Creating a digital copy of a real instance from at least one image

Abstract

Creation of a digital copy of a real instance from at least one image. The invention relates to a method for generating a digital copy of a real instance, the method comprising the following steps: - obtaining (300) at least one image representative of an environment; - detecting (301) at least one given real instance in said at least one received image; - obtaining (302-304) at least one piece of information relating to the given real instance; - generating (305) a digital copy of said given real instance, the digital copy comprising said at least one piece of information relating to the given real instance. Figure for the abstract: [Fig 3]

Description

Title of the invention: Creation of a digital copy of a real instance from at least one image technical field

[0001] The invention relates to the field of virtual representation of a real instance, object or person, also called twin or digital copy.

[0002] Its purpose is, in particular, to facilitate the creation of a digital copy or twin for a given real instance. State of the art

[0003] A digital copy or twin, or “digital twin” in English, is a virtual representation of a real instance, for example of an object or product, a process or a person.

[0004] In order for the digital twin to model the real instance to which it corresponds, one or more sensors internal to the real instance, or external but capable of obtaining descriptive data of the real instance, can collect data which are sent back to a device or server which stores the digital twin, in order to update the digital twin with the collected data.

[0005] In the case of a product or object, such data include, for example, operating data of the product or object, temperature, weather conditions to which the product or object is subjected, dimensions of the object, state of use of the object, etc.

[0006] The digital twin can be used for the design, simulation, monitoring, optimization and / or maintenance of this product or object. Actions can then be taken on the real instance, in order to improve its performance or lifespan, for example.

[0007] In the context of product modeling, there are different types of digital twins that can model a particular component of a product, an interaction between two components of a product, or the entire product as a system of interacting components, when the product includes several components.

[0008] Thus, the real instance corresponding to the digital twin can be a system of objects.

[0009] In order to create a new digital copy for a real instance not yet virtually duplicated, it is currently required for an operator to manually define the real instance via a user interface on a user terminal for storage of the digital copy thus defined in the user terminal or on a remote server. It is also necessary that the operator determines the identity of each real instance for which a digital copy is created.

[0010] It is thus possible to create digital copies for the real instances comprising a simple environment with few real instances. However, such creation requires significant human and technical resources: it involves a human operator and requires a user interface to access an application dedicated to the digital twin creation service. Learning to use such an application, if it is not very intuitive, requires a lengthy adaptation period for the human operator.

[0011] However, there is no solution that allows for the simple generation of a digital copy of a real instance, requiring little or no human resources, particularly in an environment comprising a large number of real instances for which respective digital twins need to be created.

[0012] The invention offers a solution that does not present the disadvantages of the prior art. Description of the invention

[0013] To this end, from a functional perspective, the invention relates to a method for generating a digital copy of a real instance, the method comprising the following steps: - obtaining at least one representative image of an environment; - detection of at least one real instance given in said at least one received image; - obtaining at least one piece of information relating to the given real instance; - generating a digital copy of said given real instance, the digital copy including said at least one piece of information relating to the given real instance.

[0014] Thus, the invention enables the automated creation of a digital copy of a given real-world instance from an image acquired of an environment containing the given real-world instance. The digital copy may include one or more pieces of information relating to the given real-world instance: this not only facilitates the creation of the digital copy, but also its enrichment with information relating to the given real-world instance. However, the digital copy can then be enriched with data acquired by sensors other than the image acquisition device that acquired at least one image.

[0015] According to embodiments, the information relating to the given real instance may include location information for the given real instance and / or type information representative of a type of the given real instance.

[0016] Location information is advantageous for modeling the environment, and in particular the interactions between the different real-world instances that make up an environment. Information of this type facilitates obtaining information complementary to that of the other type.

[0017] In addition, obtaining location information and / or type information may include image analysis of at least one received image.

[0018] It is thus possible to obtain such information without requiring access to external resources.

[0019] In addition, obtaining location information and / or type information may include an analysis of at least one image received by a convolutional neural network.

[0020] A convolutional neural network is particularly suited for image segmentation, and / or for classifying the given real instance into a type among a set of predetermined types for which the convolutional neural network has been trained beforehand.

[0021] According to some embodiments, the at least one piece of information may include at least one visible characteristic of the given actual instance, including: - a size, a color, a mark and / or a model of the given actual instance; and / or - a text inscribed on the actual instance given; and / or - an interaction of the given real instance with at least one other real instance in the environment.

[0022] Such visible characteristics make it possible to automatically enrich the digital copy.

[0023] In addition, the visible feature can be identified by image analysis of a part of at least one image comprising the given real instance.

[0024] It is thus possible to obtain such information without requiring access to external resources.

[0025] According to embodiments, said at least one piece of information may include at least one internal and / or functional characteristic of the given actual instance, obtained from a resource storing data distinct from the at least one image obtained.

[0026] Thus, it becomes possible to enrich the digital copy automatically with information acquired from an external resource. In some embodiments, the digital copy can therefore include both visible features, resulting from an analysis of at least one acquired image, and non-visible (internal and / or functional) features obtained from the resource. external. This makes it possible to automate the generation of a digital copy with a complete description.

[0027] In addition, at least one internal and / or functional characteristic can be obtained from said resource via a generative artificial intelligence interface programming interface.

[0028] The use of a generative AI (Artificial Intelligence) API (“Application Programming Interface”) makes it possible to obtain complete internal and / or functional characteristics, determined from very large databases.

[0029] In addition or alternatively, obtaining said at least one internal and / or functional characteristic may include: - the generation of at least one instruction based on data representative of the given real instance, the data representative of the given real instance being obtained from said at least one image; - obtaining at least one response, following the transmission of at least one generated instruction, the response including said at least one technical and / or functional characteristic.

[0030] It is thus possible to obtain targeted internal and / or functional characteristics through the use of at least one instruction, or prompt. Furthermore, it is possible to provide a series of instructions to refine the characteristics thus obtained.

[0031] In addition, the data representing the given real instance can be: - a part of at least one image comprising the given real instance; or - information relating to the given real instance, obtained from an image analysis applied to a part of at least one image comprising the given real instance.

[0032] It is thus possible to generate an instruction or prompt from information obtained automatically in the preceding steps of the process according to the invention. The invention may, in particular, provide for image analysis, enriched by the retrieval of internal and / or functional information obtained from an external resource, by describing the actual instance based on the information determined previously during the image analysis.

[0033] In addition, the instruction can be generated from a given visible feature obtained from image analysis applied to the part of at least one image comprising the given real instance, the given visible feature being the text written on the given real instance.

[0034] Text inscribed on an object can allow for precise identification of the object and the retrieval of precise internal / functional information about the identified object. Furthermore, the extraction of inscribed text can be based on known and robust character recognition solutions.

[0035] In addition or alternatively, the generated instruction may further indicate a given format, the given format being a predetermined format associated with the digital copy, the response may include said at least one technical and / or functional characteristic according to the given format and the digital copy may be generated according to the given format. Thus, the generation of the digital copy is simplified in that it does not require formatting of at least some of the information relating to the given real instance.

[0036] According to some embodiments, the process may further comprise: - detection of at least one other real instance in said at least one image obtained in addition to the given real instance; - obtaining at least one piece of information relating to the other actual instance; - creation of a digital copy of said other real instance given, the digital copy including said at least one piece of information relating to the other real instance.

[0037] It is thus made possible to create digital copies of all the real instances detected in a scene, in an automated way.

[0038] According to some embodiments, the method may comprise obtaining at least one new image, acquired subsequent to said at least one image, the method further comprising: - detection of at least one new real instance given in said at least one new image obtained; - obtaining at least one new piece of information relating to the new actual instance given; - in the event of determination that the new real instance given and the real instance given are the same real instance, update the digital copy of the real instance given according to the new information obtained.

[0039] It is thus possible to automatically update the digital copy of a real instance, by acquiring successive images representative of an environment including the real instance.

[0040] If the given real instance is not detected in at least one new image, the method may include deleting the digital copy of the given real instance. This makes it possible to automatically delete the digital copy of a real instance when it leaves the environment.

[0041] According to a material aspect, the invention relates to a device for generating a digital copy of a real instance, the device comprising an interface configured to obtain at least one representative image of an environment, and a processor configured to: - detect at least one real instance given in said at least one received image; - to obtain at least one piece of information relating to the actual instance given; - generate a digital copy of said given real instance, the digital copy including said at least one piece of information relating to the given real instance.

[0042] According to another material aspect, the invention also relates to a computer program suitable for implementation on a device, the program comprising code instructions which, when the program is executed by a processor, carries out the steps of the defined process.

[0043] Such programs can use any programming language. They can be downloaded from a communication network and / or stored on a computer-readable medium.

[0044] According to another material aspect, the invention relates to a data carrier on which at least one series of program code instructions for the execution of the process defined above has been stored. Brief description of the drawings

[0045] The invention will be better understood upon reading the following description, given by way of example and with reference to the accompanying drawings in which:

[0046] Fig. 1 illustrates a system for generating a digital copy of a real instance from at least one representative image of an environment, according to embodiments of the invention;

[0047] Figure 2 illustrates an image acquired by a copy generation system. digital representation of a real instance, according to embodiments of the invention;

[0048] Fig. 3 illustrates the steps of a method for generating and updating a digital copy of a real instance from an image of an environment, according to embodiments of the invention;

[0049] Figure 4 illustrates a device for generating a digital copy of an instance real, according to embodiments of the invention. Description of the implementation methods

[0050] Fig. 1 illustrates a system 100 for generating a digital copy of a real instance in an environment 120.

[0051] The environment 120 may comprise a set of at least one real instance. "Real instance" means any object or product, any component of such an object or product, any system of objects or products, any process, any person, or any animal or plant. In the example of [Fig. 1], the environment 120 comprises, by way of illustration, a first real instance 110, which is a fire extinguisher, and a second real instance 112, which is a chair.

[0052] The fire extinguisher 110 can be considered as composed of a set of individual components forming the extinguisher, each of these components being an actual instance. Alternatively, and as considered in the remainder of the description, the fire extinguisher 110 is considered as a single first actual instance. The same applies to the chair 112, which is considered in the following as a single second actual instance.

[0053] At least some of the actual instances may include inscribed text, particularly for actual instances of object or component type. This is the case in the example shown in [Fig. 1], in which text 111 is inscribed on the fire extinguisher 110. Text 111 may, for example, be instructions for using the fire extinguisher 110.

[0054] The system 100 according to the invention includes an image acquisition device 101 capable of obtaining at least one image representative of the environment 120. The image acquisition device 101 is capable of obtaining an image, a series of images or a video stream comprising video images or video frames.

[0055] For the sake of simplifying the description of the invention, the image acquisition device 101 is considered in the following to be a camera.

[0056] In the example of [Fig. 1], the first real instance 110 and the second real instance 112 are both located within a field of view of the camera 101. Such a field of view depends on intrinsic characteristics of the camera 101, notably its focal length, and on the position of the camera 101 relative to the environment 120. The field of view may be fixed, and include the first real instance 110 and the second real instance 112. Alternatively, the camera 101 is movable and the field of view may vary, so as to acquire images of different fields of view corresponding to respective positions / orientations of the camera 101. For example, a first image may include the first real instance 110 and a second image, acquired before or after the first image, may include the second real instance 112.

[0057] In what follows, it is considered, by way of illustration, that the camera is capable of acquiring an image 200 representative of the first real instance 110 and the second real instance 112, as represented on the [Fig.2], described below.

[0058] The system 100 further includes a digital copy generation device 102 capable of communicating with the camera 101. As described below, the device 102 is capable of generating a digital copy of at least one of the real instances, from at least one representative image of the environment 120, acquired by the camera 101. An update of the digital copy thus generated can also be implemented by the device 102.

[0059] The device 102 can store the digital copy thus generated or updated, locally in a memory of the device 102 or in a memory in Communication with device 102, or with a remote storage device 104, which device 102 can access via a wide area network 103, such as an IP (Internet Protocol) network. Alternatively, network 103 is a local area network. No restrictions are attached to the storage device 104, which can be a storage server 104 dedicated to a digital twin service. The dedicated server can thus process descriptive data of the real instances corresponding to the stored digital copies, for the purposes of designing, simulating, monitoring, optimizing, and / or maintaining the real instances.

[0060] The storage device 104 can in particular be shared between several devices 102. The storage device 104 can thus store digital copies of real instances located in separate places, or even in separate sites.

[0061] In the example of [Fig.1], the device 102 is connected to a single camera 101. The device 102 can communicate with a plurality of cameras 101, for example located on the same site, in order to generate and update digital copies of part or all of the real instances located in the respective fields of vision of the cameras 101.

[0062] The system 100 may further include an external resource 105 accessible via the network 103, which may be an external database. The database 105 may, in particular, be queried via a software interface, such as an application programming interface, API, which may be a local API 106, or an edge API, executed in the device 102 or in another device that is part of the local network of the device 102, which may be a remote application programming interface 107 (of the "cloud" type), accessible by the device 102 via the network 103, or which may be an API hosted by the device 102 itself. Database 105 can store detailed information on a set of objects or people, and querying database 105 thus makes it possible to obtain information relating to the real instances detected in image 200, as described with reference to [Fig.3] describing the method according to the invention.

[0063] According to the invention, several external databases 105 can be queried by different respective APIs, which can be local or remote.

[0064] Figure 2 shows an image 200 acquired by camera 101 and to which the Device 102 applied a first image analysis in order to identify the first real instance 110 and the second real instance 112 in first and second parts 210 and 212 of image 200.

[0065] Figure 3 shows the steps of a process for creating a digital copy of a actual instance, according to embodiments of the invention.

[0066] The method can be implemented in the device 102 described above with reference to [Fig.1].

[0067] At a step 300, the device 102 receives at least one image from the camera 101, the image being representative of an environment, such as the environment 120 described previously. As described previously, the device 102 can receive one image or a series of images, such as a series of video images, at step 300.

[0068] At a step 301, the device 102 can detect at least one real instance in said at least one image received at step 300. Such detection can include determining a portion of the image, for example a rectangle, also called a "bounding box," comprising the real instance. In the example shown in [Fig. 2], step 301 thus includes determining the first and second portions 210 and 212, comprising respectively the first real instance 110 and the second real instance 112. When several real instances are detected at step 301, the subsequent steps of the method are implemented for at least one of the detected real instances, for example, for the first real instance 110, which is a fire extinguisher in the example considered above. Step 301 can thus include determining the first portion 210 of the image 200, comprising the first real instance 110.In addition, step 301 may include, starting from the first determined part 210, determining the location information of the first real instance in the environment 120. Step 301 is based on an initial analysis of the image 200 received in step 300, or of the image stream, when several images are received in step 300. No restrictions are attached to the initial image analysis, which may be based on a known image analysis algorithm capable of detecting instances of an image, for example, by image segmentation. As an example, the initial image analysis may be implemented by submitting the input image to a first convolutional neural network (CNN) capable of detecting real instances in the received input image and segmenting the image based on the detected real instances.Such a convolutional neural network can be derived from machine learning, particularly deep learning when the convolutional neural network is a deep neural network. Other technical solutions can be used to implement the initial image analysis according to the invention.

[0069] The first image analysis can be based on one of the following algorithms: You Only Look Once (or YOLO, which is a real-time instance detection algorithm based on a CNN), Faster R-CNN, SSD (Single Shot Detector), Mask R-CNN, RetinaNet, Inception, OpenCV, or any other image analysis algorithm capable of detecting a real instance in a part of the image. Alternatively, the first image analysis can be based on an algorithm such as, for example, Meta™'s Segment Anything Model, which allows for the fine delineation of a instance in an image: the first part 210 thus determined during the detection of the fire extinguisher 110 has in this case a shape close to the fire extinguisher, and is not a rectangle as represented on the [Fig.2].

[0070] The first image analysis can be implemented by the device 102 (software stored in the device 102 and implementing the first image analysis), or by another device on the local network of the device 102, or can be implemented by a Cloud-type software service, which the device 102 accesses via the network 103.

[0071] At step 302, the device 102 can identify type information for the first real instance detected at step 301. Such type information identifies a type, family, or class of the real instance. There are no restrictions on the level of generality corresponding to the identified type / family / class. For example, if the first real instance is a fire extinguisher, the type information can identify the type "fire extinguisher," or a more general type such as "object," "actionable object," "security device," or any other general designation that includes the instance "fire extinguisher." The precision associated with the type depends on the second image analysis performed at step 302.The aforementioned examples of algorithms are also suitable for implementing the second image analysis, namely the following algorithms: You Only Look Once, Faster R-CNN, SSD, Mask R-CNN, RetinaNet, Inception, OpenCV, or any other image analysis algorithm capable of detecting an instance in an image or in a part of the image.

[0072] The second image analysis can be implemented by the device 102 (software stored in the device 102 and implementing the second image analysis), or by another device on the local network of the device 102, or can be implemented by a Cloud-type software service, which the device 102 accesses via the network 103.

[0073] According to some embodiments, steps 301 and 302 can be implemented jointly by image analysis, performed by a single software solution. For example, the aforementioned algorithms YOLO, Faster R-CNN, SSD, Mask R-CNN, RetinaNet, Inception, and OpenCV allow steps 301 and 302 to be performed jointly.

[0074] According to a first embodiment, following step 302, the process proceeds directly to step 305, which generates a digital copy of the first real instance, or updates the first real instance, as described later. Alternatively, following step 302, the process proceeds to step 303 or step 304, which are described below.

[0075] At a step 303, the device 102 can obtain at least one visible feature of the first actual instance 110, by a third image analysis applied to the part of the image comprising the given real instance. By "visible feature" we mean a feature that can be deduced or estimated from the visual appearance of the given real instance, and therefore by an image analysis.

[0076] The at least one visible feature is any of the following features, or any combination thereof: - a size (including a set of dimensions), a color, a brand and / or a model of the given actual instance; and / or - a text inscribed on the given actual instance, such as text 111 inscribed on the first actual instance 110; and / or - an interaction of the given real instance with at least one other real instance detected in the at least image.

[0077] The third image analysis can be implemented by software, stored by device 102 or by another device on device 102's local network, or implemented as a cloud service, accessible remotely via network 103, optionally via one of APIs 106 or 107. The software solution implementing the third image analysis can be, for example, one of the following solutions, listed without limitation: - Google Cloud Vision AI™, which is capable of recognizing objects, scenes, and people in images. In addition, such a solution can be used to extract non-visible technical information from recognized objects (in accordance with step 304 described below); - Amazon Rekognition™, which is capable of recognizing objects. In addition, such a solution can be used to extract technical, non-visible information from recognized objects (in accordance with step 304 described below); - Microsoft Azure Computer Vision™, which is capable of analyzing the content of an image; - IBM Watson Visual Recognition™, which is capable of implementing object detection and classification of objects detected in an image; - Clarifai™, which is a platform hosting artificial intelligence tools to recognize objects and classify images; - TesseractTM, which is a text extraction service, from an image; - Open Source Computer Vision Library, which is a library, from which the third image analysis can be trained; - Mobile Vision API, which is an API that provides a computer vision function.

[0078] Note that the third image analysis may include the application of at least two separate software solutions, for example, one to obtain the size, color, brand and / or model characteristics of the given real instance, and the other to extract text inscribed on the given real instance. Text extraction recorded on the given real instance can notably be implemented by an optical character recognition tool, also called OCR, for "Optical Character Recognition" in English.

[0079] Following step 303, the process can proceed directly to step 305 described later, or can proceed to step 304 described below.

[0080] At a step 304, the device 102 can obtain at least one additional piece of information to the visible information obtained at step 303, and / or at least one internal and / or functional characteristic, from an external resource, such as the database 105 described previously.

[0081] The term "supplementary information to the visible information" means external and visible information of the given real instance, but which cannot be obtained by analyzing the at least one image received in step 300, or which allows increasing the precision of a visible feature obtained by analyzing the at least one image received in step 300. The supplementary information may in particular include the precise dimensions of the given real instance, which can be obtained after model identification in step 303, and by querying the database 105, which can store such supplementary information in association with data representative of the given real instance (for example, the model of the given real instance).

[0082] An internal and / or functional characteristic is understood to be a non-visible characteristic of the given real instance, and therefore not deducible by simple visual observation of the given real instance in an image. Internal and / or functional characteristics may include: - any chemical composition of a product stored in the given real instance; and / or - an identifier of an API for controlling the given real instance, when the given real instance is a connected object; and / or - a mode of operation of the given real instance; and / or - a default setting of the given real instance; and / or - any other internal and / or functional characteristic, which depends on the real instance considered.

[0083] Such internal and / or functional characteristics can, for example, be detailed in a technical notice associated with the given actual instance, which can be stored in the database 105 in association with a model identifier. Thus, the device 102 can query the database 105 based on the model of the given actual instance obtained in step 303, or, alternatively, based on the object type determined in step 302.

[0084] More generally, the device 102 can query the database 105 using any data representative of the given real instance, or any what combination of data is representative of the given real-world instance. The data representative of the given real-world instance can be: - the part of the image comprising the given actual instance; and / or - any information, or combination of information, relating to the given actual instance obtained in step 301, step 302 and / or step 303.

[0085] According to advantageous embodiments, step 304 may include querying a generative artificial intelligence API capable of returning a response based on an input instruction, also called a "prompt". The generative artificial intelligence API may be an artificial intelligence conversational agent, such as ChatGPT™. Alternatively, the generative artificial intelligence API may be Llama2™, Claude™, Mistral AI, or any other known solution.

[0086] Such a generative artificial intelligence API can be an edge API 106, a remote cloud-type API 107, or an API embedded in the device 102.

[0087] Thus, according to the invention, at least one instruction is generated in step 304 by the device 102, from at least one data point representative of the given real instance. This at least one instruction is submitted to the generative AI API, and at least one response is received in return, this response including at least one internal and / or functional characteristic of the given real instance and / or at least one of the aforementioned additional pieces of information.

[0088] For example, all or some of the information relating to the given real instance obtained in steps 301 to 303 can be included in a single instruction, and a response comprising at least one internal and / or functional piece of information about the given real instance, and / or at least one supplementary piece of information about its visible characteristics, is received in return. Alternatively, the device 102 can be configured to generate a series of instructions, each instruction comprising at least one representative piece of data about the object. A series of responses to the instructions is received in return, each response in the series comprising at least one internal and / or functional piece of information, and / or at least one supplementary piece of information about its visible characteristics, distinct from those contained in the other responses.

[0089] According to embodiments of the invention, the at least one generated instruction may further specify a response format, and the at least one response received when querying the external resource conforms to the response format specified in the at least one instruction. Such a possibility is offered by certain generative AI APIs. The specified response format may advantageously correspond to a predetermined format of the digital copy to be generated. For example, the predetermined format could be a JSON format.

[0090] In step 305, the device 102 determines whether a digital copy has been previously created for the given real instance. Such a determination may consist of checking in the storage device 104, or in an internal memory of the device 102, whether at least some of the information relating to the given real instance corresponds to relevant information contained in a previously generated digital copy. For example, if the type information and location information, indicating, for example, a site or a room, of the given real instance correspond to similar information stored in a previously generated digital copy, the device 102 can determine that the previously generated digital copy is the digital copy of the given real instance.No restrictions are attached to the information, or information, relating to the given real instance that is / are used to verify whether a digital copy has already been generated for the given real instance. If device 102 determines that no digital copy has been previously created for the given real instance, device 102 generates, at step 305, a digital copy of the real instance, the digital copy including at least one piece of information relating to the given real instance, the at least one piece of information relating to the given real instance being one or any combination of the following information: - the location information determined at step 301; - the type of information determined in step 302; - at least one visible characteristic determined in step 303; - at least one additional piece of information regarding the visible characteristic determined in step 304; and / or - at least one internal and / or functional characteristic determined in step 304, optionally according to the predetermined format.

[0091] The generated digital copy can be stored in an internal memory of the device 102 or can be stored in the digital copy storage device 104.

[0092] If device 102 determines in step 305 that a digital copy already exists for the given real instance, device 102 can update the digital copy based on at least one characteristic relating to the given real instance. Such an update may include: - the detection of a difference between a value indicated by the information relating to the given real instance, and determined in one of steps 301 to 304, and a stored value in the digital copy for that same information. Such a stored value may originate from a previous iteration of the process implemented for the same given real instance, or from a previous update of the digital copy. associated with the given real instance, either during the generation of the digital copy associated with the given real instance;

[0093] - the modification / update of the value stored in the digital copy with the value determined at one of the steps 301 to 304. The modified stored value can thus be updated in a local memory of the device 102 and / or transmitted to the storage device 104 for updating the digital copy associated with the given real instance.

[0094] Alternatively or in addition, at least one piece of information relating to the given actual instance determined in one of steps 301 to 304 is not included in the digital copy previously generated for the given actual instance, in which case the update includes the addition of the information not yet included in the digital copy.

[0095] The invention thus allows for the automatic updating of certain information stored in the digital copy. The steps of the process can therefore be iterated, by being applied to images, or series of images, acquired at different times by the same camera 101.

[0096] By way of example, in a previous iteration of the process applied to the fire extinguisher 110, the digital copy may indicate a "not used" usage state. In a current iteration, the third image analysis of step 303 may determine a "used" usage state, for example, because an element of the activation mechanism of the fire extinguisher 110 is in a position indicating that the extinguisher has been activated and used. In this case, in step 305 of the current iteration, the device 102 detects a difference between the usage state stored in the digital copy from the previous iteration, which is a "not used" state, and a usage state determined in step 303 of the current iteration.

[0097] According to the invention, the update can apply to information relating to the given actual instance other than its state of use. For example, an update of the digital copy can be performed for any information indicating a change of location and / or a change of form, and / or for any other change.

[0098] Note that in the absence of identification of a real instance corresponding to a previously generated digital copy (during a previous iteration of the steps of the process) in the image obtained in step 300, the device 102 can delete the previously generated digital copy from its internal memory or from the storage device 104.

[0099] After generation and storage of the digital copy for the given real instance, or updating of the digital copy of the given real instance, steps 302 to 304 can be implemented for at least one other real instance detected in the at least one image received in step 300. Alternatively, steps 302 to 304 are applied in parallel to the given real instance and to at least one other real instance.

[0100] Several generations or updates of digital copies can be implemented in parallel, or sequentially one after the other.

[0101] As described previously, any of the combinations of steps 302 to 304 can be implemented beforehand to generate or update the digital copy in step 305 according to the invention.

[0102] In a first embodiment, the device 102 is capable of implementing steps 300 and 301, step 302 of identifying type information, and then implementing step 305 after step 302 (without implementing steps 303 and 304). In this case, the digital copy of the given real instance generated or updated in step 305 may include the type information, and optionally the location information determined in step 301.

[0103] In a second embodiment, the device 102 is capable of implementing steps 300 and 301, step 302 and step 303, and then implementing step 305 after step 303 (without implementing step 304). In this case, the digital copy of the given real instance generated or updated in step 305 may include: - the type information determined in step 302; - the location information determined in step 301; and / or - at least one visible characteristic determined in step 303.

[0104] In a third embodiment, the device is capable of performing steps 300 and 301, step 302, then step 304, and then step 305 after step 304. In this case, the digital copy of the given real instance generated or updated in step 305 may include:

[0105] - the type information determined in step 302; - the location information determined in step 301; and / or - at least one internal and / or functional characteristic determined in step 304; - at least one complementary characteristic to a visible characteristic, determined in step 304.

[0106] In the third embodiment, the type information determined in step 302 can be used to generate at least one instruction from which the database 105 is queried to obtain at least one internal and / or functional characteristic, or at least one additional characteristic, determined in step 304.

[0107] In a fourth embodiment, the device 102 is capable of carrying out all of steps 300 to 304 before step 305 is carried out. In this case, the digital copy of the given real instance generated or updated in step 305 may include:

[0108] - the type information determined in step 302; - the location information determined in step 301; - at least one visible characteristic determined in step 303; - at least one internal and / or functional characteristic determined in step 304; - at least one additional piece of information, or at least one visible characteristic, determined in step 304.

[0109] It should be noted that the at least one visible feature determined in step 303 and / or the type information determined in step 302 may not be integrated into the generated or updated digital copy, but may be used by device 102 as representative data of the given actual instance to generate the at least one instruction in step 304, and thus obtain the at least one internal and / or functional feature, and / or the at least one additional piece of information, in step 304.

[0110] In particular, when text written on the given actual instance is extracted in step 303, device 102 can use the text to generate at least one instruction, or prompt, in step 304, without including the extracted text in the digital copy in step 305.

[0111] For all the embodiments described above, each piece of information relating to the given real instance can be determined in association with a confidence score. The confidence score represents the degree of certainty with which the information relating to the given real instance is determined. Each confidence score determined in association with a piece of information relating to the given real instance can be incorporated into the generated or updated digital copy of the given real instance, if the information relating to the given real instance is incorporated therein. The confidence score can be expressed as a percentage.

[0112] In what follows, two detailed examples of generating a digital copy are given for illustrative purposes only.

[0113] In a first example, the actual instance given is the first actual instance 110 of [Fig.1], which is a fire extinguisher.

[0114] Creating a digital copy of a fire extinguisher is useful in the context of fire safety training, the management and location of safety equipment, or fire simulations for training purposes. Manually generating a digital copy of a fire extinguisher according to the prior art may require skills in 3D modeling, software development, and data integration. In particular, it may be desirable to create a digital copy modeling fire extinguisher 110, reflecting its characteristics, dimensions, condition, and location.

[0115] Examples of information relating to a fire extinguisher that can be included in a digital copy are given below: - physical properties (internal characteristics): weight, capacity, type of liquid or gas it contains, pressure; - condition of the fire extinguisher (internal / functional characteristic): condition among a set of predetermined conditions (new, users), date of last inspection; - location (location information): descriptive information of an area of ​​a building in which the fire extinguisher is located; - interactions with the digital copy (functional characteristics): it is thus made possible to create an interactive digital copy, to allow a user to grasp, manipulate, place, activate the mechanism of the digital copy of the fire extinguisher; - Maintenance data (internal / functional characteristics): data from the last inspection of the fire extinguisher, and the date of the next scheduled inspection; - Integration with other systems: if the fire extinguisher is used in a broader context such as fire safety management of a building in which it is located. In this case, the fire extinguisher object is a component of a larger system of which a digital copy can be created, requiring the definition of digital copies for the other components of the management system; - three-dimensional modeling from visible and internal characteristics of the fire extinguisher, such as its dimensions, shape, design and components; - visualization from visible and internal characteristics of the fire extinguisher: the digital copy can be used with visualization software allowing display and interaction with the digital copy, on computer, smartphone, tablet or virtual reality headset for example.

[0116] Thus, a digital copy template can be predefined from any combination of the fire extinguisher information given above, and the analyses and tools implemented in device 102 are intended to obtain the fire extinguisher information provided in the predefined template.

[0117] In the first example, an image 200 including the fire extinguisher 110 is received at step 300. At a step 301, the fire extinguisher is detected in the image 200, in a first part 210 of the image 200.

[0118] Device 102 implements step 301 and determines information of the type "packaged goods" associated with a confidence score of 82%. In this first example, step 301 does not allow for the determination of a more precise type ("fire extinguisher" or "fire safety device," for example) than the type "packaged goods." However, the level of certainty associated with the type information is high.

[0119] In the first example, device 102 implements step 302, based on OCR software, and extracts text 111 written on fire extinguisher 110, from the first part 210 of image 200.

[0120] In the first example, device 102 implements step 303, by generating an instruction including the text 111 extracted in the previous step, and submitting the instruction to a generative AI API, in order to obtain a response including the following internal and / or functional characteristics, and additional information on visible characteristics: - the type of fire extinguisher (additional information to the visible characteristics): ABC type powder fire extinguisher; - a brand of fire extinguisher (additional information on visible characteristics); - a volume of 2kg (internal characteristic); - a type of ABC powder (internal characteristic); - a use according to which the fire extinguisher is suitable for use on class A fires, of solid combustibles type, B, flammable liquids, and C, gases (functional characteristics); - instructions for use of the 110 fire extinguisher (functional characteristics).

[0121] An additional instruction can be generated by device 102 to request the exact dimensions of the fire extinguisher (complementary visible features), associated with the brand and type of fire extinguisher identified in the response received to the previous instruction. The response to the additional instruction thus includes the dimensions of fire extinguisher 110, which allows for the creation of a three-dimensional representation of fire extinguisher 110 from its generated digital copy.

[0122] As described above, the instruction and the additional instruction may indicate a response format corresponding to the predetermined format for creating the digital copy, such as the JSON format.

[0123] The digital copy of fire extinguisher 110 can thus be generated in step 305 from the information relating to fire extinguisher 110 obtained in step 304. It is not necessary to add the information of the type "packaged goods" determined in step 302, since more precise information of type was obtained in step 304, nor to include the text 111 extracted in step 303, which was used to generate the instruction.

[0124] As previously stated, the digital copy may further include a usage state of the 110 fire extinguisher. During the generation of the digital copy described herein, the determined usage state may be a "not used" state. The digital copy of the 110 fire extinguisher may be updated during a subsequent iteration of the process if a state other than "not used" is detected, for example, a "used" state.

[0125] In a second example, the given real instance is the second real instance 112 of [Fig.1], which is a chair.

[0126] In the second example, the device 102 can detect the chair 112 in the second part 212 of the image 200 at step 301, by applying the first image analysis to the image 200.

[0127] In step 302, the "chair" type information can be determined by applying the second image analysis. The "chair" type information can be determined in association with a high confidence score, for example 94%.

[0128] According to the second example, the device 102 can implement step 304 after step 302 (without performing step 303), by generating an instruction for a generative AI API, the instruction comprising the second part 212 of the image 200 as representative data of the chair 112. Indeed, some generative AI APIs are capable of receiving an image as input and returning a response to the instruction, which may include the following internal / functional and complementary visible features: - the dimensions of the chair 112 and / or each of the chair components, such as the legs, seat, frame, etc. (additional characteristics of visible characteristics); - a material for the chair and / or a material for each component of the chair (additional visible information and internal information); - a model and / or brand of the chair (additional characteristic to visible characteristics); - a type of chair design (complementary characteristic to visible characteristics); - a type of tissue (internal characteristic).

[0129] Each of the above pieces of information can be associated with a confidence score indicated in the response.

[0130] As with the fire extinguisher 110, the digital copy associated with the chair 112 can also be updated during subsequent iterations of the steps of the method according to the invention. For example, during the generation of the digital copy, an occupancy state of "occupied" can be determined in step 303 if a person is sitting on the chair in image 200. In a subsequent iteration of the method, upon obtaining a new image or series of images in step 300, the device 102 can determine an occupancy state other than the occupied state, for example, an "empty" state, if no one is sitting on the chair in the new image or series of images of the subsequent iteration. If the chair is not detected in the new image or series of images of the subsequent iteration, the device 102 can delete the copy digital associated with the chair, in its internal memory and / or with the storage device 104.

[0131] The above examples are given by way of illustration. The invention can be applied to the detection of real instances in any environment 120, not only inside a building. The environment 120 could, for example, be: - a public park, the real instances being trees; - a public space, the actual instances being elements of street furniture; - a network infrastructure, with the actual instances being elements of the network infrastructure, etc.

[0132] Figure 4 shows the structure of the device 102 for creating a digital copy, according to embodiments of the invention.

[0133] The device 102 includes a processor 401 configured to communicate unidirectionally or bidirectionally, via one or more buses or via a direct wired connection, with a memory 402 such as a Random Access Memory (RAM), a Read Only Memory (ROM), or any other type of memory (Flash, EEPROM, etc.). Alternatively, the memory 402 comprises several memories of the aforementioned types.

[0134] Memory 402 includes at least one non-volatile memory in which are stored, temporarily or permanently, the data used and / or resulting from the implementation of the steps of the process described in reference to [Fig.3].

[0135] In particular, memory 402 can store, according to certain embodiments, the software algorithm(s) implementing the first image analysis, the second image analysis, and / or the third image analysis described above. Memory 402 can also store instruction formats corresponding to a digital copy template for implementing step 304 described above.

[0136] Alternatively, device 102 comprises separate modules for implementing steps 301 to 304 described above. Device 102 may thus comprise any combination of the following modules: - a first module configured for the implementation of step 301; - a second module configured for the implementation of step 302; - a third module configured for the implementation of step 303; - a fourth module configured for the implementation of step 304.

[0137] The processor 401 is capable of executing instructions, stored in memory 402, for the implementation of steps 300 to 305 described with reference to [Fig.3].

[0138] The device 102 includes a first interface 403 capable of communicating with the camera 101, in particular for obtaining at least one image during the step 300 described above.

[0139] Device 102 may further include a second interface 404 capable of accessing the extended network 103 and / or the local network of device 102, to communicate remotely with entities accessing the network, in particular with the storage device 104 of digital copies for transmission of a generated digital copy or for updating or deleting a digital copy stored in the storage device 104, but also with APIs 106 and 107.

Claims

Demands

1. A method for generating a digital copy of a real instance (110; 112), the method comprising the following steps: - obtaining (300) at least one image (200) representative of an environment (120); - detecting (301) at least one given real instance in said at least one received image; - obtaining (302-304) at least one piece of information relating to the given real instance; - generating (305) a digital copy of said given real instance, the digital copy comprising said at least one piece of information relating to the given real instance, in which said at least one piece of information comprises at least one internal feature of the given real instance (110; 112) obtained from a resource (105) storing data distinct from the at least one image (200) obtained.

2. A method according to claim 1, wherein at least one other piece of information is obtained relating to the given actual instance, said at least one other piece of information comprising location information of the given actual instance and / or type information representative of a type of the given actual instance.

3. A method according to claim 2, wherein obtaining location information and / or type information includes image analysis of at least one (200) received image.

4. A method according to any one of the preceding claims, wherein at least one other piece of information is obtained, said at least one other piece of information comprising at least one visible feature of the given real instance (110; 112), comprising: - a size, a color, a mark and / or a model of the given real instance; and / or - a text inscribed (111) on the given real instance; and / or - an interaction of the given real instance with at least one other real instance of the environment.

5. A method according to claim 4, wherein the visible feature is identified by image analysis of a portion (210; 212) of at least one image (200) comprising the given actual instance.

6. A method according to claim 1, wherein at least one internal feature is obtained from said resource (105) via a generative artificial intelligence interface programming interface (106; 107).

7. A method according to claim 6, wherein obtaining (304) said at least one internal feature comprises: - generating at least one instruction based on data representative of the given real instance (110; 112), the data representative of the given real instance being obtained from said at least one image; - obtaining at least one response, following transmission of the at least one generated instruction, the response comprising said at least one internal feature.

8. A method according to claim 7, wherein the data representing the given real instance is: - a part (210;212) of at least one image (200) comprising the given real instance (110; 112); or - information relating to the given real instance, obtained from an image analysis applied to a part of at least one image comprising the given real instance.

9. A method according to claim 8 and claim 5, wherein the instruction is generated from a given visible feature obtained from image analysis applied to part (210; 212) of at least one image (200) comprising the given actual instance (110; 112), the given visible feature being the text (111) written on the given actual instance.

10. A method according to any one of claims 7 to 9, wherein the generated instruction further indicates a given format, the given format being a predetermined format associated with the digital copy, wherein the response includes said at least one internal feature according to the given format and wherein the digital copy is generated (305) according to the given format.

11. A method according to any one of the preceding claims, further comprising: - detection (301) of at least one other real instance in said at least one image obtained in addition to the given real instance; - obtaining (302-304) at least one piece of information relating to the other real instance; - generation (305) of a digital copy of said other real instance given, the digital copy including said at least one piece of information relating to the other real instance.

12. A method according to any one of the preceding claims, comprising obtaining at least one new image, acquired subsequent to said at least one image, the method further comprising: - detection (301) of at least one new real instance given in said at least one new image obtained; - obtaining (302-304) at least one new piece of information relating to the new real instance given; - in the event of determination that the new real instance given and the real instance given are the same real instance, updating (305) the digital copy of the real instance given according to the new information obtained.

13. Device (102) for generating a digital copy of a real instance, the device comprising an interface (403) configured to obtain at least one image (200) representative of an environment (120), and a processor (401) configured to: - detect at least one given real instance (110; 112) in said at least one received image; - obtain at least one piece of information relating to the given real instance; - generate a digital copy of said given real instance, the digital copy comprising said at least one piece of information relating to the given real instance, in which said at least one piece of information comprises at least one internal characteristic of the given real instance (110; 112) obtained from a resource (105) storing data distinct from the at least one image (200) obtained.14 Computer program capable of being implemented in a device as defined in claim 13, the program comprising code instructions which, when executed by a processor (401), carries out the steps of the process defined in any one of claims 1 to 12.

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