Method for requesting production of a drawing and drawing production method, communication terminal, and program implementing same

Abstract

The invention relates to a drawing production method, a communication terminal, and a program implementing same. According to the invention, a method for requesting production of a drawing (RDP) is implemented by a communication terminal, the request method (RDP) comprising: generating (ICGN) an input command (ic) for graphic generation by artificial intelligence (GGN), wherein the input command (ic) is generated on the basis of a current neural signal (sn, snj) of a user (U). The quality of the images produced is thus improved for the average user, since the input commands are determined on the basis of the image conceived (either verbally or graphically) by the user.

Description

Drawing production request method and drawing production method, communication terminal and program implementing them

[0001] The invention relates to a drawing production method, a communication terminal and a program implementing it. State of the art

[0002] Drawing, whether artistic or technical, remains today the preserve of certain individuals more gifted than others. This is despite increasingly sophisticated digital tools that now allow for the production of high-quality images.

[0003] After several years of development, computer-aided design (CAD) software gradually allowed users to draw directly on a computer using a keyboard and mouse. This type of drawing enabled both technical drawing (plans, diagrams, etc.) and artistic drawing (posters, video games, animated films, etc.). Later, the use of graphics tablets and touchscreens, combined with this CAD software, allowed artists to employ more natural drawing techniques, as they once again used a stylus.

[0004] Recent years have seen the emergence of generative artificial intelligences (AI) such as MidJourney TM or Dall-E TM whose input commands are formulated in natural language, such as LLM-type language models.

[0005] Whether using traditional digital drawing tools (especially CAD) such as the Adobe suite TMor generative AI, producing a quality image still requires knowing how to use them properly and having access to the right resources. This is why, despite this evolution in drawing tools, it is still necessary today to call upon a professional designer, graphic artist, or artist, and / or a specialist in writing "prompts" (i.e., input commands for generative AI) to generate a quality image, even using graphic AI.

[0006] One of the aims of the present invention is to provide improvements over the prior art.

[0007] An object of the invention is a drawing production request method implemented by a communication terminal, said request method comprising: generating an input command for graphic generation by artificial intelligence, the input command being generated based on a current neural signal from a user.

[0008] Thus, the quality of the images produced will be improved for the average user because the input commands will be determined based on the image thought of (either verbally or graphically) by the user.

[0009] Advantageously, the request process involves: receiving a neural signal captured by a direct neural interface worn by the user.

[0010] Advantageously, the input command is generated based on a succession of neural signals from the user, each neural signal corresponding to at least one element of the image to be generated.

[0011] Thus the quality of the images produced will be improved more precisely, that is to say, it will not be improved for a single element of the image but for all the most important elements of the image considered by the user.

[0012] Advantageously, the user's sequence of neural signals corresponds to a set of neural signals from among the following: - a sequence of neural signals during a predefined time period; - a sequence of neural signals interrupted by an end command;

[0013] - a succession of neural signals ending with a neural signal associated with an end command.

[0014] Thus, the completion of the input command will either be automatic (because it corresponds to a succession of signals during a predefined time period), or controlled in a neural way, thus avoiding the user having to change the control mode which could cause errors (in particular an input command generated by forgetting the last neural signal or taking into account a neural signal distinct from the input command).

[0015] Advantageously, the input command function of a neural signal includes at least one of the following: - a word function of a current neural signal of the user thinking about a word; - a word or several words function of a current neural signal of the user thinking about an image; - an image function of a current neural signal of the user thinking about a word; - an image function of a current neural signal of the user thinking about an image.

[0016] Advantageously, when the user thinks of an image, the generation of the input command involves: - analyzing the image associated with the user's current neural signal, the analysis determining a set of words describing the image; - converting the resulting set of words from the analysis into an input command for the AI-generated graphics.

[0017] Thus, the visual thinking of a single image nevertheless allows for an image whose quality has been improved by a complex input command.

[0018] Advantageously, the request process involves: generating a subcommand of the input command by associating with the input command triggering the generation of an image by an artificial intelligence graphics generator a parameterization of at least one element of the image to be generated according to a neural signal from the user.

[0019] Thus, image quality is improved by taking into account all of the user's thoughts relating to the image to be generated.

[0020] An object of the invention is also a drawing production method implemented by a communication terminal, said drawing production method comprising: - requesting a drawing production according to the invention by providing an input command for graphic generation by artificial intelligence; and - generating by artificial intelligence an image according to the input command provided by the drawing production request.

[0021] Advantageously, according to one implementation of the invention, the various steps of the process according to the invention are implemented by a software or computer program, this software comprising software instructions intended to be executed by a data processor of a device forming part of a communication terminal and designed to control the execution of the various steps of this process.

[0022] The invention therefore also relates to a program comprising program code instructions for the execution of the steps of the drawing production request process or the drawing production process according to the invention when said program is executed by a processor.

[0023] This program can use any programming language and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form or in any other desirable form.

[0024] An object of the invention is also a communication terminal comprising: - a drawing production requester capable of generating an input command from a graphics generator by artificial intelligence, the input command being generated according to a neural signal from a user; - a visual reproduction device capable of reproducing the image provided by the graphics generator by artificial intelligence according to the generated input command.

[0025] Advantageously, the communication terminal includes a device for obtaining neural signals from the user.

[0026] Advantageously, the device for obtaining neural signals is one of the following: - a neural signal receiver connected to a user-worn direct neural interface; - a user-worn direct neural interface.

[0027] Advantageously, when the signal acquisition device is a user-worn direct neural interface, the direct neural interface has several electrodes arranged on the direct neural interface so as to be positioned relative to predefined areas of the user's skull when wearing the direct neural interface among the following areas: - Broca's area, - visual area, - occipital lobe.

[0028] Advantageously, the communication terminal includes at least one of the following devices: - a communication interface with the artificial intelligence graphics generator; - the artificial intelligence graphics generator.

[0029] The features and advantages of the invention will become clearer upon reading the description, given by way of example, and the related figures which represent:

[0030] , a simplified diagram of a drawing production request method according to the invention and of a drawing production method according to a first variant of the invention,

[0031] , a simplified diagram of a drawing production process according to a second variant of the invention,

[0032] , a simplified diagram of a communication terminal according to the invention,

[0033] , a simplified diagram of a first use case of the invention during a simple request,

[0034] , a simplified diagram of a second use case of the invention during a simple query,

[0035] , a simplified diagram of a third use case of the invention during a complex query,

[0036] , a simplified diagram of a fourth use case of the invention during a complex query.

[0037] Laillustre a simplified diagram of a drawing production request method according to the invention and of a drawing production method according to a first variant of the invention.

[0038] The RDP drawing production request process is implemented by a communication terminal. It includes: generating an input command (ICGN) for a graphic generation by artificial intelligence (GGN), the input command (ic) being generated based on a neural signal (sn). j current of a user U.

[0039] In particular, the RDP request process includes: receiving SN_RCV a neural signal captured by a direct neural interface BCI worn by the user U.

[0040] In particular, the input command ic is generated based on a succession of neural signals {sn j} j of the user: ic({sn j} j ), each neuronal signal sn jcorresponding to at least one element of the image to be generated e i .

[0041] In particular, the sequence of neural signals of the user {sn j} j corresponds to a set of neural signals among the following: - a succession of neural signals during a predefined time period T; - a succession of neural signals interrupted by an end command, in particular an end command from a human-machine interface such as a mouse, a keyboard, etc.;

[0042] - a succession of neuronal signals ending with a neuronal signal sn J associated with a thnd_rq end command.

[0043] In particular, a neuronal signal sn, sn j current according to which the input command ic is generated is a neural signal captured when the user U thinks of at least one of the following objects:- a word twd;- an image timg.

[0044] Thus, according to user U, they will be able to choose the image generation mode with which they are most comfortable in order to improve the quality of the generated image: textual or visual thinking.

[0045] In particular, the input command ic(sn), ic({sn j} j ) function of a neural signal includes at least one of the following elements: - a word wd, a function of a current neural signal sn of the user U thinking of a word twd; - a word or more words wd, {wd n} n based on a current neuronal signal sn of the user U thinking about an image timg;- an image img based on a current neuronal signal sn of the user U thinking about a word twd;- an image img based on a current neuronal signal sn of the user U thinking about an image timg.

[0046] In particular, when the user thinks of a timg image, the generation of the ICGN input command involves: - analyzing the img image (IMG_NZ)i associated with the current neuronal signal sn of user U, the IMG_NZ analysis determines a set of words {wd n} n describing the image; - convert the set of words resulting from the analysis into an input command for the graphic generation by artificial intelligence.

[0047] In particular, the RDP request process involves: generating a subcommand of the input command ic by associating it with the input command that triggers the generation GGN, GGN m of a dg image by a graphics generator using artificial intelligence GGT, GGT m a pr, pr setting i , pr ki of at least one element e, e i of the image to be generated based on a neuronal signal sn j , sn i , sn ki of user U.

[0048] In particular, the parameterization subcommand includes at least one pr parameter, pr i , pr kiamong the following:- position of the element on the image pos(e) ;- plane of the image in which the element is located pln(e);- style sty(e);- color clr(e);- color range cg(e).

[0049] In the illustrated examples, a single parameter is associated with a neural signal. However, in general, a single neural signal can determine several distinct parameters of the image to be generated, notably from the list above.

[0050] In particular, a direct neural interface carried by a user U of a communication terminal T implementing the RDP request process provides at least one current neural signal sn, snj of the user U captured by the direct neural interface BCI.

[0051] In particular, the RDP request process includes:

[0052] brain-controlled BCGN, the graphic generation by artificial intelligence GGN, GGN m .

[0053] In particular, the brain control of graphic generation by BCGN artificial intelligence involves the reception of SN_RCV neural signals.

[0054] In particular, the RDP request process includes:

[0055] analyze SN_NZ the neuronal signal sn, sn j provided by the direct neural interface (BCI) to determine a content wd(sn), img(sn), pr(sn), pr k (s k ), wd i (sn i ), img i (sn i ), pr iki (sn ki ), i <ki<i+1pensé par l’utilisateur U.

[0056] In particular, the SN_NZ neural signal analysis uses a BDSN neural signal database in which the neural signals sn, sn j are associated, prior to the implementation of the RDP request process, with wd, img, pr content during a learning of the thought of user U.

[0057] In particular, the SN_NZ neural signal analysis interprets the sn, sn neural signals j by means of an initial artificial intelligence device, notably following prior training in the RDP request process.

[0058] In particular, the brain control of graphic generation by BCGN artificial intelligence involves the analysis of SN_NZ neural signals.

[0059] In particular, the reception of neuronal signals SN_RCV provides the neuronal signals sn, sn j received from the user's direct neural interface to the SN_NZ neural signal analysis.

[0060] In particular, the RDP request process includes:

[0061] check the type ty? of content thought by the user, in particular the type of content determined by the analysis of SN_NZ neural signals.

[0062] In particular, in the case where the content type ty is an image [fi], the query process includes

[0063] analyze IMG_NZ an image img, img i determined based on the current neuronal signal sn, sn j of user U, specifically the image determined by the analysis of the SN_NZ neural signals.

[0064] In particular, the analyzed image img, img i by analysis IMG_NZ is an image associated with the current neuronal signal sn of user U.

[0065] In particular, the IMG_NZ analysis determines a set of words {wd n} n describing the image img, img i determined based on the current neuronal signal sn, sn j .

[0066] When the user thinks of several words or images to trigger the generation of a GGN or GGNm image, then the RDP request process involves:

[0067] temporarily store TST data relating to a content function of a neural signal sn, particularly in a temporary storage device TM, such as a temporary memory, a temporary content database…

[0068] In particular, in the case where the content type ty is an image [fi], the RDP request process temporarily stores TST a set of words {wd n} n describing the image img, img i determined based on the current neuronal signal sn, sn j .

[0069] In particular, in the case where the content type ty is content related to an image form [f] (notably a word wd, wd i relating to an image form [f]=[fw] or an image img, img i relating to an image shape [f]=[fi]), the RDP request process temporarily stores this content relating to the image shape e, ei: e=wd, e=img, e i =wd i , e i =img i …

[0070] In particular, a set e, ei of data relating to a single piece of content constitutes a description (including a textual description: one or more words and / or a description per image) of the shape of an element of an image to be generated. The data relating to the content includes, in particular, the content itself: for example, a word wd, an image img, or descriptive data about the content: for example, a set of words {wd n} n describing an image (img) as a function of a neuronal signal (sn): .

[0071] In particular, when the content type ty is a parameter [pr] of an image to be generated, the RDP request process involves: associating PR_CP with the parameter data prj, pri ji to a data or set of stored data relating to e, ei to one and the same content, in particular in the temporary storage device TM.

[0072] Thus, it is no longer just a single piece of data or a set of data that will be stored, but a pair or, more generally, a (J+1)-tuple containing, in addition to a stored piece of data or a set of data related to e, {e i} i to a single and the same content one or more parameters pr j , pr iji of the image element to be generated: (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I,

[0073] When the RDP request process uses multiple neural signals sn j To generate the ICGN input command ic, the RDP request process iterates j=j+1 through the previous steps for a new neural signal sn j In particular, the RDP request process includes: check STP? if a new neural signal should be taken into account in the ICGN generation of the current input command ic.

[0074] In a first variant of the STP? check, this STP? check possibly includes a check of the time elapsed between the first neuronal signal sn j=1 taken into account in the ICGN generation of the current input command ic at the current time, if the elapsed time is greater than a predefined time period T. Thus, as long as the elapsed time is not greater than this predefined time period [N], then the RDP request process iterates through the previous steps. Otherwise, that is, as soon as the elapsed time is greater than this predefined time period [Y], then the STP? check determines the end of the consideration of new neural signals in the ICGN generation of the current input command ic. Consequently, as soon as the request process includes at least one iteration j=j+1, then the ICGN generation of the current input command ic is a function of a succession of neural signals {sn j} j>1 .

[0075] In a first variant of the STP? check, this STP? check may include a neural control check. Specifically, the STP? check determines whether the current neural signal sn j corresponds or does not correspond to a command to end the consideration of new neural signals in the ICGN generation of the current input command ic. If not [N], particularly when the current neural signal sn j If the RDP query process corresponds to one or more data points relating to an image to be generated: form [f] or parameter [pr], then the RDP query process iterates through the previous steps. Otherwise, that is, if the current neural signal sn j corresponds or not to a termination command for taking into account new neural signals in the ICGN generation of the current ic input command [Y], then this termination command is implemented.

[0076] In particular, as soon as the STP? check determines the end [Y] of considering new neural signals in the ICGN generation of the current input command ic, the RDP request process includes: reading temporarily stored TRD data, notably in the temporary storage device, such as a set of stored data relating {e i} i to a single content or a (J+1)-tuple comprising, in addition to a stored data item or set of data items, e, {e i} i to a single and the same content one or more parameters pr j , pr iji of the image element to be generated: (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I…

[0077] In particular, the RDP request process includes

[0078] - convert LG_CNV, LG_CNV m the data relating to an image to be generated e, {e i} i , (e, pr), (e, {prk} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of the graphic generation by artificial intelligence GGN, GGN m .

[0079] Specifically, when a single neural signal sn is used to determine the data e relating to a single element of the image to be generated: e=wd, e=img, e={wd n} n The RDP query process does not involve iteration but performs, as soon as this data e is determined (for example, either simply by analyzing the neuronal signal SN_NZ, or after analyzing IMG_NZ of an image img resulting from the interpretation of a neuronal signal sn by analyzing neuronal signals SN_NZ, etc.), the LG_CNV, LG_CNV conversion m of the data relating to the image to be generated e in the input command ic of the graphic generation by artificial intelligence GGN, GGN m .

[0080] In particular, in the case where the RDP request process is configured to generate input commands from several distinct artificial intelligence-based graphical generations (GGN) m Then the RDP request process may include: selecting GGT_SLCT and generating a graphic using artificial intelligence (GGN). m among the several distinct artificial intelligence-powered graphics generations available {GGN m} m .

[0081] Optionally, the GGT_SLCT selection is a function of a neural signal sn from the user U following the end [Y] of taking into account new neural signals in the ICGN generation of the current input command ic and / or data relating to the image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I…, in particular the type of parameterization of the image elements to be generated.

[0082] In particular, the LG_CNV conversion m is a function of the selected artificial intelligence-generated graphics (GGN) m .

[0083] The LG_CNV, LG_CNV conversion m is notably carried out on the basis of a language model such as a large language model, also called LLM or "large language model" in English.

[0084] In particular, the generation of ICGN input commands includes one or more of the following steps: - verifying the type `ty` of content intended by the user; - analyzing an image (`img`, `img`) using IMG_NZ. i determined based on the current neuronal signal sn, sn j of user U; - temporarily store TST data relating to a content function of a neural signal sn, particularly in a temporary storage device TM; - associate PR_CP with the parameter data prj, pri jito a stored data point or set of data points relating to a single content; - check STP? if a new neural signal should be taken into account in the ICGN generation of the current input command ic; - read the temporarily stored data TRD; - convert LG_CNV, LG_CNV m the data relating to an image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of the graphic generation by artificial intelligence GGN, GGN m ;- Select GGT_SLCT, a graphical generation by artificial intelligence GGN m among the several distinct artificial intelligence-powered graphics generations available {GGN m} m .

[0085] In particular, the brain control of graphic generation by BCGN artificial intelligence includes one or more of the following steps: - verify the type ty? of content intended by the user; - analyze IMG_NZ an image img, img i determined based on the current neuronal signal sn, sn j of user U; - temporarily store TST data relating to a content function of a neural signal sn, particularly in a temporary storage device TM; - associate PR_CP with the parameter data prj, pri ji to a stored data point or set of data points relating to a single content; - check STP? if a new neural signal should be taken into account in the ICGN generation of the current input command ic; - read the temporarily stored data TRD; - convert LG_CNV, LG_CNV m the data relating to an image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K )J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of the graphic generation by artificial intelligence GGN, GGN m ;- Select GGT_SLCT, a graphical generation by artificial intelligence GGN m among the several distinct artificial intelligence-powered graphics generations available {GGN m} m ;- generate an ICGN input command from a graphical generation by artificial intelligence GGN, GGN m .

[0086] A particular embodiment of the RDP drawing production request method according to the invention is a program comprising program code instructions for executing the steps of the RDP drawing production request method according to the invention when said program is executed by a processor.

[0087] In particular, a DAP drawing production method is implemented by a communication terminal. This DAP drawing production method comprises: - requesting RDP to produce a drawing according to the invention by providing an input command ic for graphic generation by artificial intelligence GN, GN m ; and- generate GGN, GGN m by artificial intelligence an image dg according to the input command ic provided by the RDP drawing production request.

[0088] In particular, the GGN generation, GGN m Artificial intelligence image generation involves: - either local artificial intelligence generation (LGGN, LGGN) m , notably through an LGGT artificial intelligence graphics generator, LGGT m implemented in the T communication terminal implementing the DAP drawing production process of the dg image,

[0089] - either: + a D_RQ emission of a request to a graphic generator using artificial intelligence GGT, GGT m remote from the communication terminal T implementing the DAP drawing production process of the image dg, the request issued d_rq including in particular the input command generated ic ;+ a reception D_NSW of a response from the graphics generator by artificial intelligence GGT, GGT m remote from the d_rq request issued, the response containing the image generated dg by the GGT artificial intelligence graphics generator, GGT m distant.

[0090] In particular, when generating GGN graphics, GGN mAn image's artificial intelligence can use several distinct graphics generators: local LGTm (implementing local graphics generation LGGNm) or remote GGTm. The GGT_SLCT selection then triggers the provision of the generated input command ic to the selected graphics generator GGTm / LGTm. For example, in the case of a remote graphics generator GGTm, the emitted request d_rq is either addressed to the selected remote graphics generator GGTm, or the emitted request d_rq includes an ggtm identification of the selected remote graphics generator GGTm.

[0091] Thus, the DAP drawing production process provides a dg image generated according to at least one neural signal from a user, including a DRPR visual reproduction device from the communication terminal T.

[0092] A particular embodiment of the DAP drawing production process according to the invention is a program comprising program code instructions for executing the steps of the DAP drawing production process according to the invention when said program is executed by a processor.

[0093] Laillustre a simplified diagram of a drawing production process according to a second variant of the invention.

[0094] The DAP drawing production process includes: - requiring RDP drawing production according to the invention, in particular as illustrated by the diagram - generating GGN, GGN m graphically, using artificial intelligence, an image dg, dg l based on an input command ic provided by the drawing production request.

[0095] In particular, the drawing process involves: capturing a neuronal signal sn, sn (SN_CPT) j , sn l , sn j lof a user U of the communication terminal implementing the drawing production process, the captured neural signal sn, sn j, sn, sn j , sn l , sn j l being provided to the RDP drawing production request to generate the input command ic.

[0096] In particular, the drawing production process includes: visually reproducing the generated image dg, dg using GRPR. l .

[0097] In particular, the drawing production process includes an iteration l=l+1 of the drawing production based on a generated image dg l through the production of drawing during the previous iteration l.

[0098] For example, to generate a first image l=1, user U thinks of a word twd or an image timg (j=1), user U's brain then produces a neural signal sn l=1 , sn j=1 l=1 . This first neuronal signal sn 1 , sn1 1is captured SN_CPT and provided to the RDP drawing production request.

[0099] Optionally, still to generate this first image l=1, user U thinks of one or more other words twd or an image timg (j=2…J), the brain of user U then successively produces one or more other neural signals sn2 1 ,{sn j 1}. Each jth neuronal signal sn j 1 is successively captured SN_CPT and provided to the RDP drawing production request.

[0100] The RDP production request generates an initial input command. l=1 based on a neuronal signal sn 1 or several successive neuronal signals {sn j 1} j .

[0101] Next, the graphic generation by artificial intelligence GGN, GGN m generates a first image dg l=1depending on the first input command ic1, itself a function of one or more neural signals sn 1 ,{sn j 1} j .

[0102] And, the graphic generation by artificial intelligence GGN, GGN m provides the first generated image dg 1à la reproduction visuelle GRPR. Ainsi la première image générée dg1est reproduite par exemple sur un écran, par un dispositif de réalité immersive, tel qu’un casque ou des lunettes RA, RV ou RX, par un projecteur 2D, 3D, holographique…

[0103] User U viewing the first generated image dg l=1 Users may want to modify the reproduced image. Therefore, to generate a new image l=l+1=2 following this first generated image dg1, user U looks at the first generated image dg1 reproduced (j=1). User U's brain then produces a neural signal sn j=1 l=2 . This first neuronal signal sn1 2 is captured SN_CPT and provided to the RDP drawing production request.

[0104] Then, still to generate this second image l=2, user U thinks of one or more other words twd or an image timg (j=2…J) depending on the desired modification(s); user U's brain then successively produces one or more other neural signals sn2 2 ,{sn j 2}. Each jth neuronal signal sn j 2 is successively captured SN_CPT and provided to the RDP drawing production request.

[0105] The RDP production request generates a second input command. l=2 based on these successive neuronal signals {sn j 2} j .

[0106] Next, the graphic generation by artificial intelligence GGN, GGN m generates a second image dg l=2 depending on the second input command ic2, itself a function of these neural signals {sn j 2} j .

[0107] And, the graphic generation by artificial intelligence GGN, GGN m provides the second generated image dg 2à la reproduction visuelle GRPR. Ainsi la deuxième image générée dg2est reproduite par exemple sur un écran, par un dispositif de réalité immersive, tel qu’un casque ou des lunettes RA, RV ou RX, par un projecteur 2D, 3D, holographique…

[0108] If user U is viewing the l-th generated image dg l If the reproduced version is to be modified, the above modification steps are repeated as many times as necessary l=3…L.

[0109] The invention also relates to a storage medium. The information storage medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, for example a CD-ROM or a microelectronic circuit ROM, or a magnetic recording means, for example a floppy disk or a hard disk drive.

[0110] On the other hand, the information medium can be a transmissible medium such as an electrical or optical signal that can be transmitted via an electrical or optical cable, by radio, or by other means. The program according to the invention can, in particular, be downloaded onto a network, notably the Internet.

[0111] Alternatively, the information carrier may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the process in question.

[0112] In another implementation, the invention is implemented using software and / or hardware components. In this context, the term "module" can refer to either a software component or a hardware component. A software component corresponds to one or more computer programs, one or more subroutines of a program, or more generally, any element of a program or software capable of implementing a function or set of functions as described above. A hardware component corresponds to any element of a hardware assembly capable of implementing a function or set of functions.

[0113] In an unillustrated variant of the invention, a communication terminal includes a processor capable of implementing the RDP request process and / or the DAP drawing production process according to the invention.

[0114] Laillustre a simplified diagram of a communication terminal according to the invention.

[0115] The communication terminal 1 comprises: - a drawing production query 15, 145 capable of generating an input command ic from a graphics generator 140, 4 by artificial intelligence, the input command ic being generated according to a neural signal sn, sn j of a user U ;- a visual reproduction device 11 capable of reproducing the image generated dg provided by the graphic generator 140, 4 by artificial intelligence according to the input command generated ic.

[0116] In particular, the communication terminal 1 includes a device 12 for obtaining neural signals from the user U.

[0117] In particular, the neuronal signal acquisition device 12 is one of the following: - a receptor 12 R of neuronal signals sn, sn jconnected to a direct neural interface 2 worn by the user U; - a direct neural interface 12 I user-supported U.

[0118] In particular, the direct neural interface 12 I , 2 includes one or more electrodes arranged on the direct neural interface 12 I , 2 so as to be positioned relative to predefined areas of the user's skull U when wearing the direct neural interface 12 I , 2 from the following areas:- Broca's area,- visual area,- occipital lobe,- etc.

[0119] In particular, the communication terminal 1 includes at least one of the following devices: - a communication interface 13 with the graphic generator 4 by artificial intelligence; - the graphic generator 140 by artificial intelligence.

[0120] In particular, a direct neural interface 2, 12 Icarried by a user U from the communication terminal 1 provides at least one current neural signal sn, snj from user U captured by the direct neural interface 2, 12 I .

[0121] In particular, requester 15, 145 of drawing production includes:

[0122] An input command generator 155 for a graphics generator 140, 4 using artificial intelligence. The input command generator 155 is specifically capable of generating an input command for a graphics generator 140, 4 using artificial intelligence.

[0123] In particular, requester 15, 145 for drawing production includes or consists of:

[0124] A brain controller for a 140.4 graphics generator, powered by artificial intelligence based on a user's current neural signal. Specifically, the brain controller for a graphics generator is capable of generating an input command for the 140.4 graphics generator using artificial intelligence based on a user's current neural signal.

[0125] In particular, the drawing production query 15, 145 includes a neural signal interface 151, for example a neural signal receiver.

[0126] In particular, requester 15, 145 of drawing production includes:

[0127] a 152 device for analyzing neural signals sn, sn j provided by a direct neural interface 2.12 I , the neural signal analysis device 152 being capable of determining a content wd(sn), img(sn), pr(sn), pr k (s k ), wd i (sn i ), img i (sni ), pr iki (sn ki ), i <ki<i+1pensé par l’utilisateur U.

[0128] In particular, the neural signal analysis device 152 is capable of using a neural signal database 153 in which the neural signals sn, sn j are associated, prior to the implementation of the drawing production query 15, 145, with wd, img, pr content during a learning of the user's thought U.

[0129] In particular, the neural signal analysis device 152 is capable of interpreting neural signals sn, sn j by means of a first artificial intelligence device, in particular following prior learning the implementation of the query 15, 145 of drawing production.

[0130] In particular, the brain controller includes the 152 device for analyzing neural signals.

[0131] In particular, the neural signal interface 151 provides the neural signals sn, sn j obtained, received from the direct neural interface 2, 12 I from user U to the neural signal analysis device 152.

[0132] In particular, requester 15, 145 of drawing production includes:

[0133] a 1550 content type checker thought of by user U, including the content type determined by the neural signal analysis device 152.

[0134] In particular, requester 15, 145 of drawing production includes:

[0135] an image analyzer 1551.

[0136] The 1551 image analysis device is capable of analyzing img, img images i determined based on the current neuronal signal sn, snj of user U in the case where verifier 1550 determines that the content type ty is an image: ty=fi.img, The determined image img, img iis an image determined by the 152 neural signal analysis device.

[0137] In particular, the analyzed image img, img i by the 1551 image analysis device is an image associated with the current neuronal signal sn of the user U.

[0138] In particular, the 1551 image analysis device is capable of determining a set of words {wd n} n describing the image img, img i determined based on the current neuronal signal sn, sn j .

[0139] In particular, requester 15, 145 of drawing production includes:

[0140] a temporary recording device 1554 of data relating to a content function of a neuronal signal sn in particular in a temporary storage device 1553, such as a temporary memory, a temporary content base… The temporary recording device 1554 is capable of temporarily storing this data relating to a content when the user thinks of several words, images to trigger the generation of an image GGN, GGNm.

[0141] In particular, the temporary recording device 1554 is capable of temporarily storing a set of words {wd n} n describing the image img, img i determined based on the current neuronal signal sn, sn j in the case where the content type ty is an image [fi].

[0142] In particular, the temporary recording device 1554 is capable of temporarily storing content relating to the image form e, ei: e=wd, e=img, e i =wdi , e i =img i … in the case where the type of this content ty is content relating to an image form [f] (in particular a word wd, wd i relating to an image form [f]=[fw] or an image img, img i relative to an image form [f]=[fi]),

[0143] In particular, the drawing production query 15, 145 includes: a coupler 1552 capable of associating, in the case where the content type ty is a parameter data [pr] of an image to be generated, a parameter data prj, pri ji to a data or set of stored data relating to e, ei to one and the same content, in particular in the temporary storage device 1553.

[0144] Thus, it is no longer just a single piece of data or a set of data that will be stored, but a pair or, more generally, a (J+1)-tuple containing, in addition to a stored piece of data or a set of data related to e, {e i} ito a single and the same content one or more parameters pr j , pr iji of the image element to be generated: (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I,

[0145] When the drawing production query 15, 145 is capable of using multiple neural signals sn j To generate ICGN, the input command ic, then the drawing production query 15, 145 includes: an iteration trigger 1555 capable of iterating j=j+1, the use of the previous devices for a new neural signal sn j .

[0146] Specifically, iteration trigger 1555 is capable of triggering sn_trg to take into account a new neuronal signal sn j=j+1 For example, iteration trigger 1555 is capable of triggering sn_trg to obtain a new neuronal signal sn j=j+1by the neuronal signal acquisition device 12, such as capture by the direct neuronal interface 12I or reception by the neuronal signal receptor 12R of the new neuronal signal sn j=j+1 .

[0147] In particular, the drawing production query 15, 145 includes: a checker 1555 capable of checking whether a new neural signal should be taken into account in the generation of the current input command ic by the drawing production query 15, 145.

[0148] In particular, the drawing production query 15, 145 includes: a data reader 1556. The data reader 1556 is capable of reading, as soon as the verifier 1555 determines the end of the consideration of new neural signals in the generation of the current input command ic, data temporarily stored, in particular in the temporary storage device 1553, such as a set of stored data relating {e i} ito a single content or a (J+1)-tuple comprising, in addition to a stored data item or set of data items, e, {e i} i to a single and the same content one or more parameters pr j , pr iji of the image element to be generated: (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I…

[0149] In particular, requester 15, 145 of drawing production includes:

[0150] - a 1557 converter capable of converting data relating to an image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of a graphic generator by artificial intelligence 140, 4.

[0151] Specifically, when a single neural signal sn is used to determine the data e relating to a single element of the image to be generated: e=wd, e=img, e={wdn} n , the drawing production query 15, 145, in particular the iteration trigger 1555, is capable of not implementing an iteration but provides the data e, as soon as it is determined (for example, either simply by the neural signal analyzer 152, or by the analyzer 1551 of an image img resulting from the interpretation of a neural signal sn by the neural signal analyzer 152, etc.), to the converter 1557 of data relating to the image to be generated e into the input command ic of the graphic generation by artificial intelligence 140, 4.

[0152] In particular, in the case where the drawing production query 15, 145 is configured to generate input commands from several distinct artificial intelligence graphics generators (not shown), then the drawing production query 15, 145 may include: a selector (not shown) of an artificial intelligence graphics generator from among the several distinct artificial intelligence graphics generators available.

[0153] Optionally, the selector is capable of performing the selection based on a neural signal sn from the user U following the completion of the processing of new neural signals in the generation of the current input command ic and / or the data relating to the image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I…, in particular the type of parameterization of the image elements to be generated.

[0154] In particular, the 1557 converter is capable of implementing a conversion function of the selected AI-powered graphics generator.

[0155] In particular, the input control generator 155 of a graphics generator 140, 4 by artificial intelligence includes one or more of the following devices: - the user-defined content type checker 1550 U; - the image analyzer 1551 capable of analyzing an image img, img i determined based on the current neuronal signal sn, sn j of the user U; - the temporary recording device 1554 capable of temporarily storing data relating to a content function of a neuronal signal sn in particular in a temporary storage device 1553; - the temporary storage device 1553;

[0156] - the 1552 coupler capable of associating the parameter data prj, pri jito a stored data item or set of stored data relating to a single content; - the checker 1555 capable of determining whether a new neural signal should be taken into account in the generation of the current input command ic; - the data reader 1556 capable of reading temporarily stored data; - the converter 1557 capable of converting the data relating to an image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of the graphics generator 140, 4 by artificial intelligence;- the selector capable of selecting a graphics generator by artificial intelligence from among the several distinct graphics generators by artificial intelligence available.

[0157] In particular, the brain controller (not illustrated) of a graphics generator 140, 4 by artificial intelligence includes one or more of the following devices:- the neural signal interface 151;

[0158] - the neural signal analyzer 152; - the neural signal database 153;

[0159] - the input control generator 155 of a graphic generator 140, 4 by artificial intelligence;

[0160] - the 1550 user-defined content type checker U; - the 1551 image analyzer capable of analyzing an image img, img i determined based on the current neuronal signal sn, sn j of the user U; - the temporary recording device 1554 capable of temporarily storing data relating to a content function of a neuronal signal sn in particular in a temporary storage device 1553; - the temporary storage device 1553;

[0161] - the 1552 coupler capable of associating the parameter data prj, pri ji to a stored data item or set of stored data relating to a single content; - the checker 1555 capable of determining whether a new neural signal should be taken into account in the generation of the current input command ic; - the data reader 1556 capable of reading temporarily stored data; - the converter 1557 capable of converting the data relating to an image to be generated e, {e i} i , (e, pr), (e, {pr k} k=1…K ) J=K , {(e i ,pr i )}i=1…I, J=I… in an input command ic of the graphics generator 140, 4 by artificial intelligence;- the selector capable of selecting a graphics generator by artificial intelligence from among the several distinct graphics generators by artificial intelligence available.

[0162] The drawing production query 15, 145 and / or the input command generator 155 and / or the converter 1557 is capable of providing the input command ic as a function of at least one current neural signal sn, {sn j} j from user U to the 140, 4 graphic generator by artificial intelligence.

[0163] In particular, the communication interface 13 is capable of relaying the input command ic provided by the drawing production requester 15, 145 and / or the input command generator 155 and / or the converter 1557 to a graphics generator 4 by artificial intelligence separate from the communication terminal 1.

[0164] In particular, the communication interface 13 is capable of relaying the input command ic provided by the drawing production requester 15, 145 and / or the input command generator 155 and / or the converter 1557 to a graphics generator 4 by artificial intelligence remote from the communication terminal 1 via a communication network 3.

[0165] In particular, the communication interface 13 is capable of transmitting the input command ic provided by the drawing production requester 15, 145 and / or the input command generator 155 and / or the converter 1557 to a graphics generator 4 by remote artificial intelligence from the communication terminal 1 via a communication network 3.

[0166] In particular, communication interface 13 is capable of receiving a generated dg, dg image lby the graphic generator 4 by remote artificial intelligence from the communication terminal 1 via a communication network 3 according to the previously relayed input command ic, issued by the communication interface 13.

[0167] In particular, the communication interface 13 includes a transmitter 130 and a receiver 131. The transmitter 130 is notably capable of transmitting the input command ic provided by the drawing production requester 15, 145 and / or the input command generator 155 and / or the converter 1557 to a graphics generator 4 by artificial intelligence remote from the communication terminal 1 via a communication network 3.

[0168] Receiver 131 is notably capable of receiving a dg-generated image. lby the graphic generator 4 by artificial intelligence remote from the communication terminal 1 via a communication network 3 according to the previously relayed input command ic, issued by the transmitter 130.

[0169] In particular, the 140,4 graphic generator by artificial intelligence is a generator by generative adversarial networks or GANs (Generative Adversarial Networks).

[0170] In particular, the reproduction device 11 is capable of reproducing the generated image dg, dg l provided by the graphics generator 140, 4 by artificial intelligence, where appropriate via the communication interface 13 and the communication network 3.

[0171] Figures 4a and 4b illustrate two use cases of the invention during a simple query.

[0172] Laillustre a simplified diagram of a first use case of the invention during a simple query triggered by the thought of a word.

[0173] User U is thinking of a word twd. In our example, it is the word car. The direct neural interface 12 I , 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 according to the invention.

[0174] The communication terminal 1 generates, based on the captured neural signal sn, an input command ic of an artificial intelligence graphics generator which, following this input command ic, produces a generated image dg.

[0175] The communication terminal 1, in particular the reproduction device 11 of this communication terminal 1, then reproduces the image thus generated dg. In this case, the reproduction device 11 of the communication terminal 1 reproduces an image of a car generated by artificial intelligence.

[0176] Laillustre a simplified diagram of a second use case of the invention during a simple request triggered by the thought of an image.

[0177] User U thinks of an image. In our example, this is an image of a car; that is, they visualize any car in their mind. The direct neural interface 12 I , 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 according to the invention.

[0178] The communication terminal 1 generates, based on the captured neural signal sn, an input command ic of an artificial intelligence graphics generator which, following this input command ic, produces a generated image dg.

[0179] The communication terminal 1, in particular the reproduction device 11 of this communication terminal 1, then reproduces the image thus generated dg. In this case, the reproduction device 11 of the communication terminal 1 reproduces an image of a car generated by artificial intelligence.

[0180] Figures 5a and 5b illustrate two use cases of the invention during a complex query.

[0181] Laillustre a simplified diagram of a third use case of the invention during a complex query triggered by several successive thoughts relating to the same element of an image to be generated.

[0182] Initially, t1, user U thinks of a first image, timg1. In our example, this is an image of a car; that is, they visualize any car in their mind. The direct neural interface 12 I, 2 captures the current neuronal signal during (i.e., during) this thought of the user U and provides it to the communication terminal 1 according to the invention as the first neuronal signal sn1 relating to the image to be generated.

[0183] The communication terminal 1 stores the image and / or words describing the image determined according to the first neuronal signal captured sn1 as the first image thought timg1 by the user U.

[0184] Then, in a second step t2, user U thinks of a second image timg2. In our example, this is a colored image (in this example, the color green), meaning they visualize this color in their mind. The direct neural interface 12 I , 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 as a second neuronal signal sn2 related to the image to be generated.

[0185] The communication terminal 1 stores a color parameter (determined based on the second captured neuronal signal sn2 as the second thought image timg2 by the user U) in association with the previously stored data, in this case the image and / or words describing the image determined based on the first captured neuronal signal sn1 as the first thought image timg1 by the user U.

[0186] And, in a third step t3, user U has a third thought. In this case, they think of a word twd3. In our example, it's a word describing a style: sty (in this example, graffiti style). The direct neural interface 12 I , 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 as a third neuronal signal sn3 related to the image to be generated.

[0187] The communication terminal 1 stores a style parameter (determined based on the third captured neural signal sn3 as the third thought twd3 of user U) in association with the previously stored data, in this case the image and / or words describing the image determined based on the first captured neural signal sn1 as the first thought image timg1 by user U, and the color parameter.

[0188] In an unillustrated example, a single neural signal is used instead of the second and third neural signals to determine both the color and style of the image to be generated.

[0189] Finally, in a fourth step:

[0190] The communication terminal 1 generates, based on the stored data (in this case, the image and / or words describing the determined image, i.e., a car in this example, based on the first neural signal captured sn1 as the first image thought of timg1 by user U; and the associated parameters in our example, namely the color parameter - in this example: green - determined based on the second neural signal captured sn2 as the second image thought of timg2 by user U, and the style parameter - in this example: graffiti - determined based on the third neural signal captured sn3 as the third thought of user U twd3), an input command ic from an artificial intelligence graphics generator which, following this input command ic, produces a generated image dg. The input command ic is, in particular, a "prompt" such as: "green car in graffiti style".

[0191] The communication terminal 1, in particular the reproduction device 11 of this communication terminal 1, then reproduces the image thus generated dg. In this case, the reproduction device 11 of the communication terminal 1 reproduces an image of a green car in graffiti style generated by artificial intelligence.

[0192] Laillustre a simplified diagram of a fourth use case of the invention during a complex query triggered by several successive thoughts relating to distinct elements of an image to be generated.

[0193] Initially, t1, user U thinks of a first image, timg1. In our example, this is an image of a car; that is, they visualize any car in their mind. The direct neural interface 12 I, 2 captures the current neuronal signal during (i.e., during) this thought of the user U and provides it to the communication terminal 1 according to the invention as the first neuronal signal sn1 relating to the image to be generated.

[0194] The communication terminal 1 stores the image and / or words describing the image determined according to the first neuronal signal captured sn1 as the first image thought timg1 by the user U.

[0195] Then, in a second step t2, the user U thinks of a second image timg2. In our example, this is the plane of the image in which the previous shape must be positioned (in this example, the foreground); that is, they visualize this position in their mind. The direct neural interface 12 I, 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 as a second neuronal signal sn2 related to the image to be generated.

[0196] The communication terminal 1 stores the plan parameter (determined based on the second captured neuronal signal sn2 as the second thought image timg2 by the user U) in association with the previously stored data, in this case the image and / or words describing the image determined based on the first captured neuronal signal sn1 as the first thought image timg1 by the user U.

[0197] Then, in a third step t3, user U has a third thought. In this case, they think of a third image timg3. In our example, this is an image of the sea; that is, they visualize any sea in their mind. The direct neural interface 12 I, 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 as a third neuronal signal sn3 related to the image to be generated.

[0198] The communication terminal 1 stores the image and / or words describing the image determined according to the third neuronal signal captured sn1 as the third image thought timg3 by the user U.

[0199] And, in a fourth step t4, user U thinks of a fourth image timg4. In our example, this is the image plane in which the previous shape, in this case the sea, must be positioned in the image (in this example, background); that is, they visualize this position in their mind. The direct neural interface 12 I, 2 captures the current neuronal signal sn during (that is to say during) this thought of the user U and provides it to the communication terminal 1 as a fourth neuronal signal sn4 related to the image to be generated.

[0200] The communication terminal 1 stores the plan parameter (determined based on the fourth captured neuronal signal sn4 as the fourth thought image timg4 by the user U) in association with the previously stored data, in this case the image and / or words describing the image determined based on the third captured neuronal signal sn3 as the third thought image timg3 by the user U.

[0201] Finally, in a fifth step t5:

[0202] The communication terminal 1 generates, based on the stored data (in this case, images and / or words describing the determined images, i.e., a car and the sea in this example, respectively based on the first and third captured neural signals sn1 and sn3 as the first and third images thought of by user U; and the associated parameters in our example, namely the respective shot parameters - in this example: foreground for the car and background for the sea - determined respectively based on the second and fourth captured neural signals sn2 and sn4 as the second and fourth images thought of by user U), an input command ic from an artificial intelligence graphics generator which, following this input command ic, produces a generated image dg. The input command ic is notably a "prompt" such as: "car in the foreground and sea in the background".

[0203] The communication terminal 1, in particular the reproduction device 11 of this communication terminal 1, then reproduces the image thus generated dg. In this case, the reproduction device 11 of the communication terminal 1 reproduces an image of a car in front of the sea generated by artificial intelligence.

[0204] The invention allows, in particular, the generation by thought of: - simple drawings with a single shape: primitive shapes (circle, square, sphere, cube…) or complex shapes (car, house, object, etc.) - complex drawings (landscapes, portraits, posters…) using colors such as primary colors, and / or complementary colors and / or colors from among 10, 50, 256… colors with particular styles (comics, science fiction, painting, photorealistic…).

[0205] The invention may also generate an image, such as a scene, from the user's thoughts, including a standardized object like an 8mm bolt, an E27 light bulb, or a referenced piece of furniture. The generated image could be an industrial drawing, an assembly diagram, or even a 2D or 3D interior design plan.

[0206] The neural signal analyzer may eventually draw from a database of 2D or 3D drawings to provide the thought image that allows the artificial intelligence generator to control the image of the scene.

[0207] This invention makes it possible to draw by thought thanks to a direct neural interface, notably based on the user's EEG or electroencephalogram.

[0208] This invention is a design tool accessible to a wider audience than just professionals or experienced amateurs for image creation. It also allows for the generation of sketches from a simple idea.

[0209] "A picture is worth a thousand words." This saying will be all the easier to verify by using invention in communication.

[0210] Finally, since the control is neural, the invention allows a disabled user to access image generation by artificial intelligence.

Claims

Drawing production request method implemented by a communication terminal, said request method comprising: generating an input command for graphic generation by artificial intelligence, the input command being generated based on a current neural signal from a user. A query method according to the preceding claim, wherein the query method comprises: receiving a neural signal captured by a direct neural interface worn by the user. A query method according to any one of the preceding claims, wherein the input command is generated based on a succession of neural signals from the user, each neural signal corresponding to at least one element of the image to be generated. A query method according to the preceding claim, wherein the sequence of user neural signals corresponds to a set of neural signals among the following: - a sequence of neural signals during a predefined time period; - a sequence of neural signals interrupted by an end command; - a sequence of neural signals ending with a neural signal associated with an end command. A query method according to any one of the preceding claims, wherein the input command, which is a function of a neural signal, comprises at least one of the following: - a word, which is a function of a current neural signal of the user thinking about a word; - a word or several words, which are a function of a current neural signal of the user thinking about an image; - an image, which is a function of a current neural signal of the user thinking about a word; - an image, which is a function of a current neural signal of the user thinking about an image. A query method according to any one of the preceding claims, wherein, when the user thinks of an image, the generation of the input command comprises: - analyzing the image associated with the user's current neural signal, the analysis determining a set of words describing the image; - converting the resulting set of words from the analysis into an input command for the AI-generated graphics. A query method according to any one of the preceding claims, wherein the query method comprises: generating a subcommand of the input command by associating with the input command triggering the generation of an image by an artificial intelligence graphics generator a parameterization of at least one element of the image to be generated according to a neural signal from the user. Drawing production method implemented by a communication terminal, said drawing production method comprising: - requesting a drawing production according to one of the preceding claims by providing an input command for graphic generation by artificial intelligence; and - generating by artificial intelligence an image according to the input command provided by the drawing production request. Program comprising program code instructions for executing the steps of the drawing production request process according to any one of claims 1 to 7 and / or the drawing production process according to the preceding claim when said program is executed by a processor. Communication terminal comprising: - a drawing production requester capable of generating an input command from a graphic generator by artificial intelligence, the input command being generated according to a neural signal from a user; - a visual reproduction device capable of reproducing the generated image provided by the graphic generator by artificial intelligence according to the generated input command. Communication terminal according to the preceding claim, wherein the communication terminal includes a device for obtaining neural signals from the user. Communication terminal according to the preceding claim, wherein the neural signal acquisition device is one of the following: - a neural signal receiver connected to a user-worn direct neural interface; - a user-worn direct neural interface. Communication terminal according to the preceding claim, wherein, when the signal acquisition device is a user-worn direct neural interface, the direct neural interface comprises several electrodes arranged on the direct neural interface so as to be positioned relative to predefined areas of the user's skull when wearing the direct neural interface among the following areas: - Broca's area, - visual area, - occipital lobe. Communication terminal according to any one of claims 10 to 13, wherein the communication terminal comprises at least one of the following devices: - a communication interface with the artificial intelligence graphics generator; - the artificial intelligence graphics generator.

Images