system

Abstract

The system according to this embodiment aims to create a sense of seasonality at a low cost. [Solution] The system according to the embodiment comprises a generation unit, an application unit, and an output unit. The generation unit generates face frames according to the season. The application unit applies the face frames generated by the generation unit to the serving robot. The output unit outputs the face frames applied by the application unit in transparent PNG format.

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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the cost for creating a sense of season was high.

[0005] The system according to the embodiment aims to create a sense of season at low cost.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, an application unit, and an output unit. The generation unit generates a face frame according to a season. The application unit applies the face frame generated by the generation unit to a food delivery robot. The output unit outputs the face frame applied by the application unit in a transparent PNG format.

Effects of the Invention

[0007] The system according to this embodiment can create a sense of the seasons at a low cost. [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 serving robot system according to an embodiment of the present invention is a system for creating a sense of the seasons. This serving robot system uses a generation AI to generate face frames appropriate to the season and applies these frames to the serving robot, thereby creating a sense of the seasons while keeping costs down. For example, the generation AI automatically creates frames such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. This allows users and employees to easily feel the sense of the seasons, which helps to simplify promotional campaigns and improve employee productivity. Furthermore, since the generation of face frames is automated by using the generation AI, the time and cost can be significantly reduced compared to manual decoration. In addition, the generated face frames are output in transparent PNG format and composited with the face of the serving robot. This makes it possible to create a sense of the seasons without changing the appearance of the serving robot. This mechanism allows for the creation of a sense of the seasons in various places such as stores and factories, contributing to increased employee motivation and productivity. For example, stores can easily implement seasonal promotional campaigns, and factories can improve employee motivation and enable more efficient work. This allows the serving robot system to generate a face frame to create a sense of the season, apply it to the serving robot, and output it in transparent PNG format.

[0029] The serving robot system according to the embodiment comprises a generation unit, an application unit, and an output unit. The generation unit generates face frames according to the season. For example, the generation unit generates face frames according to the season using a generation AI. The generation unit inputs "Please generate a spring face frame" as a prompt to the generation AI, and the generation AI generates a frame with cherry blossom petals falling. The generation unit can also input "Please generate a summer face frame" to the generation AI, and the generation AI can generate a frame of the sea or fireworks. Furthermore, the generation unit can input "Please generate an autumn face frame" to the generation AI, and the generation AI can generate a frame of autumn leaves. The application unit applies the generated face frames to the serving robot. For example, the application unit composites the generated face frames onto the face of the serving robot. The application unit creates a sense of the season by superimposing the generated face frames onto the face of the serving robot. The application unit can also display the generated face frames on the display of the serving robot. Furthermore, the application unit can also attach the generated face frame to the exterior of the serving robot. The output unit outputs the generated face frame in transparent PNG format. The output unit saves the generated face frame in transparent PNG format, for example. The output unit displays the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame in transparent PNG format via email. Thus, the serving robot system according to this embodiment can generate a face frame to create a sense of the season, apply it to the serving robot, and output it in transparent PNG format.

[0030] The generation unit generates face frames appropriate for each season. For example, the generation unit uses a generation AI to generate face frames appropriate for each season. The generation unit can input "Generate a spring face frame" as a prompt to the generation AI, and the generation AI will generate a frame with cherry blossom petals fluttering. The generation unit can also input "Generate a summer face frame" to the generation AI, and the generation AI will generate a frame with the sea and fireworks. Furthermore, the generation unit can input "Generate an autumn face frame" to the generation AI, and the generation AI will generate a frame with autumn leaves. By inputting specific prompts to the generation AI, the generation unit generates face frames that reflect the characteristics of each season. For example, if "Generate a winter face frame" is input, the generation AI can generate a frame with snowflakes and Christmas decorations. The generation AI generates designs that capture the characteristics of each season based on a pre-trained dataset. The generation unit can also check the face frames output by the generation AI and make fine adjustments as needed. For example, it can adjust the color scheme and design elements of the generated frames to make them more attractive. The generation unit efficiently utilizes the output of the generation AI to generate a variety of face frames in a short time. This allows the generation unit to quickly provide face frames that match seasonal events and themes, giving the appearance of the serving robot a rich seasonal feel.

[0031] The application unit applies the generated face frame to the serving robot. For example, the application unit composites the generated face frame onto the serving robot's face. By superimposing the generated face frame onto the serving robot's face, the application unit creates a sense of the season. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the serving robot's exterior. The application unit uses image processing algorithms to automate the face frame application process. For example, it automatically adjusts the position and size of the face frame to naturally composite it onto the serving robot's face. The application unit can also adjust the transparency and color of the face frame, dynamically changing it in accordance with the serving robot's expressions and movements. When displaying the face frame on the serving robot's display, the application unit can also add animation effects to the face frame. For example, by adding animations of cherry blossoms falling or fireworks going off, a more dynamic and attractive presentation can be created. After the application of the face frame is complete, the application unit synchronizes it with the serving robot's movements and sounds to achieve a more integrated presentation. This allows the application unit to effectively apply the generated face frame to the serving robot, creating a sense of the season and providing a fun experience for the user.

[0032] The output unit outputs the generated face frame in transparent PNG format. For example, the output unit saves the generated face frame in transparent PNG format. The output unit displays the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame via email in transparent PNG format. When generating transparent PNG files, the output unit optimizes the image resolution and file size. For example, it uses compression techniques to generate high-resolution images while keeping the file size down. The output unit can also save the generated face frame to cloud storage and share it with other devices and systems as needed. The output unit can also add metadata when generating transparent PNG files. For example, embedding the generation date and time, seasonal information, and design version information as metadata makes later management and searching easier. When sending the generated face frame via email, the output unit can not only send it as an attachment but also embed a preview image in the email body. This allows recipients to see the face frame design the moment they open the email. The output unit automatically adjusts print settings when printing the generated face frames, resulting in high-quality print output. This allows the output unit to output the generated face frames in various formats and provide them to users in a variety of ways, such as on serving robot displays, printed materials, or via email.

[0033] The generation unit can generate seasonal face frames using a generation AI. For example, the generation unit uses the generation AI to generate seasonal face frames. The generation unit inputs "Generate a spring face frame" as a prompt to the generation AI, and the generation AI generates a frame with cherry blossom petals fluttering. The generation unit can also input "Generate a summer face frame" to the generation AI, and the generation AI can generate a frame with the sea or fireworks. Furthermore, the generation unit can input "Generate an autumn face frame" to the generation AI, and the generation AI can generate a frame with autumn leaves. In this way, seasonal face frames can be automatically generated by using the generation AI. The generation AI is implemented using technologies such as deep learning or GAN (Generative Adversarial Network). Some or all of the above-mentioned processes in the generation unit are performed using the generation AI.

[0034] The application unit can apply the generated face frame to the serving robot. For example, the application unit can composite the generated face frame onto the face of the serving robot. By superimposing the generated face frame onto the face of the serving robot, the application unit can create a sense of the season. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the exterior of the serving robot. In this way, by applying the generated face frame to the serving robot, a sense of the season can be created. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of compositing the generated face frame onto the face of the serving robot.

[0035] The output unit can output the generated face frame in transparent PNG format. For example, the output unit can save the generated face frame in transparent PNG format. The output unit can display the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame in transparent PNG format via email. This allows for the creation of seasonal themes without altering the appearance of the serving robot by outputting the generated face frame in transparent PNG format. The transparent PNG format must meet specifications such as resolution, color depth, and transparency criteria. Some or all of the above-described processes in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of saving the generated face frame in transparent PNG format.

[0036] The generation unit can generate frames with different designs for each season, such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. For example, the generation unit uses a generation AI to generate a frame with cherry blossom petals in spring. The generation unit prompts the generation AI with "Generate a spring face frame," and the generation AI generates a frame with cherry blossom petals. The generation unit can also use the generation AI to generate frames with the sea and fireworks in summer. The generation unit prompts the generation AI with "Generate a summer face frame," and the generation AI generates a frame with the sea and fireworks. Furthermore, the generation unit can also use the generation AI to generate a frame with autumn leaves in fall. The generation unit prompts the generation AI with "Generate an autumn face frame," and the generation AI generates a frame with autumn leaves. In this way, by generating face frames with different designs for each season, a sense of the seasons can be created. Some or all of the above processing in the generation unit is performed using a generation AI.

[0037] The application unit can combine the generated face frame with the face of the serving robot. For example, the application unit can create a sense of the season by superimposing the generated face frame onto the face of the serving robot. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the exterior of the serving robot. This allows for the creation of a sense of the season without changing the appearance of the serving robot by combining the generated face frame with the face of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of combining the generated face frame with the face of the serving robot.

[0038] The generation unit can generate special face frames corresponding to seasonal events and holidays. For example, using the generation AI, the generation unit can generate face frames with Santa Claus and Christmas tree motifs for Christmas. The generation unit prompts the generation AI with "Generate a Christmas face frame," and the generation AI generates face frames with Santa Claus and Christmas tree motifs. The generation unit can also use the generation AI to generate face frames with pumpkin and ghost motifs for Halloween. The generation unit prompts the generation AI with "Generate a Halloween face frame," and the generation AI generates face frames with pumpkin and ghost motifs. Furthermore, the generation unit can use the generation AI to generate face frames with heart and chocolate motifs for Valentine's Day. The generation unit prompts the generation AI with "Generate a Valentine's Day face frame," and the generation AI generates face frames with heart and chocolate motifs. In this way, by generating special face frames corresponding to seasonal events and holidays, a special sense of the season can be created. Some or all of the above processing in the generation unit is performed using the generation AI.

[0039] The generation unit can incorporate the user's past preferences and feedback when generating face frames. For example, the generation unit can use a generation AI to generate face frames that reflect the user's past preferences and feedback. The generation unit can prompt the generation AI with "Generate a face frame based on designs the user has liked in the past," and the generation AI will generate a similar face frame. The generation unit can also prompt the generation AI with "Generate a face frame based on feedback the user has provided in the past," and the generation AI will generate a face frame with an improved design. Furthermore, the generation unit can prompt the generation AI with "Generate a face frame that reflects the colors and themes the user has chosen in the past," and the generation AI will generate a face frame based on that. In this way, by incorporating the user's past preferences and feedback, it is possible to generate face frames that are more suitable for the user. Some or all of the above processes in the generation unit are performed using a generation AI.

[0040] The generation unit can consider regional cultures and customs when generating face frames appropriate for each season. For example, the generation unit can use a generation AI to generate a face frame with a cherry blossom motif for spring in Japan. The generation unit prompts the generation AI with "Generate a face frame for spring in Japan," and the generation AI generates a face frame with a cherry blossom motif. The generation unit can also use the generation AI to generate a face frame with an Independence Day fireworks motif for summer in America. The generation unit prompts the generation AI with "Generate a face frame for summer in America," and the generation AI generates a face frame with an Independence Day fireworks motif. Furthermore, the generation unit can use the generation AI to generate a face frame with a Day of the Dead motif for autumn in Mexico. The generation unit prompts the generation AI with "Generate a face frame for autumn in Mexico," and the generation AI generates a face frame with a Day of the Dead motif. This allows for a more appropriate sense of the season to each region by considering regional cultures and customs. Some or all of the above processing in the generation unit is performed using a generation AI.

[0041] The generation unit can analyze the user's social media activity and generate a face frame that matches current trends when generating face frames. For example, the generation unit uses a generation AI to analyze the user's social media activity and generate a face frame that matches current trends. The generation unit prompts the generation AI with "Analyze the user's social media activity and generate a face frame that matches current trends," and the generation AI generates a face frame based on images and videos that the user frequently shares. The generation unit can also prompt the generation AI with "Analyze the content of posts from influencers that the user follows and generate a face frame that matches current trends," and the generation AI generates a face frame based on the influencers' posts. Furthermore, the generation unit can prompt the generation AI with "Generate a face frame based on the trends of online communities that the user participates in," and the generation AI generates a face frame based on the community trends. In this way, by analyzing the user's social media activity, it is possible to generate face frames with designs that match current trends. Some or all of the above processes in the generation unit are performed using a generation AI.

[0042] The application unit can apply the face frame at the optimal timing, taking into account the movement and position information of the serving robot. For example, the application unit can apply the face frame when the serving robot approaches the user. The application unit can apply the face frame when the serving robot reaches a specific position. The application unit can also apply the face frame when the serving robot stops. In this way, by taking into account the movement and position information of the serving robot, the face frame can be applied at the optimal timing. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the timing of face frame application based on the movement and position information of the serving robot.

[0043] The application unit can harmonize the face frame with other decorations and designs of the serving robot when applying it. For example, the application unit can apply a face frame that matches the body color of the serving robot. The application unit can apply a face frame with a design that is integrated with the other decorations of the serving robot. The application unit can also apply a face frame that matches the theme of the serving robot. This allows for a more cohesive and seasonal feel by harmonizing with the other decorations and designs of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the design of the face frame based on the body color and theme of the serving robot.

[0044] The application unit can adjust the application method of the face frame according to the operating environment of the serving robot when applying it. For example, in a store, the application unit can apply the face frame to attract the customer's attention. In a factory, the application unit can apply the face frame in a way that does not hinder work efficiency. Furthermore, the application unit can apply a face frame that matches the theme at an event venue. In this way, by adjusting the application method according to the operating environment of the serving robot, the face frame can be applied in a more appropriate manner. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the application method of the face frame based on the operating environment of the serving robot.

[0045] The application unit can select the optimal application method when applying a face frame by referring to the operation history of the serving robot. For example, the application unit can select the optimal application method based on application methods that have been successful in the past for the serving robot. The application unit applies the face frame at the most effective timing based on the operation history of the serving robot. The application unit can also analyze the operation history of the serving robot and select the optimal application order. In this way, the optimal application method can be selected by referring to the operation history of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of selecting the application method of the face frame based on the operation history of the serving robot.

[0046] The output unit can optimize the output of face frames to match the display resolution and color settings of the serving robot. For example, the output unit can adjust the resolution of the face frame to match the display resolution of the serving robot. The output unit can also adjust the color tone of the face frame to match the color settings of the serving robot. Furthermore, the output unit can adjust the size of the face frame to match the display size of the serving robot. By optimizing for the display resolution and color settings of the serving robot, a more appropriate face frame can be output. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the display resolution and color settings of the serving robot.

[0047] The output unit can select an output format that reflects the user's past feedback when outputting face frames. For example, the output unit can use a generation AI to output face frames that reflect the user's past feedback. The output unit can prompt the generation AI with "Generate a face frame based on the output format the user has preferred in the past," and the generation AI will output a face frame based on that prompt. Alternatively, the output unit can input "Generate a face frame based on feedback the user has provided in the past," and the generation AI can select an improved output format. Furthermore, the output unit can input "Generate a face frame that reflects the colors and themes the user has previously selected," and the generation AI can output a face frame based on that prompt. This allows for the output of face frames that are more suitable for the user by reflecting the user's past feedback. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have the AI ​​perform the process of selecting the output format of the face frame based on the user's past feedback.

[0048] The output unit can adjust the output format of the face frame according to the operating environment of the serving robot. For example, in a store, the output unit can output a face frame that attracts the customer's attention. In a factory, the output unit can output a face frame that does not hinder work efficiency. Furthermore, the output unit can output a face frame that matches the theme at an event venue. By adjusting the output format according to the operating environment of the serving robot, it is possible to output the face frame in a more appropriate format. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the operating environment of the serving robot.

[0049] The output unit can make adjustments to the face frame output to harmonize it with other display content of the serving robot. For example, the output unit can output a face frame whose colors match those of other display content of the serving robot. The output unit can output a face frame with a design that is integrated with the other display content of the serving robot. The output unit can also output a face frame that matches the theme of the serving robot. This allows for a more integrated display by harmonizing it with the other display content of the serving robot. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the other display content of the serving robot.

[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 application unit can synchronize the generated face frames with the movements of the serving robot. For example, a dynamic face frame can be applied when the serving robot is moving, and a static face frame can be applied when it is stopped. Furthermore, when the serving robot performs a specific action, a face frame that matches that action can be applied. Additionally, when the serving robot approaches the user, a face frame designed to attract the user's attention can be applied. This allows for a more dynamic and seasonal feel by applying face frames synchronized with the serving robot's movements.

[0052] The output unit can send the generated face frames to the user's device. For example, it can send the face frames to the user's smartphone or tablet, allowing the user to view them on their device. Furthermore, the user can share the face frames sent to their device on social media. In addition, the user can customize the face frames sent to their device. This allows users to utilize the generated face frames more freely by sending them to their device.

[0053] The application unit can synchronize the generated face frames with the voice output of the serving robot. For example, when the serving robot outputs a specific voice, a face frame matching that voice can be applied. Furthermore, when the serving robot speaks to a user, a face frame corresponding to the user's response can be applied. Additionally, when the serving robot plays music, a face frame matching that music can be applied. By applying face frames synchronized with the serving robot's voice output, a more cohesive and seasonal atmosphere can be created.

[0054] The application unit can synchronize the generated face frames with the lighting of the serving robot. For example, when the serving robot's lighting changes, a face frame matching that lighting can be applied. Furthermore, when the serving robot reaches a specific location, a face frame matching the lighting at that location can be applied. Additionally, when the serving robot performs a specific event, a face frame matching the lighting for that event can be applied. This allows for a more cohesive and seasonal feel by applying face frames synchronized with the serving robot's lighting.

[0055] The application unit can apply the generated face frames based on the operation history of the serving robot. For example, it can apply face frames based on application methods that have been successful in the past for the serving robot. It can also apply face frames at the most effective timing based on the serving robot's operation history. Furthermore, it can analyze the serving robot's operation history and select the optimal application order. By selecting an application method for face frames based on the serving robot's operation history, a more effective sense of seasonality can be created.

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

[0057] Step 1: The generation unit generates face frames appropriate for the season. Using the generation AI, the generation unit receives the prompt "Generate a spring face frame" and generates a frame with cherry blossom petals fluttering. It can also receive the prompt "Generate a summer face frame" and generate frames with the sea and fireworks. Furthermore, it can receive the prompt "Generate an autumn face frame" and generate a frame with autumn leaves. Step 2: The application unit applies the generated face frame to the serving robot. The application unit composites and overlays the generated face frame onto the serving robot's face to create a seasonal feel. The generated face frame can also be displayed on the serving robot's display or attached to its exterior. Step 3: The output unit outputs the generated face frame in transparent PNG format. The output unit saves the generated face frame in transparent PNG format and displays it on the serving robot's display. It can also be printed in transparent PNG format or sent via email.

[0058] (Example of form 2) The serving robot system according to an embodiment of the present invention is a system for creating a sense of the seasons. This serving robot system uses a generation AI to generate face frames appropriate to the season and applies these frames to the serving robot, thereby creating a sense of the seasons while keeping costs down. For example, the generation AI automatically creates frames such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. This allows users and employees to easily feel the sense of the seasons, which helps to simplify promotional campaigns and improve employee productivity. Furthermore, since the generation of face frames is automated by using the generation AI, the time and cost can be significantly reduced compared to manual decoration. In addition, the generated face frames are output in transparent PNG format and composited with the face of the serving robot. This makes it possible to create a sense of the seasons without changing the appearance of the serving robot. This mechanism allows for the creation of a sense of the seasons in various places such as stores and factories, contributing to increased employee motivation and productivity. For example, stores can easily implement seasonal promotional campaigns, and factories can improve employee motivation and enable more efficient work. This allows the serving robot system to generate a face frame to create a sense of the season, apply it to the serving robot, and output it in transparent PNG format.

[0059] The serving robot system according to the embodiment comprises a generation unit, an application unit, and an output unit. The generation unit generates face frames according to the season. For example, the generation unit generates face frames according to the season using a generation AI. The generation unit inputs "Please generate a spring face frame" as a prompt to the generation AI, and the generation AI generates a frame with cherry blossom petals falling. The generation unit can also input "Please generate a summer face frame" to the generation AI, and the generation AI can generate a frame of the sea or fireworks. Furthermore, the generation unit can input "Please generate an autumn face frame" to the generation AI, and the generation AI can generate a frame of autumn leaves. The application unit applies the generated face frames to the serving robot. For example, the application unit composites the generated face frames onto the face of the serving robot. The application unit creates a sense of the season by superimposing the generated face frames onto the face of the serving robot. The application unit can also display the generated face frames on the display of the serving robot. Furthermore, the application unit can also attach the generated face frame to the exterior of the serving robot. The output unit outputs the generated face frame in transparent PNG format. The output unit saves the generated face frame in transparent PNG format, for example. The output unit displays the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame in transparent PNG format via email. Thus, the serving robot system according to this embodiment can generate a face frame to create a sense of the season, apply it to the serving robot, and output it in transparent PNG format.

[0060] The generation unit generates face frames appropriate for each season. For example, the generation unit uses a generation AI to generate face frames appropriate for each season. The generation unit can input "Generate a spring face frame" as a prompt to the generation AI, and the generation AI will generate a frame with cherry blossom petals fluttering. The generation unit can also input "Generate a summer face frame" to the generation AI, and the generation AI will generate a frame with the sea and fireworks. Furthermore, the generation unit can input "Generate an autumn face frame" to the generation AI, and the generation AI will generate a frame with autumn leaves. By inputting specific prompts to the generation AI, the generation unit generates face frames that reflect the characteristics of each season. For example, if "Generate a winter face frame" is input, the generation AI can generate a frame with snowflakes and Christmas decorations. The generation AI generates designs that capture the characteristics of each season based on a pre-trained dataset. The generation unit can also check the face frames output by the generation AI and make fine adjustments as needed. For example, it can adjust the color scheme and design elements of the generated frames to make them more attractive. The generation unit efficiently utilizes the output of the generation AI to generate a variety of face frames in a short time. This allows the generation unit to quickly provide face frames that match seasonal events and themes, giving the appearance of the serving robot a rich seasonal feel.

[0061] The application unit applies the generated face frame to the serving robot. For example, the application unit composites the generated face frame onto the serving robot's face. By superimposing the generated face frame onto the serving robot's face, the application unit creates a sense of the season. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the serving robot's exterior. The application unit uses image processing algorithms to automate the face frame application process. For example, it automatically adjusts the position and size of the face frame to naturally composite it onto the serving robot's face. The application unit can also adjust the transparency and color of the face frame, dynamically changing it in accordance with the serving robot's expressions and movements. When displaying the face frame on the serving robot's display, the application unit can also add animation effects to the face frame. For example, by adding animations of cherry blossoms falling or fireworks going off, a more dynamic and attractive presentation can be created. After the application of the face frame is complete, the application unit synchronizes it with the serving robot's movements and sounds to achieve a more integrated presentation. This allows the application unit to effectively apply the generated face frame to the serving robot, creating a sense of the season and providing a fun experience for the user.

[0062] The output unit outputs the generated face frame in transparent PNG format. For example, the output unit saves the generated face frame in transparent PNG format. The output unit displays the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame via email in transparent PNG format. When generating transparent PNG files, the output unit optimizes the image resolution and file size. For example, it uses compression techniques to generate high-resolution images while keeping the file size down. The output unit can also save the generated face frame to cloud storage and share it with other devices and systems as needed. The output unit can also add metadata when generating transparent PNG files. For example, embedding the generation date and time, seasonal information, and design version information as metadata makes later management and searching easier. When sending the generated face frame via email, the output unit can not only send it as an attachment but also embed a preview image in the email body. This allows recipients to see the face frame design the moment they open the email. The output unit automatically adjusts print settings when printing the generated face frames, resulting in high-quality print output. This allows the output unit to output the generated face frames in various formats and provide them to users in a variety of ways, such as on serving robot displays, printed materials, or via email.

[0063] The generation unit can generate seasonal face frames using a generation AI. For example, the generation unit uses the generation AI to generate seasonal face frames. The generation unit inputs "Generate a spring face frame" as a prompt to the generation AI, and the generation AI generates a frame with cherry blossom petals fluttering. The generation unit can also input "Generate a summer face frame" to the generation AI, and the generation AI can generate a frame with the sea or fireworks. Furthermore, the generation unit can input "Generate an autumn face frame" to the generation AI, and the generation AI can generate a frame with autumn leaves. In this way, seasonal face frames can be automatically generated by using the generation AI. The generation AI is implemented using technologies such as deep learning or GAN (Generative Adversarial Network). Some or all of the above-mentioned processes in the generation unit are performed using the generation AI.

[0064] The application unit can apply the generated face frame to the serving robot. For example, the application unit can composite the generated face frame onto the face of the serving robot. By superimposing the generated face frame onto the face of the serving robot, the application unit can create a sense of the season. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the exterior of the serving robot. In this way, by applying the generated face frame to the serving robot, a sense of the season can be created. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of compositing the generated face frame onto the face of the serving robot.

[0065] The output unit can output the generated face frame in transparent PNG format. For example, the output unit can save the generated face frame in transparent PNG format. The output unit can display the generated face frame in transparent PNG format on the serving robot's display. The output unit can also print the generated face frame in transparent PNG format. Furthermore, the output unit can send the generated face frame in transparent PNG format via email. This allows for the creation of seasonal themes without altering the appearance of the serving robot by outputting the generated face frame in transparent PNG format. The transparent PNG format must meet specifications such as resolution, color depth, and transparency criteria. Some or all of the above-described processes in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of saving the generated face frame in transparent PNG format.

[0066] The generation unit can generate frames with different designs for each season, such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. For example, the generation unit uses a generation AI to generate a frame with cherry blossom petals in spring. The generation unit prompts the generation AI with "Generate a spring face frame," and the generation AI generates a frame with cherry blossom petals. The generation unit can also use the generation AI to generate frames with the sea and fireworks in summer. The generation unit prompts the generation AI with "Generate a summer face frame," and the generation AI generates a frame with the sea and fireworks. Furthermore, the generation unit can also use the generation AI to generate a frame with autumn leaves in fall. The generation unit prompts the generation AI with "Generate an autumn face frame," and the generation AI generates a frame with autumn leaves. In this way, by generating face frames with different designs for each season, a sense of the seasons can be created. Some or all of the above processing in the generation unit is performed using a generation AI.

[0067] The application unit can combine the generated face frame with the face of the serving robot. For example, the application unit can create a sense of the season by superimposing the generated face frame onto the face of the serving robot. The application unit can also display the generated face frame on the serving robot's display. Furthermore, the application unit can attach the generated face frame to the exterior of the serving robot. This allows for the creation of a sense of the season without changing the appearance of the serving robot by combining the generated face frame with the face of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of combining the generated face frame with the face of the serving robot.

[0068] The generation unit can estimate the user's emotions and adjust the design of the face frame according to the season based on the estimated emotions. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the design of the face frame based on the estimated emotions. The generation unit can input a prompt to the generation AI, "Generate a face frame for when the user is relaxed," and the generation AI will generate a face frame with soft colors and a calm design. The generation unit can also input a prompt to the generation AI, "Generate a face frame for when the user is excited," and the generation AI will generate a face frame with vibrant colors and a dynamic design. Furthermore, the generation unit can input a prompt to the generation AI, "Generate a face frame for when the user is sad," and the generation AI will generate a face frame with calm colors and a simple design. In this way, by adjusting the design of the face frame according to the user's emotions, a more appropriate sense of the season can be created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI.

[0069] The generation unit can generate special face frames corresponding to seasonal events and holidays. For example, using the generation AI, the generation unit can generate face frames with Santa Claus and Christmas tree motifs for Christmas. The generation unit prompts the generation AI with "Generate a Christmas face frame," and the generation AI generates face frames with Santa Claus and Christmas tree motifs. The generation unit can also use the generation AI to generate face frames with pumpkin and ghost motifs for Halloween. The generation unit prompts the generation AI with "Generate a Halloween face frame," and the generation AI generates face frames with pumpkin and ghost motifs. Furthermore, the generation unit can use the generation AI to generate face frames with heart and chocolate motifs for Valentine's Day. The generation unit prompts the generation AI with "Generate a Valentine's Day face frame," and the generation AI generates face frames with heart and chocolate motifs. In this way, by generating special face frames corresponding to seasonal events and holidays, a special sense of the season can be created. Some or all of the above processing in the generation unit is performed using the generation AI.

[0070] The generation unit can incorporate the user's past preferences and feedback when generating face frames. For example, the generation unit can use a generation AI to generate face frames that reflect the user's past preferences and feedback. The generation unit can prompt the generation AI with "Generate a face frame based on designs the user has liked in the past," and the generation AI will generate a similar face frame. The generation unit can also prompt the generation AI with "Generate a face frame based on feedback the user has provided in the past," and the generation AI will generate a face frame with an improved design. Furthermore, the generation unit can prompt the generation AI with "Generate a face frame that reflects the colors and themes the user has chosen in the past," and the generation AI will generate a face frame based on that. In this way, by incorporating the user's past preferences and feedback, it is possible to generate face frames that are more suitable for the user. Some or all of the above processes in the generation unit are performed using a generation AI.

[0071] The generation unit can estimate the user's emotions and select a theme for the face frame to be generated based on the estimated emotions. For example, the generation unit can use a generation AI to estimate the user's emotions and select a theme for the face frame based on the estimated emotions. The generation unit can prompt the generation AI with "Please select a theme for when the user is relaxed," and the generation AI will select a face frame with a nature or landscape theme. The generation unit can also prompt the generation AI with "Please select a theme for when the user is excited," and the generation AI will select a face frame with a sports or action theme. Furthermore, the generation unit can prompt the generation AI with "Please select a theme for when the user is sad," and the generation AI will select a face frame with a healing or tranquility theme. By selecting a face frame theme according to the user's emotions, a more appropriate sense of the season can be created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI.

[0072] The generation unit can consider regional cultures and customs when generating face frames appropriate for each season. For example, the generation unit can use a generation AI to generate a face frame with a cherry blossom motif for spring in Japan. The generation unit prompts the generation AI with "Generate a face frame for spring in Japan," and the generation AI generates a face frame with a cherry blossom motif. The generation unit can also use the generation AI to generate a face frame with an Independence Day fireworks motif for summer in America. The generation unit prompts the generation AI with "Generate a face frame for summer in America," and the generation AI generates a face frame with an Independence Day fireworks motif. Furthermore, the generation unit can use the generation AI to generate a face frame with a Day of the Dead motif for autumn in Mexico. The generation unit prompts the generation AI with "Generate a face frame for autumn in Mexico," and the generation AI generates a face frame with a Day of the Dead motif. This allows for a more appropriate sense of the season to each region by considering regional cultures and customs. Some or all of the above processing in the generation unit is performed using a generation AI.

[0073] The generation unit can analyze the user's social media activity and generate a face frame that matches current trends when generating face frames. For example, the generation unit uses a generation AI to analyze the user's social media activity and generate a face frame that matches current trends. The generation unit prompts the generation AI with "Analyze the user's social media activity and generate a face frame that matches current trends," and the generation AI generates a face frame based on images and videos that the user frequently shares. The generation unit can also prompt the generation AI with "Analyze the content of posts from influencers that the user follows and generate a face frame that matches current trends," and the generation AI generates a face frame based on the influencers' posts. Furthermore, the generation unit can prompt the generation AI with "Generate a face frame based on the trends of online communities that the user participates in," and the generation AI generates a face frame based on the community trends. In this way, by analyzing the user's social media activity, it is possible to generate face frames with designs that match current trends. Some or all of the above processes in the generation unit are performed using a generation AI.

[0074] The application unit can estimate the user's emotions and adjust the timing of applying the face frame based on the estimated emotions. For example, the application unit can use a generative AI to estimate the user's emotions and adjust the timing of applying the face frame based on the estimated emotions. The application unit can prompt the generative AI with "Adjust the application timing when the user is relaxed," and the generative AI can apply the face frame at a slow pace. Alternatively, the application unit can input "Adjust the application timing when the user is excited," and the generative AI can apply the face frame quickly. Furthermore, the application unit can input "Adjust the application timing when the user is sad," and the generative AI can apply the face frame at a calm pace. By adjusting the timing of applying the face frame according to the user's emotions, the face frame can be applied at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the application unit is performed using a generative AI.

[0075] The application unit can apply the face frame at the optimal timing, taking into account the movement and position information of the serving robot. For example, the application unit can apply the face frame when the serving robot approaches the user. The application unit can apply the face frame when the serving robot reaches a specific position. The application unit can also apply the face frame when the serving robot stops. In this way, by taking into account the movement and position information of the serving robot, the face frame can be applied at the optimal timing. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the timing of face frame application based on the movement and position information of the serving robot.

[0076] The application unit can harmonize the face frame with other decorations and designs of the serving robot when applying it. For example, the application unit can apply a face frame that matches the body color of the serving robot. The application unit can apply a face frame with a design that is integrated with the other decorations of the serving robot. The application unit can also apply a face frame that matches the theme of the serving robot. This allows for a more cohesive and seasonal feel by harmonizing with the other decorations and designs of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the design of the face frame based on the body color and theme of the serving robot.

[0077] The application unit can estimate the user's emotions and determine the order in which face frames are applied based on the estimated emotions. For example, the application unit can use a generative AI to estimate the user's emotions and determine the order in which face frames are applied based on the estimated emotions. The application unit can prompt the generative AI with "Determine the application order when the user is relaxed," and the generative AI will apply a face frame with a calm design first. Alternatively, the application unit can input "Determine the application order when the user is excited," and the generative AI will apply a face frame with a vibrant design first. Furthermore, the application unit can input "Determine the application order when the user is sad," and the generative AI will apply a face frame with a calm design first. By determining the order in which face frames are applied according to the user's emotions, it is possible to apply face frames in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the application unit is performed using a generating AI.

[0078] The application unit can adjust the application method of the face frame according to the operating environment of the serving robot when applying it. For example, in a store, the application unit can apply the face frame to attract the customer's attention. In a factory, the application unit can apply the face frame in a way that does not hinder work efficiency. Furthermore, the application unit can apply a face frame that matches the theme at an event venue. In this way, by adjusting the application method according to the operating environment of the serving robot, the face frame can be applied in a more appropriate manner. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of adjusting the application method of the face frame based on the operating environment of the serving robot.

[0079] The application unit can select the optimal application method when applying a face frame by referring to the operation history of the serving robot. For example, the application unit can select the optimal application method based on application methods that have been successful in the past for the serving robot. The application unit applies the face frame at the most effective timing based on the operation history of the serving robot. The application unit can also analyze the operation history of the serving robot and select the optimal application order. In this way, the optimal application method can be selected by referring to the operation history of the serving robot. Some or all of the above processing in the application unit may be performed using AI or not. For example, the application unit can have AI perform the process of selecting the application method of the face frame based on the operation history of the serving robot.

[0080] The output unit can estimate the user's emotions and adjust the format of the output face frame based on the estimated emotions. For example, the output unit can use a generative AI to estimate the user's emotions and adjust the format of the face frame based on the estimated emotions. The output unit can prompt the generative AI with "Generate a face frame for when the user is relaxed," and the generative AI will output a face frame with soft colors. The output unit can also prompt the generative AI with "Generate a face frame for when the user is excited," and the generative AI will output a face frame with vivid colors. Furthermore, the output unit can prompt the generative AI with "Generate a face frame for when the user is sad," and the generative AI will output a face frame with calm colors. In this way, by adjusting the format of the face frame according to the user's emotions, a more appropriate format can be output. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the output section is performed using a generating AI.

[0081] The output unit can optimize the output of face frames to match the display resolution and color settings of the serving robot. For example, the output unit can adjust the resolution of the face frame to match the display resolution of the serving robot. The output unit can also adjust the color tone of the face frame to match the color settings of the serving robot. Furthermore, the output unit can adjust the size of the face frame to match the display size of the serving robot. By optimizing for the display resolution and color settings of the serving robot, a more appropriate face frame can be output. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the display resolution and color settings of the serving robot.

[0082] The output unit can select an output format that reflects the user's past feedback when outputting face frames. For example, the output unit can use a generation AI to output face frames that reflect the user's past feedback. The output unit can prompt the generation AI with "Generate a face frame based on the output format the user has preferred in the past," and the generation AI will output a face frame based on that prompt. Alternatively, the output unit can input "Generate a face frame based on feedback the user has provided in the past," and the generation AI can select an improved output format. Furthermore, the output unit can input "Generate a face frame that reflects the colors and themes the user has previously selected," and the generation AI can output a face frame based on that prompt. This allows for the output of face frames that are more suitable for the user by reflecting the user's past feedback. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have the AI ​​perform the process of selecting the output format of the face frame based on the user's past feedback.

[0083] The output unit can estimate the user's emotions and determine the order of the face frames to output based on the estimated emotions. For example, the output unit can use a generative AI to estimate the user's emotions and determine the order of the face frames based on the estimated emotions. The output unit can prompt the generative AI with "Determine the order of face frames when the user is relaxed," and the generative AI will output face frames with a calm design first. Alternatively, the output unit can prompt the generative AI with "Determine the order of face frames when the user is excited," and the generative AI will output face frames with a vibrant design first. Furthermore, the output unit can prompt the generative AI with "Determine the order of face frames when the user is sad," and the generative AI will output face frames with a calm design first. By determining the order of face frames according to the user's emotions, it is possible to output face frames in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the output section is performed using a generating AI.

[0084] The output unit can adjust the output format of the face frame according to the operating environment of the serving robot. For example, in a store, the output unit can output a face frame that attracts the customer's attention. In a factory, the output unit can output a face frame that does not hinder work efficiency. Furthermore, the output unit can output a face frame that matches the theme at an event venue. By adjusting the output format according to the operating environment of the serving robot, it is possible to output the face frame in a more appropriate format. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the operating environment of the serving robot.

[0085] The output unit can make adjustments to the face frame output to harmonize it with other display content of the serving robot. For example, the output unit can output a face frame whose colors match those of other display content of the serving robot. The output unit can output a face frame with a design that is integrated with the other display content of the serving robot. The output unit can also output a face frame that matches the theme of the serving robot. This allows for a more integrated display by harmonizing it with the other display content of the serving robot. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can have AI perform the process of adjusting the output format of the face frame based on the other display content of the serving robot.

[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 generation unit can estimate the user's emotions and adjust the color tone of the face frame based on those emotions. For example, if the user is relaxed, the generation unit will generate a face frame with soft colors. If the user is excited, the generation unit can also generate a face frame with vibrant colors. Furthermore, if the user is sad, the generation unit can generate a face frame with calm colors. This allows for the creation of face frames with color tones that match the user's emotions, thereby creating a more appropriate sense of the season.

[0088] The application unit can synchronize the generated face frames with the movements of the serving robot. For example, a dynamic face frame can be applied when the serving robot is moving, and a static face frame can be applied when it is stopped. Furthermore, when the serving robot performs a specific action, a face frame that matches that action can be applied. Additionally, when the serving robot approaches the user, a face frame designed to attract the user's attention can be applied. This allows for a more dynamic and seasonal feel by applying face frames synchronized with the serving robot's movements.

[0089] The output unit can send the generated face frames to the user's device. For example, it can send the face frames to the user's smartphone or tablet, allowing the user to view them on their device. Furthermore, the user can share the face frames sent to their device on social media. In addition, the user can customize the face frames sent to their device. This allows users to utilize the generated face frames more freely by sending them to their device.

[0090] The generation unit can estimate the user's emotions and dynamically change the design of the face frame based on those emotions. For example, if the user is relaxed, the generation unit will generate a face frame with a calm design. If the user is excited, the generation unit can also generate a face frame with a dynamic design. Furthermore, if the user is sad, the generation unit can generate a face frame with a simple design. This allows for the creation of face frames with designs that match the user's emotions, thereby creating a more appropriate sense of the season.

[0091] The application unit can synchronize the generated face frames with the voice output of the serving robot. For example, when the serving robot outputs a specific voice, a face frame matching that voice can be applied. Furthermore, when the serving robot speaks to a user, a face frame corresponding to the user's response can be applied. Additionally, when the serving robot plays music, a face frame matching that music can be applied. By applying face frames synchronized with the serving robot's voice output, a more cohesive and seasonal atmosphere can be created.

[0092] The generation unit can estimate the user's emotions and adjust the animation of the face frame based on those emotions. For example, if the user is relaxed, the generation unit can generate a face frame with slow animation. If the user is excited, the generation unit can also generate a face frame with fast animation. Furthermore, if the user is sad, the generation unit can generate a face frame with gentle animation. This allows for the creation of face frames with animations that correspond to the user's emotions, resulting in a more appropriate sense of the season.

[0093] The application unit can synchronize the generated face frames with the lighting of the serving robot. For example, when the serving robot's lighting changes, a face frame matching that lighting can be applied. Furthermore, when the serving robot reaches a specific location, a face frame matching the lighting at that location can be applied. Additionally, when the serving robot performs a specific event, a face frame matching the lighting for that event can be applied. This allows for a more cohesive and seasonal feel by applying face frames synchronized with the serving robot's lighting.

[0094] The generation unit can estimate the user's emotions and select a face frame theme based on those emotions. For example, if the user is relaxed, the generation unit will generate face frames themed around nature or scenery. If the user is excited, the generation unit can also generate face frames themed around sports or action. Furthermore, if the user is sad, the generation unit can generate face frames themed around healing or tranquility. By generating face frames with themes that match the user's emotions, it is possible to create a more appropriate sense of the season.

[0095] The application unit can apply the generated face frames based on the operation history of the serving robot. For example, it can apply face frames based on application methods that have been successful in the past for the serving robot. It can also apply face frames at the most effective timing based on the serving robot's operation history. Furthermore, it can analyze the serving robot's operation history and select the optimal application order. By selecting an application method for face frames based on the serving robot's operation history, a more effective sense of seasonality can be created.

[0096] The output unit can output the generated face frame based on the user's emotions. For example, if the user is relaxed, the output unit will output a face frame with soft colors. If the user is excited, the output unit can output a face frame with vibrant colors. Furthermore, if the user is sad, the output unit can output a face frame with calm colors. This allows for a more appropriate sense of the season by outputting face frames with colors that match the user's emotions.

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

[0098] Step 1: The generation unit generates face frames appropriate for the season. Using the generation AI, the generation unit receives the prompt "Generate a spring face frame" and generates a frame with cherry blossom petals fluttering. It can also receive the prompt "Generate a summer face frame" and generate frames with the sea and fireworks. Furthermore, it can receive the prompt "Generate an autumn face frame" and generate a frame with autumn leaves. Step 2: The application unit applies the generated face frame to the serving robot. The application unit composites and overlays the generated face frame onto the serving robot's face to create a seasonal feel. The generated face frame can also be displayed on the serving robot's display or attached to its exterior. Step 3: The output unit outputs the generated face frame in transparent PNG format. The output unit saves the generated face frame in transparent PNG format and displays it on the serving robot's display. It can also be printed in transparent PNG format or sent via email.

[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, including the generation unit, application unit, and output unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates seasonal face frames using generation AI. The application unit is implemented by the control unit 46A of the smart device 14 and synthesizes the generated face frames onto the face of the serving robot. The output unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs the generated face frames in transparent PNG format. 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, including the generation unit, application unit, and output unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates seasonal face frames using generation AI. The application unit is implemented by the control unit 46A of the smart glasses 214 and synthesizes the generated face frames onto the face of the serving robot. The output unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs the generated face frames in transparent PNG format. 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 generation unit, application unit, and output unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates seasonal face frames using generation AI. The application unit is implemented by the control unit 46A of the headset terminal 314 and synthesizes the generated face frames onto the face of the serving robot. The output unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs the generated face frames in transparent PNG format. 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, including the generation unit, application unit, and output unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates seasonal face frames using generation AI. The application unit is implemented by the control unit 46A of the robot 414, which composites the generated face frames onto the face of the serving robot. The output unit is implemented by the specific processing unit 290 of the data processing unit 12, which outputs the generated face frames in transparent PNG format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[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 generation unit that generates face frames according to the season, An application unit that applies the face frame generated by the generation unit to the serving robot, The system includes an output unit that outputs the face frame applied by the application unit in transparent PNG format. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates face frames according to the season. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned application unit is Apply the generated face frame to the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, Output the generated face frame in transparent PNG format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It generates frames such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned application unit is The generated face frame is combined with the face of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the design of the face frame according to the season based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Generate special face frames for seasonal events and holidays. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating face frames, the user's past preferences and feedback are reflected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and selects a theme for the generated face frame based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating face frames according to the season, regional culture and customs should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating face frames, the system analyzes the user's social media activity and generates designs that match current trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned application unit is It estimates the user's emotions and adjusts the timing of applying the face frame based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned application unit is When applying the face frame, the optimal timing is determined by considering the movement and position information of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned application unit is When applying the face frame, ensure it harmonizes with other decorations and designs of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned application unit is The system estimates the user's emotions and determines the order in which face frames are applied based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned application unit is When applying a face frame, adjust the application method according to the operating environment of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned application unit is When applying a face frame, the optimal application method is selected by referring to the operation history of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, It estimates the user's emotions and adjusts the format of the output face frame based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, When outputting the face frame, optimize it for the display resolution and color settings of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When outputting face frames, the output format is selected based on past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and determines the order of the face frames to output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When outputting the face frame, adjust the output format according to the operating environment of the serving robot. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When outputting the face frame, adjustments are made to harmonize it with the other display content of the serving robot. 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 generation unit that generates face frames according to the season, An application unit that applies the face frame generated by the generation unit to the serving robot, The system includes an output unit that outputs the face frame applied by the application unit in transparent PNG format. A system characterized by the following features.

2. The generating unit is Generating AI to create face frames appropriate for each season. The system according to feature 1.

3. The aforementioned application unit is Apply the generated face frame to the serving robot. The system according to feature 1.

4. The output unit is, Output the generated face frame in transparent PNG format. The system according to feature 1.

5. The generating unit is It generates frames such as cherry blossom petals in spring, the sea and fireworks in summer, autumn leaves in fall, and snowflakes in winter. The system according to feature 1.

6. The aforementioned application unit is The generated face frame is combined with the face of the serving robot. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the design of the face frame according to the season based on the estimated user emotions. The system according to feature 1.

8. The generating unit is Generate special face frames for seasonal events and holidays. The system according to feature 1.

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