Artificial intelligence based imaging system and method for interacting with individuals via a web environment - Patents.com

Abstract

An artificial intelligence-based system and method for interacting with an individual via a web environment is described. A provisioning server is configured to receive a request from a web browser executing on a client device for a predefined imaging code stored in a memory of the provisioning server. The provisioning server responds to the request by forwarding the predefined imaging code to the web browser. The predefined imaging code is configured to be executed by the client device to render an interactive graphical user interface (GUI) within the web browser on a display of the client device, load one or more images of the individual into a memory of the client device, determine one or more person attributes of the individual based on an image analysis of the one or more images of the individual, and render the one or more person attributes of the individual within the interactive GUI.

Description

[Technical field]

[0001] This disclosure generally relates to artificial intelligence based Imaging The present invention relates to a system and method, more particularly to an artificial intelligence based method for interacting with individuals via a web environment. Imaging The present invention relates to a system and method. [Background technology]

[0002] Traditional web-based communications typically require a web browser, or a native application (app) running on a client device's native operating system, to perform multiple back-and-forth, round-trip communications with a server to operate. Such communications typically require the client device to offload complex tasks, and may require extensive processing and memory resources for the server, since the server typically has more computer processing resources to handle such tasks when compared to traditional client device tasks. However, such back-and-forth, round-trip communications may create several issues related to performance and security. First, the performance of a traditional web browser or native app depends on the speed, latency, and potential errors of the computer network over which it communicates, in which case the web browser or native app may not be able to load or operate over a bad or impaired network or connection. Furthermore, a bad connection may cause a traditional web application to perform at a reduced speed or capacity. This is especially true for software applications that transmit or use large files or information, such as images or videos. For example, images and / or video generally require a large network bandwidth and may cause network delays, and without such a large bandwidth, both the client and server sides may experience delays and / or errors. Some native apps can process images, but such native apps are undesirable because they require at least two different development operating systems and must check compatibility across multiple operating systems and multiple devices with various hardware to operate effectively.For these reasons, native apps may introduce errors, may not be maintained, and must be reviewed and approved by a third-party platform vendor (e.g., the APPLE APP STORE or GOOGLE PLAY STORE), which would otherwise require constant versioning and maintenance and may require distribution fees. Additionally, consumers are becoming increasingly reluctant to download non-essential native apps, further reducing the desirability of such native apps.

[0003] Additional problems arise because traditional web-based communications are not secure. Although security-based protocols such as Transport Layer Security (TLS) and secure sockets layer (SSL) can improve the security of web-based communications, such protocols are not necessarily secure and can be tampered with or accessed, allowing cyber attackers to access the underlying information of intercepted web-based communications. Such cyber attacks are particularly problematic with respect to personal information (e.g., personally identifiable information (PII)), where users and the companies that provide the websites or servers with which users communicate are typically very sensitive about the protection and privacy of personal information. This therefore indicates that personal information is becoming increasingly important, especially as it is a primary target of privacy regulations. Summary of the Invention [Problem to be solved by the invention]

[0004] For the aforementioned reasons, artificial intelligence-based systems for interacting with individuals via web environments Imaging A system and method are needed. [Means for solving the problem]

[0005] Generally, an artificial intelligence-based system for interacting with an individual via a web environment, as described herein, Imaging The systems and methods provide a more secure environment for processing personal information, such as PII. Generally, as described herein, PII is data that can potentially be used to identify a particular individual or person. Examples of PII include an individual's full name, social security number, driver's license number, bank account number, passport number, and / or email address. PII may also include images of an individual. For example, such PII may include "selfie" images, and the like.

[0006] As described herein, the artificial intelligence-based Imaging With the systems and methods, private information does not have to be transmitted over a computer network and is therefore not susceptible to interception by cyber attacks. In particular, the disclosed systems and methods can operate without requiring a constant connection to the Internet, where only the initial connection or transmission (which does not necessarily involve the transfer of private information) occurs over a predefined Imaging It is necessary to transmit the code and execute it on the client device. Any private information, such as PII, is not included in the predefined Imaging The code may be stored or processed locally on a user's client device. Imaging The systems and methods provide artificial intelligence image analysis to generate user and / or user-specific product recommendations, providing more powerful capabilities by enhancing, annotating, and / or performing image analysis on images (e.g., selfies).

[0007] More specifically, as described herein, Imaging The system is configured to interact with an individual via a web environment. ImagingThe system includes a provisioning server having one or more processors and one or more memories. The provisioning server may be configured to respond to requests from a web browser executing on a client device. Additionally, the provisioning server may include a predefined Imaging The code may be stored in one or more memories of the server. The computer processing instructions may be configured to execute on one or more processors of the provisioning server. The computer processing instructions may cause the provisioning server to, upon receiving a request from the web browser, Imaging It allows you to transfer a code to a web browser. Imaging The code is predefined Imaging When the code is received by the web browser, it may be configured to be stored by the web browser in the memory of the client device. Imaging The code is predefined Imaging Once the code is received by the web browser, it may be configured to be executed by a client processor of the client device. Imaging The code, upon execution by a client processor, may be configured to render an interactive graphical user interface (GUI) in a web browser on a display of a client device, load one or more images of the individual into a memory of the client device, determine one or more person attributes of the individual based on an image analysis of the one or more images of the individual, and render the one or more person attributes of the individual in the interactive GUI. Additionally or alternatively, in some embodiments, a predefined Imaging The code may be executed within a web browser without communicating with a provisioning server. Still further, additionally or alternatively, in some embodiments, a predefined Imaging The code may be cached in the memory of the client device, where it is stored in a predefined ImagingThe code is permitted to access at least a portion of the memory as allocated to the web browser.

[0008] Further, as described herein, an artificial intelligence based system for interacting with an individual via a web environment may be provided. Imaging A method is disclosed. Imaging The method is predefined in the provisioning server. Imaging receiving a request from a web browser for code, the provisioning server receiving a predefined Imaging Access memory that stores code. Intelligence-based Imaging The method is that the provisioning server responds to the request with a predefined Imaging The method further includes transmitting the code to a web browser, the web browser being executable on the client device. Imaging The code is predefined Imaging When the code is received by the web browser, it may be configured to be stored by the web browser in the memory of the client device. Imaging The code is predefined Imaging Once the code is received by the web browser, it may be configured to be executed by a client processor of the client device. Imaging The method is predefined Imaging The method may further include using the code to render a GUI in a web browser on a display of the client device. Imaging The method is predefined Imaging The method may further include loading, using the code, into a memory of the client device, one or more images of the individual. Imaging The method includes, based on image analysis of one or more images of an individual, Imaging The method may further include using the code to determine one or more personality attributes of the individual. Imaging The method is predefined ImagingThe present invention may further include using the code to render one or more person attributes of the individual in an interactive GUI. Additionally or alternatively, in some embodiments, a predefined Imaging The code may be executable within a web browser without communicating with a provisioning server. Still further, additionally or alternatively, in some embodiments, a predefined Imaging The code may be cached in the memory of the client device and stored in a predefined Imaging The code is permitted to access at least a portion of the memory as allocated to the web browser.

[0009] Further, as described herein, artificial intelligence-based Imaging The system is configured to interact with an individual via a web environment. Imaging The system includes a provisioning server having one or more processors and one or more memories. The provisioning server may be configured to respond to requests from a web browser executing on a client device. Additionally, the provisioning server may include a predefined Imaging The code may be configured to be stored in one or more memories of the server. Still further, the computer processing instructions may be configured to execute on one or more processors of the provisioning server, the computer processing instructions configured to cause the provisioning server to, upon receiving a request from the web browser, generate a predefined Imaging It can also forward predefined code to a web browser. Imaging The code is predefined Imaging When the code is received by the web browser, it may be configured to be stored by the web browser in a memory of the client device. Imaging The code is predefined Imaging Once the code is received by the web browser, it may be configured to be executed by a client processor of the client device. ImagingThe code, upon execution by a client processor, may be configured to render a GUI in a web browser on a display of a client device, load one or more images of the individual into a memory of the client device, determine one or more person attributes of the individual based on an image analysis of the one or more images of the individual, and render the one or more person attributes of the individual in the interactive GUI. Additionally or alternatively, in some embodiments, the one or more person attributes may include one or more facial features, one or more mouth features, or one or more hair-based features of the individual.

[0010] Further, as described herein, an artificial intelligence based system for interacting with an individual via a web environment may be provided. Imaging A method is disclosed. Imaging The method is predefined in the provisioning server. Imaging receiving a request from a web browser for code, the provisioning server receiving a predefined Imaging Access memory that stores code. Intelligence-based Imaging The method is predefined Imaging The method further includes responding to the request by the provisioning server by forwarding the code to a web browser, the web browser being executable on the client device. Imaging The code is predefined Imaging When the code is received by the web browser, it may be configured to be stored by the web browser in the memory of the client device. Imaging The code is predefined Imaging Once the code is received by the web browser, it may be configured to be executed by a client processor of the client device. Imaging The method is predefined Imaging The method may further include using the code to render a GUI in a web browser on a display of the client device. ImagingThe method is predefined Imaging The method may further include loading, using the code, into a memory of the client device, one or more images of the individual. Imaging The method includes, based on image analysis of one or more images of an individual, Imaging The method may further include using the code to determine one or more personality attributes of the individual. Imaging The method is predefined Imaging The method may further include using the code to render one or more person attributes of the individual within the interactive GUI. Additionally or alternatively, in some embodiments, the one or more person attributes may include one or more facial features, one or more mouth features, or one or more hair-based features of the individual.

[0011] According to the disclosure hereinabove, for example, the security of the client device may be predefined. Imaging To illustrate the improvements to the client device when enhanced by the use of code, the present disclosure includes improvements to computer functionality, or at least other technology. Imaging The code, once received and executed by the client device, is sufficient in itself to not transmit private or personal information of the user over a public network (e.g., the Internet). This prevents such information from being intercepted by cyber attackers. That is, the present disclosure describes an improvement in the functionality of the computer itself or "any other technology or technical field" since the data is secure because it does not have to be transmitted over a network that has inherent security vulnerabilities. This is an improvement over the prior art, at least because traditional web communications are subject to security issues / cyber attacks and can be intercepted and compromised even when transmitted over secure protocol standards such as SSL and / or TLS.

[0012] Further, for example, the client device may be ImagingThrough the use of code that runs exclusively, or at least mostly, on the client device, higher resolution Imaging Because the present disclosure describes how the present disclosure can provide analytics, the present disclosure includes improvements in computer capabilities, or at least improvements over other technologies. These advantages may be particularly important for apps where images and other personal information are required during the operation of the app, and where the app uses large files (e.g., images) and requires extensive processing. Imaging Using the code, faster data processing is achieved since the client device no longer experiences delays from typical network communications, especially over the transmission of large files or data, e.g., image data. Imaging With the code, the client device does not need a permanent Internet connection. In various embodiments, after being transmitted and loaded (e.g., once each time before use), the predefined Imaging The code can then run with little or no data connection to the server.

[0013] Further, the present disclosure provides a method for detecting and detecting a variety of well-understood, routine, special features outside of traditional activities in the field, or specific useful applications, such as artificial intelligence-based systems for interacting with individuals via a web environment as described herein. Imaging The systems and methods include adding non-conventional steps that limit the scope of the claims.

[0014] The advantages will become more apparent to those skilled in the art from the following description of preferred embodiments shown and described by way of example. As will be understood, the present embodiments may enable other and different embodiments, and their details may be modified in various aspects. Accordingly, the drawings and description should be regarded as illustrative in nature, and not as restrictive. [Brief description of the drawings]

[0015] The figures described below depict various aspects of the systems and methods disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed systems and methods, and that each of the figures is intended to correspond to a possible embodiment thereof. Moreover, wherever possible, the following description refers to reference numerals contained in the following figures, in which features that are shown in multiple figures are designated with consistent reference numerals.

[0016] Although the drawings illustrate configurations currently under consideration, it is understood that the present embodiments are not limited to the precise configuration and instrumentalities shown. [Figure 1] 1 illustrates an exemplary artificial intelligence based imaging system configured to interact with an individual via a web environment, according to various embodiments disclosed herein. [Diagram 2] 2 illustrates a schematic diagram of an exemplary client device implementing a predefined imaging code when received from a provisioning server of the artificial intelligence-based imaging system of FIG. 1 in accordance with various embodiments disclosed herein. [Diagram 3] 1 illustrates a schematic diagram of an exemplary artificial intelligence based imaging method for interacting with an individual via a web environment, according to various embodiments disclosed herein. [Figure 4] 4 shows a schematic diagram of a further exemplary artificial intelligence based imaging method for interacting with an individual via a web environment according to the method of FIG. 3 and according to various embodiments disclosed herein, where one or more person attributes of the individual include one or more facial features, one or more mouth features, or one or more hair-based features of the individual. [Diagram 5] 1-4 show an exemplary image of any of the artificial intelligence based imaging systems or methods as described with respect to FIGS. 1-4, including an individual showing one or more person attributes of the individual, including one or more facial features, one or more mouth features, or one or more hair-based features of the individual, in accordance with various embodiments disclosed herein. [Figure 6]6 illustrates an exemplary display or user interface for recommending one or more products based on one or more personality attributes of the individual of FIG. 5.

[0017] These figures depict preferred embodiments for purposes of example only. Alternative embodiments of the systems and methods illustrated herein may be used without departing from the inventive principles described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] FIG. 1 illustrates an exemplary artificial intelligence-based system configured to interact with an individual via a web environment, in accordance with various embodiments disclosed herein. Imaging 1 illustrates a system 100. In the exemplary embodiment of FIG. Imaging The system 100 includes a server 102, which may include one or more computer servers. In various embodiments, the server 102 includes multiple servers, which may include multiple redundant or replicated servers as part of a server farm. In yet further embodiments, the server 102 may be implemented as a cloud-based server. For example, the server 102 may be any one or more cloud-based platforms, such as MICROSOFT AZURE, AMAZON AWS, etc. The server 102 may include one or more processors 104, as well as one or more computer memories 106. The server 102 may be referred to herein as a "provisioning server."

[0019] The memory 106 may include one or more forms of fixed and / or removable memory of volatile and / or non-volatile memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and / or other hard drives, flash memory, MicroSD cards, etc. The memory 106 may store an operating system (OS) (e.g., Microsoft Windows, Linux, Unix, etc.) that may facilitate functions, apps, methods, or other software as discussed herein. The memory 106 may also store predefined operating system (OS) programs for transmission or transfer to a web browser or client device as described herein. Imaging A code 108 may also be stored. Additionally or alternatively, a predefined Imaging The code 108 may also be stored in a database 105, which is accessible to or communicatively coupled to the server 102. The memory 106 may also store machine-readable instructions, including any of one or more applications, one or more software components, and / or one or more application programming interfaces (APIs), which may be implemented to facilitate or perform features, functions, or other disclosures described herein, such as any methods, processes, elements, or limitations, as illustrated, shown, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein. For example, at least some of the applications, software components, or APIs may: ImagingBased on machine learning models or components, and / or predefined Imaging It may be, include, or be portions of code 108, each configured to facilitate various functions thereof as discussed herein, it being understood that one or more other applications may be envisioned and executed by the processor 104.

[0020] The processor 104 is connected to the memory 106 via a computer bus that is responsible for transmitting electronic data, data packets, or electronic signals between the processor 104 and the memory 106, and can implement or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, shown, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein.

[0021] The processor 104 may interface with the memory 106 via a computer bus to execute an operating system (OS). The processor 104 may also interface with the memory 106 via a computer bus to create, read, update, delete, or otherwise access or interact with data stored in the memory 106 and / or in the database 104 (e.g., a relational database such as Oracle, DB2, MySQL, or a NoSQL based database such as MongoDB). The data stored in the memory 106 and / or in the database 104 may include all or a portion of any of the data or information described herein, including, for example, one or more search requests, one or more transaction details, and user profile information.

[0022] The server 102 may further include a communication component configured to communicate (e.g., send and receive) data via one or more external / network ports to one or more network or local terminals, such as the computer network 120 and / or terminal 109 (for rendering or visualization) described herein. In some embodiments, the server 102 may include a client-server platform technology, such as ASP.NET, JavaJ2EE, Ruby on Rails, Node.js, web services, or online APIs, responsible for receiving and responding to electronic requests. The server 102 may implement a client-server platform technology that may interact with a memory 106 (including applications, components, APIs, data, etc. stored therein) and / or a database 105 via a computer bus to realize or execute machine-readable instructions, methods, processes, elements, or limitations as illustrated, depicted, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein. According to some embodiments, server 102 may include or interact with one or more transceivers (e.g., WWAN, WLAN, and / or WPAN transceivers) that function according to IEEE, 3GPP, or other standards and may be used to receive and transmit data via an external / network port connected to computer network 120. In some embodiments, computer network 120 may include a private network or a local area network (LAN). Additionally or alternatively, computer network 120 may include a public network, such as the Internet.

[0023] The server 102 may further include or implement an operator interface configured to present information to and / or receive input from an administrator or operator. As shown in FIG. 1, the operator interface may provide a display screen (e.g., via terminal 109). The server 102 may also provide I / O components (e.g., ports, capacitive or resistive touch-sensitive input panels, keys, buttons, lights, LEDs), which may be directly accessible via or attached to the server 102, or indirectly accessible via or attached to the terminal 109. According to some embodiments, an administrator or operator may access the server 102 via the terminal 109 to review information, make changes, enter training data, and / or perform other functions.

[0024] As described above in this specification, in some embodiments, server 102 may perform functions as discussed herein as part of a “cloud” network and / or communicate with other hardware or software components in the cloud to transmit, retrieve, and / or analyze data or information as described herein.

[0025] Typically, a computer program or computer-based product, application, or code (e.g., the predefined ImagingThe computer-readable program code or computer instructions (program code or other computer processing instructions) may be stored on a computer usable storage medium having such computer-readable program code or computer instructions embodied therein, or on a tangible non-transitory computer-readable medium (e.g., standard random access memory (RAM), optical disk, universal serial bus (USB) drive, etc.), and the computer-readable program code or computer instructions may be installed on or adapted to be executed by the processor 104 (e.g., working in conjunction with a respective operating system in memory 106) and may facilitate, implement, or perform the machine-readable instructions, methods, processes, elements, or limitations as illustrated, shown, or described in the various flow diagrams, illustrations, diagrams, figures, and / or other disclosures herein. In this regard, the program code may be implemented in any desired programming language and may be implemented as machine code, assembly code, bytecode, interpreted source code, or the like (e.g., via Golang, Python, C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).

[0026] 1, server 102 is communicatively coupled to one or more client devices 111c1-111c3 via computer network 120 and / or to client devices 112c1-112c3 via base stations 111b and 112b. In some embodiments, base stations 111b and 112b may include cellular base stations, such as cell towers, that communicate with one or more client devices 111c1-111c3 and 112c1-112c3 via wireless communication 121 based on any one or more of a variety of mobile telephony standards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, etc. Additionally or alternatively, base stations 111b and 112b may include routers, wireless switches, or other such wireless connection points that communicate with one or more client devices 111c1-111c3 and 112c1-112c3 via wireless communication 122 based on any one or more of a variety of wireless standards, including, by way of non-limiting example, IEEE 802.11a / b / c / g, BLUETOOTH standards, etc.

[0027] Any of the one or more client devices 111c1-111c3 and / or 112c1-112c3 may include a mobile phone, a mobile telephone, a tablet device, a personal data assistance (PDA), or the like, including, by non-limiting example, an APPLE iPhone or iPad device, or a GOOGLE ANDROID based mobile phone or table. Additionally, one or more of the client devices 111c1-111c3 and / or 112c1-112c3 may implement or run an operating system (OS) or mobile platform, such as Apple's iOS and / or Google's Android operating systems. Any of the one or more client devices 111c1-111c3 and / or 112c1-112c3 may execute computer processing instructions or code (e.g., predefined operating systems, ... ImagingThe device may include one or more processors and / or one or more memories for storing, implementing, or executing the program (eg, code).

[0028] FIG. 2 is an illustration of the artificial intelligence-based system of FIG. Imaging The predefined, when received from the provisioning server 102 of the system 100 Imaging 2 illustrates a simplified diagram of an exemplary client device 200 for implementing code 108. Client device 200 may be any of client devices 111c1-111c3 and / or 112c1-112c3, as described herein with respect to FIG. 1. In FIG. 2, client device 200 is illustrated as a mobile phone, such as, for example, an Apple iPhone device or a Google Android device. However, it should be understood that client device 200 may be, for example, a tablet, a PDA, or other such similar client device, as described herein.

[0029] In the example of FIG. 2, client device 200 includes a CPU 202 (e.g., a processor) and a camera 204. Camera 204 is configured to take, capture, or generate digital images (e.g., pixel-based images), which, in at least some embodiments, can store in memory 203 of client device 200. Memory 203 is communicatively coupled to or accessible by CPU 202. CPU 202 can implement or execute computer process instructions, including instructions for implementing a web browser 210 on display screen 201 of client device 200. In various embodiments, web browser 210 can be implemented as a mobile application (app) or a native app for a given operating system of client device 200. Use of web browser 210 can provide artificial intelligence-based Imaging Very broad distribution and / or availability of the systems and methods described herein may be provided, ImagingThat is, the web browser 210 drives traffic and usage of the system and method, regardless of the operating system or client device model, e.g., the client device 200. Imaging Enables the implementation and execution of systems and methods.

[0030] As described herein, the client device 200 may include a wireless transceiver for receiving wireless communications 121 and / or 122 from the base stations 111b and / or 112b. In various embodiments described herein, the client device 200 and / or the web browser 210 may receive predefined Imaging The instructions for requesting code 108 may be implemented or executed. For example, as shown with respect to FIG. 2, in some embodiments, a predefined Imaging The code 108 may be transferred to the client device 200 and / or the web browser 210 as a script, such as, by way of non-limiting example, a JavaScript (.js) file, a web assembly module (WASM), and / or the like. In various embodiments, the code 108 may be a predefined Imaging The code 108 may be stored or cached in memory 203 of the client device 200. In some embodiments, the code 108 may be a predefined Imaging Code 108 may further include, implement, or have access to at least partially render or expose a graphical user interface (GUI) 218 ​​for displaying text and / or images on display screen 201. Additionally or alternatively, web browser 210 may include, implement, or have access to at least partially render or expose a GUI 218 for displaying text and / or images on display screen 201.

[0031] Predefined Imaging The code 108 is for a digital image captured by the camera 204. Imaging Analysis and / or Imaging For example, in various embodiments, such an algorithm is provided. Imaging Analysis and / or Imaging The algorithm is predefined Imaging The code 108 may be implemented by predefined or compiled code. Such code may include, but is not limited to, Web Assembly (WASM). Typically, WASM is a portable binary code format for generating executable programs that can be executed or implemented within a host environment, such as a web browser (e.g., web browser 210). Because this WASM is an executable program, it provides high performance within a web browser compared to traditional browser-based technologies, including, for example, standalone JavaScript and cascading style sheets (CSS). Additionally or alternatively, predefined Imaging Code 108 may also include scripts or code, such as code from a third party library, such as the TENSORFLOW library (e.g., Tensorflow.js). For example, as described herein, the TENSORFLOW library uses artificial intelligence algorithms to analyze digital images using predefined Imaging Code 108. Additionally or alternatively, a predefined Imaging Code 108 may include its own JavaScript and CSS that loads, caches, and / or interacts with WASM code and / or third party code (e.g., TENSORFLOW). In various embodiments, code 108 may include predefined JavaScript and CSS, as described herein. Imaging Code 108 includes one or more of WASM and JavaScript code (e.g., Tensorflow.js and / or proprietary JavaScript) for implementing a method, flow diagram, algorithm, or portion thereof.

[0032] As shown in FIG. 2, in various embodiments, the camera 204 may be used (e.g., when requested through the GUI 218) to capture a digital image such as a "selfie" 220, e.g., a photograph taken by oneself. It should be understood that while a selfie is described in FIG. 2, other digital images are contemplated herein (e.g., photographs taken by persons other than the target individual). In some embodiments, the "selfie" 220 may be optionally displayed via the GUI 218 as shown in FIG. 2. Additionally or alternatively, the "selfie" 220 may include determining one or more person attributes of an individual based on image analysis of one or more images of the individual, as described herein. Imaging For the analysis, a predefined Imaging In addition, this may further include rendering one or more person attributes of the individual in an interactive GUI on or as part of the GUI 218. Imaging The results 222 of the analysis (e.g., any of one or more person attributes) may be displayed on the GUI 218. In this manner, an artificial intelligence-based Imaging The system 100 is configured to interact with the individual through a web environment. As illustrated by FIG. 2, it is noted that none of the captured and analyzed digital images are transmitted to a server over any network (e.g., computer network 120). In this way, the PII (e.g., an individual's selfie) is not at risk of being intercepted over a computer network, thereby enabling artificial intelligence-based analytics. Imaging The security of the system 100 and the client device 200 as a whole is increased.

[0033] FIG. 3 illustrates an exemplary artificial intelligence-based system for interacting with an individual via a web environment, according to various embodiments disclosed herein. Imaging 3 shows a simplified diagram of a method 300. In block 302, an artificial intelligence based Imaging The method 300 may include, at a provisioning server (e.g., provisioning server 102), ImagingCode (e.g., predefined Imaging The provisioning server (e.g., provisioning server 102) may receive a request (e.g., a hypertext transfer (HTTP) request) from a web browser (e.g., web browser 210) for a predefined Imaging A memory (eg, database 105 and / or memory 106) that stores the code is accessed.

[0034] At block 304, an artificial intelligence based Imaging The method 300 may include, in response to a request, providing a predefined Imaging Code (e.g., predefined Imaging The method further includes forwarding the request, code 108, to a web browser (e.g., web browser 210), where the web browser is executing on a client device (e.g., client device 200). In various embodiments, the web browser 210 is configured to receive hypertext markup language (HTML) from the provisioning server 102 for use in rendering at least a portion of a user interface (e.g., GUI 218) or display of the web browser 210.

[0035] In various embodiments, a predefined Imaging Code 108 is, for example, a predefined Imaging When the code 108 is received by the web browser, the code 108 is configured to be stored by the web browser (e.g., web browser 210) in a memory (e.g., memory 203) of the client device (e.g., client device 200). Additionally or alternatively, in various embodiments, the predefined Imaging Code 108 is a predefined ImagingWhen the code 108 is received by the web browser 210, it is configured to be executed by a client processor (eg, CPU 202) of a client device (eg, client device 200).

[0036] At block 306, an artificial intelligence based Imaging The method 300 is a predefined Imaging The method further includes using code 108 to render an interactive graphical user interface (e.g., GUI 218) within a web browser (e.g., web browser 210) on a display (e.g., display screen 201) of a client device (e.g., client device 200).

[0037] At block 308, an artificial intelligence based Imaging The method 300 is a predefined Imaging Code (e.g., predefined Imaging The method further includes loading, using code 108, one or more images of the individual (eg, as captured by camera 204) into a memory (eg, memory 203) of the client device.

[0038] At block 310, an artificial intelligence based Imaging The method 300 is a predefined Imaging Code (e.g., predefined Imaging code 108) for determining one or more person attributes of the individual based on image analysis of one or more images of the individual. In various embodiments, the image analysis includes training and / or using a machine learning model, neural network, or other artificial intelligence model to determine one or more person attributes of the individual based on pixel data of the one or more images of the individual. For example, in various embodiments, machine learning, as described herein, may be used to determine one or more person attributes of the individual. ImagingThe model may be trained using a supervised or unsupervised machine learning program or algorithm. The machine learning program or algorithm may use a neural network, which may be a convolutional neural network, a deep learning neural network, or a blended learning module or program that learns two or more features or feature data sets within a particular area of ​​interest. These machine learning programs or algorithms may also include natural language processing, semantic analysis, automated reasoning, regression analysis, support vector machine (SVM) analysis, decision tree analysis, random forest analysis, K-nearest neighbor analysis, naive Bayes analysis, clustering, reinforcement learning, and / or other machine learning algorithms and / or techniques. Machine learning may involve identifying and recognizing patterns within existing data (such as training a model based on pixel data within an image having multiple person attributes of an individual) to facilitate making predictions or identifications for subsequent data (such as using a model on new pixel data of a new individual to determine one or more new person attributes for such new individual).

[0039] Machine learning as described herein ImagingMachine learning models, such as those of the model, can be created and trained based on exemplary (e.g., "training data") inputs or data (which may be referred to as "features" and "labels") to make valid and reliable predictions of new inputs, such as test-level or production-level data or inputs. In supervised machine learning, a machine learning program running on a server, computing device, or processor is provided with exemplary inputs (e.g., "features") and their associated or observed outputs (e.g., "labels"), and the machine learning program or algorithm can determine or discover rules, relationships, or machine learning "models" that map such inputs (e.g., "features") to outputs (e.g., labels), for example, by determining and / or assigning weights or other measurements for the model across various feature categories of the model. Such rules, relationships, or models can then be provided with subsequent inputs and the model executed on the server, computing device, or processor to predict expected outputs based on the discovered rules, relationships, or models.

[0040] In unsupervised machine learning, the server, computing device, or processor may be required to find its own structure in unlabeled example inputs, where, for example, multiple training iterations are performed by the server, computing device, or processor to train multiple generations of models until a satisfactory model is generated, e.g., a model that provides sufficient predictive accuracy given test or production level data or inputs. The disclosure herein may employ one or both of such supervised or unsupervised machine learning techniques.

[0041] Image analysis is a machine learning technique based on pixel data from images of one or more individuals. Imaging Additionally or alternatively, the image analysis may include training a model that has previously been trained using machine learning. ImagingThis may include using the model to determine one or more person attributes of the individual based on one or more images of the individual. In this manner, the pixel data (e.g., to select one or more features of the individual, such as the mouth, hairline, face, or parts thereof) may be used to perform machine learning. Imaging The model can be trained or used to determine one or more person attributes of an individual, which may include, but are not limited to, features of the individual's facial features (e.g., skin or facial hair features), mouth, e.g., tooth(s)-related needs (e.g., plaque, staining, or other oral issues), or hair-based features (e.g., hair length, hair color, etc.).

[0042] In some embodiments, a predefined Imaging When the code 108 is transmitted to the client device 200, it includes a Web Assembly (WASM) based module and one or more scripts (e.g., JavaScript code). In various embodiments, the WASM based module (as described herein) may include machine learning Imaging As described herein, machine learning Imaging The model is configured to input one or more images of an individual and determine person attributes for that individual. In some embodiments, the model is configured to input one or more scripts, or more generally, predefined Imaging Code 108 is machine learning Imaging It can contain models, in this case machine learning Imaging The model implements or uses at least a TENSORFLOW-based library. Additionally or alternatively, machine learning Imaging The model may include, implement, or use other artificial intelligence based third party packages or libraries, including the PYTORCH library or the SCIKIT-LEARN Python library.

[0043] Referring to FIG. 3, at block 312, an artificial intelligence based Imaging The method 300 is a predefined Imaging Code (e.g., predefined Imaging The method further includes using the predefined person attribute to render one or more person attributes of the individual in an interactive GUI (e.g., GUI 218) using the predefined person attribute. Imaging The code 108, upon execution by a client processor (eg, CPU 202), is further configured to render one or more images having one or more person attributes of the individual within the interactive GUI (eg, GUI 218).

[0044] In various embodiments, a predefined Imaging The code 108 is executable within a web browser (e.g., web browser 210) without communicating with a provisioning server (e.g., provisioning server 102). In some embodiments, the code 108 may be executed within a web browser (e.g., web browser 210) without communicating with a provisioning server (e.g., provisioning server 102). Imaging The code 108 may be transferred during a single transmission from a provisioning server (e.g., the provisioning server 102) to a web browser (e.g., the web browser 210). Additionally or alternatively, a predefined Imaging The code is executable within a web browser (eg, web browser 210) without transmitting any personally identifiable information (PII) to a provisioning server (eg, provisioning server 102).

[0045] In various embodiments, a predefined Imaging The code 108 is cached in a memory (e.g., memory 203) of a client device (e.g., client device 200), in which case the predefined Imaging The code 108 can access at least a portion of the memory (e.g., memory 203) as allocated to a web browser (e.g., web browser 210). ImagingThe code 108 can access at least a portion of the memory (e.g., memory 203) when it is not allocated to a web browser (e.g., web browser 210) or is separate from memory allocated to the web browser. In some embodiments, the code 108 can access at least a portion of the memory (e.g., memory 203) when it is not allocated to a web browser (e.g., web browser 210) or is separate from memory allocated to the web browser. Imaging At least a portion of the code 108 is cached in a memory (eg, memory 203) of a client device (eg, client device 200) prior to or during capture of one or more images of the individual.

[0046] FIG. 4 illustrates a further exemplary artificial intelligence based method for interacting with an individual via a web environment, in accordance with the method 300 of FIG. Imaging A simplified diagram of a method 400 is shown in which one or more person attributes of an individual include one or more facial features, one or more mouth features, or one or more hair-based features of the individual, according to various embodiments disclosed herein.

[0047] The method 400 of FIG. 4 describes image analysis of an example image 500 of FIG. 5. More generally, FIG. 5 illustrates the artificial intelligence based image analysis of an individual 501, as described with respect to FIGS. 1-4, respectively. Imaging 5 illustrates an image 500 for image analysis by any of the systems or methods, which shows one or more person attributes 502-522 of an individual 501, including one or more facial features (e.g., skin 502, eyes 504, eyebrows 506, lips 512), one or more mouth features (e.g., teeth or through (not shown)), or one or more hair-based features (e.g., hair 522) of the individual 501, according to various embodiments disclosed herein. As illustrated by FIG. 5, the image 500 includes pixel data corresponding to each of the person attributes 502-522 and their respective regions and / or locations within the image 500 of the individual 501. The pixel data for any of the one or more person attributes 502-522 may be captured by the camera 204 and analyzed using, for example, machine learning, as described herein. Imaging Models, including predefined WASM modules Imaging It can be analyzed by code 108.

[0048] Referring to FIG. 4, at block 402, the method 400 begins by determining whether a predefined Imaging Code (e.g., predefined Imaging The provisioning server includes a client device 200 that transmits a request from a web browser (e.g., web browser 210) to a provisioning server (e.g., provisioning server 102) for a predefined Imaging The request may access a memory (e.g., database 105 and / or memory 106) that stores code 108. In various embodiments, as described herein, the request may access software, code, instructions, or other such assets (e.g., predefined Imaging Request code 108) for AI-based Imaging The request may be made from a link within a web page, such as an initial or pre-loaded web page that enables the analysis. Additionally or alternatively, the request may be made from a predefined link, such as a link or address stored in memory 203 of the client device 200.

[0049] At block 404, the method 400 continues by a provisioning server (e.g., the provisioning server 102) responding to the request by providing a predefined Imaging 4, the response from the provisioning server may include HyperText Markup Language (HTML), JavaScript (js), Cascading Style Sheets (CSS) scripts, and WASM code (e.g., for the image 500 and / or the personal image). Imaging The software, code, and / or instructions may include, for example, predefined ImagingCode 108 may include such software, code, and / or instructions when transferred in whole or in part at block 406. For example, predefined Imaging The code 108 may include WASM code, including machine learning (ML) models, in which case the remainder of the response from the provisioning server 102 may include JavaScript, such as proprietary or third party code (e.g., TensorFlow.js), and / or HTML and CSS for use by the web browser 210 to format, arrange, or generate a front-end UI (e.g., UI 218) on the display screen 201 of the client device 200. For example, in various embodiments, the web browser 210 is configured to receive HyperText Markup Language (HTML) from the provisioning server 102 for use in rendering at least a portion of a user interface (e.g., GUI 218) or display of the web browser 210.

[0050] In various embodiments, a predefined Imaging The code 108 is executable within a web browser (e.g., web browser 210) without communicating with a provisioning server (e.g., provisioning server 102). In some embodiments, the code 108 may be executed within a web browser (e.g., web browser 210) without communicating with a provisioning server (e.g., provisioning server 102). Imaging The code 108 may be transferred during a single transmission from a provisioning server (e.g., the provisioning server 102) to a web browser (e.g., the web browser 210). Additionally or alternatively, a predefined ImagingThe code can be executed within a web browser (e.g., web browser 210) without transmitting any personally identifiable information (PII) to a provisioning server (e.g., provisioning server 102). In this manner, the WASM code can interact with the web browser 210, including updating the web browser 210, without the need to process or request information via HTML, JavaScript, or from the provisioning server 102. Such interaction can include, for example, interacting with a document object model (DOM) of the web page of the web browser 210 that causes text and / or images, e.g., image 500 and associated text, to be displayed via the GUI 218 of the web browser 210.

[0051] At block 406, the method 400 continues by storing predefined caches or memory (e.g., memory 203) used by or at least shared by the web browser 210. Imaging The method further includes loading or storing the code 108. Imaging Code 108 is a predefined Imaging When the code 108 is received by a web browser (e.g., web browser 210), it is configured to be executed by a client processor (e.g., CPU 202) of a client device (e.g., client device 200). For example, as shown in FIG. 4, the JavaScript file (e.g., received by provisioning server 102) may include a predefined Imaging When the code 108 is received by the web browser 210, the web browser 210 and / or the client device 200 are Imaging The code 108 may be instructed to be stored in memory 203 of the client device 200 .

[0052] At block 408, the method 400 further includes the web browser 210 reading the HTML and CSS scripts, code, and / or instructions, such as those received by the provisioning server 102, to render the GUI 218. That is, execution of the web browser 210 on the client device 200 is performed based on the predefined Imaging The code 108 and / or HTML, JavaScript, CSS, or other instructions received by the provisioning server 102 can be used to render a GUI (e.g., GUI 218) within a web browser 210 on a display (e.g., web browser 210) of the client device 200.

[0053] At block 410, a user, such as individual 501, may use camera 204 to capture one or more images (eg, image 500, such as a "selfie" image or frame).

[0054] At block 412, the method 400 may include image optimization of the image (e.g., image 500), such as updating the size, resolution, or cropping the image (e.g., image 500). In some embodiments, such optimization may include modifying an area of ​​interest (e.g., one or more person attribute areas 502-522, etc.) by updating, modifying, selecting, and / or cropping pixels or regions of the image (e.g., image 500). Such image optimization may be performed by JavaScript scripts or other code or instructions as transferred from the provisioning server 102.

[0055] At block 420, the method 400 includes storing in a memory (e.g., the memory 203) a machine learning Imaging It further includes loading a model (e.g., a WASM-based model), which can be used to Imaging This may include loading the code 108, in whole or in part, into memory 203. ImagingCode 108 may include a WASM-based module and one or more scripts. In various embodiments, such scripts may include JavaScript files for storing, loading, and / or configuring the WASM-based module for operation or execution on client device 200.

[0056] Predefined Imaging The code 108 may be cached in a memory (e.g., memory 203) of a client device (e.g., client device 200) where the predefined Imaging The code 108 is permitted to access at least a portion of the memory (e.g., memory 203) as assigned to the web browser (e.g., web browser 210). As shown in FIG. 5, Imaging At least a portion of the code 108 is cached in a memory (e.g., memory 203) of a client device (e.g., client device 200) before or during capture of one or more images (e.g., image 500) of an individual (e.g., individual 501).

[0057] At block 422, the method 400 continues by Imaging The method further includes loading, using code 108, one or more images (e.g., image 500) of individual 501 into memory (e.g., memory 203) of a client device (e.g., client device 200). In various embodiments, the method further includes loading, using code 108, one or more images (e.g., image 500) of individual 501 into memory (e.g., memory 203) of a client device (e.g., client device 200). Imaging The JavaScript code or other such instructions when provided by code 108 may load the image. As shown in FIG. 5, one or more images may be processed by machine learning for image analysis. Imaging may be provided to the model.

[0058] As illustrated in Figure 4, the WASM-based module uses machine learning ImagingThe WASM-based module can include a model that can be executed by the CPU 202 of the client device 200 to perform image analysis of the image 500. The image analysis can include machine learning based on pixel data of an image (e.g., image 500) of one or more individuals (e.g., individual 501). Imaging Additionally or alternatively, the image analysis may include training or updating a model based on the machine learning of a WASM-based module, as previously trained. Imaging This may include using the model to determine one or more person attributes (e.g., person attributes 502-522) of an individual (e.g., individual 501) based on one or more images (e.g., image 500) of the individual. Using the pixel data of image 500 (e.g., to screen one or more features of the individual, such as a hairline, face, skin, or portions thereof), WASM-based machine learning Imaging A model can be trained or an existing machine model can be used to determine one or more person attributes of an individual. Such person attributes can include, but are not limited to, facial features (e.g., skin or facial hair features) of the individual, features of the mouth, such as tooth(s) related needs (e.g., plaque, stains, or other oral issues), or hair-based features (e.g., hair length, hair color, etc.), as shown and described with respect to image 500 of FIG. 5. Machine learning models can also be trained and can use non-image or text-based data of an individual, including, but not limited to, demographic data (e.g., age, gender, etc.) and geographic location, humidity of an area, etc.

[0059] At block 424, the method 400 continues by Imaging Using code 108, determining one or more person attributes (e.g., person attributes 502-522) of an individual 501 based on image analysis of one or more images (e.g., image 500) of the individual (e.g., 500). For example, this can include using WASM-based machine learning. Imaging Machine learning such as models Imaging The results of the model may be included.

[0060] In various embodiments, machine learning Imaging The model can input pixel data of an image (e.g., image 500) of an individual (e.g., individual 501) as feature data. Imaging The model can output label data as a result in block 424, which may include numerical data (e.g., age of individual 501), classification data (e.g., hair length with classification "1" or "2" in person attributes 522, and / or eye color "green" or "blue" in person attributes 504), or discrimination data (e.g., skin region in person attributes 502). Additionally or alternatively, machine learning Imaging The model may output, as a result at block 424, label data that may include visualizations or annotations. Such visualizations or annotations may include images or video images (e.g., image 500) highlighting possible regions of interest (e.g., person attributes 502-522). In some embodiments, such visualizations or annotations may be shown on the display screen 201 in real-time or near real-time with marked or highlighted specific regions, as illustrated by regions or portions (e.g., person attributes 502-522) of the image 500 as shown by FIG. 5. In some embodiments, such marked or highlighted specific regions may be shown as an augmented reality (AR) image, where the face, or other body parts, of an individual (e.g., individual 501) are shown in real-time or near real-time with annotated markings (e.g., person attributes 502-522) superimposed on the image 500 and visible on the display screen 201.

[0061] At block 426, the method 400 performs machine learning as described for block 424. Imaging When the model is generated or produced, the result is displayed on the GUI 218. The client device 200 may also display the predefined ImagingCode 108 (e.g., including scripts and / or other instructions described herein) may be used to render one or more images and associated person attributes (e.g., any one of 502-522) of individual 501 within an interactive GUI (e.g., display screen 201). As described above, one or more person attributes 502-522 may include one or more facial features (e.g., skin 502, eyes 504, eyebrows 506, or lips 512), one or more mouth features (e.g., teeth (not shown)), or one or more hair-based features (e.g., hair 522) of individual 501. The images may be rendered as still images, video images (i.e., frames), annotated images, AR-based images, etc., as described herein.

[0062] In various embodiments, the results or images, when determined or analyzed, may be displayed (e.g., via display screen 201) along with or used to display one or more corresponding recommended products. For example, recommended products (e.g., cosmetics or foundation) may be displayed or recommended to a user or individual that correspond to age, wrinkles, eye color, hair color, hair length, etc., as determined from image data of the individual's personal attributes (e.g., personal attributes 502-522).

[0063] 6 illustrates an exemplary display or user interface 600 for recommending one or more products (e.g., products 602p and 622p) based on one or more person attributes (e.g., 502 and 522) of an individual 501 of FIG. 5. In the example of FIG. 6, user interface 600 may be, or may be displayed as part of, user interface 218 described herein. For example, in various embodiments, machine learning may be used to recommend one or more products (e.g., products 602p and 622p) based on one or more person attributes (e.g., 502 and 522) of an individual 501 of FIG. 5. In the example of FIG. 6, user interface 600 may be, or may be displayed as part of, a user interface 218 described herein. ImagingThe output or results of the model (and / or, for example, a WASM module as described herein) can be used to generate or identify corresponding product recommendations based on image analysis (e.g., analysis of pixel data of image 500) of one or more person attributes (e.g., 502 and 522) of individual 501. Such recommendations can include products such as toothpaste (e.g., in the case of mouth attributes), or how to use a particular product (e.g., how to apply a particular recommended beauty cream). Other examples can include hair care, e.g., displaying a text or video instruction manual on how to achieve a recommended hair grooming action or hairstyle for the user or individual using the recommended product.

[0064] In the example of Figure 6, the user interface 600 includes a list 601 of recommended products as determined by image analysis of the image 500 and for one or more person attributes (e.g., 502 and 522) of the individual 501 of Figure 5. The list 601 includes at least two products 602p and 622p that correspond to the person attributes 502 and 522, respectively. The list 601 includes a first recommendation 602 for the person attribute 502 (e.g., skin attribute), where the recommended product 602p is determined by machine learning from the analysis of the image 500. Imaging Models (e.g., predefined Imaging A skin care product that can address one or more identified features (e.g., wrinkles) of an individual 501 when detected by a skin care product (e.g., skin care product) that is sensitive to the skin care product.

[0065] Similarly, the list 601 includes a second recommendation 602 for a person attribute 522 (e.g., hair attribute), where the recommended product 622p is based on machine learning from an analysis of the image 500. Imaging Models (e.g., predefined Imaging A hair care product that can address one or more identified characteristics (e.g., split ends, dandruff, or hair shine) of an individual 501 when detected by a hair care product (e.g., a ...

[0066] User interface 600 may further include selectable UI buttons 602s and 622s that allow a user (e.g., individual 501) to select a corresponding product, e.g., 602p and 622p, to be purchased or shipped, respectively. In some embodiments, selection of selectable UI buttons 602s and 622s may cause the recommended product to be shipped to the user (e.g., individual 501) and / or may notify a third party that the individual is interested in the product.

[0067] Aspects of the Disclosure The following aspects are provided as examples according to the disclosure herein and are not intended to limit the scope of the disclosure.

[0068] Aspect 1. An artificial intelligence-based system configured to interact with an individual via a web environment Imaging A system based on artificial intelligence Imaging The system includes a provisioning server having one or more processors and one or more memories and configured to respond to requests from a web browser executing on a client device, and a predefined set of predefined configuration data stored in the one or more memories of the server. Imaging and computer processing instructions configured to execute on one or more processors of a provisioning server, the computer processing instructions causing the provisioning server to, upon receiving a request from the web browser, Imaging The code is transferred to the web browser and predefined Imaging The code is predefined Imaging The code is configured to be stored by the web browser in a memory of the client device when the code is received by the web browser, and the predefined Imaging The code is predefined Imaging The code is configured to be executed by a client processor of the client device when the code is received by the web browser, and the code includes a predefined Imaging The code, when executed by the client processor, is configured to: render an interactive graphical user interface (GUI) within a web browser on a display of the client device; load one or more images of the individual into a memory of the client device; determine one or more person attributes of the individual based on an image analysis of the one or more images of the individual; and render the one or more person attributes of the individual in the interactive GUI; Imaging The code can be executed in a web browser without communicating with a provisioning server and can run on predefined Imaging The code is cached in the memory of the client device and is stored in a predefined Imaging The code is permitted to access at least a portion of the memory as allocated to the web browser. Imaging system. Aspect 2. Predefined Imaging The code includes a Web Assembly (WASM) based module and one or more scripts. Imaging system. Aspect 3. WASM-based modules use machine learning Imaging Machine learning, including models Imaging 3. The artificial intelligence-based method of claim 2, wherein the model is configured to input one or more images of an individual and determine person attributes of the individual. Imaging system. Aspect 4. Predefined Imaging The code is a machine learning library that implements at least the TENSORFLOW base library. Imaging The artificial intelligence-based method according to any one of the first to third aspects, comprising a model. Imaging system. Aspect 5. Predefined Imaging The artificial intelligence-based method according to any one of the first to fourth aspects, wherein the code is transferred from the provisioning server to the web browser during a single transmission. Imaging system. Aspect 6. Predefined Imaging6. The artificial intelligence based method of claim 5, wherein at least a portion of the code is cached in a memory of the client device before or during capture of one or more images of the individual. Imaging system. Aspect 7. The artificial intelligence-based method according to any one of aspects 1 to 6, wherein the web browser is configured to receive HyperText Markup Language (HTML). Imaging system. Aspect 8. Predefined Imaging The artificial intelligence-based method according to any one of the first to seventh aspects, wherein the code is executable within a web browser without transmitting any personally identifiable information (PII) to a provisioning server. Imaging system. Aspect 9. Predefined Imaging The artificial intelligence-based method of any one of aspects 1 to 8, wherein the code, upon execution by the client processor, is further configured to render one or more images with one or more person attributes of the individual in the interactive GUI. Imaging system. Aspect 10. Artificial Intelligence-Based Method for Interacting with Individuals via a Web Environment Imaging A method, comprising: Imaging The method is predefined in the provisioning server. Imaging receiving a request from a web browser for the code, the provisioning server receiving a predefined Imaging receiving a predefined code by a provisioning server having access to a memory storing the code; Imaging and responding to the request by forwarding code to a web browser, the web browser being executed on the client device and configured to execute a predefined Imaging The code is predefined Imaging The code is configured to be stored by the web browser in a memory of the client device when the code is received by the web browser, and the predefined Imaging The code is predefined ImagingThe code is configured to be executed by a client processor of the client device when the code is received by the web browser. Imaging Rendering an interactive graphical user interface (GUI) within a web browser on a display of a client device using the code and displaying a predefined Imaging loading, using code, one or more images of an individual into memory of a client device and Imaging determining one or more person attributes of the individual based on an image analysis of one or more images of the individual using the code; Imaging and rendering one or more person attributes of the individual in an interactive GUI using the predefined Imaging The code can be executed in a web browser without communicating with a provisioning server and can run on predefined Imaging The code is cached in the client device's memory and stored in a predefined Imaging The code is permitted to access at least a portion of the memory as allocated to the web browser. Imaging method. Aspect 11. Predefined Imaging The code includes a Web Assembly (WASM) based module and one or more scripts. Imaging system. 12. WASM-based modules are machine learning Imaging Machine learning, including models Imaging 12. The artificial intelligence-based method of claim 11, wherein the model is configured to input one or more images of an individual and determine person attributes of the individual. Imaging system. Aspect 13. Predefined Imaging The code is a machine learning library that implements at least the TENSORFLOW base library. Imaging 13. The artificial intelligence-based method according to any one of aspects 10 to 12, comprising a model. Imaging system. Aspect 14. Predefined Imaging 14. The artificial intelligence-based method according to any one of aspects 10 to 13, wherein the code is transferred from the provisioning server to the web browser during a single transmission. Imaging system. Aspect 15. Predefined Imaging 15. The artificial intelligence based method of claim 14, wherein at least a portion of the code is cached in a memory of the client device before or during capture of one or more images of the individual. Imaging system. Aspect 16. The artificial intelligence-based method according to any one of aspects 10 to 15, wherein the web browser is configured to receive HyperText Markup Language (HTML). Imaging system. Aspect 17. Predefined Imaging 17. The artificial intelligence-based method according to any one of aspects 10 to 16, wherein the code is executable within a web browser without transmitting any personally identifiable information (PII) to a provisioning server. Imaging system. Aspect 18. Predefined Imaging 18. The artificial intelligence based method of any one of aspects 10 to 17, wherein the code, upon execution by the client processor, is further configured to render one or more images having one or more person attributes of the individual within the interactive GUI. Imaging system. Aspect 19. An artificial intelligence-based system configured to interact with an individual via a web environment Imaging A system based on artificial intelligence Imaging The system includes a provisioning server having one or more processors and one or more memories and configured to respond to requests from a web browser executing on a client device, and a predefined set of predefined configuration data stored in the one or more memories of the server. Imaging and computer processing instructions configured to execute on one or more processors of a provisioning server, the computer processing instructions causing the provisioning server to, upon receiving a request from the web browser, ImagingThe code is transferred to the web browser and predefined Imaging The code is predefined Imaging The code is configured to be stored by the web browser in a memory of the client device when the code is received by the web browser, and the predefined Imaging The code is predefined Imaging The code is configured to be executed by a client processor of the client device when the code is received by the web browser, and the code includes a predefined Imaging The code, when executed by the client processor, is configured to render an interactive graphical user interface (GUI) within a web browser on a display of the client device; load one or more images of the individual into a memory of the client device; determine one or more person attributes of the individual based on an image analysis of the one or more images of the individual; and render the one or more person attributes of the individual in the interactive GUI; One or more person attributes The method comprises the steps of: (a) generating a facial image of an individual based on artificial intelligence (AI) information; Imaging system. Aspect 20. Predefined Imaging 20. The artificial intelligence based method of claim 19, wherein the code, upon execution by the client processor, is further configured to recommend one or more products to the individual based on one or more personality attributes of the individual. Imaging system. Aspect 21. Artificial intelligence-based method for interacting with individuals via a web environment Imaging A method, comprising: Imaging The method is predefined in the provisioning server. Imaging receiving a request from a web browser for the code, the provisioning server receiving a predefined Imaging receiving a predefined code by a provisioning server having access to a memory storing the code; Imagingand responding to the request by forwarding code to a web browser, the web browser being executed on the client device and configured to execute a predefined Imaging The code is predefined Imaging The code is configured to be stored by the web browser in a memory of the client device when the code is received by the web browser, and the predefined Imaging The code is predefined Imaging The code is configured to be executed by a client processor of the client device when the code is received by the web browser. Imaging Rendering an interactive graphical user interface (GUI) within a web browser on a display of a client device using the code and displaying a predefined Imaging loading, using code, one or more images of an individual into memory of a client device and Imaging determining one or more person attributes of the individual based on an image analysis of one or more images of the individual using the code; Imaging and rendering one or more person attributes of the individual in the interactive GUI using the code, the one or more person attributes including one or more facial features, one or more mouth features, or one or more hair-based features of the individual. Imaging method. Aspect 22. Predefined Imaging 22. The artificial intelligence based method of claim 21, wherein the code, upon execution by the client processor, is further configured to recommend one or more products to the individual based on one or more personality attributes of the individual. Imaging method.

[0069] Additional Considerations Although the disclosure of this specification describes detailed descriptions of many different embodiments, it should be understood that the legal scope of this specification is defined by the words of the claims set forth at the end of this patent and equivalents. This detailed description should be construed as exemplary only and does not describe all possible embodiments, as describing all possible embodiments would be impractical. Many alternative embodiments can be implemented using either current technology or technology developed after the filing date of this patent, and such embodiments would still fall within the scope of the claims.

[0070] The following additional considerations apply to the foregoing discussion. Throughout this specification, multiple examples may implement components, operations, or structures described as a single example. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed simultaneously, and the operations need not be performed in the order illustrated. Structures and functions presented as separate components in an exemplary configuration may be realized as combined structures or components. Similarly, structures and functions presented as a single component may also be realized as separate components. These and other variations, modifications, additions, and improvements are also included within the scope of the subject matter of this specification.

[0071] Additionally, certain embodiments are described herein as including logic or some routines, subroutines, applications, or instructions. These may be constructed as either software (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware, routines, etc. are tangible units capable of performing certain operations and may be configured or arranged in a particular manner. In an exemplary embodiment, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware modules of a computer system (e.g., processors or groups of processors) may be configured by software (e.g., applications or application portions) as hardware modules that operate to perform certain operations as described herein.

[0072] Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the associated operations. Such processors, whether temporarily or permanently configured, may form processor-implemented modules that operate to perform one or more operations or functions. Modules referred to herein may, in some example embodiments, include processor-implemented modules.

[0073] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. Certain performance of those operations may be distributed to one or more processors and may be spread across multiple machines as well as being present within a single machine. In some exemplary embodiments, the processor(s) may be located in a single location, whereas in other embodiments, the processor may be distributed across several locations.

[0074] Certain performance of those operations may be distributed across one or more processors and may reside within a single machine as well as be spread across multiple machines. In some exemplary embodiments, one or more processors or processor-implemented modules may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other embodiments, one or more processors or processor-implemented modules may be distributed across multiple geographic locations.

[0075] This detailed description should be construed as illustrative only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. Those skilled in the art will be able to realize numerous alternative embodiments using either current technology or technology developed after the filing date of this application.

[0076] Those skilled in the art will recognize that numerous modifications, changes, and combinations can be made to the above-described embodiments without departing from the scope of the present invention, and such modifications, changes, and combinations can be considered to be within the periphery of the inventive concept.

[0077] The claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless conventional means-plus-function language is expressly recited, such as "means for" or "step for" language. The systems and methods described herein are directed to improving computer functionality and improve upon the functionality of conventional computers.

[0078] Dimensions and values ​​disclosed herein should not be understood as being strictly limited to the exact numerical values ​​recited. Instead, unless otherwise indicated, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as "40 mm" is intended to mean "about 40 mm."

[0079] All documents cited herein, including any cross-referenced or related patents or patent applications, and any patent applications or patents to which this application claims priority or benefit, are incorporated herein by reference in their entirety, unless expressly stated to the contrary. The citation of any document shall not be deemed to be prior art to any invention disclosed or claimed herein, or to teach, suggest or disclose such invention, either alone or in combination with any other reference(s). Furthermore, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition assigned to that term in this document shall govern.

[0080] While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims

1. 1. An artificial intelligence based imaging system configured to interact with an individual via a web environment, the artificial intelligence based imaging system comprising: a provisioning server, the provisioning server comprising one or more processors and one or more memories and configured to respond to requests from a web browser executing on a client device; a predefined imaging code stored in the one or more memories of the provisioning server; and computer processing instructions configured to execute on the one or more processors of the provisioning server; Equipped with The computer processing instructions cause the provisioning server, upon receiving a request from the web browser, to transmit the predefined imaging code to the web browser; the predefined imaging code is configured to be stored by the web browser in a memory of the client device when the predefined imaging code is received by the web browser; the predefined imaging code is configured to be executed by a client processor of the client device when the predefined imaging code is received by the web browser; The predefined imaging code, upon execution by the client processor, Rendering an interactive graphical user interface (GUI) within the web browser on a display of the client device; loading one or more images of an individual into a memory of the client device; determining one or more person attributes of the individual based on image analysis of the one or more images of the individual; and and rendering the one or more person attributes of the individual within the interactive GUI, the one or more person attributes comprising one or more mouth features, or one or more hair-based features; the predefined imaging code is executable within the web browser without communicating with the provisioning server; the predefined imaging code is cached in the memory of the client device, the predefined imaging code being permitted to access at least a portion of the memory as assigned to the web browser; the predefined imaging code includes a Web Assembly (WASM) based module and one or more scripts; The WASM-based module includes a machine learning imaging model, the machine learning imaging model configured to input the one or more images of the individual and determine the person attributes of the individual.

2. The artificial intelligence based imaging system of claim 1 , wherein the predefined imaging code includes a machine learning imaging model that implements at least a TENSORFLOW based library.

3. The artificial intelligence based imaging system of claim 1 or 2, wherein the predefined imaging code is transferred from the provisioning server to the web browser during a single transmission.

4. The artificial intelligence based imaging system of claim 3 , wherein at least a portion of the predefined imaging code is cached in the memory of the client device prior to or during capture of the one or more images of the individual.

5. The artificial intelligence based imaging system of any one of claims 1 to 4, wherein the web browser is configured to receive HyperText Markup Language (HTML).

6. The artificial intelligence based imaging system of any one of claims 1 to 5, wherein the predefined imaging code is executable within the web browser without transmitting any personally identifiable information (PII) to the provisioning server.

7. 7. The artificial intelligence based imaging system of claim 1, wherein the predefined imaging code, upon execution by the client processor, is further configured to render the one or more images using the one or more person attributes of the individual within the interactive GUI.

8. 8. The artificial intelligence based imaging system of claim 1, wherein the predefined imaging code, upon execution by the client processor, is further configured to recommend one or more products to the individual based on the one or more personality attributes of the individual.

Images