Information processing system, information processing method, and program

Abstract

An information processing system, an information processing method, and a program are provided that can provide useful information based on performance data at the estimation stage. [Solution] The information processing system disclosed herein includes a control unit that executes a request information acquisition process that acquires request information including requested drawing data for an object to be manufactured, and an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database, and the output data includes information regarding whether the object to be manufactured can be manufactured or whether an order can be placed, estimated based on the similarity of past drawing data included in the performance database to the requested drawing data and status information associated with each past drawing data.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Traditionally, in the business flow of a processing company that produces a wide variety of products in small quantities, determining an estimate requires taking into consideration many factors, such as material costs, equipment owned, worker skills, and the number of man-hours that workers can take on (availability), and only a limited number of members with sufficient experience are able to prepare estimates.

[0003] In response to this, in recent years, information processing devices and the like have been proposed that are capable of accurately generating processing information for new processed products without relying on the knowledge and experience of skilled workers (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2024-180232 Summary of the Invention [Problem to be solved by the invention]

[0005] However, there is a demand for providing not only the above-mentioned processing information but also other useful information at the estimation stage.

[0006] Therefore, the present disclosure has been made in consideration of the above-mentioned problems, and its purpose is to provide an information processing system, an information processing method, and a program that can provide useful information based on performance data at the estimation stage. [Means for solving the problem]

[0007] According to the present disclosure, a request information acquisition process for acquiring request information including requested drawing data of a manufacturing request object; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database; An information processing system is provided in which the output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be accepted, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data.

[0008] According to the present disclosure, a request information acquisition process for acquiring request information including requested drawing data of an object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database, An information processing method is provided in which the output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be accepted, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data.

[0009] According to the present disclosure, a request information acquisition process for acquiring request information including requested drawing data of an object to be manufactured; an output data generation process for generating output data using a generation AI based on the request information and a pre-stored past performance database; A program is provided in which the output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be accepted, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide an information processing system, an information processing method, and a program that can provide useful information based on performance data at the estimation stage. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating a configuration example of an information processing system according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing device according to the embodiment. [Figure 3] FIG. 2 is a diagram illustrating an example of the configuration of a client terminal according to the embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of the configuration of a receiver terminal according to the embodiment. [Figure 5] FIG. 4 is a flowchart illustrating a series of controls in the system according to the embodiment. [Figure 6] FIG. 4 is a flowchart illustrating a series of controls in the system according to the embodiment. [Figure 7] FIG. 10 is a diagram showing an example of output data according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0013] FIG. 1 shows an example of an information processing system 1 according to this embodiment. The system 1 includes an information processing device (server device) 10 managed by a system administrator, a requester terminal 20 used by a requester such as a product manufacturer or trading company, and a receiver terminal 30 used by a receiver such as a processing factory that receives the request. The information processing device 10, the requester terminal 20, and the receiver terminal 30 are connected to one another via a network NW and can transmit and receive various information to and from one another. Note that there may be multiple requester terminals 20 and multiple receiver terminals 30. Furthermore, for example, if the requester or receiver directly inputs or outputs information via an input / output unit of the information processing device 10, the requester terminal 20 and the receiver terminal 30 can be omitted.

[0014] The system 1 can generate output data including information such as whether the recipient can manufacture the requested item (or whether the recipient can accept the order), how long it will take to manufacture, and how much the estimated cost will be, based on, for example, request information including requested drawing data for the item requested to be manufactured sent from the requester terminal 20 and a pre-stored past performance database. The request information includes, for example, information regarding a request for quotation or a manufacturing request.

[0015] The information processing device 10 is a server device used by a system administrator or the like when operating and managing various services, and may be, for example, a general-purpose computer such as a workstation or personal computer, or may be logically realized using cloud computing technology.

[0016] The information processing device 10 includes a control unit 11, a storage unit 12, an input unit 13, an output unit 14, and a communication unit 15, as shown in FIG.

[0017] The requester terminal 20 includes a control unit 21, a storage unit 22, an input unit 23, an output unit 24, and a communication unit 25, as shown in Fig. 3, for example. The recipient terminal 30 similarly includes a control unit 31, a storage unit 32, an input unit 33, an output unit 34, and a communication unit 35, as shown in Fig. 4, for example. The requester terminal 20 and the recipient terminal 30 are each a computer operated by a user that inputs and outputs various types of information, and are configured, for example, as a smartphone, a tablet computer, or a personal computer. The users (requester, recipient) can access the information processing device 10, for example, by an application or web browser executed on each terminal.

[0018] When the information processing device 10 receives various commands (requests) from other information processing devices or the like via the input unit 13 or the communication unit 15, the control unit 11 executes processing according to a program, and the program processing results (e.g., images, sounds, etc.) are sent to the output unit 14 or other information processing devices (requester terminal 20, recipient terminal 30), etc. Alternatively, the information processing device 10 receives various commands (requests) from other information processing devices or the like via the communication unit 15, and transmits the program processing results executed by the control unit 11 to the other information processing devices or the like. Note that part of the program may be sent to the other information processing devices and executed on the other information processing devices. In this case, the other information processing devices may be, for example, smartphones, mobile phone terminals, tablet terminals, personal computers, etc., and are connected to the information processing device 10 wirelessly or via a wired connection via a network such as the Internet.

[0019] The control units 11, 21, and 31 transfer data between each unit and control the entire device, and are realized, for example, by a CPU (Central Processing Unit), MPU (Micro Processing Unit), or GPU (Graphics Processing Unit) executing a program stored in a specified memory.

[0020] The storage units 12, 22, and 32 store various data and programs, and are, for example, non-volatile or volatile semiconductor memories such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read-Only Memory), as well as magnetic disks, flexible disks, optical disks, compact disks, minidisks, and DVDs (Digital Versatile Discs).

[0021] The input units 13, 23, 33 are used by users and system administrators to input various data, and are realized by, for example, a keyboard, a mouse, a touch panel, buttons, a microphone, and the like.

[0022] The output units 14, 24, and 34 output various information generated by the control unit, etc. The output units are, for example, a liquid crystal display (LCD), a touch panel, a printer, a speaker, etc.

[0023] The communication units 15, 25, and 35 are for communicating with other information processing devices, and have a function as a receiving unit that receives various data and signals transmitted from other information processing devices, etc., and a function as a transmitting unit that transmits various data and signals to other information processing devices, etc. in response to commands from the control unit. The communication units are realized by, for example, a NIC (Network Interface Card), an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, a wireless communication device for wireless communication, a USB (Universal Serial Bus) connector or an RS232C connector for serial communication, etc.

[0024] 2, the control unit 11 of the information processing device 10 can function as an information acquisition unit 111 and an information generation unit 112. The storage unit 12 can function as an element information storage unit 121 and an output information storage unit 122.

[0025] The information acquisition unit 111 acquires various information used to generate output data. The acquired information may include information input by a user or a system administrator, and information acquired from an external system via a network such as the Internet (for example, web information such as raw material unit price information and part unit price information published on the Internet).

[0026] The information generation unit 112 generates output information based on the input information and the information in the storage unit, by inputting the input information into the generative model as appropriate, and further editing the generated information output by the generative model as necessary. The generative model may be implemented in the information processing device 10 or in another server accessible via a communication network, but is not limited thereto. When the generative model is implemented in the information processing device 10, the information generation unit 112 inputs prompt information to the generative model. When the generative model is implemented on another server, the information generation unit 112 transmits the prompt information to the generative model via the network. The information generated by the information generation unit 112 may include information generated without using a generation AI. For example, information previously stored in the storage unit in association with each element information constituting the request information may be output (without the intervention of a generation AI) in accordance with various data included in the request information. The generation conditions for such output information (generated information) are previously stored in the storage unit.

[0027] The element information storage unit 121 stores various types of element information including performance data used to generate output data, etc. The element information storage unit 121 can store information input in advance by users such as a system administrator, a requester, or a recipient, information acquired from an external device or the Web, etc.

[0028] The output information storage unit 122 stores the output information generated by the control unit 11. The output information may include text information, image information, audio information, and the like.

[0029] When the information processing device 10 generates various types of information using a generative AI, the information acquisition unit 111 generates a prompt based on the acquired information (or based on a pre-stored prompt or the input prompt itself), and if the information is text information, the information generation unit 112 inputs the prompt into a text generation model (e.g., a large-scale language model such as ChatGPT), or if the information is an image or design, the information generation unit 112 inputs the prompt into an image generation model to generate the information. The generative model may, for example, receive a specific input vector or random noise as input and generate an image from that information. The generative model includes, for example, a generator. The generator converts the input information into appropriate features or patterns and converts them into text or an image. The generator is constructed using, for example, a convolutional neural network (CNN), a transformer, or other deep learning architecture, although other architectures may also be used.

[0030] As shown in FIG. 5, the control unit 11 of this system executes a request information acquisition process (S1) for acquiring request information including requested drawing data for a manufacturing request object, and an output data generation process (S2) for generating output data using a generation AI based on the request information and a pre-stored past performance database. In the output data generation process, the control unit inputs prompt information to the generation AI, specifying which items (element information) in the performance database to reference and which information to output, so that the AI ​​can output the specified items of information. This prompt information may be pre-stored in a storage unit (pre-stored combinations of conditions for which element information in the performance database to reference in order to obtain each piece of output information), or the prompt information may be generated by acquiring input information from a user (such as a system administrator, requester, or recipient). For example, when a user inputs element information (such as an estimated cost or delivery date) to be output, the control unit may determine which performance information to reference in order to obtain the output information based on the condition information stored in the storage unit, and then generate the prompt information.

[0031] The output data may include information regarding whether the requested manufacturing object can be manufactured or whether the order can be accepted, estimated based on the similarity of the past drawing data included in the performance database to the requested drawing data and the status information associated with each past drawing data. In other words, the control unit of the information processing device may extract similar past cases based on the requested drawing data, and determine whether the order can be accepted (or whether the manufacturing can be accepted) based on the status information (accepted, declined, etc.) of the similar cases.

[0032] In step S1, when a requester inputs and transmits request information including requested drawing data of an object to be manufactured, such as a processed part, via the requester terminal 20, the information acquisition unit 111 of the information processing device 10 receives the request information via the communication unit 15, thereby acquiring the request information. The request information may be transmitted directly from the requester terminal 20 to the information processing device 10, or may be transmitted (indirectly) to the information processing device 10 via the recipient terminal 30. The request information may be transmitted from the requester terminal 20 to both the information processing device 10 and the recipient terminal 30.

[0033] The control unit 11 may store the request information (associated with identification information) in the storage unit 12. The requested drawing data may include a two-dimensional (or three-dimensional) object image representing the target object, the target object name (processed product name), dimensional information (length, width, thickness, height, inner diameter, outer diameter), material information (quality), processing method information, quantity information, weight information, drawing number, requesting company name, component part information (type of part, manufacturer information, etc.), and the like.

[0034] In step S2, for example, the control unit (information generating unit 112) estimates the similarity of all or part of the past drawing data to the requested drawing data (S21), as shown in Fig. 6. The process of estimating the similarity is not particularly limited, but for example, the similarity is estimated by comparing the feature information of the requested drawing data with the feature information of the past drawing data.

[0035] The information generating unit 112 then executes a determination process regarding whether or not the order can be accepted or whether or not the manufacturing can be performed (S22). For example, if the status information associated with the past drawing data (similar drawing data) that satisfies a predetermined condition, such as the highest similarity, is "order accepted" (or "manufacturable"), the information generating unit 112 may determine that the order can be accepted (or "manufacturable"), and if the status information is other than "order accepted" (e.g., "rejected"), the information generating unit 112 may determine that the order cannot be accepted (or "manufacturable"), and output the determination result (order accepted, order not accepted, etc.) as output data. Alternatively, if the status information associated with the similar drawing data is other than "order accepted" (e.g., "rejected"), the information generating unit 112 may determine that the order cannot be accepted (or "manufacturable"), "confirmation required," "other," etc. Note that if no past drawing data (similar drawing data) that satisfies the predetermined condition exists in the performance database, a predetermined determination result such as "order not accepted (or "manufacturable"), "confirmation required," or "other" may be displayed. Such determination conditions regarding whether or not the order can be accepted (or manufacturable) are stored in advance in the storage unit. The determination condition information may be updated based on input information from the system administrator, the recipient, etc. For example, the recipient may input the judgment conditions himself / herself, thereby allowing the conditions to be registered (stored) and changed (updated).

[0036] The information generating unit 112 may not be limited to information on one past drawing data, such as the one with the highest similarity, but may comprehensively determine the status information associated with multiple drawing data whose similarity is equal to or greater than a predetermined specific value (threshold) or whose similarity ranking is a specific number (e.g., 3, 5, 10, etc.) that can be set as desired. For example, if all of the status information associated with multiple drawing data whose similarity to the requested drawing data is equal to or greater than a threshold is "order received," the information generating unit 112 may determine the status as "order accepted," otherwise it may determine the status as "order not accepted" or "confirmation required," etc. Alternatively, if even one of the status information associated with multiple drawing data whose similarity is equal to or greater than a threshold is "order received," the information generating unit 112 may determine the status as "order not accepted" or "confirmation required," etc. Alternatively, the percentage of the status information associated with multiple drawing data whose similarity is equal to or greater than a threshold that is "order received" may be output as a "past order rate," a "probability (%) indicating order possibility (or manufacturability)," etc. For example, if there are four pieces of past drawing data whose similarity is equal to or exceeds a threshold value and only one of them has the status "order received," the "probability (%) of order receipt possibility (or manufacturability)" can be set to 25%. In other words, the control unit may output the order receipt probability of a case similar to the request information (performance data whose similarity meets a predetermined condition) as output data.

[0037] The information generation unit 112 inputs the above-mentioned judgment condition information and the performance database into the generation AI to have it learn, and also inputs a prompt to the generation AI to generate predetermined output data (data including information regarding whether or not the product can be manufactured or whether or not the order can be accepted) based on the judgment condition information, request information, and performance database, thereby obtaining information regarding whether or not the product can be manufactured or whether or not the order can be accepted, generated by the generation AI.The information can then be displayed as output data on a user terminal (requester terminal 20, receiver terminal 30) and presented to the user (either the requester or the receiver, or both).The prompt information may be input by a system administrator and stored in advance in the storage unit, or the information generation unit 12 may cause the generation AI to generate the prompt information.

[0038] 6, the information generating unit 112 may perform a process of estimating amount information such as an estimated amount (S23), and a process of estimating period information such as a manufacturing period and a delivery date (delivery date) (S24). In this case, the estimated information can be output as output data.

[0039] In S23, for example, the control unit can extract past similar projects whose similarity satisfies a predetermined condition, and then use monetary information such as the estimated price, material cost, and profit amount of the similar projects to cause the generation AI to output various estimated prices (the estimated price, material cost, profit amount, etc. of the current requested project). The control unit can also prompt the generation AI to calculate output data such as the estimated price and manufacturing period by referring to various information contained in the request information and the performance database, such as the quantity of the manufacturing object, processing requirements (processing information), and finishing specifications. Such calculation condition information is stored in the storage unit in advance and is updated according to user input.

[0040] In S24, for example, the control unit can cause the generation AI to output various period information (such as the manufacturing period and delivery date of the current requested item) using information related to the period, such as the manufacturing period of the similar item and the period information between the date of receipt of the request and the delivery date.

[0041] FIG. 7 shows an example of the output data screen S. This screen is basically displayed on the screen of the receiver terminal 30, but may also be displayed on the screen of the requester terminal 20 or the information processing device 10. When displayed on the screen of the receiver terminal 30, the person in charge at the receiver can view the screen and decide whether to accept the request, the estimated amount, the delivery date, etc. The displayed content may be restricted depending on the terminal on which it is displayed. For example, when displayed on the requester terminal 20, it is possible to prevent certain items such as the manufacturing cost and the break-even amount from being displayed. The restriction information may be set in advance and stored in a storage unit, and may be updated by a user's input operation.

[0042] 7, the items displayed are order acceptance (manufacturing availability), estimated cost (yen), manufacturing period (hours, days, weeks, months, etc.), delivery date (date, time, etc.), manufacturing cost (yen), estimated man-hours (hours), break-even amount (yen), equipment to be used (cutting machine, milling machine, etc.), person in charge of manufacturing (name), and information on similar past projects (project name, link (URL), etc.), but are not limited to these. For example, all or any part of the information contained in a database of past projects whose similarity meets a predetermined condition may be displayed as similar past projects.

[0043] As described above, the information processing system disclosed herein includes a control unit that executes a request information acquisition process that acquires request information, including requested drawing data for a manufacturing request object, and an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database. The output data includes information regarding the feasibility of manufacturing or accepting an order for the manufacturing request object, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data. This configuration enables useful information to be provided at the quotation stage based on performance data. Furthermore, the use of a generation AI allows for quick decisions on whether to accept an order, enabling a quick response to the requester and preventing a decline in the order acceptance rate. It also reduces the burden on estimators and reduces human error in judgment.

[0044] In this system, the control unit may estimate the similarity of the previous drawing data to the requested drawing data by comparing the characteristic information of the previous drawing data with the characteristic information of the requested drawing data. This improves the accuracy of the similarity determination, thereby improving the accuracy of generating output data based on the previous drawing data.

[0045] In this system, the control unit may select one or more pieces of past drawing data whose similarity meets a predetermined condition from among a plurality of pieces of past drawing data as similar drawing data, and generate output data using information associated with the similar drawing data. This allows past cases similar to the requested case to be narrowed down and output data to be generated, thereby reducing the processing load compared to when all past data is used and increasing the accuracy of the output data.

[0046] In this system, the output data may include information indicating the probability of receiving or manufacturing past requests whose similarity satisfies a predetermined condition. This allows the probability of receiving or actually manufacturing a similar request in the past to be confirmed, which can be used as a reference when deciding whether or not to accept the request.

[0047] In this system, the output data may also include other items, such as information on the reasons for losing orders for past requests whose similarity meets a predetermined condition. In particular, if the order acceptance determination result is not "acceptable," the cause can be analyzed by also outputting information on the reasons for losing orders. Furthermore, by inputting a prompt to the generation AI to analyze the reasons for losing orders, the control unit can output the analysis results of each company's orders and losses as output data. The items included in the output data may be selected by each user (system administrator, requester, recipient, etc.). For example, the control unit may present (display on each terminal screen) options for items that can be presented as output data (items included in the performance database) to the user, allowing the user to select.

[0048] In this system, the control unit may further generate output data including information regarding whether production or order acceptance is possible based on operation information of the in-house facilities. This allows the control unit to determine whether the requested production item can be produced or whether the order can be accepted, for example, based on operation information of the in-house facilities. For example, if the in-house facilities required for production of the product included in the request information are unavailable (for example, all have been operating for a long period of time, are out of order, are undergoing maintenance, etc.), the control unit can make a decision to "decline" or "require confirmation" regardless of other conditions. Information on such judgment conditions is also stored in advance in the storage unit.

[0049] In this system, the control unit may further generate output data including information on whether production or order acceptance is possible based on the worker's work schedule information. This allows the control unit to determine whether the worker's man-hours required to manufacture the product included in the request information can be secured and, if not, to decline the request. More specifically, the control unit may estimate the man-hours required for the requested job based on man-hour information (manufacturing period information) associated with past jobs whose similarity meets a predetermined condition as the man-hours required for the requested job, and determine whether the man-hours can be secured by a predetermined time point based on the worker's work schedule information. In this case, by storing in advance in the storage unit not only the worker's work schedule but also information on the type of work (processing process) each worker can handle (e.g., cutting, surface treatment, etc.), it is possible to more accurately determine whether the worker's man-hours required to manufacture the requested product can be secured. Such various worker-related information and information on the conditions for generating the output data are also stored in the storage unit in advance. Furthermore, if a worker who was in charge of a past job in the performance data has now retired, the control unit may determine the job as "declined" or "requires confirmation" regardless of other conditions.

[0050] In this system, the output data may include estimated cost information based on the similarity of the past drawing data to the requested drawing data and the cost information associated with each past drawing data. This allows for the presentation of estimated costs based on past performance.

[0051] In this system, the output data may include time-related information regarding the delivery date or work period of the processed product estimated based on the similarity of the past drawing data to the requested drawing data and the manufacturing period information associated with each past drawing data. This makes it possible to present information regarding the delivery date or work period based on past performance.

[0052] In this system, the output data may include method-related information regarding the manufacturing method estimated based on the similarity of the past drawing data to the requested drawing data and the manufacturing method information associated with each past drawing data. This allows the manufacturing method of the requested object to be estimated from past projects similar to the request information. The manufacturing method information includes information on the processing process. For example, the processing process of a past project similar to the request information may be estimated as the processing process of the requested object. Alternatively, the processing process may be estimated based on the results of comparing the shape of the processed product of the similar past project with the shape of the processed product included in the requested drawing data. Specifically, if the number of holes in the requested drawing data is twice as many as the number of holes in the processed product of the similar past project, the number of drilling processes may be doubled, and the processing cost may also be calculated as twice the drilling cost. Processing costs for other processing processes can also be calculated based on the ratio of processing type information to processing amount information such as the number of holes (number of holes) and amount (e.g., laser processing distance). The processing type information may be, for example, drilling, laser processing, cutting, surface treatment, MC processing, horizontal hole drilling, welding, chamfering, deburring, heat treatment, inspection, etc. The estimated cost calculated by the control unit as output data may be, for example, an amount obtained by multiplying the sum of the estimated raw material cost and the estimated processing cost by a predetermined profit rate. Calculation condition information such as a calculation formula for such an estimated amount is stored in the storage unit in advance and can be updated as appropriate in response to user input.

[0053] In this system, the control unit may generate output data using a pre-stored template based on the requested drawing data. For example, a template used in a past project similar to the requested drawing data may be used. Templates are provided, for example, for each type of material or each type of processing, and information such as material cost information and processing method is pre-associated and stored. The control unit may use the template to estimate and fill in blank information using a generation AI based on the request information.

[0054] In this system, the output data may include estimated cost information regarding the manufacturing cost estimated based on estimated material information estimated from the request information and raw material information updated periodically. This allows the estimated cost information for manufacturing the requested item to be presented. In particular, for example, when the control unit acquires material cost information updated by user input or published on the Web in real time, appropriate estimated cost and estimated amount information can be presented in response to fluctuations in raw material costs. As a result, an appropriate estimated amount can be set, allowing the recipient to appropriately secure profits and avoid losses.

[0055] In this system, the output data may include break-even price information estimated based on estimated cost information and manufacturing man-hour information estimated from the request information. This makes it possible to present the amount of money needed to receive an order in order to make a profit. The manufacturing man-hour information estimated from the request information may be the value of the man-hour information associated with similar drawing data, or, if there are multiple similar drawing data, the average, maximum, minimum, etc. of the man-hour information.

[0056] In this system, the control unit may further collect and store output data for each manufacturer generated based on the performance databases of multiple manufacturers. For example, performance data entered by multiple recipients from their respective recipient terminals can be received by the information processing device and stored in the storage unit. This allows the performance databases of not just one company but multiple recipient companies to be stored together.

[0057] In this system, the control unit may further provide the requesting company with output data for each manufacturer, generated based on a performance database of multiple manufacturers. This allows the requesting company to be presented with information on multiple receiving companies. More specifically, the requesting company can compare data such as order acceptance, estimated price, and delivery time (manufacturing period) of each processing company side by side.

[0058] In this system, the information processing device 10 may also have an analysis function that performs an analysis for each recipient's manufacturing plant (processing plant) based on information from each company's performance database. The information processing device 10 may generate analysis results, such as the order rate (loss rate) and reasons for loss for each recipient, and output them as output data. Specifically, for example, the information processing device 10 displays pre-stored information options on a user terminal (requester terminal 20, receiver terminal 30, or another information processing device) and allows the user to select one of the options to accept request information. The options consist of outputtable data items such as order rate and reasons for loss. When the information processing device 10 accepts request information transmitted from the user terminal, it may generate output data corresponding to the request information and output it to the user terminal. As an output method, for example, each company's order rate and reasons for loss may be displayed on a screen in a list. With this configuration, the order rate and reasons for loss for each supplier can be easily compared and evaluated.

[0059] Here, we will explain the information in the performance database that is pre-stored in the storage unit 12 (or storage unit 22). The information in the performance database can be updated in response to input from at least one of the information processing device 10, the requester terminal 20, and the receiver terminal 30.

[0060] The performance database contains information about past requested cases. The performance database may contain, for example, information about multiple recipients, in which case, information about past requests received from each recipient (processing manufacturer, etc.) may be collected. Items included in the performance database include, for example, case identification information (case number, case ID, etc.), case name, case status, estimated amount, sales amount, profit amount (loss amount), raw material price, manufacturing method (including processing process), manufacturing man-hours, manufacturing cost, company representative (name, identification information, etc.), customer name (name and identification information of the requesting company, such as a manufacturer or trading company), customer representative (name, identification information, etc.), information about reasons for lost orders, processing requirements and finishing specifications (including internal memos and remarks), case creation date ( The information includes the request receipt date, requested drawing data, quote submission date, quote creation period information, scheduled shipping date and time, shipping date and time (delivery date and time), manufacturing period information, link information (data storage location, reference URL, etc.), item identification information (item ID, etc.), template used (the template used for the project among the pre-stored templates), quote status (completed, incomplete), drawing attributes, order identification information (order number), drawing identification information (drawing number), manufacturing drawing data (processing drawing data), item name, quantity (units), material cost (total amount, unit price [yen / kg]), material type (plate), material quality, material supplier name, contact information (telephone number, email address), plate thickness [mm], specific gravity, size (width, length, thickness), etc., which are stored in association with each project. Processing process information may also include, for example, information on the type of processing (e.g., drilling, laser processing), hole size, number, laser processing distance (mm), and processing unit price (e.g., unit price per hole or per mm of laser processing).

[0061] Information in the performance database is stored in the memory unit by being entered by the recipient, or by being automatically updated by receiving information from other systems within the company (financial management systems, project management systems, etc.) or information from other information processing devices. Project identification information is identification information unique to each requested project, and is indicated, for example, by a project number (numbers) or a project ID (text such as letters and numbers). Project name is a name indicating the content of the project. Project status is information indicating the status of each project, and may include items such as received (order successfully placed), lost (order not successfully placed), estimate submitted, declined, and others. Information on the cause of lost order is information on the cause of failure to receive an order, and may be, for example, equipment-related causes such as a lack of equipment, worker-related causes such as a lack of workers, or money-related causes such as an inconsistent amount.

[0062] The storage unit 12 also stores condition information for various information processes in advance. The condition information may include, for example, conditions for which items of information are to be extracted and stored from the request information in the request information acquisition process (S1). The condition information may also include, for example, condition information for which items of information included in the request information are to be input to the generation AI and which items of information in the performance database are to be referenced to generate output data in the output data generation process (S2). The condition information may also include prompt information to be input to the generation AI and condition information for generating prompt information (what kind of prompt information is to be generated based on the information input by the user).

[0063] The condition information may also include, for example, information on the estimation conditions for the similarity of past drawing data to requested drawing data, information on the judgment conditions for the judgment process regarding whether to accept an order or whether to manufacture, information on the estimation conditions for the process (S23) of estimating monetary information such as an estimated amount, information on the estimation conditions for the process (S24) of estimating period information such as the period related to manufacturing and the delivery date (delivery date), etc. In other words, it may include information on the generation conditions for generating output data for all items that can be output by this system. It is preferable that these condition information are stored with the generation conditions set for each item of output data.

[0064] Below, we will explain an example of a method for calculating the similarity for each drawing in order to search for a drawing similar to a target drawing (requested drawing data) from among multiple candidate drawings (past drawing data).The target "drawing" can be, for example, a drawing of a part used in various devices, and the target "object" can be, but is not limited to, a front view, side view, plan view, perspective view, cross-sectional view, etc. of the part.

[0065] The control unit 11 can execute a feature estimation process to estimate feature information of a target object (front view, plan view, side view, etc.) included in the target drawing data; an individual similarity calculation process to compare the feature information of the target object with the feature information of all candidate objects included in each of a plurality of candidate drawings to calculate the individual similarity for each candidate object; an overall similarity calculation process to calculate the overall similarity for each candidate drawing using the individual similarities of all candidate objects included in each candidate drawing; and an output data generation process to generate output data based on the overall similarity.

[0066] In this way, by calculating the overall similarity for each candidate drawing based on individual comparisons between the target object and all candidate objects, the accuracy of similar drawing searches can be improved.

[0067] For example, the system may accept an input specifying a specific area in the target drawing, and recognize only objects included in the specific area as target objects. The specific area may be specified by, for example, a user inputting a frame of a specific shape (e.g., rectangle, circle, etc.) that surrounds an object in the target drawing. By specifying the target object in this way, the user can improve the convenience of the similar drawing search function and reduce the processing load of similarity calculations by preventing objects unnecessary to the user from being selected as target objects. Alternatively, the user may select multiple candidate drawings from among multiple drawing data pre-stored in the storage unit via a user terminal, or the control unit may automatically select multiple candidate drawings. When the control unit automatically selects candidate drawings, it may select all drawings stored in the storage unit as candidate drawings, or it may select drawings that share common information associated with the target drawings. Specifically, the system may select drawings that share one or more items of information among the items associated with each drawing, such as "part name," "client company name," "material," "client's contact person," "recipient's contact person," "company name," "product name," and "directional attribute (front view, plan view, etc.)." In this case, each piece of drawing data (image data) may be associated with annotation information (attribute information) indicating the item.

[0068] In the feature estimation process, the control unit estimates feature information of a target object included in the target drawing. The feature information may be, for example, a feature amount (feature vector), but is not limited to this. The feature information is data that is uniquely determined according to at least the shape of the target object.

[0069] The method for calculating the feature information is not particularly limited. For example, the feature information can be calculated (inferred) by inputting data of the connected region forming the target object into a feature inference model, and the resulting data can be output. The inference model may be, for example, one that applies a neural network or the like, but any machine learning model can be used. Such an inference model is pre-stored in a storage unit or stored in an external information processing system that can communicate via a communication unit. The number of inference models is not limited to one. For example, multiple inference models with different conditions, such as differences in machine learning methods or data, may be stored and used selectively or in parallel. The feature inference model is machine-learned so that, for example, the more similar the target object (target connected region) and the candidate object (candidate connected region) are, the higher the similarity when comparing the features. The similarity is defined, for example, by the distance when comparing the feature values ​​of images. For example, a distance index such as Euclidean distance or Manhattan distance, or a similarity index such as cosine similarity may be used.

[0070] The feature (feature information) is output as vector data of a fixed-length numeric array, but is not limited to vector format and may be output in other data formats. The feature may be, for example, SIFT feature, SURF feature, ORB feature, AKAZE feature, etc.

[0071] Here, the target drawing data may include or be associated in advance with connected areas and feature information of the target objects included in the target drawing, or the control unit 11 may be able to detect the target objects included in the target drawing.

[0072] When the control unit 11 detects a target object, for example, it may extract a connected area (which can be, for example, a circular or polygonal annular area, but may also be a shape with some discontinuous parts rather than a completely continuous shape) defined by multiple pixels included in each drawing whose brightness values ​​are consecutive pixels that have a brightness value equal to or greater than a predetermined value (i.e., multiple pixels that form a continuous line), and detect the connected area as an object.

[0073] In addition, in the process of detecting such a target object, the brightness values ​​of multiple pixels included in each drawing may be binarized. "Binarization" refers to the process of converting, for example, pixels in each drawing that are below a predetermined brightness threshold into white and pixels that exceed the threshold into black.

[0074] Furthermore, the control unit 11 may perform a line thickening (dilation) process on each drawing. For example, the control unit 11 may thicken lines by converting or maintaining all pixels adjacent to pixels with a luminance value of "gray to black, intermediate between white and black" (pixels whose luminance exceeds a threshold) before binarization to a luminance value of "black," or by converting or maintaining pixels adjacent to pixels with a luminance value of "black" after binarization to a luminance value of "black." Furthermore, in addition to the adjacent pixels, pixels close to pixels with a predetermined luminance value before or after binarization may be thickened to a predetermined line thickness by converting them to black. By thickening lines, it is possible to connect unintentionally broken (disconnected) lines on the drawing, for example, because the original drawing's color is too light or the lines are too thin, thereby improving the accuracy of detecting the target object (the accuracy of extracting connected regions). The control unit 11 may perform the various processes described above on either or both of the target drawing and the candidate drawing. Condition information and other information required for executing each process are stored in advance in the storage unit 12.

[0075] In the individual similarity calculation process, the control unit compares the feature information of the target object with the feature information of all candidate objects included in each of the multiple candidate drawings to calculate the individual similarity for each candidate object. The target object can be a front view of the part. The target drawing may contain only one target object, or three or more target objects. The candidate drawing includes one or more candidate objects. Note that the target drawing may contain a table element in which text is surrounded by a rectangular frame, but the control unit may not recognize the table element as a target object based on the annotation information, and may instead extract the text as request information.

[0076] The control unit compares the feature amount of the target object with the feature amounts of all target objects in all candidate drawings, and calculates the individual similarity of each target object for each target object. The individual similarity is expressed, for example, as a value between 0 and 1, with the higher the similarity being expressed as a numerical value closer to 1. The value of the individual similarity is not limited to this, and may be set between a lower limit and an upper limit so that the higher the similarity is, the closer it is to the upper limit value, or the closer it is to the lower limit value. If the number of candidate objects differs for each candidate drawing, the number of individual similarities will differ for each candidate drawing.

[0077] Then, in the overall similarity calculation process, the control unit 11 calculates the overall similarity for each candidate drawing using the individual similarities of all candidate objects included in each candidate drawing.

[0078] Information such as conditions for calculating the overall similarity is stored in advance in the storage unit. The method for calculating the overall similarity is not particularly limited as long as it uses information about individual similarities. For example, the control unit calculates the overall similarity by applying individual similarity information for all candidate objects included in each candidate drawing to a predetermined calculation formula. Specifically, the control unit 11 may calculate the average value of all individual similarities for all candidate objects included in the candidate drawing to obtain the overall similarity, or may further apply the average value to a predetermined formula to calculate an index to obtain the overall similarity. For example, the harmonic mean is preferable, but arithmetic mean, geometric mean, etc. may also be used. The overall similarity may be calculated using any similarity evaluation mechanism. The overall similarity may not necessarily be calculated using the average value of individual similarities. For example, a model for predicting similarity may be constructed by combining the complexity of the shape of the drawing or vectorized data with metadata (data about the data). More specifically, the objects in each drawing may be structured (graphed) and the structures may be compared to determine partial similarity or overall structural similarity. The overall similarity calculation process determines the overall similarity for each candidate drawing. Then, in the output data generation process, the control unit generates output data based on the overall similarity.

[0079] In this embodiment, the overall similarity calculation process may calculate the overall similarity for each candidate drawing by a harmonic mean process using the individual similarities of all candidate objects included in each candidate drawing. Using the harmonic mean can improve the accuracy of detecting drawings showing highly similar solids (e.g., components) compared to using the arithmetic mean. For example, if there are three candidate objects (Drawings 1 to 3, e.g., side views, plan views, etc.), and only one of them (e.g., Drawing 1) is similar and the others are dissimilar, using the arithmetic mean will have a greater influence (individual similarities) on the values ​​of the dissimilar drawings (e.g., Drawings 2 and 3) and will likely be determined to be dissimilar. However, using the harmonic mean will have a greater influence on the similar drawing (Drawing 1), making it more likely to be determined to be similar. This allows for accurate retrieval of drawings with high similarity in solid form (shape) even when the candidate objects include drawings (candidate objects) that are the same (or similar) as solids but viewed from different directions.

[0080] In the present embodiment, the control unit may execute a ranking process for ranking the plurality of candidate drawings based on the overall similarity, and generate the output data based on the ranking, thereby making it possible to present data related to candidate drawings with high similarity to the user in an easy-to-understand manner.

[0081] In this embodiment, the control unit may perform the following process in the feature estimation process: binarizing the brightness values ​​of multiple pixels included in the target drawing; and recognizing the target object by extracting a connected area defined by pixels in which the line drawing brightness values ​​used to draw the line drawing are consecutive, out of the two binarized brightness values.

[0082] In this embodiment, the control unit may perform a thickening process to thicken the lines included in the target drawing before or after the binarization process, and recognize the target object by extracting the connected area after the thickening process, thereby improving the detection accuracy of the target object (the extraction accuracy of the connected area).

[0083] The control unit may perform annotation processing on each drawing. For example, the control unit may perform image analysis on each drawing data to associate attribute information of the areas of figures, lines, and text drawn within the frame of the drawing (e.g., inside the outermost rectangular frame along the outer edge of the drawing) and store the associated information in the storage unit. Examples of attribute information include, but are not limited to, round parts, non-round parts (e.g., square, polygonal), tables, annotation text, stamps, etc. Furthermore, handwritten text may be flagged as "handwritten," and if the drawing is unclear or distorted, a "low quality" flag may be flagged, and the information may be associated with the drawing and stored in the storage unit.

[0084] The control unit 11 may output the annotation results and display them on the user terminal 20. Furthermore, the control unit 11 may accept editing instructions from the user via the user terminal 20, allowing the user to modify the range of the area of ​​the drawing to which attribute information is assigned or the type of attribute. This can improve the recognition accuracy of elements included in the drawing. It is preferable that such processing be performed before the control unit calculates the feature information of each drawing. The annotation processing may be performed using a predetermined trained model. A trained model may be used that has previously trained a dataset (combination) of image data of elements that may be included in the drawing (round-shaped components, non-round-shaped components, tables, annotation text, stamps, etc.) and correct attribute information. Furthermore, the user may input the dataset again to perform re-training to improve accuracy.

[0085] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is clear that a person skilled in the art of the present disclosure can conceive of various modified or altered examples within the scope of the technical idea described in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0086] The devices described in this specification may be realized as a single device, or may be realized by a plurality of devices (e.g., cloud servers) partly or entirely connected via a network. For example, the control unit 11 and the storage unit 12 of the information processing device 10 may be realized by different servers connected to each other via a network.

[0087] The series of processes performed by the device described in this specification may be realized using software, hardware, or a combination of software and hardware. A computer program for realizing each function of the device and terminal according to this embodiment may be created and installed on a PC or the like. A computer-readable recording medium on which such a computer program is stored may also be provided. Examples of the recording medium include a magnetic disk, an optical disk, a magneto-optical disk, and a flash memory. The computer program may also be distributed, for example, via a network, without using a recording medium.

[0088] Furthermore, the processes described herein using flowchart diagrams do not necessarily have to be performed in the order shown. Some process steps may be performed in parallel. Additional process steps may be employed, and some process steps may be omitted.

[0089] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that will be apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects.

[0090] The following configurations also fall within the technical scope of the present disclosure. (Item 1) A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database; An information processing system in which the output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be accepted, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data. (Item 2) The control unit 2. The information processing system according to item 1, wherein the similarity of the past drawing data to the requested drawing data is estimated by comparing characteristic information of the past drawing data with characteristic information of the requested drawing data. (Item 3) The control unit Selecting one or more pieces of past drawing data similar to the requested drawing data as similar drawing data from among the plurality of pieces of past drawing data based on the information on the similarity; 3. The information processing system according to item 1 or 2, wherein output data is generated using information associated with the similar drawing data. (Item 4) 3. The information processing system according to item 1 or 2, wherein the output data includes information indicating the probability of receiving an order or manufacturing a past request for which the similarity satisfies a predetermined condition. (Item 5) 3. The information processing system according to item 1 or 2, wherein the control unit further generates output data including information regarding whether the manufacturing or order acceptance is possible based on operation information of in-house facilities. (Item 6) 3. The information processing system according to item 1 or 2, wherein the control unit further generates output data including information regarding whether the manufacturing or order acceptance is possible based on work schedule information of the worker. (Item 7) The information processing system described in item 1 or 2, wherein the output data includes estimated cost information based on the similarity of the past drawing data to the requested drawing data and the cost information associated with each past drawing data. (Item 8) The information processing system described in item 1 or 2, wherein the output data includes period-related information regarding the delivery date or work period of the processed product estimated based on the similarity of the past drawing data to the requested drawing data and manufacturing period information associated with each past drawing data. (Item 9) The information processing system described in item 1 or 2, wherein the output data includes method-related information regarding a manufacturing method estimated based on the similarity of the past drawing data to the requested drawing data and manufacturing method information associated with each past drawing data. (Item 10) 3. The information processing system according to item 1 or 2, wherein the output data includes estimated cost information relating to estimated material information estimated from the request information, material cost information estimated based on raw material information updated at predetermined intervals, and processing step information estimated based on the request information. (Item 11) 3. The information processing system according to item 1 or 2, wherein the output data includes break-even price information estimated based on the estimated cost information and manufacturing man-hour information estimated from the request information. (Item 12) 3. The information processing system according to item 1 or 2, wherein the control unit further collects and stores the output data for each manufacturer generated based on the performance databases of a plurality of manufacturers. (Item 13) 3. The information processing system according to item 1 or 2, wherein the control unit further presents the output data for each manufacturer generated based on the performance database of a plurality of manufacturers to the requesting company. (Item 14) A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database, An information processing method in which the output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be placed, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and status information associated with each past drawing data. (Item 15) A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process for generating output data using a generation AI based on the request information and a pre-stored past performance database; The output data includes information regarding whether the requested manufacturing object can be manufactured or whether an order can be placed, estimated based on the similarity of past drawing data contained in the performance database to the requested drawing data and the status information associated with each past drawing data. [Explanation of symbols]

[0091] 1. Information Processing Systems 10 Information processing device (server) 11 Control section 12 Storage section 20. Requester terminal 30 Recipient terminal

Claims

1. A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database; the output data includes information regarding whether the requested manufacturing object can be manufactured or whether the order can be accepted, estimated based on the similarity of past drawing data included in the performance database to the requested drawing data and status information associated with each past drawing data; The control unit further collects and stores the output data for each manufacturer generated based on the performance databases of a plurality of manufacturers.

2. The control unit 2. The information processing system according to claim 1, wherein the similarity of the past drawing data to the requested drawing data is estimated by comparing characteristic information of the past drawing data with characteristic information of the requested drawing data.

3. The control unit selecting one or more pieces of past drawing data similar to the requested drawing data as similar drawing data from among the plurality of pieces of past drawing data based on the information on the similarity; The information processing system according to claim 1 , wherein output data is generated using information associated with the similar drawing data.

4. 3. The information processing system according to claim 1, wherein the output data includes information indicating the probability of receiving an order or manufacturing a past request for which the similarity satisfies a predetermined condition.

5. The information processing system according to claim 1 or 2, wherein the control unit generates output data including information on whether the product can be manufactured or whether the order can be accepted, based on operation information of in-house facilities.

6. The information processing system according to claim 1 or 2, wherein the control unit generates output data including information on whether the manufacturing or order acceptance is possible, based on work schedule information of the worker.

7. 3. The information processing system according to claim 1, wherein the output data includes estimated cost information based on the similarity of the past drawing data to the requested drawing data and cost information associated with each past drawing data.

8. The information processing system described in claim 1 or 2, wherein the output data includes period-related information regarding the delivery date or work period of the processed product estimated based on the similarity of the past drawing data to the requested drawing data and manufacturing period information associated with each past drawing data.

9. The information processing system according to claim 1 or 2, wherein the output data includes method-related information regarding a manufacturing method estimated based on the similarity of the past drawing data to the requested drawing data and manufacturing method information associated with each past drawing data.

10. 3. The information processing system according to claim 1, wherein the output data includes estimated cost information relating to estimated material information estimated from the request information, material cost information estimated based on raw material information updated at predetermined intervals, and processing process information estimated based on the request information.

11. 11. The information processing system according to claim 10, wherein the output data includes break-even price information estimated based on the estimated cost information and manufacturing man-hour information estimated from the request information.

12. A request information acquisition process for acquiring request information including requested drawing data of an object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database; the output data includes information regarding whether the requested manufacturing object can be manufactured or whether the order can be accepted, estimated based on the similarity of past drawing data included in the performance database to the requested drawing data and status information associated with each past drawing data; The control unit further presents the output data for each manufacturer, generated based on the performance database of a plurality of manufacturers, to the client.

13. A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process that generates output data using a generation AI based on the request information and a pre-stored past performance database, the output data includes information regarding whether the requested manufacturing object can be manufactured or whether the order can be accepted, estimated based on the similarity of past drawing data included in the performance database to the requested drawing data and status information associated with each past drawing data; The control unit further collects and stores the output data for each manufacturer generated based on the performance databases of a plurality of manufacturers.

14. A request information acquisition process for acquiring request information including requested drawing data of the object to be manufactured; an output data generation process for generating output data using a generation AI based on the request information and a pre-stored past performance database; the output data includes information regarding whether the requested manufacturing object can be manufactured or whether the order can be accepted, estimated based on the similarity of past drawing data included in the performance database to the requested drawing data and status information associated with each past drawing data; The control unit further collects and stores the output data for each manufacturer generated based on the performance databases of a plurality of manufacturers.

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