System and method for artificial intelligence-based matchmaking

Abstract

Disclosed is an artificial intelligence-based matchmaking system (100) for suggesting compatible user profiles on a graphical user interface (GUI). The system (100) includes a user device (102) comprising an input unit adapted to receive one or more input data from a user and an output unit communicatively coupled with a server (104) through a communication network (106). The server (104) includes a processing unit configured to receive and store profile inputs comprising textual, categorical, or behavioral data such as occupation, interests, and intent parameters, process the profile inputs to generate a prioritized list of candidate profiles based on semantic correlation, contextual similarity, and inferred intent, display the candidate profiles sequentially on the GUI, receive gesture inputs indicating interest or skip actions and update a dynamic parameter-weighted preference model to adaptively reorder subsequent profiles in real-time. The present disclosure also relates to a method (200) for artificial intelligence-based matchmaking system (100).

Description

OF THE INVENTION

[0001] SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE-BASED MATCHMAKING

[0002] The present disclosure relates to the field of artificial intelligence and data-driven matchmaking technologies. More particularly, the present disclosure relates to a system and method for artificial intelligence-based matchmaking.

[0003] In recent years, matchmaking platforms spanning social, dating, professional, and networking domains have become increasingly popular for connecting individuals based on shared interests and preferences. Conventional matchmaking systems typically depend on explicit user-provided data such as age, gender, location, or interests to generate matches. These systems generally apply rule-based filtering or basic similarity computations to identify potential profiles. However, such methods are often inadequate in capturing the implicit intent or behavioral nuances of the user, resulting in recommendations that lack true contextual relevance.

[0004] Existing platforms often present a static list of profiles ranked by simplistic criteria or fixed scoring functions. This approach fails to recognize that user preferences evolve dynamically over time and may vary depending on emotional context, interaction patterns, or prior experiences. Consequently, users may repeatedly encounter irrelevant or redundant profiles, leading to reduced engagement and overall dissatisfaction.

[0005] Another limitation of traditional systems is the absence of adaptive feedback mechanisms. While many applications allow users to express “interest” or “disinterest” through gestures such as swiping or tapping, these gestures are treated as isolated actions rather than as learning signals for an intelligent model. The lack of integration between gesture-based feedback and recommendation logic results in inefficient personalization and a slow adaptation rate of the matchmaking algorithm.

[0006] Furthermore, existing systems lack the capability to semantically interpret user-generated textual inputs such as bios, prompts, or preferences to extract the deeper contextual meaning and intent. Without semantic and linguistic understanding, the system cannot distinguish between subtle differences in user expression, such as whether a user seeks casual interaction, professional collaboration, or a long-term connection. This results in mismatched suggestions and poor relevance in user recommendations.

[0007] Current approaches also fail to incorporate real-time learning mechanisms. Profile ranking and compatibility scoring often rely on precomputed datasets or collaborative filtering methods that do not update immediately in response to new user gestures or feedback. This absence of adaptive recalibration prevents the system from continuously improving its recommendation quality as the user interacts with profiles.

[0008] Moreover, many matchmaking systems do not utilize reinforcement learning or weighted parameter optimization to refine predictions based on user responses. The absence of a dynamic parameter-weighted model limits the system’s capacity to learn the relative importance of specific user attributes, such as shared interests, professions, or inferred personality traits.

[0009] In view of the above, there is a need for an improved artificial intelligence-based matchmaking system that provides adaptive, intent-driven, and preference-based profile recommendations, integrates semantic understanding of user inputs through natural language processing (NLP), enables gesture-based feedback for real-time model recalibration, and leverages reinforcement learning and behavioral data analytics to continuously refine and optimize compatibility scoring over time.

[0010] In one aspect of the present disclosure, an artificial intelligence-based matchmaking system is provided.

[0011] The artificial intelligence-based matchmaking system includes a user device. The artificial intelligence-based matchmaking system further includes an input unit that is adapted to receive one or more input data from a user. The artificial intelligence-based matchmaking system further includes an output unit that is communicatively coupled with the input unit and being communicatively coupled to a server through a communication network. The artificial intelligence-based matchmaking system further includes a server that is communicatively coupled with the user device. The artificial intelligence-based matchmaking system further includes a processing unit disposed within the server and configured to receive and store one or more profile inputs provided by a user, the profile inputs comprising textual, categorical, or behavioral data including occupation, interests, or intent parameters. The artificial intelligence-based matchmaking system further includes process the profile inputs and generate a prioritized list of candidate profiles based on semantic correlation, contextual similarity, and inferred intent. The artificial intelligence-based matchmaking system further includes display one or more of the candidate profiles sequentially on the GUI of the user device. The artificial intelligence-based matchmaking system further includes receive a first gesture input from the user corresponding to an interest action, the first gesture input comprising a directional swipe, tap, or other predefined gesture detected by the gesture-recognition interface. The artificial intelligence-based matchmaking system further includes receive a second gesture input from the user corresponding to a skip action, the second gesture input being distinct from the first gesture input. The artificial intelligence-based matchmaking system further includes update a dynamic parameter-weighted preference model based on the received first or second gesture input, wherein the model increases or decreases respective feature weights associated with parameters of the interacted profiles. The artificial intelligence-based matchmaking system further includes adaptively refresh and reorder the subsequent candidate profiles in real-time based on the updated feature weights, thereby refining future profile recommendations.

[0012] In some aspects of the present disclosure, the first gesture input comprises a swipe in a first direction or a tap gesture, and the second gesture input comprises a swipe in a second, opposite direction.

[0013] In some aspects of the present disclosure, the graphical user interface (GUI) is configured to display user posts, events, and communities.

[0014] In some aspects of the present disclosure, the processing unit comprises a natural language processing (NLP) engine configured to semantically analyze user-input text on profiles to infer contextual meaning and relationship intent.

[0015] In some aspects of the present disclosure, the dynamic parameter-weighted preference model updates parameter scores using a reinforcement learning instructions based on gesture feedback, increasing weights for parameters associated with accepted profiles and decreasing weights for parameters associated with skipped profiles.

[0016] In some aspects of the present disclosure, the processing unit is configured to detect latent preference clusters from historical gesture data and adjust to emphasize parameters corresponding to frequently accepted categories.

[0017] In some aspects of the present disclosure, the user device further includes a haptic feedback interface configured to generate a tactile response corresponding to a successful match or mutual interest event.

[0018] In some aspects of the present disclosure, the processing unit is configured to support domain-specific matchmaking including professional, academic, or entrepreneurial connections by interpreting user-provided intent indicators.

[0019] In some aspects of the present disclosure, the processing unit utilizes semantic embeddings and collaborative filtering to determine inter-profile compatibility beyond explicit user inputs.

[0020] In some aspects of the present disclosure, the processing unit continuously recalibrates profile scoring based on both user gesture history and cross-user correlation data obtained from a global behavioral dataset.

[0021] In some aspects of the present disclosure, upon each gesture input, the processing unit computes a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and updates the model accordingly.

[0022] In second aspect of the present disclosure, a computer-implemented method for artificial intelligence-based matchmaking system is provided.

[0023] The method includes receiving, by a user device, one or more profile inputs from a user through an input unit, the profile inputs comprising at least textual, categorical, or behavioral data including occupation, interests, and intent parameters. The method further includes transmitting, by the user device, the received profile inputs to a server over a communication network. The method further includes receiving, by the server, the profile inputs from the user device and storing the inputs in a database associated with the user. The method further includes performing semantic analysis of the user-provided textual content using a Natural Language Processing (NLP) engine to extract context and relationship intent. The method further includes determining semantic correlation and contextual similarity between the user profile and other profiles in the dataset. The method further includes applying a ranking instructions to generate the prioritized list of candidate profiles based on inferred intent and computed compatibility scores. The method further includes displaying, by the output unit of the user device, one or more of the prioritized candidate profiles sequentially on a graphical user interface (GUI). The method further includes receiving, by the gesture-recognition interface of the user device, a first gesture input corresponding to an interest action, the first gesture input comprising at least one of a directional swipe, a tap, or another predefined gesture. The method further includes receiving, by the gesture-recognition interface of the user device, a second gesture input corresponding to a skip action, the second gesture input being distinct from the first gesture input. The method further includes transmitting, by the user device, the received gesture inputs to the server. The method further includes updating, by the processing unit, a dynamic parameter-weighted preference model based on the received gesture inputs, by increasing one or more feature weights associated with parameters of profiles corresponding to the first gesture input, decreasing one or more feature weights associated with parameters of profiles corresponding to the second gesture input, and recalibrating the preference model using a reinforcement learning instructions configured to adaptively optimize the scoring function based on gesture-derived feedback. The method further includes adaptively refreshing and reordering, by the processing unit, the subsequent candidate profiles displayed to the user on the GUI in real time based on the updated preference model. The method further includes detecting, by the processing unit, latent preference clusters from accumulated gesture data to identify emerging interest patterns. The method further includes generating, by the processing unit, a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and uses the metric to weight future recommendations. The method further includes transmitting, by the server, refined and reordered profile recommendations to the user device for display. The method further includes optionally generating, by the user device, a haptic feedback response upon occurrence of a mutual interest or successful match event.

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. In the drawing,

[0025] illustrates an artificial intelligence-based matchmaking system, in accordance with an aspect of the present disclosure; and

[0026] illustrates a method for artificial intelligence-based matchmaking and adaptive profile recommendation, in accordance with an aspect of the present disclosure.DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, known details are not described in order to avoid obscuring the description.

[0028] References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and such references mean at least one of the embodiments.

[0029] Reference to "one embodiment", "an embodiment", “one aspect”, “some aspects”, “an aspect” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.

[0030] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided.

[0031] A recital of one or more synonyms does not exclude the use of other synonyms.The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification. Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods, and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

[0032] Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims or can be learned by the practice of the principles set forth herein.

[0033] The term “apparatus” and “device” are interchangeably used across the context.

[0034] As mentioned above, there is a need for an improved artificial intelligence-based matchmaking system that provides adaptive, intent-driven, and preference-based profile recommendations, integrates semantic understanding of user inputs through natural language processing (NLP), enables gesture-based feedback for real-time model recalibration, and leverages reinforcement learning and behavioral data analytics to continuously refine and optimize compatibility scoring over time.

[0035] illustrates an artificial intelligence-based matchmaking system 100, in accordance with an aspect of the present disclosure.

[0036] The artificial intelligence-based matchmaking system 100 may include a user device 102 and a server 104. The user device 102 and the server 104 may be coupled with each other by way of a communication network 106. In some aspects of the present disclosure, the user device 102 and the server 104 may be communicably coupled through separate communication networks 106 established therebetween. In some aspects of the present disclosure, the communication network 106 may include suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for transmission and reception of data related to operations of various entities of the system 100. Each network port may correspond to a virtual address (or a physical machine address) for the transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address), and the physical address may be a Media Access Control (MAC) address. The communication network 106 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the input device and the server. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof.

[0037] In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of a plurality of communication channels in the communication network. The communication channels may include but are not limited to, a wireless channel, a wired channel, or a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with data standards which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and / or later developed technologies.

[0038] The user device 102 may be adapted to receive one or more instructions provided by the user, share one or more results, and / or transmit data within the system 100. The user device 102 may further be adapted to display one or more output to the user. In some aspects of the present disclosure, the display unit may be provided outside the user device 102 without limiting the scope of the disclosure as the scope is intended to cover the nature of dis one or more outputs. It will be apparent to a person of ordinary skill in the art that the user may be any person using or operating the system 100, without deviating from scope of the disclosure. Examples of the user device may include but are not limited to, a desktop, a notebook, a laptop, a handheld computer, a touch-sensitive device, a keyboard, a microphone, a mouse, a joystick, a computing device, a smart-phone, and / or a smartwatch. It may be apparent to a person of ordinary skill in the art that the input device may include any device / apparatus that is capable of manipulation by the user.

[0039] In some aspects of the present disclosure, the user device 102 may include an input unit 108 and an output unit 110.

[0040] The input unit 108 may be adapted for receiving input data provided by the user. In some aspects of the present disclosure, the input unit 108 may include but is not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include and / or otherwise cover any type of interface, including known, related, and later developed interfaces.

[0041] The output unit 110 may be adapted for display or visually represent (or presenting) an output to the user. The output unit 110 may be adapted to represent movement of one or more blocks visually to the user. The output unit 110 further be adapted to represent one or more alarm indication to the user. The output unit 110 may be adapted to represent one or more alarm indications by way of a light signal or a warning image. In some aspects of the present disclosure, the output unit 110 may include but is not limited to, a display device, a printer, a projection device, and / or a speaker. In some other aspects of the present disclosure, the output interface may include but is not limited to, a digital display, an analog display, a touch screen display, a graphical user interface, a website, a webpage, a keyboard, a mouse, a light pen, an appearance of a desktop, and / or illuminated characters.

[0042] In some aspects of the present disclosure, the user device 102 may further include a communication unit 112.

[0043] The communication unit 112 may be configured to enable the input device to communicate with the server 104 and other components of the system 100 over a communication network 106, according to an aspect of the present disclosure. In some aspects of the present disclosure, the communication unit 112 may be one of but is not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit.

[0044] In some aspects of the present disclosure, the communication unit 112 may enable the user device 102 to communicate with other (e.g., remote) user device(s) (not shown) or with a server 104 by way of the communication network 106, including when the user device 102 is in the connected standby mode. The other user device(s) can include, for instance, laptop(s), tablet(s), smartphone(s), etc. The communication interface(s) can detect and / or establish communication with the other user device(s) via one or more communication protocols such as Wi-Fi Direct, Bluetooth®, ultrasound beaconing, and / or other communication protocols that provide for peer-to-peer access between devices.

[0045] In some aspects of the present disclosure, the input unit 108 can be implemented as a transparent unit through which the light from the output unit 110 penetrates such that user can visualize the one or more outputs in the output unit 110 through the input unit 108.

[0046] The output unit 110 can be implemented any of a Liquid Crystal Display (LCD), a Thin Film Transistor LCD (TFT LCD), a Light Emitting Diode (LED), an Organic LED (OLED), an Active Matrix OLED (AMOLED), a flexible display, a bended display, a 3Dimensional (3D) display, and the like. Aspects of the present disclosure are intended to include, known, well developed and later developed output units. The output unit 110 can be implemented as a transparent or semi-transparent unit through which the light penetrates.

[0047] The user device 102 may further include a communication unit 112. The communication unit 112 may be adapted to transfer the input signals collected from the any of the input device 102. The input device 102 may further be configured to share the collected input signals outside the input device 102.

[0048] The memory 114 may be configured to store logic, instructions, circuitry, interfaces, and / or codes of the processor 116 to enable the processor 116 to execute the one or more operations associated with the system 100. The memory 114 may be further configured to store therein, data associated with the user device 102, and the like. It will be apparent to a person having ordinary skill in the art that the storage unit 124 may be configured to store various types of data associated with the user device 102, without deviating from the scope of the present disclosure. Examples of the memory 114 may include but are not limited to, a Relational database, a NoSQL database, a Cloud database, an Object-oriented database, and the like. Further, the storage unit 124 may include associated memories that may include, but is not limited to, a Read-Only Memory (ROM), a Random Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM). Embodiments of the present disclosure are intended to include or otherwise cover any type of the memory 114 including known, related art, and / or later developed technologies. In some embodiments of the present disclosure, a set of centralized or distributed networks of peripheral memory devices may be interfaced with the server 104, as an example, on a cloud server.

[0049] The processor 116 may be configured to execute various operations associated with the user device 102. Specifically, the processor 116 may be configured to execute the one or more operations associated with the user device 102 by communicating one or more commands and / or instructions over the communication network 106 to the user device 102 and the server 104. Examples of the processor 116 may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), and the like. Embodiments of the present disclosure are intended to include and / or otherwise cover any type of the processor 116 including known, related art, and / or later developed technologies. The processor 116 described herein as used by programmable logic controllers may include one or more central processor 116 (CPUs), graphical processor 116 (GPUs), or any other processor known in the art. More generally, a processor 116 as used herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor 116 may also comprise memory storing machine-readable instructions executable for performing tasks. A processor 116 acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and / or by routing the information to an output device. A processor 116 may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer. A processor 116 may be coupled (electrically and / or as comprising executable components) with any other processor 116 enabling interaction and / or communication there-between. A user interface processor 116 or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device. Various devices described herein including, without limitation, the programmable logic controllers and related computing infrastructure may comprise at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein. The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to one or more processors for execution. A computer readable medium may take many forms including, but not limited to, non-transitory, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks. Non-limiting examples of volatile media include dynamic memory. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up a system bus. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. An executable application, as used herein, comprises code or machine-readable instructions for conditioning the processor 116 to implement predetermined functions, such as those of an operating system, a context data acquisition system or other information processing system, for example, in response to user command or input. An executable procedure is a segment of code or machine-readable instruction, sub-routine, or other distinct section of code or portion of an executable application for performing one or more particular processes. These processes may include receiving input data and / or parameters, performing operations on received input data and / or performing functions in response to received input parameters, and providing resulting output data and / or parameters. The functions and process steps herein may be performed automatically, wholly or partially in response to user command. An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity.

[0050] The server 104 may be configured to receive the one or more first data provided by the user. The server 104 may further be configured to authenticate the user by comparing the one or more first data, representing a username and password, with data stored in a prestored database. The server 104 may further be configured to facilitate the user in generating a profile based on the one or more first data, representing user parameters. The server 104 may further be configured to send a first set of sample profiles to the user device, each profile having variable parameters, based on the user's preferences. The server 104 may further be configured to receive one or more second data from the user device indicating the user's acceptance or rejection of the sample profiles. The server 104 may further be configured to analyze the user's preferences based on the received one or more second data. The server 104 may further be configured to display one or more subsequent profiles to the user, scored and ranked according to the maximum matching of the user's preferences. In some aspects of the present disclosure, the server 104 further includes an encryption module to securely transmit the first data and second data between the user device 102 and the server 104, ensuring data integrity and user privacy during profile matching and authentication.

[0051] The server 104 may include a network interface 118, a I / O interface 120, a storage unit 124, and one or more processing unit (of which one processor is shown and designated as 122).

[0052] The processing unit 122, the storage unit 124, the network interface 118, and the I / O interface 120 may communicate with each other by way of a first communication bus 126. It will be apparent to a person having ordinary skill in the art that the processing circuitry is for illustrative purposes and not limited to any specific combination of hardware circuitry and / or software.

[0053] The network interface 118 may include suitable logic, circuitry, and interfaces that may be configured to establish and enable a communication between the server and different components of the system 100. The network interface 118 may be implemented by use of various known technologies to support wired or wireless communication of the information processing device with the communication network. The network interface 118 may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit.

[0054] The I / O interface 120 may include suitable logic, circuitry, interfaces, and / or code that may be configured to receive inputs (e.g., orders) and transmit server outputs via a plurality of data ports in the server. The I / O interface 120 may include various input and output data ports for different I / O devices. Examples of such I / O devices may include, but are not limited to, a touch screen, a keyboard, a mouse, a joystick, a projector audio output, a microphone, an image- capture device, a liquid crystal display (LCD) screen, and / or a speaker.

[0055] The processing unit 122 may be configured to execute various operations associated with the system 100. Specifically, the processing unit 122 may be configured to execute the one or more operations associated with the system 100 by communicating one or more commands and / or instructions over the communication network 106 to the user device 102 and the server 104. Examples of the processing circuitry 122 may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a field-programmable gate array (FPGA), a Programmable Logic Control unit (PLC), and the like. Embodiments of the present disclosure are intended to include and / or otherwise cover any type of the processor including known, related art, and / or later developed technologies. The processing unit 122 described herein as used by programmable logic controllers may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art. More generally, processing circuitry 122 as used herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processing unit 122 may also comprise memory storing machine-readable instructions executable for performing tasks. Processing unit 122 acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and / or by routing the information to an output device. Processing unit 122 may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer. Processing unit 122 may be coupled (electrically and / or as comprising executable components) with any other processor enabling interaction and / or communication there-between. A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device. Various devices described herein including, without limitation, the programmable logic controllers and related computing infrastructure may comprise at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein. The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to one or more processors for execution. A computer readable medium may take many forms including, but not limited to, non-transitory, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks. Non-limiting examples of volatile media include dynamic memory. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up a system bus. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. An executable application, as used herein, comprises code or machine readable instructions for conditioning the processor to implement predetermined functions, such as those of an operating system, a context data acquisition system or other information processing system, for example, in response to user command or input. An executable procedure is a segment of code or machine readable instruction, sub-routine, or other distinct section of code or portion of an executable application for performing one or more particular processes. These processes may include receiving input data and / or parameters, performing operations on received input data and / or performing functions in response to received input parameters, and providing resulting output data and / or parameters. The functions and process steps herein may be performed automatically, wholly or partially in response to user command. An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity.

[0056] The processing unit 122 may be configured to receive the one or more first data provided by the user. The processing unit 122 may further be configured to authenticate the user by comparing the one or more first data, representing a username and password, with data stored in a prestored database. The processing unit 122 may further be configured to facilitate the user in generating a profile based on the one or more first data, representing user parameters. The processing unit 122 may further be configured to send a first set of sample profiles to the user device, each profile having variable parameters, based on the user's preferences. The processing unit 122 may further be configured to receive one or more second data from the user device indicating the user's acceptance or rejection of the sample profiles. The processing unit 122 may further be configured to analyze the user's preferences based on the received one or more second data. The processing unit 122 may further be configured to display one or more subsequent profiles to the user, scored and ranked according to the maximum matching of the user's preferences.

[0057] The processing unit 122 may be further configured to process the received profile inputs to generate a prioritized list of candidate profiles that are most compatible with the user’s inferred intent. In some aspects of the present disclosure, the processing unit 122 may employ artificial intelligence and natural language processing (NLP) algorithms to extract semantic meaning from textual content within the user’s profile, and to compute contextual similarity between profiles using learned embedding representations or correlation models. The processing unit 122 may analyze multi-dimensional attributes such as interests, location, intent indicators, and interaction patterns to compute a compatibility score for each candidate profile. Based on these computed scores, processing unit 122 may generate a prioritized or ranked list of potential matches. The ranked candidate profiles may then be transmitted to and displayed on the graphical user interface (GUI) of the user device 102 in a sequential manner, allowing the user to view and interact with each profile individually.

[0058] The processing unit 122 may be configured to receive a first gesture input from the user, corresponding to an interest or acceptance action. The first gesture input may include, but is not limited to, a directional swipe (for example, a right swipe), a tap, or any other predefined gesture captured by the gesture-recognition interface of the user device 102. The processing unit 122 may interpret the received gesture as a positive signal indicating preference toward the displayed candidate profile. Similarly, the processing unit 122 may be configured to receive a second gesture input from the user corresponding to a skip or rejection action, such as a left swipe, back swipe, or another distinct gesture recognizable by the gesture-recognition module. These gesture-based inputs may be transmitted in real time to the server 104, where they are processed as behavioral feedback to update the user’s evolving preference model.

[0059] The processing unit 122 may further be configured to update a dynamic parameter-weighted preference model based on the received gesture inputs. The preference model may store feature weights associated with various user attributes, such as interests, age group, location proximity, or behavioral similarity, and dynamically adjust these weights in response to user actions. For example, when the user performs a first gesture input indicating interest, the processing unit 122 may increase the feature weights corresponding to the characteristics of the accepted profile. Conversely, when the user performs a second gesture input indicating disinterest, the processing unit 122 may decrease the feature weights associated with that profile’s parameters. The model may employ reinforcement learning or adaptive optimization algorithms to continuously fine-tune the relative importance of parameters based on ongoing gesture-derived feedback.

[0060] The processing unit 122 may be configured to adaptively refresh and reorder subsequent candidate profiles displayed to the user on the GUI of the user device 102. The reordering process may occur in real time and may utilize the updated feature weights to reprioritize candidate profiles that more closely align with the user’s refined preferences and inferred intent. This dynamic recalibration ensures that future recommendations are context-aware, adaptive, and increasingly accurate over successive interactions. By continuously learning from user gestures and behavioral responses, the processing unit 122 enables the system 100 to deliver an intelligent matchmaking experience that evolves uniquely for each user, thereby improving compatibility precision, engagement, and satisfaction over time.

[0061] The storage unit 124 may be configured to store logic, instructions, circuitry, interfaces, and / or codes of the processing unit 122 to enable the processing unit 122 to execute the one or more operations associated with the system 100. The storage unit 124 may be further configured to store therein, data associated with the server 104, and the like. It will be apparent to a person having ordinary skill in the art that the storage unit 124 may be configured to store various types of data associated with the server 104, without deviating from the scope of the present disclosure. Examples of the storage unit may include but are not limited to, a Relational database, a NoSQL database, a Cloud database, an Object-oriented database, and the like. Further, the storage unit may include associated memories that may include, but is not limited to, a Read-Only Memory (ROM), a Random Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and / or an Electrically EPROM (EEPROM). Embodiments of the present disclosure are intended to include or otherwise cover any type of the storage unit 124 including known, related art, and / or later developed technologies.

[0062] The processing unit 122 may include receiving engine 128, an authentication engine 130, a profile creation engine 134, an AI engine 136, a token generation engine 138, a conversation establishing engine 140, and a score allocation engine 142.

[0063] The receiving engine 128 may be configured for receiving initial data (username and password) for authentication and subsequent data, such as the user's acceptance or rejection of sample profiles. The receiving engine 128 may further be configured to receive feedback as second data and user-defined preferences, including criteria such as role, experience, industry, location, investor type, technology interests, and other customizable parameters.

[0064] The authentication engine 130 may be configured to compare the received first data (username and password) with pre-stored data for user authentication. The authentication engine 130 may further be configured to ensure secure access control and validation of user identity.

[0065] The profile creation engine 134 may be configured to facilitate the creation and customization of user profiles based on initial data provided by the user. The profile creation engine 134 may further be configured to adjust profile parameters dynamically based on user preferences and predefined criteria. The profile creation engine 134 may further be configured to enable users to assign specific weights to profile parameters for customized ranking, helping to prioritize criteria such as experience, industry, or location. In some aspects of the present disclosure, the user profile may be stored in the form of audio or video based instructions. In some aspects of the present disclosure, the initial data provided by the user may include email id, username, password, date of birth, and gender. In some aspects of the present disclosure, the initial data may further include title, organization / industry. In some other aspects of the present disclosure, the title may be one of, founder, investor, student, employee, enabler, intern, and the like.

[0066] In some other aspects of the present disclosure, the industry may be one of, deeptech, medtech, pharmaceutical, automobile, Information technology, electrical and electronics, food and beverage, hotel and agriculture, and the like.

[0067] The Artificial Intelligence (AI) Engine 124 may act as the main intelligence unit responsible for performing all the operations described in the system 100. The AI Engine 124 may work together with the processing unit 122 and automatically handles all data processing, decision-making, and adaptive learning tasks. It is designed to learn from user behavior and continuously improve its performance over time.

[0068] The AI Engine 124 may receive user inputs such as login details, preferences, and profile information from the user device 102. It verifies the user’s credentials with stored data in the database and then creates a detailed user profile. Based on this information, the AI Engine 124 may generate and sends a set of sample profiles to the user device. When the user accepts or rejects these profiles, the AI Engine 124 may analyze this feedback to understand the user’s choices and preferences.

[0069] The AI Engine 124 may use artificial intelligence and natural language processing (NLP) techniques to analyze user data, interests, and behavioral patterns. It compares different profiles using compatibility scoring models and ranks them according to how well they match the user’s preferences. The AI Engine 124 may continuously update these rankings as it learns more about the user’s actions and interests.

[0070] The AI Engine 124 may also interpret gesture inputs such as swipes, taps, or clicks from the user device. A right swipe or tap may indicate interest, while a left swipe or back action may indicate rejection. Based on these gestures, the AI Engine 124 may update the user’s preference model by adjusting the importance of different profile features. For example, if the user repeatedly shows interest in profiles with similar characteristics, the AI Engine 124 increases the weight of those features in future recommendations.

[0071] Using this continuously updated information, the AI Engine 124 may reorder and refreshes the profiles displayed to the user in real time. This ensures that the system 100 shows more accurate and personalized matches as the user continues to interact. The AI Engine 124 may use deep learning, reinforcement learning, or optimization algorithms to learn user behavior and improve its accuracy.

[0072] The AI Engine 124 may also monitor its performance based on user engagement and feedback. It automatically updates its internal settings to deliver better results over time. Through this ongoing learning process, the AI Engine 124 may enable the system 100 to provide an intelligent, personalized, and adaptive matchmaking experience that becomes more accurate and relevant with every user interaction.

[0073] The token generation engine 138 may be configured to generate both fungible and non-fungible tokens that users can utilize to unlock communication capabilities with matched profiles.

[0074] The token generation engine 138 may further be configured to ensure secure and verifiable access to enhanced features or communication tools within the community connection system.

[0075] The conversation establishing engine 140 may be configured to manage and facilitate communication between the user device 102 and the server 104, enabling seamless interaction with suggested profiles. The conversation establishing engine 140 may further be configured to utilize tokens to grant access to communication channels, allowing users to connect with profiles that match their preferences.

[0076] The score allocation engine 142 may be configured to rank and scores profiles based on the user’s preferences, predefined matching criteria, and any custom weights assigned by the user. The score allocation engine 142 may further be configured to utilize a scoring algorithm that evaluates factors such as profile similarity, completeness, and user feedback to display profiles in an order of relevance.

[0077] The historical data management engine 144 may be configured to maintain a database of user interactions, including acceptance or rejection patterns, to improve the personalization of profile recommendations over time. The historical data management engine 144 may further be configured to allow the AI Engine136 to analyze trends and continuously refine profile suggestions based on historical behavior.

[0078] In some aspects of the present disclosure, the first gesture input may comprise a swipe in a first direction or a tap gesture, and the second gesture input comprises a swipe in a second, opposite direction.

[0079] The gesture-recognition interface of the user device 102 may be adapted to detect, interpret, and classify various user gestures performed on the graphical user interface (GUI). The interface may include one or more sensors, touch-sensitive panels, or motion detection modules capable of recognizing directional gestures, positional coordinates, and temporal motion parameters associated with user interactions.

[0080] The first gesture input may be represented by a rightward swipe, upward swipe, or tap on the profile image displayed on the GUI, which may correspond to an interest action, indicating that the user wishes to express approval or preference toward the displayed candidate profile. Conversely, the second gesture input may be represented by a leftward or downward swipe, or a gesture in the direction opposite to that of the first gesture input, which corresponds to a skip or rejection action, signifying that the user is not interested in the presented candidate profile.

[0081] In some aspects of the present disclosure, the graphical user interface (GUI) may further configure to display user posts, events, and communities.

[0082] The graphical user interface (GUI) of the user device 102 may be configured to display a variety of interactive content elements including user posts, events, and communities, thereby extending the functionality of the matchmaking system 100 beyond one-to-one profile recommendations. The GUI may serve as a dynamic, context-aware interface through which users can explore not only compatible profiles but also engage in broader social or professional interactions within the platform. The GUI may be implemented as an adaptive, data-driven interface capable of rendering real-time content streams sourced from the server 104. These content streams may include user-generated posts, scheduled events, and community discussions, which are algorithmically curated based on the user’s interests, preferences, and historical engagement patterns.

[0083] The user posts displayed on the GUI may include textual content, images, videos, or external links shared by users to express opinions, experiences, or personal updates. The system 100 may employ content filtering and natural language processing (NLP) techniques within the processing unit 114 of the server 104 to categorize these posts under relevant topics such as lifestyle, hobbies, professional insights. The GUI may organize the posts in a scrollable feed or modular tile arrangement, allowing users to interact through likes, comments, or gesture-based reactions. Such engagement data may be captured by the input unit 110 and transmitted to the server 104 for behavioral analysis, thereby enhancing the user’s preference model and improving accuracy recommendation.

[0084] The events section of the GUI may be configured to display upcoming or ongoing events relevant to the user’s inferred interests or geographical proximity. These events may include social gatherings, webinars, professional meetups, workshops, or online discussions organized by other users or verified hosts within the platform. The processing unit 114 may analyze user profiles and contextual intent data to recommend events that align with the user’s preferences, professional domain, or desired networking category. The GUI may present event details such as time, date, description, and participation status, along with an option to register or express interest via a simple gesture or button input. In some aspects of the present disclosure, the system 100 may provide reminders or notifications for registered events and display mutual attendees to encourage meaningful engagement.

[0085] The community section of the GUI may serve as an interactive space where users with shared interests, goals, or backgrounds can connect and collaborate. Each community may be represented as a digital group or forum dedicated to a specific domain such as entrepreneurship, creative arts, technology, education, or travel. The GUI may display community recommendations based on semantic correlation between a user’s interests and the thematic attributes of existing communities stored in the database. The user may join, follow, or participate in discussions within a selected community through the same GUI interface. The output unit 112 may render real-time updates from community threads, announcements, or trending topics, while the input unit 110 allows users to post, comment, or react.

[0086] In some aspects of the present disclosure, the GUI may integrate all three modules posts, events, and communities within a unified dashboard layout, allowing seamless navigation between matchmaking recommendations and social engagement activities. The interface may be designed to adapt dynamically based on screen size, user interaction frequency, and personalization settings stored in the user profile database. The server 104 may further employ machine learning techniques to determine which types of content (e.g., posts versus events) hold higher engagement relevance for each user and adjust the interface display accordingly. The GUI may also include visual indicators such as icons, tabs, or color-coded categories to help users easily distinguish between content types.

[0087] In some aspects of the present disclosure, the processing unit 122 may include a natural language processing (NLP) engine configured to semantically analyze user-input text on profiles to infer contextual meaning and relationship intent.

[0088] The processing unit 122 of the server 104 may comprise a Natural Language Processing (NLP) engine configured to semantically analyze user-input text on profiles to infer contextual meaning and relationship intent. The NLP engine may include suitable logic, algorithms, and machine learning models for processing and understanding natural language expressions entered by users as part of their profile descriptions, preferences, and other textual inputs. The NLP engine may operate by converting free-form text data into structured, machine-interpretable semantic representations that capture both the explicit and implicit meanings conveyed by the user. This enables the system 100 to interpret textual information not merely at a keyword or surface level, but in terms of contextual relevance, emotional tone, and intent.

[0089] In some aspects of the present disclosure, the dynamic parameter-weighted preference model updates parameter scores using reinforcement learning instructions based on gesture feedback, increasing weights for parameters associated with accepted profiles and decreasing weights for parameters associated with skipped profiles.

[0090] The processing unit 122 of the server 104 may include or execute a dynamic parameter-weighted preference model configured to continuously adapt and refine the matchmaking process based on user interactions. The preference model may be designed to learn user-specific behavioral patterns through reinforcement learning (RL) principles, wherein the system dynamically updates internal parameter weights according to the feedback received from user gestures. In this configuration, each user interaction such as a swipe, tap, or skip serves as an implicit feedback signal that reflects the user’s evolving preference structure. The system 100 interprets a positive gesture (for example, a right swipe or tap) as a form of reward feedback, while a negative gesture (for example, a left swipe or skip) is interpreted as a penalty or negative reinforcement signal. These feedback events are continuously recorded and analyzed by the preference model to optimize future matchmaking recommendations in real time.

[0091] The dynamic parameter-weighted preference model may represent each user’s preference profile as a weighted vector comprising multiple parameters such as interests, occupation, age range, geographical proximity, lifestyle attributes, communication tone, and inferred intent. Each of these parameters is assigned an adjustable weight that signifies its relative importance in determining compatibility between profiles. When a user performs an interest action on a displayed candidate profile (for example, a right swipe), the reinforcement learning instruction increases the weights associated with the dominant characteristics of that accepted profile. For instance, if the accepted profile includes a particular profession, hobby, or personality trait, the model increases the significance of those features in subsequent scoring functions. Conversely, when the user performs a skip gesture (for example, a left swipe), the model decreases the weights of the attributes associated with the skipped profile. This continuous process of incremental reward and penalty adjustment allows the model to converge toward an accurate representation of the user’s implicit decision-making logic.

[0092] The reinforcement learning framework may employ algorithms such as Q-learning, policy gradient methods, or contextual bandits to guide the weight adjustment process. Each interaction is treated as a state-action-reward sequence, where the state corresponds to the user’s current preference vector, the action corresponds to the gesture input, and the reward reflects the user’s satisfaction or interest level inferred from that gesture. Over successive iterations, the model optimizes a scoring policy that maximizes the cumulative reward, the probability of recommending profiles that elicit positive user responses. The learning process may further incorporate exploration-exploitation balancing, ensuring that the system not only reinforces known preferences but also periodically explores new or untested profile categories to discover hidden user interests.

[0093] In some aspects of the present disclosure, the processing unit 122 may further configured to detect latent preference clusters from historical gesture data and adjust to emphasize parameters corresponding to frequently accepted categories.

[0094] The processing unit 122 of the server 104 may be configured to detect latent preference clusters from accumulated or historical gesture data generated by the user during interaction with the matchmaking system 100. The term latent preference clusters refers to underlying, implicit groupings of user interests or behavioral tendencies that are not explicitly defined by the user but are inferred through consistent gesture patterns and historical interaction feedback. The processing unit 122 may include suitable machine learning algorithms, clustering engines, and data analytics models to identify these hidden correlations across multiple interaction dimensions, thereby enhancing the adaptability and intelligence of the recommendation process.

[0095] In some aspects of the present disclosure, the user device 102 may further include a haptic feedback interface configured to generate a tactile response corresponding to a successful match or mutual interest event.

[0096] The haptic feedback interface may configure to generate a tactile response corresponding to a successful match or mutual interest event detected within the matchmaking system 100. The haptic feedback interface may be implemented as an integrated hardware and software component designed to provide sensory feedback to the user through controlled vibration patterns, pressure pulses, or other tactile sensations. The inclusion of this interface enhances user engagement by delivering immediate and intuitive confirmation of key interactive events, such as when two users express mutual interest in each other’s profiles.

[0097] The haptic feedback interface may be communicatively coupled with the output unit 110 and controlled by the processing circuitry of the user device 102, which executes predefined vibration or tactile response instructions upon receiving a corresponding trigger signal from the server 104. When the processing unit 122 of the server 104 identifies a mutual interest event such as when two users perform a “first gesture input” (for example, a right swipe or tap) on each other’s profiles the server transmits a notification signal to both user devices through the communication network 106. Upon receiving this signal, the haptic feedback interface of each device activates a predefined tactile pattern, such as a short double vibration or pulsing effect, to indicate the occurrence of a successful match. This tactile confirmation provides a physical cue to the user, reinforcing positive engagement and enhancing the emotional resonance of the interaction.

[0098] In some aspects of the present disclosure, the haptic feedback interface may be implemented using vibration motors, piezoelectric actuators, or electroactive polymer layers, which convert electrical signals into mechanical movement or vibration. The system may support multiple feedback intensities and vibration patterns corresponding to different event types. For example, a light, brief vibration may indicate a received message or match request, while a stronger or rhythmic vibration may signify a confirmed mutual connection. The user device 102 may store a set of haptic feedback profiles that define specific response characteristics such as frequency, amplitude, duration, and repetition pattern for various system events, allowing for consistent and recognizable tactile signalling.

[0099] In some aspects of the present disclosure, the processing unit 122 may be configured to support domain-specific matchmaking including professional, academic, or entrepreneurial connections by interpreting user-provided intent indicators.

[0100] The processing unit 122 may be configured to support domain-specific matchmaking, including but not limited to professional, academic, and entrepreneurial connections, by interpreting user-provided intent indicators. The processing unit 122 employs intelligent data interpretation techniques to analyze the context, tone, and semantic content of user-submitted information to identify the specific purpose or intent of interaction within the matchmaking ecosystem.

[0101] In some aspects of the present disclosure, users may input explicit intent indicators during registration or while updating their profiles. Such indicators may include predefined options like “seeking professional collaboration,” “academic research partnership,” “startup co-founder,” “mentorship opportunity,” or “social discovery.” The processing unit 122 interprets these inputs using a combination of natural language processing (NLP), contextual keyword extraction, and semantic intent classification models. For example, if a user states “looking for investment partners for a new AI venture,” the NLP engine categorizes the intent as “entrepreneurial” and prioritizes profile matching with users who have similar entrepreneurial or investment interests.

[0102] In some aspects of the present disclosure, the processing unit 122 may further infer intent implicitly through behavioral cues, such as the types of profiles the user engages with, the communities they follow, or the nature of posts they create. Using machine learning-based pattern recognition, the system dynamically updates a domain-intent vector for each user that quantifies their likelihood of engaging in specific types of connections such as professional networking versus social companionship. This domain-intent vector is continuously refined based on the user’s gesture feedback (e.g., profile swipes, likes, or skips), thereby allowing the system to evolve its understanding of user intent over time.

[0103] The processing unit 122 may then employ this intent interpretation to filter, rank, and present candidate profiles that align with the same or compatible intent domains. For instance, in the case of professional matchmaking, the system may emphasize parameters such as occupation, skill set, industry sector, and experience level. In an academic context, it may prioritize shared research areas, educational qualifications, and institutional affiliations. For entrepreneurial matchmaking, it may highlight complementary business interests, funding stage, or startup role preferences. By tailoring its recommendation logic to these domain-specific parameters, the system ensures that users receive more relevant and goal-aligned matches.

[0104] In some aspects of the present disclosure, processing unit 122 may utilize semantic embeddings and collaborative filtering to determine inter-profile compatibility beyond explicit user inputs.

[0105] The processing unit 122 is configured to utilize semantic embeddings in conjunction with collaborative filtering techniques to determine inter-profile compatibility beyond the scope of explicit user inputs. Traditional matchmaking systems typically rely on direct parameter matching such as shared interests, age, or location which often fails to capture the deeper contextual or behavioral alignment between users. To overcome this limitation, the present system 100 introduces a hybrid intelligence model that learns latent relationships and compatibility factors from both textual and behavioral data through machine learning representations.

[0106] The semantic embedding framework implemented by the processing unit 122 converts user-provided textual inputs such as bios, interests, posts, and intent statements into high-dimensional vector representations. Each vector encodes the semantic meaning and contextual associations of words, phrases, and sentences, enabling the system to quantify similarity between profiles based on meaning rather than just keyword overlap. For instance, two users may describe their professional interests differently one stating “AI researcher” and another “machine learning scientist” yet the embedding model maps these expressions to closely related positions in the semantic vector space, thereby recognizing a strong contextual similarity.

[0107] In some aspects of the present disclosure, processing unit 122 integrates the outputs of both semantic embeddings and collaborative filtering into a unified compatibility scoring model. This hybrid scoring system assigns a composite compatibility value to each candidate profile pair, considering both semantic proximity (derived from text analysis) and behavioral similarity (derived from interaction data). For example, if two users exhibit comparable communication styles or have historically liked similar types of profiles, the system increases their compatibility score, even in the absence of explicitly matched profile attributes.

[0108] Furthermore, the semantic embedding model may be trained using deep learning architectures, such as transformer-based neural networks or recurrent networks, that enable contextual understanding of language across multiple domains professional, academic, or social. This allows the system to capture nuanced distinctions in user intent, tone, and personality expression. The embeddings may also evolve dynamically, being fine-tuned periodically as the user’s behavioral and textual data expands, ensuring that compatibility computations remain current and reflective of ongoing user development.

[0109] In some aspects of the present disclosure, the collaborative filtering component may employ either user-based or item-based approaches. In the user-based method, the system identifies clusters of users who demonstrate similar engagement behaviors and leverages their collective preferences to enhance matchmaking accuracy for individuals within the cluster. In the item-based method, the system determines relationships among profiles that attract similar users, thereby identifying hidden affinities among candidate profiles. The processing unit 122 may also apply matrix factorization techniques or graph-based collaborative learning to decompose large-scale behavioral data into latent features that represent unobserved compatibility factors.

[0110] In some aspects of the present disclosure, the processing unit 122 may continuously recalibrate profile scoring based on both user gesture history and cross-user correlation data obtained from a global behavioral dataset.

[0111] The processing unit 122 is configured to continuously recalibrate profile scoring by dynamically integrating two primary data sources, the individual user’s gesture history and cross-user correlation data derived from a global behavioral dataset. This continuous recalibration mechanism allows the matchmaking system 100 to maintain an evolving understanding of user preferences, ensuring that compatibility assessments remain accurate and contextually relevant over time.

[0112] The gesture history refers to the sequence and nature of interaction gestures performed by a user on the graphical user interface (GUI), such as directional swipes, taps, long presses, or skips that indicate varying levels of interest or disinterest toward displayed profiles. The processing unit 122 continuously monitors and records these gestures, assigning quantitative feedback values (for instance, positive reinforcement for “interest” actions and negative reinforcement for “skip” actions). This gesture data forms the foundation for an adaptive reinforcement learning framework, wherein the system adjusts the feature weights associated with profile attributes such as age, interests, communication tone, or occupation based on observed interaction outcomes. Over time, attributes correlated with profiles that receive frequent positive gestures are weighed more heavily, while those linked to skipped or ignored profiles are deprioritized.

[0113] The global behavioral dataset, which may aggregate anonymized user interaction patterns from across the entire matchmaking network. This dataset captures large-scale behavioral correlations such as how users with similar preferences or demographic attributes respond to certain types of profiles. By performing cross-user correlation analysis, the processing unit identifies macro-level compatibility trends, for example, recognizing that users interested in “entrepreneurial networking” tend to favor profiles with indicators of business leadership or innovation, even across diverse geographical regions. These cross-user insights are used to fine-tune the scoring algorithms for each individual user, ensuring that profile recommendations benefit from the collective intelligence of the broader user base.

[0114] The continuous recalibration process is executed through periodic or event-driven updates, wherein the system reevaluates all candidate profiles considering newly acquired gesture feedback and evolving correlation data. The recalibration engine within the processing unit 122 employs machine learning optimization algorithms such as gradient-based weight adjustment or Bayesian updating to change the internal scoring parameters. As a result, the matchmaking system can detect subtle preference shifts, such as a user developing an affinity for new interests or connection types and immediately reflect these shifts in next recommendations.

[0115] In some aspects of the present disclosure, the global behavioral dataset may be structured as a distributed database that aggregates anonymized feature response pairs, preserving privacy while enabling large-scale statistical learning. The processing unit 122 may employ federated learning techniques to extract and apply global insights without directly accessing or compromising individual user data. This ensures that while scoring logic benefits from network-wide learning, user-specific privacy and data integrity remain intact.

[0116] Furthermore, the recalibrated scoring system may continuously synchronize with other analytical components such as the semantic embedding engine and collaborative filtering module to ensure that both contextual meaning and behavioral tendencies are reflected in the updated compatibility rankings. The outcome of this integrated recalibration is a real-time adaptive matchmaking experience, where profile suggestions evolve organically as user interactions accumulate, and where scoring precision improves with every engagement cycle.

[0117] In some aspects of the present disclosure, upon each gesture input, the processing unit 122 may compute a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and updates the model accordingly.

[0118] The processing unit 122 may be configured to compute a gesture-response confidence metric upon receiving each gesture input from the user. This metric quantitatively represents the predictive reliability of the system’s currently inferred user preferences based on the nature, consistency, and contextual strength of the user’s interactive behavior. The resulting gesture-response metric functions as a probabilistic weight that determines the extent to which the underlying preference model should be updated in response to the new interaction.

[0119] In one embodiment, every gesture input such as a directional swipe, tap, or long-press triggers a micro-level inference operation within the processing unit 122. The system analyzes the gesture’s type, timing, and contextual association with displayed profile attributes. For instance, if the user performs a rapid “interest” gesture immediately after viewing profiles with similar characteristics (e.g., shared occupation or hobbies), the system interprets the gesture as having a high confidence level, signifying that the current preference model accurately reflects the user’s true interests. Conversely, if the user unexpectedly skips a profile that the system had predicted as a high match based on prior data, the resulting confidence metric is low, signaling a potential misalignment between the model’s inference and the user’s actual intent.

[0120] The processing unit 122 may compute the confidence metric using a combination of Bayesian inference, probabilistic weighting, and entropy-based uncertainty modeling. Each new gesture event contributes to a confidence update, calculated as a function of historical consistency (how similar this gesture is to prior actions), contextual similarity (how related the interacted profile is to past accepted ones), and model variance (how confident the model was in predicting this outcome). The confidence value typically ranges between 0 and 1, where values approaching 1 indicate strong reinforcement of the existing model, and lower values indicate potential deviation requiring recalibration.

[0121] Once the gesture-response confidence metric is computed, the processing unit 122 uses it to adjust the learning rate or update strength applied to the user’s preference model. High-confidence gestures trigger stronger reinforcement learning updates, increasing the feature weights associated with the accepted profile parameters (such as “education,” “career domain,” or “interest cluster”). Conversely, low-confidence gestures trigger moderated or exploratory updates, encouraging the system to reassess or diversify its weighting strategy to reduce overfitting to uncertain behavior. Over time, this selective update mechanism ensures that the model evolves in proportion to the statistical reliability of the user’s feedback rather than treating all gestures equally.

[0122] In some embodiments, the gesture-response confidence metric may also be aggregated across multiple sessions to form a temporal confidence profile. This allows the processing unit 122 to identify patterns of stable versus volatile user preferences. For example, consistent high-confidence responses to profiles with similar personality traits over multiple sessions may indicate a long-term preference pattern, whereas fluctuating low-confidence responses may suggest exploratory behavior or evolving intent. The system uses these longitudinal insights to modulate the sensitivity of future updates making the model more adaptive when confidence patterns vary and more stable when confidence remains high over time.

[0123] Furthermore, the confidence metric may interact with the reinforcement learning engine and the dynamic parameter-weighted model to modulate feature reweighting in real time. For instance, when confidence metrics consistently validate certain parameters (such as “communication style” or “shared goals”), the model autonomously strengthens their predictive importance. In contrast, parameters frequently associated with low-confidence gestures may be temporarily deprioritized or re-evaluated for contextual inconsistencies.

[0124] In an exemplary scenario, a user initially logs in to the artificial intelligence-based matchmaking system 100, where the receiving engine 128 accepts their username and password. The authentication engine 130 verifies these credentials against pre-stored data within the server 104 to ensure secure access. Upon successful authentication, the user may input personal preferences such as occupation, interests, location, relationship intent, or domain-specific objectives like professional collaboration or entrepreneurial networking. The profile creation engine 134 utilizes these inputs to generate a detailed user profile and allows the user to assign specific weights to preferred attributes, such as prioritizing professional background over geographical proximity, thereby tailoring the matching process to individual preferences. As the user begins interacting with the system 100, the graphical user interface (GUI) of the user device 102 displays a sequence of candidate profiles curated by the processing unit 122 based on semantic similarity and contextual relevance. The user may express interest through a first gesture input, such as a right swipe or tap, or indicate disinterest through a second gesture input, such as a left swipe or skip. These gestures are detected by the gesture-recognition interface and transmitted to the server 104 for processing. The AI engine 136 analyzes these interaction patterns in real time and updates a dynamic parameter-weighted preference model, wherein the feature weights of accepted profiles are increased, and those of rejected profiles are decreased. The system 100 further employs a reinforcement learning framework that enables the AI engine 136 to continuously optimize profile ranking logic based on gesture-derived feedback. Each gesture interaction contributes to recalibrating compatibility scores, ensuring that subsequent recommendations are more closely aligned with the user’s evolving interests. In parallel, the NLP engine semantically interprets textual information within the user’s profile and the candidate profiles to extract contextual meaning, intent, and behavioral tone. This allows the AI engine 136 to generate compatibility rankings that go beyond surface-level parameter matching. To enhance user engagement, the token generation engine 138 provides the user with fungible or non-fungible tokens that unlock communication privileges or special matchmaking features. The conversation establishing engine 140 utilizes these tokens to facilitate seamless communication between users who exhibit mutual interest, enabling chat, voice, or video interactions directly through the platform. During these interactions, the system 100 may generate a tactile notification through the haptic feedback interface upon detecting a successful match event. Meanwhile, the historical data management engine 144 maintains a repository of past interactions, accepted and rejected profiles, and communication history. This stored data enables the AI engine 136 to identify latent preference clusters, refine long-term behavioral models, and dynamically adjust scoring logic in future sessions. The score allocation engine 142 evaluates and ranks candidate profiles based on user-defined weight parameters, reinforcement learning outcomes, and the gesture-response confidence metric computed by the processing unit 122. The ranking dynamically updates with each new interaction, ensuring that the profiles displayed on the GUI remain contextually relevant, personalized, and adaptive to the user’s ongoing behavioral evolution. Ultimately, this adaptive feedback cycle allows the artificial intelligence-based matchmaking system 100 to evolve continuously with user behavior, providing increasingly accurate, intent-driven, and semantically contextual profile recommendations, thereby ensuring an intelligent, personalized, and rewarding matchmaking experience.

[0125] In another exemplary scenario, a user looking for investment opportunities logs in to the artificial intelligence-based matchmaking system 100 through the graphical user interface (GUI) of the user device 102. The receiving engine 128 accepts the user’s login credentials, such as username and password, while the authentication engine 130 verifies the credentials against securely stored data within the server 104 to ensure authenticated access. Once successfully authenticated, the user specifies investment preferences such as preferred industries, funding range, geographical focus, startup maturity level, and desired partnership type. The profile creation engine 134 processes these inputs to generate a personalized investor profile, enabling the user to assign weights to specific parameters such as industry sector, business model, or investment stage thereby customizing how potential opportunities will be prioritized and displayed. The processing unit 122, in coordination with the AI engine 136, analyzes these investor preferences using semantic correlation and contextual similarity techniques to retrieve a list of startup or entrepreneur profiles that align with the investor’s interests. The graphical user interface displays these profiles sequentially, allowing the investor to express interest through a first gesture input, such as a right swipe or tap, or to reject profiles through a second gesture input, such as a left swipe or skip. Each gesture is detected by the gesture-recognition interface and transmitted in real time to the server 104. The AI engine 136 interprets these gestures as reinforcement feedback and dynamically updates the parameter-weighted preference model to strengthen the correlation between the user’s gestures and the attributes of accepted or rejected startup profiles. As the investor continues to interact, the reinforcement learning framework within the AI engine 136 continually recalibrates the recommendation logic, ensuring that each subsequent set of investment opportunities more accurately reflects the investor’s evolving focus and interests. The natural language processing (NLP) engine within the processing unit 122 semantically analyzes textual content from startup descriptions, business proposals, and founders’ bios to infer intent, growth potential, and contextual relevance. Using these analyses, the AI engine 136 generates a compatibility score for each opportunity based on multiple dimensions, including domain alignment, scalability potential, and past interaction patterns. To facilitate secure engagement between investors and entrepreneurs, the token generation engine 138 issues digital tokens that grant the investor controlled access to advanced communication tools such as private chat, video calls, or document sharing. The conversation establishing engine 140 utilizes these tokens to create authenticated communication channels between the investor and the selected startup, ensuring data security and traceability throughout the discussion. Meanwhile, the historical data management engine 144 continuously stores and analyzes interaction data such as accepted profiles, discussion outcomes, and investment preferences to identify latent investment trends and refine the system’s predictive accuracy over time. The score allocation engine 142 evaluates and ranks all candidate startup profiles by combining user-defined weights, reinforcement learning feedback, and contextual NLP insights to ensure that the most promising investment opportunities appear at the top of the recommendation list. The system 100 dynamically updates these rankings with each new interaction or feedback instance, ensuring that the displayed profiles evolve in alignment with the investor’s decision-making behavior. Through this intelligent, adaptive feedback cycle, the artificial intelligence-based matchmaking system 100 empowers investors to discover, evaluate, and engage with highly compatible opportunities in a secure, data-driven, and context-aware environment.

[0126] In another exemplary scenario, consider a user seeking potential collaborators for a technology startup who logs in to the artificial intelligence-based matchmaking system 100 via the graphical user interface (GUI) of the user device 102. The receiving engine 128 accepts the user’s credentials, such as username and password, while the authentication engine 130 verifies the credentials against pre-stored authentication data within the server 104 to ensure secure access. Upon successful authentication, the user specifies collaboration-related preferences including desired co-founder skill sets, technical expertise, domain focus (such as AI, IoT, or blockchain), geographical availability, and preferred collaboration model (for example, equity-based or remote partnership). The profile creation engine 134 utilizes this information to create a structured collaboration profile and allows the user to assign weights to parameters such as technical skill alignment, industry experience, or business role preference to prioritize collaboration matches accordingly. The processing unit 122, in association with the AI engine 136, analyzes these preferences to generate a prioritized list of candidate collaborators whose profiles semantically and contextually align with the user’s stated requirements. The graphical user interface (GUI) of the user device 102 then displays these profiles sequentially, allowing the user to express interest through a first gesture input (for example, a right swipe or tap) or indicate disinterest through a second gesture input (for example, a left swipe or skip). These gestures are detected by the gesture-recognition interface and transmitted to the server 104, where the AI engine 136 interprets them as behavioral feedback. Each gesture serves as a reinforcement signal that enables the system to update the dynamic parameter-weighted preference model in real time, adjusting the relative importance of profile attributes such as technical expertise, entrepreneurial background, and location proximity. The NLP engine within the processing unit 122 semantically analyzes textual content from collaborator profiles, bios, and skill descriptions to infer contextual meaning and intent, identifying complementary capabilities such as business development, software engineering, or product design expertise. The AI engine 136 combines this semantic understanding with reinforcement learning feedback to adaptively refine the compatibility scores of potential collaborators. Consequently, profiles that better match the user’s startup goals and skill requirements are prioritized for display. The processing unit 122 continuously recalibrates ranking weights by integrating both user gesture history and cross-user correlation data from a global behavioral dataset, ensuring that recommendations evolve dynamically as the user’s interaction patterns mature. Once mutual interest is established between two users, the token generation engine 138 issues secure digital tokens that unlock access to advanced collaboration features such as direct chat, document sharing, or project workspace integration. The conversation establishing engine 140 utilizes these tokens to initiate secure and authenticated communication channels between both parties, facilitating discussions around partnership terms, business goals, and shared visions for the startup. The historical data management engine 144 concurrently stores all user interactions and feedback, enabling long-term behavioral analysis and identifying emerging collaboration trends or skill gaps. The score allocation engine 142 evaluates each collaborator profile based on weighted preference criteria, semantic intent, reinforcement learning feedback, and past engagement success rates, ensuring that the most relevant and capable collaborators are displayed at the top of the list. As the user continues interacting with the system, these scores and rankings are automatically updated to reflect evolving project needs and personal preferences. Through this adaptive feedback mechanism, the artificial intelligence-based matchmaking system 100 delivers a highly intelligent, context-aware, and goal-oriented collaboration experience, enabling entrepreneurs to identify, engage, and partner with the most suitable collaborators for building and scaling their technology startups efficiently.

[0127] In another exemplary scenario, a user seeking potential research collaborators logs in to the artificial intelligence-based matchmaking system 100 through the graphical user interface (GUI) of the user device 102. The receiving engine 128 accepts the user’s login credentials, while the authentication engine 130 verifies these credentials against pre-stored authentication data within the server 104 to ensure secure and authorized access. Upon successful authentication, the user may input preferences such as research domain, area of specialization, academic position, institution type, geographical location, and desired collaboration mode (for instance, co-authoring publications, joint grant applications, or technology development partnerships). The profile creation engine 134 processes these preferences to generate a personalized research profile, allowing the user to assign priority weights to specific criteria such as publication relevance, expertise level, or institutional ranking, ensuring that recommendations are optimized for meaningful academic collaborations. As the user begins exploring the platform, the processing unit 122 retrieves and ranks candidate collaborator profiles based on contextual, semantic, and intent-based similarity. The AI engine 136 analyzes the user’s research interests, publication records, and described objectives, comparing them with those of potential collaborators using semantic correlation models. The graphical user interface displays these profiles sequentially, enabling the user to express interest by performing a first gesture input (such as a right swipe or tap) or to skip unsuitable profiles through a second gesture input (such as a left swipe or skip). Each gesture is detected by the gesture-recognition interface and transmitted to the server 104, where the AI engine 136 interprets the input as behavioral feedback and updates the user’s preference model accordingly. The reinforcement learning framework integrated within the AI engine 136 dynamically adjusts feature weights corresponding to accepted or rejected profiles, continuously refining the accuracy of subsequent recommendations. Simultaneously, the natural language processing (NLP) engine semantically analyzes academic keywords, research abstracts, and project descriptions from user profiles to infer deeper contextual intent—such as identifying collaborators with similar research goals, complementary expertise, or compatible methodologies. As more gesture data and feedback are accumulated, the system 100 learns implicit user behavior, allowing it to automatically adapt and present increasingly relevant collaborator profiles that align with the user’s evolving research interests. In parallel, the token generation engine 138 issues secure digital tokens to users who wish to initiate direct communication with potential collaborators. These tokens grant access to advanced communication features such as proposal discussions, project document exchange, and video conferencing capabilities. The conversation establishing engine 140 utilizes these tokens to enable verified and secure communication between researchers, ensuring privacy and integrity in academic interactions. The historical data management engine 144 stores all user interactions, feedback, and collaboration outcomes, which helps the AI engine 136 identify latent preference clusters such as recurring interest in specific domains or collaboration types and incorporate these patterns into future matchmaking processes. Finally, the score allocation engine 142 evaluates and ranks each potential collaborator’s profile using the user’s assigned weight parameters, compatibility metrics, and past feedback data, ensuring that the most suitable and contextually relevant profiles appear first. As the system continuously learns and refines its algorithms based on ongoing user interactions, it delivers progressively accurate, domain-specific, and intent-driven research collaboration suggestions. Through this adaptive and intelligent feedback loop, the artificial intelligence-based matchmaking system 100 enables researchers to efficiently identify, connect, and collaborate with academic peers or experts who share complementary skills, thereby fostering innovation and accelerating interdisciplinary research outcomes.

[0128] In another exemplary scenario, an academic institution utilizes the artificial intelligence-based matchmaking system 100 to identify and connect with potential global research and innovation partners. The receiving engine 128 accepts the institution’s login credentials, while the authentication engine 130 verifies them against pre-stored organizational data within the server 104 to ensure authorized and secure access. Once successfully authenticated, the institution’s representative enters collaboration preferences such as research domain, funding area, project scale, partner type (for example, university, corporate lab, or government body), and target geography. The profile creation engine 134 processes these preferences to generate an institutional research profile, allowing the representative to assign weighted priorities to specific criteria such as technology domain alignment, funding eligibility, or innovation capability, thereby customizing how the system ranks potential global partners. The processing unit 122, in conjunction with the AI engine 136, analyzes the provided preferences and compares them with profiles of research organizations, funding agencies, and innovation centers stored in the system database. The graphical user interface (GUI) of the user device 102 displays these potential partner profiles sequentially, providing concise summaries of institutional strengths, ongoing projects, and collaboration history. The user can indicate interest through a first gesture input, such as a right swipe or tap, or reject irrelevant profiles through a second gesture input, such as a left swipe or skip. Each gesture is detected by the gesture-recognition interface and transmitted to the server 104, where the AI engine 136 processes the input as adaptive feedback and dynamically updates the institution’s preference model. The NLP engine within the processing unit 122 semantically analyzes textual content such as institutional mission statements, research abstracts, and funding proposals to infer contextual meaning, research intent, and innovation focus. The reinforcement learning framework embedded in the AI engine 136 uses this semantic understanding along with gesture-based feedback to continuously adjust the matchmaking algorithm, ensuring that subsequent recommendations better reflect the institution’s evolving partnership goals. Over successive interactions, the system 100 autonomously learns patterns of institutional preference such as recurring interest in certain countries, technology domains, or funding types and adapts its scoring logic accordingly. To facilitate formal engagement, the token generation engine 138 provides secure digital tokens to enable advanced communication features such as proposal exchange, memorandum-of-understanding (MoU) discussions, or videoconferencing with potential partners. The conversation establishing engine 140 utilizes these tokens to initiate authenticated communication channels between institutional representatives, allowing them to explore collaboration frameworks, funding mechanisms, and joint project possibilities. Meanwhile, the historical data management engine 144 records previous interaction data, accepted partnerships, and feedback patterns to assist in identifying long-term collaboration trends and research impact clusters. The score allocation engine 142 evaluates and ranks partner institutions based on weighted preference parameters, compatibility indicators, and prior engagement outcomes, ensuring that the most relevant and high-potential global collaborators are displayed at the top of the list. The system 100 continuously recalibrates these scores as it gathers new data, creating an evolving, intelligence-driven recommendation cycle. Through this adaptive and context-aware process, the artificial intelligence-based matchmaking system 100 empowers academic and research organizations to efficiently identify, evaluate, and connect with compatible global partners, thereby enhancing innovation capacity, fostering interdisciplinary cooperation, and accelerating the development of impactful research programs worldwide.

[0129] illustrates a flowchart that depicts a method 200 for artificial intelligence-based matchmaking and adaptive profile recommendation, in accordance with an aspect of the present disclosure. The method 200 may include the following steps:

[0130] At step 202, receiving, by a user device 102, one or more profile inputs from a user through an input unit 108, the profile inputs comprising at least textual, categorical, or behavioral data including occupation, interests, and intent parameters.

[0131] At step 204, transmitting, by the user device 102, the received profile inputs to a server 104 over a communication network 106.

[0132] At step 206, receiving, by the server 104, the profile inputs from the user device 102 and storing the inputs in a database 108 associated with the user.

[0133] At step 208, performing (208) semantic analysis of the user-provided textual content using a Natural Language Processing (NLP) engine to extract context and relationship intent.

[0134] At step 210, determining semantic correlation and contextual similarity between the user profile and other profiles in the dataset.

[0135] At step 212, applying a ranking instruction to generate the prioritized list of candidate profiles based on inferred intent and computed compatibility scores.

[0136] At step 214, displaying, by the output unit of the user device 102, one or more of the prioritized candidate profiles sequentially on a graphical user interface (GUI).

[0137] At step 216, receiving, by the gesture-recognition interface of the user device 102, a first gesture input corresponding to an interest action, the first gesture input comprising at least one of a directional swipe, a tap, or another predefined gesture.

[0138] At step 218, receiving, by the gesture-recognition interface of the user device 102, a second gesture input corresponding to a skip action, the second gesture input being distinct from the first gesture input.

[0139] At step 220, transmitting, by the user device 102, the received gesture inputs to the server 104.

[0140] At step 222, updating, by the processing unit 122, a dynamic parameter-weighted preference model based on the received gesture inputs, by increasing one or more feature weights associated with parameters of profiles corresponding to the first gesture input, decreasing one or more feature weights associated with parameters of profiles corresponding to the second gesture input, and recalibrating the preference model using a reinforcement learning instructions configured to adaptively optimize the scoring function based on gesture-derived feedback.

[0141] At step 224, adaptively refreshing and reordering, by the processing unit 122, the subsequent candidate profiles displayed to the user on the GUI in real time based on the updated preference model.

[0142] At step 226, detecting, by the processing unit 122, latent preference clusters from accumulated gesture data to identify emerging interest patterns.

[0143] At step 228, generating, by the processing unit 122, a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and uses the metric to weight future recommendations.

[0144] At step 230, transmitting, by the server 104, refined and reordered profile recommendations to the user device (102) for display.

[0145] At step 232, optionally generating, by the user device 102, a haptic feedback response upon occurrence of a mutual interest or successful match event.

[0146] The implementation set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detain above, other modifications or additions are possible. In particular, further features and / or variations can be provided in addition to those set forth herein. For example, the implementation described can be directed to various combinations and sub combinations of the disclosed features and / or combinations and sub combinations of the several further features disclosed above. In addition, the logic flows depicted in the accompany figures and / or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.

Claims

An artificial intelligence-based matchmaking system (100) for suggesting compatible user profiles on a graphical user interface (GUI), the system (100) comprising:a user device (102) comprises:an input unit (108) that is adapted to receive one or more input data from a user; andan output unit (110) that is communicatively coupled with the input unit (110);a server (104) that is communicatively coupled with the user device (102), comprises:a processing unit (122) disposed within the server (104) and configured to:receive and store one or more profile inputs provided by a user, the profile inputs comprising textual, categorical, or behavioral data including occupation, interests, or intent parameters;process the profile inputs and generate a prioritized list of candidate profiles based on semantic correlation, contextual similarity, and inferred intent;display one or more of the candidate profiles sequentially on the GUI of the user device (102);receive a first gesture input from the user corresponding to an interest action, the first gesture input comprising a directional swipe, tap, or other predefined gesture detected by the gesture-recognition interface;receive a second gesture input from the user corresponding to a skip action, the second gesture input being distinct from the first gesture input;update a dynamic parameter-weighted preference model based on the received first or second gesture input, wherein the model increases or decreases respective feature weights associated with parameters of the interacted profiles; andadaptively refresh and reorder the subsequent candidate profiles in real-time based on the updated feature weights, thereby refining future profile recommendations.The system (100) as claimed in claim 1, wherein the first gesture input comprises a swipe in a first direction or a tap gesture, and the second gesture input comprises a swipe in a second, opposite direction.The system (100) as claimed in claim 1, wherein the graphical user interface (GUI) is configured to display user posts, events, and communities.The system (100) as claimed in claim 1, wherein the processing unit (122) comprises a natural language processing (NLP) engine configured to semantically analyze user-input text on profiles to infer contextual meaning and relationship intent.The system (100) as claimed in claim 1, wherein the dynamic parameter-weighted preference model updates parameter scores using a reinforcement learning instructions based on gesture feedback, increasing weights for parameters associated with accepted profiles and decreasing weights for parameters associated with skipped profiles.The system (100) as claimed in claim 1, wherein the processing unit (122) is configured to detect latent preference clusters from historical gesture data and adjust to emphasize parameters corresponding to frequently accepted categories.The system (100) as claimed in claim 1, wherein the user device (102) further includes a haptic feedback interface configured to generate a tactile response corresponding to a successful match or mutual interest event.The system (100) as claimed in claim 1, wherein the processing unit (122) is configured to support domain-specific matchmaking including professional, academic, or entrepreneurial connections by interpreting user-provided intent indicators.The system (100) as claimed in claim 1, wherein the processing unit (122) utilizes semantic embeddings and collaborative filtering to determine inter-profile compatibility beyond explicit user inputs.The system (100) as claimed in claim 1, wherein the processing unit (122) continuously recalibrates profile scoring based on both user gesture history and cross-user correlation data obtained from a global behavioral dataset.The system (100) as claimed in claim 1, wherein upon each gesture input, the processing unit (122) computes a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and updates the model accordingly.A computer-implemented method (200) for artificial intelligence-based matchmaking and adaptive profile recommendation, comprising:receiving (202), by a user device (102), one or more profile inputs from a user through an input unit (108), the profile inputs comprising at least textual, categorical, or behavioral data including occupation, interests, and intent parameters;transmitting (204), by the user device (102), the received profile inputs to a server (104) over a communication network (106);receiving (206), by the server (104), the profile inputs from the user device (102) and storing the inputs in a database associated with the user;performing (208) semantic analysis of the user-provided textual content using a Natural Language Processing (NLP) engine to extract context and relationship intent;determining (210) semantic correlation and contextual similarity between the user profile and other profiles in the dataset; andapplying (212) a ranking instructions to generate the prioritized list of candidate profiles based on inferred intent and computed compatibility scores;displaying (214), by the output unit of the user device (102), one or more of the prioritized candidate profiles sequentially on a graphical user interface (GUI);receiving (216), by the gesture-recognition interface of the user device (102), a first gesture input corresponding to an interest action, the first gesture input comprising at least one of a directional swipe, a tap, or another predefined gesture;receiving (218), by the gesture-recognition interface of the user device (102), a second gesture input corresponding to a skip action, the second gesture input being distinct from the first gesture input;transmitting (220), by the user device (102), the received gesture inputs to the server (104);updating (222), by the processing unit (122), a dynamic parameter-weighted preference model based on the received gesture inputs, by increasing one or more feature weights associated with parameters of profiles corresponding to the first gesture input, decreasing one or more feature weights associated with parameters of profiles corresponding tothe second gesture input, and recalibrating the preference model using a reinforcement learning instructions configured to adaptively optimize the scoring function based on gesture-derived feedback;adaptively refreshing and reordering (224), by the processing unit (122), the subsequent candidate profiles displayed to the user on the GUI in real time based on the updated preference model;detecting (226), by the processing unit (122), latent preference clusters from accumulated gesture data to identify emerging interest patterns;generating (228), by the processing unit (122), a gesture-response confidence metric that quantifies the predictive reliability of inferred preferences and uses the metric to weight future recommendations;transmitting (230), by the server (104), refined and reordered profile recommendations to the user device (102) for display; andoptionally generating (232), by the user device (102), a haptic feedback response upon occurrence of a mutual interest or successful match event.

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