use this for marketing purposes.For example, it's possible to understand the sales performance of targeted products for specific age groups or genders and utilize this information in marketing strategies. In this way, SmartOrderAI adds voice ordering functionality to restaurant tablets and vending machines, making it easy for anyone to order. Furthermore, because the AI ​​responds in a natural conversational manner, order errors are reduced, making the job easier for staff. It can also be used for marketing, contributing to increased store sales. Thus, the SmartOrderAI system adds voice ordering functionality to restaurant tablets and vending machines, making it easy for anyone to order.

[0059] The SmartOrderAI system according to this embodiment comprises a reception unit, a conversion unit, and a processing unit. The reception unit receives orders from the user by voice. Users can use voice commands or natural language to enter orders by voice. For example, a user can enter an order such as "I want two hamburgers" by voice. The conversion unit recognizes the voice input by the reception unit and converts it into text. The conversion unit converts the user's voice into text data using, for example, a speech recognition algorithm. For example, the conversion unit can convert the voice "I want two hamburgers" into text. The processing unit processes the order based on the text data converted by the conversion unit. The processing unit processes the order according to, for example, an order confirmation method and processing procedure. For example, the processing unit can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers. Thus, the SmartOrderAI system according to this embodiment makes it easy for users to place orders by voice input, converting them to text, and processing them. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without using AI. For example, the reception unit can input the user's voice into the AI, which can then perform speech recognition. Some or all of the above-described processes in the conversion unit may be performed using the AI, or not. For example, the conversion unit can input the user's voice into the AI, which can then convert it into text. Some or all of the above-described processes in the processing unit may be performed using the AI, or not. For example, the processing unit can input text data into the AI, which can then process the order details.

[0060] The reception desk allows users to enter orders by voice. Users can use voice commands or natural language to enter orders. Specifically, users can voice orders such as "I want two hamburgers" into their smartphone or a dedicated device. The reception desk can use a microphone with noise-canceling capabilities to capture these voice inputs with high accuracy. Furthermore, the reception desk captures the user's voice in real time and saves it as audio data. This audio data is stored in the reception desk's temporary memory and sent to the next processing step. The reception desk provides a user-friendly interface to smoothly accept user voice input. For example, it can provide a visual indicator to show when voice input has started and audio feedback to show when voice input is complete. This allows users to confirm whether their order has been correctly received. Additionally, the reception desk can support multiple languages, accommodating users who speak different languages. For example, it can accept voice input in multiple languages ​​such as English, Japanese, and Spanish. This allows the reception desk to cater to a wider user base and improve convenience.

[0061] The conversion unit recognizes the voice input from the reception unit and converts it into text. The conversion unit uses, for example, a speech recognition algorithm to convert the user's voice into text data. Specifically, the conversion unit uses a speech recognition engine to analyze the voice data and identify phonemes and words. The speech recognition engine is trained using deep learning technology and has high recognition accuracy. For example, the conversion unit can convert the voice "I want two hamburgers" into text. When converting voice data into text data, the conversion unit considers context and grammar to generate accurate text. For example, it considers context to generate accurate text so that the voice input "I want two hamburgers" is not misrecognized as "I want two hamburgers." Furthermore, the conversion unit can learn the user's pronunciation and speaking characteristics to improve the accuracy of speech recognition. For example, if the user has a specific accent or dialect, it can learn these characteristics to improve recognition accuracy. The conversion unit generates the speech recognition results in real time and sends them to the next processing step. This allows the user to enter orders smoothly and have them processed quickly.

[0062] The processing unit processes the order based on the text data converted by the conversion unit. The processing unit processes the order according to the order confirmation method and processing procedure, for example. Specifically, the processing unit analyzes the text data to identify the order details. For example, it can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers. After confirming the order details, the processing unit generates an order confirmation message and presents it to the user. For example, it generates a confirmation message such as "Your order for two hamburgers has been accepted" and displays it on the user's device. Furthermore, the processing unit saves the order details to a database and manages the order history. This allows the user to refer to their past order history and makes reordering easier. After confirming the order details, the processing unit executes the order according to the order processing procedure. For example, it sends the order details to the kitchen or delivery department to arrange cooking and delivery. The processing unit can monitor the progress of the order in real time and notify the user. For example, it can notify the user when the order is being cooked or delivered, allowing them to keep track of the order's progress. This enables the processing unit to process the user's order quickly and accurately, providing a smooth ordering experience.

[0063] The processing unit can translate the menu language in real time based on the order content. The processing unit translates the order content in real time based on, for example, the translation algorithm used and the accuracy of the translation. For example, the processing unit can translate the order content from English to Japanese. It can also translate the order content from Japanese to English. Furthermore, the processing unit can translate the order content into multiple languages. For example, the processing unit can translate the order content into English, Japanese, French, etc. This makes it possible to order across language barriers by translating the menu language in real time based on the order content. Some or all of the above processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the order content into AI, and the AI ​​can translate it in real time.

[0064] The processing unit can generate natural responses based on the user's voice and confirm the order details. The processing unit generates natural responses based, for example, on the dialogue generation algorithm used and the criteria for evaluating the naturalness of the response. For example, the processing unit can generate a response such as "Two hamburgers, correct?" to the user's voice. The processing unit can also generate a response such as "Is that all for your order?" Furthermore, the processing unit can generate a response such as "Would you like something to drink?" By generating natural responses, ordering errors are reduced and user convenience is improved. Some or all of the above processing in the processing unit may be performed using, for example, AI, or not using AI. For example, the processing unit can input the user's voice into an AI, which can then generate a natural response.

[0065] The processing unit can identify age group and gender from voice and utilize this information for marketing purposes. For example, the processing unit identifies age group and gender based on methods for analyzing voice features and the accuracy of the identification. For instance, the processing unit can identify age group from a user's voice and understand the sales performance of targeted products for a specific age group. It can also identify gender from a user's voice and understand the sales performance of targeted products for a specific gender. Furthermore, the processing unit can formulate marketing strategies based on age group and gender. For example, the processing unit can propose effective marketing strategies for specific age groups and genders. This allows for understanding the sales performance of targeted products by identifying age group and gender, and utilizing this information in marketing strategies. Some or all of the above processing in the processing unit may be performed using AI, or without AI. For example, the processing unit can input the user's voice into an AI, which can then identify age group and gender.

[0066] The reception desk can receive voice input specifying specific menu items and quantities. The reception desk inputs voice commands specifying specific menu items and quantities based on, for example, the format and recognition accuracy of the voice command. For example, the reception desk can receive voice input from the user such as, "I want two hamburgers." The reception desk can also receive voice input from the user such as, "I want one cola." Furthermore, the reception desk can also receive voice input from the user such as, "I want three salads." This makes ordering easy by allowing users to input voice commands specifying specific menu items and quantities. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's voice into the AI, which can recognize specific menu items and quantities.

[0067] The conversion unit can convert the user's voice into text data using speech recognition technology. The conversion unit converts the user's voice into text data using specific speech recognition technologies, such as deep learning-based speech recognition or HMM-based speech recognition. For example, the conversion unit can convert the user's voice into text data with high accuracy using deep learning-based speech recognition technology. The conversion unit can also convert the user's voice into text data using HMM-based speech recognition technology. Furthermore, the conversion unit can combine speech recognition technologies to convert the user's voice into text data. For example, the conversion unit can convert the user's voice into text data using speech recognition technology that combines deep learning and HMM. This allows for accurate conversion of the user's voice into text data using speech recognition technology. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's voice into AI, which can then perform speech recognition.

[0068] The reception desk can estimate the user's emotions and adjust the timing of order acceptance based on the estimated emotions. The reception desk estimates the user's emotions based, for example, on methods for analyzing voice features or on emotion recognition algorithms. For example, if the reception desk is stressed, it can delay order acceptance to give the user time to relax. Conversely, if the user is in a hurry, it can expedite order acceptance for a quicker response. Furthermore, if the reception desk is enjoying itself, it can adjust the order acceptance timing to match the natural flow of conversation. By adjusting the order acceptance timing based on the user's emotions, orders can be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's voice into the AI, which can estimate the emotions.

[0069] The reception desk can analyze a user's past order history and select the optimal reception method. The reception desk analyzes a user's past order history based on data mining techniques and analytical algorithms, for example. For example, the reception desk can prioritize displaying menus that the user has frequently ordered in the past. The reception desk can also prioritize suggesting ordering methods (voice, touch, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest menus that the user will use at a particular time of day based on their past order history. In this way, by analyzing past order history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past order history into AI, which can then select the optimal reception method.

[0070] The reception desk can filter orders based on the user's current situation and areas of interest. For example, the reception desk can identify the user's current situation and areas of interest based on methods for collecting real-time data and identifying areas of interest. For instance, if the user is health-conscious, the reception desk can prioritize displaying low-calorie or organic menus. It can also prioritize displaying allergen-free menus if the user has specific allergies. Furthermore, if the user is participating in a particular event, the reception desk can prioritize displaying menus related to that event. This allows for the provision of more appropriate menus by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation and areas of interest into an AI, which can then perform the filtering.

[0071] The reception desk can estimate the user's emotions and determine the priority of orders to be received based on the estimated emotions. The reception desk estimates the user's emotions based, for example, on methods for analyzing voice features or on emotion recognition algorithms. For example, if the user is in a hurry, the reception desk can set the order to a high priority and respond quickly. If the user is relaxed, the reception desk can set the order to a low priority and prioritize other orders. Furthermore, if the user is stressed, the reception desk can set the order to a medium priority and provide appropriate service. This allows orders to be processed in a more appropriate order by determining the order priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's voice into an AI, which can estimate the emotions.

[0072] The reception desk can prioritize accepting orders that are highly relevant to the user, taking into account the user's geographical location information. The reception desk considers the user's geographical location information based on, for example, the method of acquiring location information and the criteria for evaluating relevance. For example, if the user is in a specific region, the reception desk can prioritize displaying local specialties from that region. Similarly, if the user is in a tourist destination, the reception desk can prioritize displaying menus related to that tourist destination. Furthermore, if the user is at a specific event venue, the reception desk can prioritize displaying menus related to that event. This allows the reception desk to prioritize accepting orders that are highly relevant to the user by considering their geographical location information. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into an AI, which can then prioritize accepting highly relevant orders.

[0073] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. The reception desk analyzes a user's social media activity based on, for example, data collection methods and analysis algorithms. For example, if the reception desk mentions a particular menu item on social media, it can prioritize displaying that menu item. Similarly, if the reception desk participates in a particular event on social media, it can prioritize displaying menu items related to that event. Furthermore, if the reception desk indicates a particular health-conscious trend on social media, it can prioritize displaying health-conscious menu items. This allows for the priority acceptance of orders relevant to the user by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's social media activity into an AI, which can then accept relevant orders.

[0074] The conversion unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions. The conversion unit estimates the user's emotions based, for example, on a method for analyzing speech features or an emotion recognition algorithm. For example, if the user is nervous, the conversion unit can increase the accuracy of speech recognition to perform accurate recognition. The conversion unit can also set the accuracy of speech recognition to normal if the user is relaxed. Furthermore, if the user is in a hurry, the conversion unit can increase the accuracy of speech recognition to perform rapid recognition. In this way, more accurate speech recognition is possible by adjusting the accuracy of speech recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI, for example, or not using AI. For example, the conversion unit can input the user's voice into an AI, and the AI ​​can estimate the emotions.

[0075] The conversion unit can adjust the level of detail in the conversion based on the importance of the order during speech recognition. For example, the conversion unit adjusts the level of detail based on criteria for evaluating the importance of the order and methods for adjusting the level of detail. For example, if the order is important, the conversion unit can perform detailed speech recognition and accurate text conversion. If the order is general, the conversion unit can perform normal speech recognition and standard text conversion. Furthermore, if the order is simple, the conversion unit can perform simplified speech recognition and rapid text conversion. By adjusting the level of detail in the conversion based on the importance of the order, more appropriate speech recognition becomes possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the importance of the order into the AI, and the AI ​​can adjust the level of detail in the conversion.

[0076] The conversion unit can apply different recognition algorithms depending on the order category during speech recognition. For example, the conversion unit applies different recognition algorithms based on the classification method of the order category and the type of algorithm to be applied. For example, in the case of a food order, the conversion unit can apply a speech recognition algorithm specialized for food. In the case of a beverage order, the conversion unit can also apply a speech recognition algorithm specialized for beverages. Furthermore, in the case of a dessert order, the conversion unit can also apply a speech recognition algorithm specialized for desserts. By applying different recognition algorithms depending on the order category, more accurate speech recognition becomes possible. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the order category into the AI, and the AI ​​can apply an appropriate recognition algorithm.

[0077] The conversion unit can estimate the user's emotions and adjust the speech recognition speed based on the estimated emotions. The conversion unit estimates the user's emotions based, for example, on speech feature analysis methods or emotion recognition algorithms. For example, if the user is in a hurry, the conversion unit can speed up speech recognition for faster recognition. It can also set the speech recognition speed to normal if the user is relaxed. Furthermore, if the user is stressed, the conversion unit can slow down speech recognition for more accurate recognition. This allows for speech recognition at a more appropriate speed by adjusting the speed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can input the user's voice into an AI, which can then estimate the emotions.

[0078] The conversion unit can determine the recognition priority based on the order submission time during speech recognition. The conversion unit determines the recognition priority based, for example, on evaluation criteria and methods for determining priority based on the submission time. For example, if an order is submitted during peak hours, the conversion unit can set a high recognition priority to enable a quick response. The conversion unit can also set a normal recognition priority if the order is submitted during normal hours. Furthermore, if an order is submitted during off-peak hours, the conversion unit can set a low recognition priority to prioritize other orders. This allows for speech recognition in a more appropriate order by determining the recognition priority based on the order submission time. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the order submission time into the AI, and the AI ​​can determine the recognition priority.

[0079] The conversion unit can adjust the recognition order based on the relevance of orders during speech recognition. The conversion unit adjusts the recognition order based, for example, on criteria for evaluating order relevance and methods for adjusting the order. For example, the conversion unit can prioritize the recognition order if an order is related to a specific campaign. It can also set the recognition order to normal if an order is related to a general menu item. Furthermore, the conversion unit can prioritize the recognition order if an order is related to a specific event. This allows for more appropriate speech recognition by adjusting the recognition order based on order relevance. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the relevance of orders into the AI, which can then adjust the recognition order.

[0080] The processing unit can estimate the user's emotions and adjust the order processing method based on the estimated emotions. The processing unit estimates the user's emotions based, for example, on a method for analyzing speech features or an emotion recognition algorithm. For example, if the user is in a hurry, the processing unit can process the order quickly. Also, if the user is relaxed, the processing unit can process the order normally. Furthermore, if the user is stressed, the processing unit can process the order carefully. This allows for more appropriate order processing by adjusting the order processing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI or not using AI. For example, the processing unit can input the user's voice into an AI, which can then estimate the emotions.

[0081] The processing unit can analyze the user's past order history and select the optimal processing method when processing an order. For example, the processing unit can analyze the user's past order history based on past order history analysis methods and optimization algorithms. For example, the processing unit can prioritize processing menus that the user has frequently ordered in the past. The processing unit can also prioritize processing orders using methods that the user has used in the past (voice, touch, etc.). Furthermore, the processing unit can predict and process menus that the user will use at a specific time of day based on their past order history. In this way, by analyzing past order history, the processing unit can provide the user with the most optimal processing method. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past order history into AI, and the AI ​​can select the optimal processing method.

[0082] The processing unit can customize the processing methods based on the user's current situation when processing an order. For example, the processing unit evaluates the user's current situation based on evaluation criteria and customization methods. For example, if the user is health-conscious, the processing unit can prioritize low-calorie or organic menus. It can also prioritize allergen-free menus if the user has specific allergies. Furthermore, if the user is participating in a specific event, the processing unit can prioritize menus related to that event. This allows for more appropriate order processing by customizing the processing methods based on the user's situation. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's current situation into the AI, which can then customize the processing methods.

[0083] The processing unit can estimate the user's emotions and determine the order processing priority based on the estimated emotions. The processing unit estimates the user's emotions based, for example, on a voice feature analysis method or an emotion recognition algorithm. For example, if the user is in a hurry, the processing unit can set the order processing priority high and respond quickly. The processing unit can also set the order processing priority to normal if the user is relaxed. Furthermore, if the processing unit is stressed, it can set the order processing priority to medium and respond appropriately. This allows orders to be processed in a more appropriate order by determining the order processing priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI or not using AI. For example, the processing unit can input the user's voice into an AI, which can estimate the emotions.

[0084] The processing unit can select the optimal processing method when processing an order, taking into account the user's geographical location. The processing unit considers the user's geographical location based, for example, on the method of acquiring location information and the criteria for evaluating relevance. For example, if the user is in a specific region, the processing unit can prioritize processing local specialties from that region. Similarly, if the user is in a tourist destination, the processing unit can prioritize processing menus related to that tourist destination. Furthermore, if the user is at a specific event venue, the processing unit can prioritize processing menus related to that event. This allows the processing unit to provide the user with the most optimal processing method by considering geographical location. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location into an AI, which can then select the optimal processing method.

[0085] The processing unit can analyze a user's social media activity and suggest processing methods when processing an order. For example, the processing unit analyzes a user's social media activity based on data collection methods and analysis algorithms. For example, if a user mentions a particular menu item on social media, the processing unit can prioritize processing that menu item. Similarly, if a user participates in a particular event on social media, the processing unit can prioritize processing menu items related to that event. Furthermore, if a user indicates a particular health-conscious trend on social media, the processing unit can prioritize processing health-conscious menu items. This allows for the prioritization of orders relevant to a user by analyzing their social media activity. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity into an AI, which can then suggest processing methods.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can analyze the tone and speed of the user's voice to estimate their emotions. For example, if the user's voice is excited, the reception desk can estimate that the user is excited and process the order quickly. Conversely, if the user's voice is calm, the reception desk can estimate that the user is relaxed and process the order at a normal speed. Furthermore, if the user's voice sounds urgent, the reception desk can estimate that the user is in a hurry and prioritize processing the order. This allows for more appropriate service by adjusting the order processing speed based on the user's emotions.

[0088] The voice conversion unit can analyze the characteristics of the user's voice and estimate the user's emotions. For example, if the user's voice is trembling, the conversion unit can estimate that the user is nervous and improve the accuracy of speech recognition. Also, if the user's voice is cheerful, the conversion unit can estimate that the user is enjoying themselves and set the speech recognition speed to normal. Furthermore, if the user's voice is low, the conversion unit can estimate that the user is depressed and improve the accuracy of speech recognition. In this way, by adjusting the accuracy of speech recognition based on the user's emotions, more accurate speech recognition becomes possible.

[0089] The processing unit can analyze the tone and speed of the user's voice to estimate their emotions. For example, if the user's voice is excited, the processing unit can estimate that the user is excited and process the order quickly. Conversely, if the user's voice is calm, the processing unit can estimate that the user is relaxed and process the order at a normal speed. Furthermore, if the user's voice is urgent, the processing unit can estimate that the user is in a hurry and prioritize processing the order. This allows for more appropriate responses by adjusting the order processing speed based on the user's emotions.

[0090] The reception desk can analyze the characteristics of the user's voice and estimate their emotions. For example, if the user's voice is trembling, the reception desk can estimate that the user is nervous and adjust the timing of order acceptance. Conversely, if the user's voice is cheerful, the reception desk can estimate that the user is enjoying themselves and set the order acceptance timing to normal. Furthermore, if the user's voice is low, the reception desk can estimate that the user is depressed and adjust the order acceptance timing accordingly. By adjusting the order acceptance timing based on the user's emotions, more appropriate responses become possible.

[0091] The processing unit can analyze the tone and speed of the user's voice to estimate their emotions. For example, if the user's voice is excited, the processing unit can estimate that the user is excited and process the order quickly. Conversely, if the user's voice is calm, the processing unit can estimate that the user is relaxed and process the order at a normal speed. Furthermore, if the user's voice is urgent, the processing unit can estimate that the user is in a hurry and prioritize processing the order. This allows for more appropriate responses by adjusting the order processing speed based on the user's emotions.

[0092] The reception desk can analyze a user's past order history and select the most suitable ordering method. For example, it can prioritize displaying menu items that the user has frequently ordered in the past. It can also prioritize suggesting ordering methods (voice, touch, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest menu items that the user will use at a particular time of day based on their past order history. In this way, by analyzing past order history, the reception desk can provide the user with the most suitable ordering method.

[0093] The ordering system can filter orders based on the user's current situation and areas of interest. For example, if a user is health-conscious, the system can prioritize displaying low-calorie or organic menu items. It can also prioritize displaying allergen-free menu items if the user has specific allergies. Furthermore, if a user is attending a particular event, the system can prioritize displaying menu items related to that event. This allows for more appropriate menu options to be provided by filtering based on the user's situation and areas of interest.

[0094] The reception desk can prioritize orders that are highly relevant to the user by considering their geographical location. For example, if the reception desk is in a specific region, it can prioritize displaying local specialties from that region. Similarly, if the reception desk is in a tourist area, it can prioritize displaying menus related to that tourist destination. Furthermore, if the reception desk is at a specific event venue, it can prioritize displaying menus related to that event. In this way, by considering geographical location, the reception desk can prioritize orders that are highly relevant to the user.

[0095] The reception desk can analyze a user's social media activity when taking an order and accept relevant orders. For example, if the reception desk mentions a particular menu item on social media, it can prioritize displaying that menu item. Similarly, if the reception desk is participating in a particular event on social media, it can prioritize displaying menu items related to that event. Furthermore, if the reception desk indicates a specific health-conscious trend on social media, it can prioritize displaying health-conscious menu items. This allows the system to prioritize orders relevant to the user by analyzing their social media activity.

[0096] The conversion unit can adjust the level of detail in the conversion based on the importance of the order during speech recognition. For example, for important orders, the conversion unit can perform detailed speech recognition and accurate text conversion. For general orders, the conversion unit can perform normal speech recognition and standard text conversion. Furthermore, for simple orders, the conversion unit can perform simplified speech recognition and rapid text conversion. By adjusting the level of detail in the conversion based on the importance of the order, more appropriate speech recognition becomes possible.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk allows users to enter their orders by voice. Users can enter their orders using voice commands or natural language. For example, they can enter an order by voice such as "I want two hamburgers." Step 2: The conversion unit recognizes the voice input by the reception unit and converts it into text. The conversion unit uses a speech recognition algorithm to convert the user's voice into text data. For example, it can convert the voice saying "I want two hamburgers" into text. Step 3: The processing unit processes the order details based on the text data converted by the conversion unit. The processing unit processes the order details according to the order confirmation method and processing procedure. For example, it can analyze the text data "I want two hamburgers" and confirm an order for two hamburgers.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, conversion unit, and processing unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and receives the user's voice. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts the user's voice into text data using a speech recognition algorithm. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and processes the order details based on the converted text data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, conversion unit, and processing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and receives the user's voice. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts the user's voice into text data using a speech recognition algorithm. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and processes the order details based on the converted text data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements, including the reception unit, conversion unit, and processing unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and receives the user's voice. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the user's voice into text data using a speech recognition algorithm. The processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes the order details based on the converted text data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, conversion unit, and processing unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives the user's voice. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts the user's voice into text data using a speech recognition algorithm. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and processes the order details based on the converted text data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception area where users enter their orders by voice, A conversion unit that recognizes the voice input by the reception unit and converts it into text, A processing unit that processes the order details based on the text data converted by the conversion unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned processing unit, The menu language is translated in real time based on the order. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned processing unit, It generates natural responses based on the user's voice and confirms the order details. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned processing unit, Identifying age group and gender from voices and using that information for marketing purposes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Enter your voice to specify a particular menu item or quantity. The system described in Appendix 1, characterized by the features described herein. (Note 6) The conversion unit is Speech recognition technology is used to convert the user's voice into text data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of order acceptance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past order history and select the optimal order processing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When taking an order, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of orders to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting orders, the system prioritizes orders that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking an order, the system analyzes the user's social media activity and accepts relevant orders. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is During voice recognition, the level of detail in the conversion is adjusted based on the importance of the order details. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is When using voice recognition, different recognition algorithms are applied depending on the order category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the speech recognition speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During speech recognition, the system prioritizes recognition based on when the order was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During speech recognition, the recognition order is adjusted based on the relevance of the order. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned processing unit, The system estimates the user's emotions and adjusts the order processing method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned processing unit, During order processing, the system analyzes the user's past order history to select the optimal processing method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned processing unit, During order processing, the processing method is customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned processing unit, The system estimates the user's emotions and prioritizes order processing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned processing unit, When processing an order, the system selects the optimal processing method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned processing unit, During order processing, we analyze the user's social media activity and suggest processing methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their orders by voice, A conversion unit that recognizes the voice input by the reception unit and converts it into text, A processing unit that processes the order details based on the text data converted by the conversion unit, Equipped with A system characterized by the following features.

2. The aforementioned processing unit, The menu language is translated in real time based on the order. The system according to feature 1.

3. The aforementioned processing unit, It generates natural responses based on the user's voice and confirms the order details. The system according to feature 1.

4. The aforementioned processing unit, Identifying age group and gender from voices and using that information for marketing purposes. The system according to feature 1.

5. The aforementioned reception unit is Enter your voice to specify a particular menu item or quantity. The system according to feature 1.

6. The conversion unit is Speech recognition technology is used to convert the user's voice into text data. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of order acceptance based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past order history and select the optimal order processing method. The system according to feature 1.

9. The aforementioned reception unit is When taking an order, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of orders to be accepted based on those estimated emotions. The system according to feature 1.

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