System

Abstract

An object of the system according to the embodiment is to integrally generate and propose part arrangement and exterior design based on a customer's request.SOLUTION: A system according to an embodiment includes a parts arrangement design unit, an exterior design generation unit, an integration unit, and a proposal unit. The part arrangement design unit performs internal space design of part arrangement on the basis of a customer's request. The exterior design generation unit generates an exterior design in consideration of the volume of the internal space and the component arrangement designed by the parts arrangement design unit. The integration unit integrates the parts arrangement and the exterior design generated by the parts arrangement design unit and the exterior design generation unit. The proposal unit proposes the product design integrated by the integration unit to the customer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to generate part placement and exterior design in an integrated manner in product design, and efficient design is required.

[0005] The system of the embodiment aims to generate and propose parts placement and exterior design in an integrated manner based on customer requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a parts layout design unit, an exterior design generation unit, an integration unit, and a proposal unit. The parts layout design unit designs the interior space of the product based on customer requests. The exterior design generation unit generates an exterior design taking into consideration the volume of the interior space and the layout of parts designed by the parts layout design unit. The integration unit integrates the parts layout and exterior design generated by the parts layout design unit and the exterior design generation unit. The proposal unit proposes the product design integrated by the integration unit to the customer. [Effects of the Invention]

[0007] The system according to the embodiment can generate and propose parts placement and exterior design in an integrated manner based on customer requests. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A product design system according to an embodiment of the present invention is a system that designs an interior space for part placement based on customer requests, generates an exterior design taking into account the volume of the interior space and the placement of parts, integrates the part placement and the exterior design, and proposes it to the customer. This allows the product design system to provide an optimal product design that meets the customer's requests.

[0029] A product design system according to an embodiment includes a parts layout design unit, an exterior design generation unit, an integration unit, and a proposal unit. The parts layout design unit performs interior space design based on customer requests. For example, if a customer requests, "I want the camera and sensor to be placed at the front and the battery at the back," the generation AI proposes an optimal parts layout based on that request. The generation AI receives prompts containing the customer's requests, and the generation AI performs interior space design based on those prompts. The exterior design generation unit generates an exterior design taking into account the volume and component layout of the interior space designed by the parts layout design unit. For example, it may provide vents to efficiently cool the internal components or adjust the position of ports for easy external access. The generation AI then proposes an exterior design based on the results of the interior space design. For example, it may propose the shape, color, and material of the exterior based on a customer request such as, "I prefer a simple, modern design." The integration unit integrates the parts layout generated by the parts layout design unit and the exterior design generation unit with the exterior design. For example, it is possible to provide a product with efficient internal component layout and a beautiful exterior design. The proposal unit proposes the product design integrated by the integration unit to the customer. For example, if a customer requests that the product be made a little more compact, the generation AI regenerates the design in accordance with that request. This allows the product design system according to the embodiment to provide optimal product designs that meet customer requests. For example, it can achieve efficient and safe parts placement and beautiful exterior designs for precision equipment such as home appliances, digital gadgets, and hobby robots.

[0030] Based on customer requests, the part layout design department can optimize not only the layout of parts, but also the signal transmission paths and power supply paths between parts. For example, the part layout design department uses a generation AI to optimize signal transmission paths based on the part layout specified by the customer. For example, it designs the signal transmission between the camera and sensor to take the shortest route. In addition to part layout, the generation AI also optimizes the power supply path. For example, it designs wiring to efficiently supply power from the battery to each part. Furthermore, based on customer requests, the generation AI optimizes both signal transmission and power supply between parts. For example, it designs shielding to avoid signal interference. This enables efficient part layout by optimizing the signal transmission paths and power supply paths between parts.

[0031] In addition to arranging parts based on customer requests, the part layout design department can also design a cooling system to maximize the cooling efficiency of the parts. For example, the generative AI in the part layout design department designs a cooling system based on the part layout. For example, it places cooling fans around parts that generate heat. In addition to the part layout, the generative AI also suggests the placement of heat sinks to maximize cooling efficiency. For example, it may install a heat sink on top of the CPU or GPU. The generative AI also designs the entire cooling system based on customer requests. For example, it may introduce a liquid cooling system and optimize the flow path of the coolant. This maximizes the cooling efficiency of the parts, thereby improving the stability and lifespan of the system.

[0032] The part placement design department is able to propose optimal parts, not just part placement, but also part selection, based on customer requests. In the part placement design department, for example, the generation AI selects the optimal parts based on customer requests. For example, it proposes camera resolution and sensor type. In addition to part placement, the generation AI also selects parts. For example, it proposes battery capacity and shape. Furthermore, the generation AI simultaneously selects and places parts according to customer requests. For example, it selects the optimal SoC and board and proposes placement based on that. This allows the optimal parts to be selected based on customer requests, achieving efficient part placement.

[0033] Based on customer requests, the parts placement design unit can propose a placement that takes into account the durability and lifespan of parts in addition to the placement of parts. In the parts placement design unit, for example, the generation AI performs a design that takes durability into account based on the placement of parts. For example, it proposes a placement that is resistant to vibration and impact. The generation AI also proposes the optimal placement taking into account the lifespan of parts. For example, it performs a placement that prevents deterioration due to heat. The generation AI also proposes a parts placement that takes durability and lifespan into account based on customer requests. For example, it prioritizes the placement of parts with long lifespans. In this way, by proposing a placement that takes into account the durability and lifespan of parts, the reliability of the system is improved.

[0034] In addition to the exterior design, the exterior design generation unit can also select the exterior material and suggest the optimal material. For example, the generation AI selects the optimal material based on the exterior design. For example, it can suggest a lightweight and durable material. In addition to the exterior design, the generation AI also selects the material. For example, it can suggest a material with high heat dissipation properties. Furthermore, based on customer requests, the generation AI simultaneously performs the exterior design and selects the material. For example, it can suggest a material with a color and texture that matches the design. In this way, by selecting the exterior material, the optimal material can be suggested and the quality of the product can be improved.

[0035] In addition to the exterior design, the exterior design generation unit can also optimize the exterior manufacturing process. For example, the generative AI in the exterior design generation unit optimizes the manufacturing process based on the exterior design. For example, it may propose a manufacturing method using 3D printing. In addition to the exterior design, the generative AI also optimizes the manufacturing process. For example, it may propose a cost-effective manufacturing method. Furthermore, based on customer requests, the generative AI simultaneously optimizes the exterior design and manufacturing process. For example, it may propose a process that allows for manufacturing in a short delivery time. In this way, optimizing the exterior manufacturing process improves manufacturing efficiency.

[0036] In addition to the exterior design, the exterior design generation unit also selects the exterior color and texture, allowing it to propose the optimal design. In the exterior design generation unit, for example, the generation AI selects the optimal color and texture based on the exterior design. For example, it proposes a color palette that matches the customer's preferences. In addition to the exterior design, the generation AI also selects the color and texture. For example, it proposes materials that feel good to the touch. Furthermore, based on the customer's requests, the generation AI simultaneously selects the exterior design and color / texture. For example, it proposes colors and textures that match the design. In this way, by selecting the exterior color and texture, it is possible to provide a design that matches the customer's preferences.

[0037] The exterior design generation unit can propose a design that takes into account the durability and waterproofness of the exterior in addition to the exterior design. In the exterior design generation unit, for example, the generation AI creates a design that takes durability into account based on the exterior design. For example, it proposes materials that are resistant to impacts. In addition to the exterior design, the generation AI also creates a design that takes waterproofness into account. For example, it proposes waterproof seals and rubber gaskets. Furthermore, based on customer requests, the generation AI proposes an exterior design that takes durability and waterproofness into account. For example, it proposes a robust design for outdoor use. In this way, by proposing a design that takes into account the durability and waterproofness of the exterior, the reliability of the product is improved.

[0038] In addition to integrating part placement and exterior design, the integration unit can also design products that take into account ease of assembly. For example, in the integration unit, the generative AI integrates part placement and exterior design to create a design that takes into account ease of assembly. For example, it optimizes the position of screws and clips. In addition to part placement and exterior design, the generative AI also designs products that take into account ease of assembly. For example, it proposes modularized parts. Furthermore, based on customer requests, the generative AI integrates part placement and exterior design to create a design that takes into account ease of assembly. For example, it proposes a design that can be assembled without tools. This improves manufacturing efficiency by creating designs that take into account ease of assembly of products.

[0039] In addition to integrating part layout and exterior design, the integration unit can also create designs that take into account the maintainability of the product. For example, in the integration unit, the generation AI integrates part layout and exterior design to create a design that takes maintainability into account. For example, it may propose a layout that makes part replacement easy. In addition to part layout and exterior design, the generation AI may create a design that takes maintainability into account. For example, it may propose a design that is easy to clean. Furthermore, based on customer requests, the generation AI may integrate part layout and exterior design to create a design that takes maintainability into account. For example, it may propose a design that makes disassembly easy. In this way, by creating a design that takes into account the maintainability of the product, it is possible to use the product for a long period of time.

[0040] In addition to integrating part placement and exterior design, the integration unit can also design products with consideration for recyclability. For example, the integration unit's generation AI integrates part placement and exterior design to create a design that takes recyclability into account. For example, it uses recyclable materials. In addition to part placement and exterior design, the generation AI also creates a design that takes recyclability into account. For example, it proposes a design that is easy to disassemble. Furthermore, based on customer requests, the generation AI integrates part placement and exterior design to create a design that takes recyclability into account. For example, it uses reusable parts. This reduces the environmental impact by designing products with consideration for recyclability.

[0041] In addition to integrating part placement and exterior design, the integration unit can also design products with energy efficiency in mind. For example, the integration unit's generative AI integrates part placement and exterior design to create a design that takes energy efficiency into consideration. For example, it uses parts that consume low power. In addition to part placement and exterior design, the generative AI also creates a design that takes energy efficiency into consideration. For example, it proposes an efficient cooling system. Furthermore, based on customer requests, the generative AI integrates part placement and exterior design to create a design that takes energy efficiency into consideration. For example, it uses solar panels. This reduces power consumption by designing a product with energy efficiency in mind.

[0042] The proposal unit reflects customer feedback in real time and can instantly modify and optimize the design. In the proposal unit, for example, the generation AI receives customer feedback in real time and instantly modifies the design. For example, if a customer requests a color change, this is immediately reflected. The generation AI also optimizes the design based on customer feedback. For example, it adjusts the size or changes the placement of parts. In addition, a system is built in which the generation AI receives feedback in real time and modifies the design. For example, it performs online real-time design modifications. This allows customer feedback to be reflected in real time and the design to be instantly modified and optimized.

[0043] The suggestion unit can generate multiple design variations based on customer feedback and provide options. In the suggestion unit, for example, the generation AI generates multiple design variations based on customer feedback. For example, it proposes designs with different colors and shapes. Furthermore, the generation AI generates multiple design variations and provides options in response to customer requests. For example, it proposes designs with different materials and finishes. Furthermore, a system is constructed in which the generation AI generates design variations based on feedback and provides options to customers. For example, an online design selection function is provided. This provides multiple design variations based on customer feedback, increasing the options and improving customer satisfaction.

[0044] The suggestion unit can automatically suggest design improvements based on customer feedback. In the suggestion unit, for example, the generation AI automatically suggests design improvements based on customer feedback. For example, adding a function desired by the customer. The generation AI also suggests design improvements based on customer feedback. For example, solving problems pointed out by the customer. A system is also constructed in which the generation AI automatically suggests design improvements based on feedback. For example, making online design improvement suggestions. This improves the quality of the design by automatically suggesting design improvements based on customer feedback.

[0045] The proposal department can share design improvements based on customer feedback with other customers and find common areas for improvement. For example, the proposal department may use the generative AI to share design improvements with other customers based on customer feedback. For example, it may provide an online platform for finding common areas for improvement. The generative AI may also share design improvements based on customer feedback and collect opinions from other customers. For example, it may promote the exchange of opinions in an online forum. The proposal department may also build a system for the generative AI to share design improvements with other customers based on feedback. For example, it may provide a database for finding common areas for improvement. This allows design improvements to be shared based on customer feedback and common areas for improvement to be found, thereby improving the overall design quality.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Based on customer requests, the part layout design department can optimize not only the layout of parts, but also the signal transmission paths and power supply paths between parts. For example, the generation AI optimizes signal transmission paths based on the part layout specified by the customer. For example, it designs the signal transmission between the camera and sensor to take the shortest route. In addition to part layout, the generation AI also optimizes power supply paths. For example, it designs wiring to efficiently supply power from the battery to each part. Furthermore, based on customer requests, the generation AI optimizes both signal transmission and power supply between parts. For example, it designs shielding to avoid signal interference. This enables efficient part layout by optimizing the signal transmission paths and power supply paths between parts.

[0048] In addition to arranging parts based on customer requests, the part layout design department can also design a cooling system to maximize the cooling efficiency of the parts. For example, the generative AI designs a cooling system based on the layout of parts. For example, it places cooling fans around parts that generate heat. In addition to the layout of parts, the generative AI also suggests the placement of heat sinks to maximize cooling efficiency. For example, it may install a heat sink on top of the CPU or GPU. The generative AI also designs the entire cooling system based on customer requests. For example, it may introduce a liquid cooling system and optimize the flow path of the coolant. This maximizes the cooling efficiency of the parts, improving the stability and lifespan of the system.

[0049] The parts placement design department can not only place parts but also select parts based on customer requests, proposing optimal parts. For example, the generation AI selects the optimal parts based on customer requests. For example, it proposes camera resolution and sensor type. In addition to part placement, the generation AI also selects parts. For example, it proposes battery capacity and shape. Furthermore, the generation AI simultaneously selects and places parts based on customer requests. For example, it selects the optimal SoC and board and proposes placement based on that. This allows the optimal parts to be selected based on customer requests, achieving efficient part placement.

[0050] Based on customer requests, the parts placement design department can propose a placement that takes into account the durability and lifespan of parts in addition to the placement of parts. For example, the generation AI will create a design that takes durability into account based on the placement of parts. For example, it will propose a placement that is resistant to vibration and impact. The generation AI will also propose the optimal placement taking into account the lifespan of parts. For example, it will make a placement that prevents deterioration due to heat. The generation AI will also propose a parts placement that takes durability and lifespan into account based on customer requests. For example, it will prioritize the placement of parts with long lifespans. In this way, by proposing a placement that takes into account the durability and lifespan of parts, the reliability of the system will be improved.

[0051] In addition to the exterior design, the exterior design generation unit can also select the exterior material and suggest the optimal material. For example, the generation AI selects the optimal material based on the exterior design. For example, it can suggest a lightweight and durable material. In addition to the exterior design, the generation AI also selects the material. For example, it can suggest a material with high heat dissipation properties. Furthermore, based on customer requests, the generation AI simultaneously selects the exterior design and material. For example, it can suggest a material with a color and texture that matches the design. In this way, by selecting the exterior material, the optimal material can be suggested and the quality of the product can be improved.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The parts layout design department designs the interior space for parts placement based on the customer's requests. For example, if a customer requests "I want the camera and sensor to be placed at the front and the battery at the back," the generation AI will propose the optimal parts placement based on that request. The input to the generation AI is a prompt that includes the customer's request, and the generation AI designs the interior space based on that prompt. Step 2: The exterior design generation unit generates an exterior design taking into account the volume of the interior space and the layout of parts designed by the parts layout design unit. For example, it may provide vents to allow for efficient cooling of the parts placed inside, or adjust the position of ports to allow for easy access from the outside. The generation AI proposes an exterior design based on the results of the interior space design. For example, it proposes the shape, color, and material of the exterior based on customer requests such as "I prefer a simple, modern design." Step 3: The integration unit integrates the part layout and exterior design generated by the part layout design unit and the exterior design generation unit. For example, it is possible to provide a product with an efficient internal part layout and a beautiful exterior design. Step 4: The proposal unit proposes the product design integrated by the integration unit to the customer. For example, if the customer requests something like "I want it to be a little more compact," the generation AI regenerates the design in accordance with that request. This allows the product design system according to the embodiment to provide the optimal product design that meets the customer's needs.

[0054] (Example 2) A product design system according to an embodiment of the present invention is a system that designs an interior space for part placement based on customer requests, generates an exterior design taking into account the volume of the interior space and the placement of parts, integrates the part placement and the exterior design, and proposes it to the customer. This allows the product design system to provide an optimal product design that meets the customer's requests.

[0055] A product design system according to an embodiment includes a parts layout design unit, an exterior design generation unit, an integration unit, and a proposal unit. The parts layout design unit performs interior space design based on customer requests. For example, if a customer requests, "I want the camera and sensor to be placed at the front and the battery at the back," the generation AI proposes an optimal parts layout based on that request. The generation AI receives prompts containing the customer's requests, and the generation AI performs interior space design based on those prompts. The exterior design generation unit generates an exterior design taking into account the volume and component layout of the interior space designed by the parts layout design unit. For example, it may provide vents to efficiently cool the internal components or adjust the position of ports for easy external access. The generation AI then proposes an exterior design based on the results of the interior space design. For example, it may propose the shape, color, and material of the exterior based on a customer request such as, "I prefer a simple, modern design." The integration unit integrates the parts layout generated by the parts layout design unit and the exterior design generation unit with the exterior design. For example, it is possible to provide a product with efficient internal component layout and a beautiful exterior design. The proposal unit proposes the product design integrated by the integration unit to the customer. For example, if a customer requests that the product be made a little more compact, the generation AI regenerates the design in accordance with that request. This allows the product design system according to the embodiment to provide optimal product designs that meet customer requests. For example, it can achieve efficient and safe parts placement and beautiful exterior designs for precision equipment such as home appliances, digital gadgets, and hobby robots.

[0056] Based on customer requests, the part layout design department can optimize not only the layout of parts, but also the signal transmission paths and power supply paths between parts. For example, the part layout design department uses a generation AI to optimize signal transmission paths based on the part layout specified by the customer. For example, it designs the signal transmission between the camera and sensor to take the shortest route. In addition to part layout, the generation AI also optimizes the power supply path. For example, it designs wiring to efficiently supply power from the battery to each part. Furthermore, based on customer requests, the generation AI optimizes both signal transmission and power supply between parts. For example, it designs shielding to avoid signal interference. This enables efficient part layout by optimizing the signal transmission paths and power supply paths between parts.

[0057] In addition to arranging parts based on customer requests, the part layout design department can also design a cooling system to maximize the cooling efficiency of the parts. For example, the generative AI in the part layout design department designs a cooling system based on the part layout. For example, it places cooling fans around parts that generate heat. In addition to the part layout, the generative AI also suggests the placement of heat sinks to maximize cooling efficiency. For example, it may install a heat sink on top of the CPU or GPU. The generative AI also designs the entire cooling system based on customer requests. For example, it may introduce a liquid cooling system and optimize the flow path of the coolant. This maximizes the cooling efficiency of the parts, thereby improving the stability and lifespan of the system.

[0058] The part placement design department is able to propose optimal parts, not just part placement, but also part selection, based on customer requests. In the part placement design department, for example, the generation AI selects the optimal parts based on customer requests. For example, it proposes camera resolution and sensor type. In addition to part placement, the generation AI also selects parts. For example, it proposes battery capacity and shape. Furthermore, the generation AI simultaneously selects and places parts according to customer requests. For example, it selects the optimal SoC and board and proposes placement based on that. This allows the optimal parts to be selected based on customer requests, achieving efficient part placement.

[0059] Based on customer requests, the parts placement design unit can propose a placement that takes into account the durability and lifespan of parts in addition to the placement of parts. In the parts placement design unit, for example, the generation AI performs a design that takes durability into account based on the placement of parts. For example, it proposes a placement that is resistant to vibration and impact. The generation AI also proposes the optimal placement taking into account the lifespan of parts. For example, it performs a placement that prevents deterioration due to heat. The generation AI also proposes a parts placement that takes durability and lifespan into account based on customer requests. For example, it prioritizes the placement of parts with long lifespans. In this way, by proposing a placement that takes into account the durability and lifespan of parts, the reliability of the system is improved.

[0060] In addition to the exterior design, the exterior design generation unit can also select the exterior material and suggest the optimal material. For example, the generation AI selects the optimal material based on the exterior design. For example, it can suggest a lightweight and durable material. In addition to the exterior design, the generation AI also selects the material. For example, it can suggest a material with high heat dissipation properties. Furthermore, based on customer requests, the generation AI simultaneously performs the exterior design and selects the material. For example, it can suggest a material with a color and texture that matches the design. In this way, by selecting the exterior material, the optimal material can be suggested and the quality of the product can be improved.

[0061] In addition to the exterior design, the exterior design generation unit can also optimize the exterior manufacturing process. For example, the generative AI in the exterior design generation unit optimizes the manufacturing process based on the exterior design. For example, it may propose a manufacturing method using 3D printing. In addition to the exterior design, the generative AI also optimizes the manufacturing process. For example, it may propose a cost-effective manufacturing method. Furthermore, based on customer requests, the generative AI simultaneously optimizes the exterior design and manufacturing process. For example, it may propose a process that allows for manufacturing in a short delivery time. In this way, optimizing the exterior manufacturing process improves manufacturing efficiency.

[0062] In addition to the exterior design, the exterior design generation unit also selects the exterior color and texture, allowing it to propose the optimal design. In the exterior design generation unit, for example, the generation AI selects the optimal color and texture based on the exterior design. For example, it proposes a color palette that matches the customer's preferences. In addition to the exterior design, the generation AI also selects the color and texture. For example, it proposes materials that feel good to the touch. Furthermore, based on the customer's requests, the generation AI simultaneously selects the exterior design and color / texture. For example, it proposes colors and textures that match the design. In this way, by selecting the exterior color and texture, it is possible to provide a design that matches the customer's preferences.

[0063] The exterior design generation unit can propose a design that takes into account the durability and waterproofness of the exterior in addition to the exterior design. In the exterior design generation unit, for example, the generation AI creates a design that takes durability into account based on the exterior design. For example, it proposes materials that are resistant to impacts. In addition to the exterior design, the generation AI also creates a design that takes waterproofness into account. For example, it proposes waterproof seals and rubber gaskets. Furthermore, based on customer requests, the generation AI proposes an exterior design that takes durability and waterproofness into account. For example, it proposes a robust design for outdoor use. In this way, by proposing a design that takes into account the durability and waterproofness of the exterior, the reliability of the product is improved.

[0064] In addition to integrating part placement and exterior design, the integration unit can also design products that take into account ease of assembly. For example, in the integration unit, the generative AI integrates part placement and exterior design to create a design that takes into account ease of assembly. For example, it optimizes the position of screws and clips. In addition to part placement and exterior design, the generative AI also designs products that take into account ease of assembly. For example, it proposes modularized parts. Furthermore, based on customer requests, the generative AI integrates part placement and exterior design to create a design that takes into account ease of assembly. For example, it proposes a design that can be assembled without tools. This improves manufacturing efficiency by creating designs that take into account ease of assembly of products.

[0065] In addition to integrating part layout and exterior design, the integration unit can also create designs that take into account the maintainability of the product. For example, in the integration unit, the generation AI integrates part layout and exterior design to create a design that takes maintainability into account. For example, it may propose a layout that makes part replacement easy. In addition to part layout and exterior design, the generation AI may create a design that takes maintainability into account. For example, it may propose a design that is easy to clean. Furthermore, based on customer requests, the generation AI may integrate part layout and exterior design to create a design that takes maintainability into account. For example, it may propose a design that makes disassembly easy. In this way, by creating a design that takes into account the maintainability of the product, it is possible to use the product for a long period of time.

[0066] In addition to integrating part placement and exterior design, the integration unit can also design products with consideration for recyclability. For example, the integration unit's generation AI integrates part placement and exterior design to create a design that takes recyclability into account. For example, it uses recyclable materials. In addition to part placement and exterior design, the generation AI also creates a design that takes recyclability into account. For example, it proposes a design that is easy to disassemble. Furthermore, based on customer requests, the generation AI integrates part placement and exterior design to create a design that takes recyclability into account. For example, it uses reusable parts. This reduces the environmental impact by designing products with consideration for recyclability.

[0067] In addition to integrating part placement and exterior design, the integration unit can also design products with energy efficiency in mind. For example, the integration unit's generative AI integrates part placement and exterior design to create a design that takes energy efficiency into consideration. For example, it uses parts that consume low power. In addition to part placement and exterior design, the generative AI also creates a design that takes energy efficiency into consideration. For example, it proposes an efficient cooling system. Furthermore, based on customer requests, the generative AI integrates part placement and exterior design to create a design that takes energy efficiency into consideration. For example, it uses solar panels. This reduces power consumption by designing a product with energy efficiency in mind.

[0068] The proposal unit reflects customer feedback in real time and can instantly modify and optimize the design. In the proposal unit, for example, the generation AI receives customer feedback in real time and instantly modifies the design. For example, if a customer requests a color change, this is immediately reflected. The generation AI also optimizes the design based on customer feedback. For example, it adjusts the size or changes the placement of parts. In addition, a system is built in which the generation AI receives feedback in real time and modifies the design. For example, it performs online real-time design modifications. This allows customer feedback to be reflected in real time and the design to be instantly modified and optimized.

[0069] The suggestion unit can generate multiple design variations based on customer feedback and provide options. In the suggestion unit, for example, the generation AI generates multiple design variations based on customer feedback. For example, it proposes designs with different colors and shapes. Furthermore, the generation AI generates multiple design variations and provides options in response to customer requests. For example, it proposes designs with different materials and finishes. Furthermore, a system is constructed in which the generation AI generates design variations based on feedback and provides options to customers. For example, an online design selection function is provided. This provides multiple design variations based on customer feedback, increasing the options and improving customer satisfaction.

[0070] The suggestion unit can automatically suggest design improvements based on customer feedback. In the suggestion unit, for example, the generation AI automatically suggests design improvements based on customer feedback. For example, adding a function desired by the customer. The generation AI also suggests design improvements based on customer feedback. For example, solving problems pointed out by the customer. A system is also constructed in which the generation AI automatically suggests design improvements based on feedback. For example, making online design improvement suggestions. This improves the quality of the design by automatically suggesting design improvements based on customer feedback.

[0071] The proposal department can share design improvements based on customer feedback with other customers and find common areas for improvement. For example, the proposal department may use the generative AI to share design improvements with other customers based on customer feedback. For example, it may provide an online platform for finding common areas for improvement. The generative AI may also share design improvements based on customer feedback and collect opinions from other customers. For example, it may promote the exchange of opinions in an online forum. The proposal department may also build a system for the generative AI to share design improvements with other customers based on feedback. For example, it may provide a database for finding common areas for improvement. This allows design improvements to be shared based on customer feedback and common areas for improvement to be found, thereby improving the overall design quality.

[0072] The suggestion unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will provide the customer with the most satisfaction. The suggestion unit, for example, uses the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will provide the highest level of satisfaction. For example, it reflects the customer's preferred colors and shapes. Furthermore, based on the customer's emotion data, the generation AI proposes a design that will provide the highest level of satisfaction. For example, it prioritizes designs that make the customer feel relaxed. Furthermore, the emotion estimation function is used to analyze the customer's emotions in response to feedback and propose an optimal design. For example, it proposes a design that gives the customer a sense of security. In this way, customer satisfaction is improved by analyzing the customer's emotions in response to feedback and proposing the most satisfying design.

[0073] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will give the customer the most peace of mind. The proposal unit, for example, uses the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will give a sense of security. For example, it reflects colors and shapes that make the customer feel safe. Furthermore, based on the customer's emotion data, the generation AI proposes a design that will give the customer the most peace of mind. For example, it prioritizes designs that make the customer feel relaxed. Furthermore, the emotion estimation function is used to analyze the customer's emotions in response to feedback and propose an optimal design. For example, it proposes a design that will give the customer a sense of security. In this way, the proposal unit gains the customer's trust by analyzing the customer's emotions in response to feedback and proposing the most secure design.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] Based on customer requests, the part layout design department can optimize not only the layout of parts, but also the signal transmission paths and power supply paths between parts. For example, the generation AI optimizes signal transmission paths based on the part layout specified by the customer. For example, it designs the signal transmission between the camera and sensor to take the shortest route. In addition to part layout, the generation AI also optimizes power supply paths. For example, it designs wiring to efficiently supply power from the battery to each part. Furthermore, based on customer requests, the generation AI optimizes both signal transmission and power supply between parts. For example, it designs shielding to avoid signal interference. This enables efficient part layout by optimizing the signal transmission paths and power supply paths between parts.

[0076] In addition to arranging parts based on customer requests, the part layout design department can also design a cooling system to maximize the cooling efficiency of the parts. For example, the generative AI designs a cooling system based on the layout of parts. For example, it places cooling fans around parts that generate heat. In addition to the layout of parts, the generative AI also suggests the placement of heat sinks to maximize cooling efficiency. For example, it may install a heat sink on top of the CPU or GPU. The generative AI also designs the entire cooling system based on customer requests. For example, it may introduce a liquid cooling system and optimize the flow path of the coolant. This maximizes the cooling efficiency of the parts, improving the stability and lifespan of the system.

[0077] The parts placement design department can not only place parts but also select parts based on customer requests, proposing optimal parts. For example, the generation AI selects the optimal parts based on customer requests. For example, it proposes camera resolution and sensor type. In addition to part placement, the generation AI also selects parts. For example, it proposes battery capacity and shape. Furthermore, the generation AI simultaneously selects and places parts based on customer requests. For example, it selects the optimal SoC and board and proposes placement based on that. This allows the optimal parts to be selected based on customer requests, achieving efficient part placement.

[0078] Based on customer requests, the parts placement design department can propose a placement that takes into account the durability and lifespan of parts in addition to the placement of parts. For example, the generation AI will create a design that takes durability into account based on the placement of parts. For example, it will propose a placement that is resistant to vibration and impact. The generation AI will also propose the optimal placement taking into account the lifespan of parts. For example, it will make a placement that prevents deterioration due to heat. The generation AI will also propose a parts placement that takes durability and lifespan into account based on customer requests. For example, it will prioritize the placement of parts with long lifespans. In this way, by proposing a placement that takes into account the durability and lifespan of parts, the reliability of the system will be improved.

[0079] In addition to the exterior design, the exterior design generation unit can also select the exterior material and suggest the optimal material. For example, the generation AI selects the optimal material based on the exterior design. For example, it can suggest a lightweight and durable material. In addition to the exterior design, the generation AI also selects the material. For example, it can suggest a material with high heat dissipation properties. Furthermore, based on customer requests, the generation AI simultaneously selects the exterior design and material. For example, it can suggest a material with a color and texture that matches the design. In this way, by selecting the exterior material, the optimal material can be suggested and the quality of the product can be improved.

[0080] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will satisfy the customer most. For example, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose a design that will provide the highest level of satisfaction. For example, it can reflect the customer's preferred colors and shapes. Furthermore, based on the customer's emotion data, the generation AI can propose a design that will provide the highest level of satisfaction. For example, it can prioritize designs that make the customer feel relaxed. Furthermore, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose the optimal design. For example, it can propose a design that gives the customer a sense of security. In this way, customer satisfaction can be improved by analyzing the customer's emotions in response to feedback and proposing the most satisfying design.

[0081] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will give the customer the most peace of mind. For example, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose a design that will give a sense of security. For example, it can reflect colors and shapes that make the customer feel safe. Furthermore, based on the customer's emotion data, the generation AI can propose a design that will give the customer the most peace of mind. For example, it can prioritize designs that make the customer feel relaxed. Furthermore, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose the optimal design. For example, it can propose a design that will give the customer a sense of security. In this way, by analyzing the customer's emotions in response to feedback and proposing the most secure design, the trust of the customer can be gained.

[0082] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will excite the customer the most. For example, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose a design that will excite them. For example, it can reflect colors and shapes that excite customers. Furthermore, based on the customer's emotion data, the generation AI can propose a design that will excite them the most. For example, it can prioritize designs that make customers feel adrenaline. Furthermore, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose an optimal design. For example, it can propose a design that will excite the customer. In this way, by analyzing the customer's emotions in response to feedback and proposing the most exciting design, the product attracts the customer's interest.

[0083] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will provide the most relaxation for the customer. For example, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose a design that will provide a sense of relaxation. For example, it can reflect colors and shapes that make the customer feel relaxed. Furthermore, based on the customer's emotion data, the generation AI can propose a design that will provide the most relaxation. For example, it can prioritize designs that do not cause stress to the customer. Furthermore, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose the optimal design. For example, it can propose a design that will provide relaxation to the customer. In this way, analyzing the customer's emotions in response to feedback and proposing the most relaxing design promotes customer relaxation.

[0084] The proposal unit can use the emotion estimation function to analyze the customer's emotions in response to feedback and propose a design that will bring the customer the most joy. For example, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose a design that will bring joy. For example, it can reflect colors and shapes that bring joy to customers. Furthermore, based on the customer's emotion data, the generative AI can propose a design that will bring the most joy. For example, it can prioritize designs that make the customer smile. Furthermore, the emotion estimation function can be used to analyze the customer's emotions in response to feedback and propose the optimal design. For example, it can propose a design that will bring joy to the customer. In this way, by analyzing the customer's emotions in response to feedback and proposing a design that will bring the most joy, customer satisfaction can be improved.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The parts layout design department designs the interior space for parts placement based on the customer's requests. For example, if a customer requests "I want the camera and sensor to be placed at the front and the battery at the back," the generation AI will propose the optimal parts placement based on that request. The input to the generation AI is a prompt that includes the customer's request, and the generation AI designs the interior space based on that prompt. Step 2: The exterior design generation unit generates an exterior design taking into account the volume of the interior space and the layout of parts designed by the parts layout design unit. For example, it may provide vents to allow for efficient cooling of the parts placed inside, or adjust the position of ports to allow for easy access from the outside. The generation AI proposes an exterior design based on the results of the interior space design. For example, it proposes the shape, color, and material of the exterior based on customer requests such as "I prefer a simple, modern design." Step 3: The integration unit integrates the part layout and exterior design generated by the part layout design unit and the exterior design generation unit. For example, it is possible to provide a product with an efficient internal part layout and a beautiful exterior design. Step 4: The proposal unit proposes the product design integrated by the integration unit to the customer. For example, if the customer requests something like "I want it to be a little more compact," the generation AI regenerates the design in accordance with that request. This allows the product design system according to the embodiment to provide the optimal product design that meets the customer's needs.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0101] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0137] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0138] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0139] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0140] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0141] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0142] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0143] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0144] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0145] 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.

[0146] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0147] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0148] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0149] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0150] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0151] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A parts layout design department that designs the interior space of parts based on customer requests; an exterior design generation unit that generates an exterior design in consideration of the volume of the internal space and the part layout designed by the part layout design unit; an integration unit that integrates the part layout and exterior design generated by the part layout design unit and the exterior design generation unit; a proposal unit that proposes the product design integrated by the integration unit to a customer. A system characterized by:

2. The parts layout design unit Based on the customer's requirements, not only the layout of the parts but also the signal transmission paths and power supply paths between the parts are optimized.

2. The system of claim 1.

3. The parts layout design unit Based on the customer's requirements, in addition to arranging the parts, we also design the cooling system to maximize the cooling efficiency of the parts.

2. The system of claim 1.

4. The parts layout design unit Based on the customer's request, we not only arrange the parts but also select the parts and propose the most suitable parts.

2. The system of claim 1.

5. The parts layout design unit Based on the customer's request, we propose a layout that takes into consideration the durability and lifespan of the parts in addition to the layout of the parts.

2. The system of claim 1.

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