System and method for dynamically matching request with member group

Abstract

A method of dynamically matching a request from a requester to a population of members is provided. In one embodiment, the present invention enables an application to receive a request from a user based on at least one group profile identifier, a sample size, and a predetermined time period of a response, and enables the application to generate a member group for sending the request to the member group, and to determine whether the response satisfies a predetermined condition, a result of the activity is then presented to the user.

Description

Technical Field

[0001] The present invention generally relates to dynamic matching of requests from requestors. More specifically, the present invention relates to enabling requestors to dynamically connect with other users of a network based on dynamic requests and dynamically determining connections with prospective candidates based on predetermined attributes that the requestors have selected. Background Art

[0002] The following discussion of the background technology of the present invention is intended to assist in understanding the present invention. However, it should be appreciated that the discussion is not an acknowledgement or admission that any of the material referred to is published, known or part of the common general knowledge in any jurisdiction as of the priority date of the present application.

[0003] Existing traditional methods of collecting responses from group users usually require long waiting times and most of them do not have any guarantee of success. For example, in the post and wait method, users post an advertisement or survey notice and then wait, hoping for a response. Passive waiting is required, and the longer the wait, the higher the number of responses, but there is no guarantee that there will be a response. Another method is to send out an action communication, such as email, text message, flyer, to multiple users through various channels such as email, social media groups or work groups, and users simply wait and hope for a response. In addition to the waiting time, the success of any result also depends on the amount of material sent, which often occupies network bandwidth, floods the recipient's mailbox, and generates excessive waste.

[0004] No one seems to have attempted to address this problem. Therefore, the present invention seeks to at least partially overcome some of the above-mentioned shortcomings and provide an improved approach for addressing the above-mentioned challenges. Summary of the invention

[0005] According to a first aspect of the present disclosure, a method for dynamically matching a request from a requester with a member group is provided, the method comprising: receiving a request from a server from a requester, wherein the request comprises at least one group profile identifier, a sample size, and a response time period for a predetermined time period; generating, by the processor, an initial member group comprising one or more candidates, each candidate being associated with the at least one group profile identifier; and generating, by the processor, a final member group comprising a subset of the initial member group based on candidate preferences and candidate profile identities. The method also includes sending a first request by the server to a first subset of the final member population; determining whether the number of responders to the first request within an elapsed time is greater than or equal to a sample size; if the number of responders to the first request is less than the sample size and if the elapsed time is less than a predetermined time period for responses, the server sending subsequent requests to subsequent subsets of the final member population; determining whether the total number of responders to the first request and the subsequent requests within the elapsed time period is greater than or equal to the sample size, and if the total number of responders to the first request and the subsequent requests is less than the sample size and if the elapsed time is less than a predetermined response time period, sending subsequent requests to subsequent subsets of the final member population; when the total number of responders is equal to or greater than the sample size and the elapsed time exceeds a predetermined response time period, the server transmits to the requester a visual representation of the response obtained by the total number of responders.

[0006] According to other embodiments, the first subset of the final population of members is generated by determining an oversampling number, wherein the oversampling number comprises a sum of a sample size and a predetermined percentage of the sample size.

[0007] According to other embodiments, each of the one or more candidates is selected based on its association with at least one population profile identifier.

[0008] According to other embodiments, the candidate preferences include a temporal availability to respond to the first request.

[0009] According to other embodiments, the candidate profile identity represents the qualifications, reputation, or status of each of the one or more candidates within the user population.

[0010] According to other embodiments, the elapsed time represents the time that has elapsed since the first request was sent.

[0011] According to other embodiments, the visual representation includes one or more of: statistics, graphs, charts, and tables.

[0012] According to other embodiments, the at least one group profile identifier comprises one or more of: age group, gender, date of birth, current geographical location, religion, nationality and country of residence.

[0013] According to other embodiments, the initial population of members includes one or more candidates located within a selected geographic location.

[0014] According to other embodiments, the first requests sent to the first subset of the end-group of members are sent at predetermined time intervals.

[0015] According to other embodiments, the method further includes generating a relevance score for the initial group of members reflecting relevance to the at least one group profile identifier.

[0016] According to other embodiments, the relevance score is calculated based on an association of each candidate with at least one population profile identifier.

[0017] According to a second aspect of the present disclosure, a system for dynamically matching requests with member groups is provided, comprising: at least one processor, and at least one memory comprising computer program code; together with the at least one processor, the at least one memory and the computer program code are configured so that the system at least has: receiving a request from a requester from a server, wherein the request comprises at least one group profile identifier, a sample size, and a response time period for a predetermined time period; generating, by the processor, an initial member group comprising one or more candidates, each candidate being associated with the at least one group profile identifier; generating, by the processor, a final member group comprising a subset of the initial member group based on a candidate preference and a candidate profile ranking. Further comprising: sending a first request by the server to a first subset of the final member group; determining whether the number of responders to the first request within an elapsed time is greater than or equal to a sample size; if the number of responders to the first request is less than the sample size and if the elapsed time is less than a predetermined time period for responses, the server sending subsequent requests to subsequent subsets of the final member group; determining whether the total number of responders to the first request and the subsequent requests within the elapsed time is greater than or equal to the sample size, if the total number of responders to the first request and the subsequent requests is less than the sample size and if the elapsed time is less than a predetermined time period for responses, sending subsequent requests to subsequent subsets of the final member group; when the total number of responders is equal to or greater than the sample size and the elapsed time exceeds a predetermined time period for responses, the server sending a visual representation of the responses obtained by the total number of responders to the requester.

[0018] According to other embodiments, the first subset of the final population of members is generated by determining an oversampling number, wherein the oversampling number comprises a sum of a sample size and a predetermined percentage of the sample size.

[0019] According to other embodiments, each of the one or more candidates is selected based on its association with at least one population profile identifier.

[0020] According to other embodiments, the candidate preferences include a temporal availability to respond to the first request.

[0021] According to other embodiments, the candidate profile identity represents the qualifications, reputation, or ranking of each of the one or more candidates within the user population.

[0022] According to other embodiments, the elapsed time represents the time that has elapsed since the first request was sent.

[0023] According to other embodiments, the visual representation includes one or more of: statistics, graphs, charts, and tables.

[0024] According to other embodiments, the at least one group profile identifier comprises one or more of: age group, gender, date of birth, current geographical location, religion, nationality and country of residence.

[0025] According to other embodiments, the initial population of members includes one or more candidates located within a selected geographic location.

[0026] According to other embodiments, the first requests sent to the first subset of the end-group of members are sent at predetermined time intervals.

[0027] According to other embodiments, the system further comprises generating a relevance score for the initial group of members reflecting relevance to the at least one group profile identifier.

[0028] According to other embodiments, the relevance score is calculated based on an association of each candidate with at least one population profile identifier. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In the accompanying drawings, the same reference numerals generally refer to the same parts in different views. The drawings are not necessarily drawn to scale, but emphasis is generally placed on illustrating the principles of the invention. The dimensions of various features or elements may be arbitrarily expanded or reduced for clarity. In the following description, various embodiments of the invention are described with reference to the following drawings, in which:

[0030] Figure 1 is a system diagram of an environmental embodiment of the present invention, which may be implemented according to an embodiment of the present invention;

[0031] Figure 2 is an embodiment of a client device used in implementing a method or system of the present invention according to an embodiment of the present invention;

[0032] Figure 3 shows a flow chart of an embodiment of the present invention according to an embodiment of the present invention;

[0033] Figure 4A flow chart showing another embodiment of the present invention according to an embodiment of the present invention is shown;

[0034] Figure 5 A flow chart showing another embodiment of the present invention according to an embodiment of the present invention is shown;

[0035] Figure 6 An embodiment of an application of the present invention according to an embodiment of the present invention is shown; and

[0036] Figure 7 An embodiment of another application of the present invention according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] Exemplary embodiments of the present invention are now described in detail, examples of which are shown in the accompanying drawings. Although the present invention will be described in conjunction with embodiments, it should be understood that they are not intended to limit the present invention to these embodiments. On the contrary, the present invention is intended to encompass substitutions, modifications and equivalents that may be included in the concept and scope of the present invention as defined by the accompanying description. In addition, in the following detailed description of embodiments of the present invention, many specific details are set forth in order to provide a thorough understanding of the present invention. However, those of ordinary skill in the art will recognize that the present invention can be implemented without these specific details. In other cases, known methods, processes, components and circuits are not described in detail to avoid unnecessarily obscuring the various aspects of embodiments of the present invention.

[0038] In the specification, the term "comprising" should be understood to have a broad meaning similar to the term "comprising", and can be understood to include the proposed integers or steps or groups of integers or groups of steps, but not to exclude any other integers or steps or groups of integers or groups of steps. This definition also applies to variations of the term "comprising", such as "consisting of" and "comprising".

[0039] Throughout the specification and claims, unless the context clearly indicates otherwise, the following terms adopt the meanings explicitly associated herein. In addition, the words "in one embodiment" as used herein do not necessarily refer to the same embodiment, although it can be the same embodiment. In addition, the words "in another embodiment" as used herein do not necessarily refer to a different embodiment, although it can. Therefore, as described below, various embodiments of the present invention can be easily combined together without departing from the scope or concept of the present invention.

[0040] In addition, as used herein, the term "or" is an inclusive "or" operator and is equivalent to the term "and / or" unless the context clearly indicates otherwise. The term "based on" is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise. In addition, throughout the specification, the meanings of "a", "an", and "the" include plural references. The meaning of "in" includes "in" and "on the surface".

[0041] Some portions of the following description are presented either explicitly or implicitly in terms of algorithms and functional or symbolic representations of data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, generally considered to be a self-consistent sequence of steps leading to a desired result. These steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0042] Unless specifically stated otherwise, and as will become apparent from the following, it will be understood that throughout this specification, discussions utilizing terms such as “monitor,” “utilize,” “retrieve,” “provide,” “generate,” “quantify,” “compute,” “output,” “optimize,” “reconstruct,” “store,” “map,” “check,” “identify,” “collect,” “search,” “perform,” “cross-check,” “aggregate,” “determine,” “regenerate,” “update,” “compare,” “adjust,” “compile,” “process,” “trigger,” “execute,” “obtain,” and the like refer to actions and processes of a computer system or similar electronic device that manipulate and transform data represented as physical quantities within a computer system into other data similarly represented as physical quantities within a computer system or other information storage, transmission, or display device.

[0043] As used herein, the term "receiving" a request, response, communication, and any type of multimedia content from a device or component includes indirectly receiving the request, response, communication, and any type of multimedia content, such as when forwarded by one or more other devices or components. Similarly, "sending" a request, response, communication, and any type of multimedia content to a device or component includes indirectly sending the request, response, communication, and any type of multimedia content, such as when forwarded by one or more other devices or components.

[0044] In addition, this specification also implicitly discloses a computer program, which will be apparent to those skilled in the art that the various steps of the method described herein can be effected by computer code. The computer program is not intended to be limited to any particular programming language and its implementation method. It should be understood that the teachings of the present disclosure contained herein can be implemented using various programming languages ​​and their encodings. In addition, the computer program is not intended to be limited to any particular control flow. There are many other variations of the computer program, which can use different control flows without departing from the scope of this specification.

[0045] To achieve the features, advantages and purposes described, the present invention relates to a system and method for dynamically matching a requester's request with a network user. More specifically, the present invention is directed to enabling a requester to dynamically connect with other users of a network based on a predetermined or structured request, and identifying, determining, suggesting prospective candidates to connect with based on the predetermined or structured request for the user to establish a connection with and presenting such candidates to the user. This provides the user with the advantage of identifying potential clients, replacing the traditional method of screening users or candidates by matching statistical data in a database. In addition, the system is capable of providing user identification for a predetermined number of potential candidates within a predetermined time period.

[0046] Embodiments provide systems and methods for dynamically matching requests from requesters (or users) to prospective candidates on a network. The purpose of the present invention is to enable users to dynamically connect with other users of the network based on dynamic requests, and to dynamically determine connections with other users of the network based on predetermined attributes that the user has selected.

[0047] In another embodiment, the present invention determines potential or prospective candidate connections for a user; generates a list of potential or prospective candidate connections that are not connected to the user based on predetermined attributes that the user has selected; presents the list of potential or prospective candidate connections that are not connected to the user, and enables the user to make new connections and / or connect to one or more candidate connections from the list of potential or prospective candidate connections.

[0048] In another embodiment, the present invention enables a user to send a request to multiple users based on input and selection of predetermined criteria, and enables a server to receive the request and determine the expected and suggested candidates and present them to the user to dynamically establish a connection or communication with one or more selected candidates for conducting one or more activities, actions, events, communications, collaborations, polls, surveys and presenting the results of the activities to the user.

[0049] Figure 1 A block diagram of a system for dynamically matching requests from users to prospective candidates is shown. Figure 1Components of one embodiment of an environment in which the present invention may be practiced are shown. Not all components may be required to practice the present invention, and the arrangement and type of components may be varied without departing from the concept or scope of the present invention. As shown, Figure 1 The system 100 includes a client device 500. The system also includes a network or wireless network 400, a server 200, a third-party server 300, and client devices 600, 610, 620, 630 that are prospective candidates.

[0050] According to the present invention, various client devices can be used. Client devices (users and candidates) can include mobile devices, smart phones, smart devices, tablet computers, PCs such as personal computers and media centers, and other client devices. Generally, mobile devices can actually include any portable computing device capable of receiving and sending messages through a network, such as network 400, etc. Mobile devices can also be generally described as client devices 500 configured as portable. Mobile devices can have the ability to connect to a network using wireless technology, wired technology, or a combination of wired and wireless technology. Therefore, mobile devices can actually include any portable computing device capable of connecting to another computing device and receiving information. Such devices include portable devices such as cellular phones, smart phones, display pagers, radio frequency (RF) devices, infrared (IR) devices, personal digital assistants (PDAs), handheld computers, sensors, laptop computers, wearable computers, tablet computers, integrated devices combining one or more of the aforementioned devices, etc. Therefore, mobile devices generally have a wide range in terms of capabilities and features. In one example, a cellular phone or a web-enabled mobile device can have a touch-sensitive screen, a stylus, and several rows of color LCD displays that can display both text and graphics.

[0051] The client device 500 may include one or more other client applications configured to receive content from another computing device. The client application may include the ability to provide and receive text content, graphic content, audio content, video content, etc. The client application may also provide information identifying itself, including type, capabilities, name, etc. In one embodiment, the client device 500 may uniquely identify itself by any of a variety of mechanisms, including a telephone number, a mobile identification number (MIN), an electronic serial number (ESN), or other mobile device identifiers. The information may also indicate the content format that the mobile device can adopt. This information may be provided in a message sent to the server 200 or other computing devices, etc.

[0052] The term "server" used herein may refer to a single computing device or multiple interconnected computing devices operating together to perform a specific function. That is, a server may be contained within a single hardware unit or distributed between several or many different hardware units.

[0053] The client device 500 communicates with the server 200, and the server communicates with other client devices of the expected candidate and / or one or more third-party servers 300. In one embodiment, the user uses the client device 500 to send or receive request data to other client devices of the expected candidate. The server 200 receives and processes the request data from the client device 500. The server 200 searches and matches the expected candidate set based on the request data. The server 200 can use the identifier or address of the expected candidate. For example, it can use the user id or email address of the sender and / or the receiver. When communicating with the sender and / or the receiver, the server 200 can use any one or more of these identifiers or addressing mechanisms. As used herein, the terms "sender" and "receiver" can be used to refer to a person, a device, or a combination of a person and a device.

[0054] In one embodiment, a user can register him / herself and create a user account on the system 100. The system 100 communicates with a database. The database stores user profile data related to registered users and includes username, user address, user account identifier, password, given name, ID number, account number, contact number, facial image, biometric data, application settings. In other embodiments, user data may include the content of the website or the database and the server of the website, the data of the social network, data, the resource digital content and index data of the search engine, the data generated by the user, the shared data, the data input, selected and disclosed by the user, the user activities, events, actions, behaviors, current location and place, transactions and interactions. As used herein, user profile data may be referred to interchangeably with the user profile identifier and refers to any data identifying a user, a consumer, a provider, an employee. In another embodiment, the user is a system administrator, a business entity or an institution communicating with a server or a third-party server.

[0055] Figure 2A diagram of an exemplary client device 500 consistent with the disclosed embodiments is shown, which shows a computing system configuration that can be associated with a user or candidate. In some embodiments, the client device 500 may include a database 550, one or more processors 530, one or more memories 520, and one or more input / output (I / O) devices 540. In some embodiments, the client device 500 can take the form of a server, a general-purpose computer, a mainframe computer, a laptop computer, a smart phone, a mobile device, or any combination of these components. In some embodiments, the client device 500 can be configured as a specific device, system, etc. based on the storage, execution, and / or implementation of software instructions, which can perform one or more operations consistent with the disclosed embodiments. The client device 500 can be independent, or it can be part of a subsystem, which can be part of a larger system.

[0056] The client device 500 includes a dynamic matching application including a user interface for searching, matching, navigating, browsing, selecting, sorting, accessing, connecting to other candidate devices. The dynamic matching application provides an interface to enable communication with each of the candidate devices, servers, and / or third-party servers. The application may include computer executable instructions that, when executed by the client device 500, send, receive, and / or otherwise process request data, messages, content, and enable communication with another user of another client device.

[0057] The client device 500 includes a GPS transceiver 514 to determine the physical coordinates of the client device. The GPS transceiver 514 may employ other geolocation mechanisms or systems, including but not limited to triangulation, to further determine the physical location of the client device. In other embodiments, the client device may provide other information, such as a MAC address, an IP address, etc., that may be used to determine the physical location of the client device through other components.

[0058] The system 100 provides an application programming interface ("API") to facilitate such communication. Such an API can be part of a user interface, which can include a graphical user interface (GUI), a web-based interface, a programming interface such as an application programming interface (API) and / or a remote procedure call (RFC) set corresponding to an interface element, a messaging interface in which the interface element corresponds to a message of a communication protocol, and / or a suitable combination thereof. Examples of APIs include REST APIs, etc. In various embodiments, the system 100 can be implemented as a technology layer. In various embodiments, the system 100 can be deployed as a stand-alone application, where the company uses it specifically as a dynamic matching application. In some embodiments, a social network provider can integrate the system 100 into a social network interface and database.

[0059] The processor 530 may include a microprocessor, an analog circuit, a digital circuit, a mixed signal circuit, a logic circuit, an integrated circuit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. or any combination thereof. Any other type of implementation of the corresponding functions described in further detail below may also be understood as the processor 530.

[0060] In some embodiments, the client device 500 may also include a memory 520. The memory may be used by the processor 530 to permanently or temporarily store, for example, request data to be processed by the dynamic matching application to be sent to the server 200 for matching with the expected candidate. The memory 520 may include one or more storage devices configured to store instructions used by the processor to perform functions related to the disclosed embodiments. The memory may include, but is not limited to, cloud storage, server storage, and physical storage devices such as RAM (random access memory), HDD (hard disk drive), SSD (solid state drive), others or any combination thereof.

[0061] The memory may also include user data. User data may include information about a particular user of the system. For example, user data may include the content of a website or database and server of the website, data of a social network, data, digital content and index data of search engines, user-generated data, shared data, user-entered, selected and disclosed data, user activities, events, actions, behaviors, current location and place, transactions and interactions.

[0062] I / O device 540 may be one or more devices configured to allow data to be received and / or sent by client device 500. I / O devices may include one or more digital and / or analog communication devices that allow client device 200 to communicate with other machines and devices, such as Figure 1 Other components of the system 100 are shown. For example, the client device 500 may include an interface component that may provide an interface to one or more input devices, such as one or more keyboards, mouse devices, etc., which may enable the client device 500 to receive input from an operator of the device.

[0063] The client device may also include one or more databases 550. Optionally, the client device 500 may be communicatively connected to the database 550 via a network. The database 550 may include one or more memory devices that store information and are accessed and / or managed by the client device 500. The database may include a computing component (e.g., a database management system, a database server, etc.) that is configured to receive and process requests for data stored in the memory devices of the database and provide data from the database.

[0064] Figure 3 A flow chart illustrating an embodiment of a method for dynamically matching a request from a user to a member group of a dynamic matching application is provided. The method may be performed by a server 200 of the system 100, executing instructions encoded on a computer-readable medium storage device. It should be understood that one or more steps of the process may be implemented by other components of the system 100 including client devices 200, 600. In some embodiments, members are subscribers or members of a dynamic matching application, which may be paid or unpaid, or a combination of paid and unpaid services. Such services may be purchased once, monthly, or annually. The application may also provide paid services to users based on usage requirements to purchase a single request or a predetermined number of requests. In some embodiments, the user is an individual, system administrator, commercial entity, or institution that controls a client device 500 that communicates with a server or a third-party server. In some embodiments, the application allows a user to register and use the application for free for a limited period of time, or allows the user to limit use to certain services, or both.

[0065] The request or query may include a string of words that a user may enter into an input device, such as a mobile device. For example, a request or query may include a string of words that a user may type into a search field or query field of an application. The request or query may be referred to as a search query. In some embodiments, the application may be installed on the input device and include one or more of the following applications:

[0066] (i) Academic or market research tools in the fields of social sciences, politics, economics, etc.:

[0067] (ii) Instant opinion gathering tools for organizations and companies to conduct quick informal surveys, feedback or investigations;

[0068] (iii) Personal companion tools, which are used to verify personal and external opinions, as well as to clarify questions and enable users to make more informed decisions;

[0069] (iv) real-time opinion tools where users can supplement subject matter expertise from other users or be used to inform or moderate discourse;

[0070] (v) social and cultural experiments that allow for randomized social experiments;

[0071] (vi) brand or social barometer tools used to measure products or services under brand or socio-political sentiment and trends;

[0072] (vii) professional knowledge or consulting tools used to obtain expert opinions;

[0073] (viii) Survey tools for conducting rapid individual surveys with short turnaround time;

[0074] (ix) peer matching tools for finding connections with other users with similar careers, skill sets or interests;

[0075] (x) Target distribution of digital aircraft;

[0076] (xi) SOS tools for matching medical emergencies, life-threatening cases, or potential suicide cases with appropriate medical, psychiatric, or psychological expertise or donors.

[0077] In step 310, the server 200 receives a request from the user from the user to dynamically match the request with the member group. The request may include one or more activities, actions, events, transactions, requests, search queries, preferences, matching preferences, interactions, behaviors, feelings, locations, places, current locations, or places of the user. In some embodiments, the request is configured by the user based on a predetermined question that requests the user to input a user response that will collate the request data. In another embodiment, the user configures the request based on one or more of the following indicators or criteria: a group profile identifier; a sample size, N; a deadline for responding to the request or a predetermined time period for responding to the request; and a response metric. The response metric refers to the type or criterion of the respondent's response. The response metric may be configured by the user or by an application. For example, the response metric may be a single instance response, an automatically repeated response, or within a predetermined geographic location. In another embodiment, the request includes user input and / or user selection via one or more types of controls, including a list, a checkbox, a predefined or user-created or system-created standard, a condition, a rule, a word, a phrase, a sentence, a command, a matching preference, a database field, an attribute, a user profile, a user connection, or user data.

[0078] In some embodiments, the group profile identifier includes gender, date of birth, race, nationality, religion, education, occupation, skill set, interest group, place of residence or country, or other socioeconomic data or demographic data, where information about a group is collected to better understand the composition or behavior of a group of members. It also includes any other identifying information about the individual user profile that indicates the similarity, association, and / or relevance of the user to the group of members. In some examples, the group profile identifier corresponds to a member that has some association with a location identifier. Example location identifiers may include locations (e.g., country of residence, location of interest), locations of interest groups, events, places (e.g., towns, cities, neighborhoods), etc. The data provided in the request may be processed to identify a group of users that are common, associated, or related to the group profile identifier. In some embodiments, the request may be a relatively simple query that provides a wide range of results. Using a location (e.g., a cafe, a restaurant, a theater) as an example topic, an example request may be directed to a user who has visited the location.

[0079] In some embodiments, the processor may include a request understanding module. The request understanding module may include one or more machine learning models. In some embodiments, the request understanding module may be or include a collection of machine learning models, such as entity recognition, query correction, query rewriting, and intent recognition. The machine learning models may work together to process user submitted requests and provide insights that can be used by the processor to generate a member group that can be matched by the user to a target user profile identifier and a subject identifier.

[0080] The request understanding module may receive input data related to the request and predict the search intent from the request. For example, if the request includes "travel", the request understanding module may predict whether the request for "travel" is intended for a vacation trip or a trip to a destination. The prediction may be performed based on machine learning techniques. In some embodiments, the search intent may correspond to each field that may be based on the application. For example, in a partner matching program, the predefined search intent may include a vacation trip or a trip to a destination.

[0081] In step 320, the server 200 searches its database including members of the application or network and generates an initial member group including one or more prospective candidates or users among the members of the database, the application or network users being similar, related or associated with one or more group profile identifiers. In another embodiment, the server 200 sends the request to a third-party server 300, such as a client server. The third-party server 300 includes a third-party database or member database and searches the third-party database including user data of prospective candidates and generates an initial member group including one or more prospective candidates among the users or members of the application or network based on the request. As described above, the initial member group is generated based on similarity, association or correlation with one or more group profile identifiers.

[0082] In step 330, the server generates a final member population including a subset of the initial member population based on the candidate preferences and the candidate profile identities. In some embodiments, the candidate preferences refer to each candidate preference, which may include, for example, the time availability of the candidate for response. For example, a candidate may specify that he prefers to request a response once a week, and another candidate may specify that she does not want to be contacted at all. The candidate profile identity is an indicator of the candidate's seniority, occupation, or ranking in a member group or group or occupation. In another embodiment, the candidate profile identity may be determined by the server through artificial intelligence and assigned to members based on their responsiveness, their seniority, status, activity, credibility, member profile, etc.

[0083] In some embodiments, the server generates a relevance score for each member that reflects the relevance to one or more group profile identifiers in the request. The processor can generate a relevance score by parsing the request into one or more group profile identifiers. For example, if the request involves "I want to find 2 female travel companions aged 20 to 30, graduated from college, living in Singapore, and traveling to China together in July 2022" enters the search field of the application server. Examples of group profile identifiers include "female", "20-30 years old", "college graduates", "living in Singapore". Other examples of group profile identifiers include "traveler", "China", "July 2022". A relevance score for each member is generated based on the commonality of the group profile identifier with the members in the database. In some embodiments, the relevance score can be based on the frequency with which a particular member interacts with the group profile identifier. For example, in the case of "travel companion", the relevance score can be based on the frequency with which the member travels outside the country of residence. The more frequently the member travels outside the country of residence, the higher the relevance score. In some embodiments, it can be determined whether to present a responsive member to the member submitting the request based on a threshold relevance score. For example, the relevance score generated for each member can be compared with a threshold relevance score. If the relevance score is greater than or equal to the threshold relevance score, the identified respondent member is deemed sufficiently relevant to the user and is presented to the user. If the relevance score is not greater than or equal to the threshold relevance score, the particular member is not deemed sufficiently relevant to the user and will not be selected for the next step.

[0084] In step 340, the server 200 sends a first request to a first subset of the final member population to obtain a response to the first request within a predetermined time period. As described above, the predetermined response time period is configured by the user or by the application as a default time period for the response. From the final member population, the first subset of the final member population includes a smaller number of expected candidates for the final member population. The first subset of the final member population is generated by oversampling based on a sample size configured by the user or a sample size limit predetermined by the application. Based on the sample size configured by the user or the sample size limit, the first subset will further add an additional sample size of a predetermined percentage of the sample size limit or the sample size limit. The advantage of doing so is to provide the user with an increased opportunity to obtain responses from the final member population in the event of a low response rate. In other words, the application determines the first subset of the final member population by determining the oversampling amount based on the following formula:

[0085] N R >(Nn(t))+X%

[0086] where R is the number of sampling iterations (1, 2, 3, ...) and X is the additional sample size in percentage terms defined by the application.

[0087] Once the first subset of the final member group is determined by obtaining the oversampling quantity, a request is sent to the first subset, and a candidate within the final member group is randomly selected.

[0088] In step 350, the server 200 determines whether the number of responses of the first subset of the final member group within the elapsed time is greater than or equal to the sample size. The elapsed time (t) is the time that has elapsed since the first request was sent to the first subset of the final member group. If the number of responders from the first subset of the final member group within the elapsed time (t) is greater than or equal to the sample size (N), then step 370 will be triggered to send a visual representation of the result of the responses obtained from the responders to the user. If the number of responders (n) from the first subset of the final member group is less than the sample size (N), then in step 360, if the elapsed time period (t) is less than a predetermined response time period or deadline (T), the server sends the same request to the non-responders in the first subset of the final member group. In other words, the subsequent or second request will not be sent to the responders of the first request.

[0089] The predetermined response time period (T) is configured by the user submitting the request, or the application imposes a default predetermined time period in response to the request. For example, the range of the predetermined response time period (T) for a responder to respond to a request can be from as short as one minute to as long as several days. This is done to increase the chance of obtaining the sample size (N) within the predetermined time period or within the deadline (T). The server monitors and determines the number of responders within the elapsed time period (t), and repeats the process until the sample size (N) is satisfied or the elapsed time period (t) reaches or exceeds the predetermined time period (T). To achieve a certain level of accuracy of the request result, the remaining number of users is determined from the user group based on the following formula:

[0090] N n (t),

[0091] where:

[0092] (i) N is the responder size predetermined by the user or the system;

[0093] (ii) N n (t) > N - n(t);

[0094] (iii) n(t) is the response collected so far at time t; and

[0095] (iv) t < T, where t is the elapsed time and T is the predetermined time period.

[0096] In the event that the elapsed time (t) has exceeded the predetermined period of time (T), the server will proceed to step 380 and send the user a visual representation of the results of the responses obtained from the responders of the request.

[0097] In step 370, the server 200 determines whether the total number of responses from the first subset and the subsequent or second subset of the final member population during the elapsed time is greater than or equal to the sample size. If the total number of responses from the first subset and the second or subsequent subset does not equal or exceed the sample size, the server 200 will continue to send subsequent requests as long as the elapsed time does not meet or exceed the predetermined response time period. The termination condition is reached when the total number of responses from the first and / or subsequent subsets equals or exceeds the sample size, or when the elapsed time meets the predetermined response time period.

[0098] In step 380, once the time elapsed equals or exceeds the predetermined time period (T), the server sends a visual representation of the response results obtained from the responders of the request to the user. The visual representation can be in the form of statistics, tables, graphs, charts, etc., so that the results can be visually understood and read by the user. In some embodiments, the user identifier data of the responder can be provided to the user. In some embodiments, the server will also send one or more user identifier data of the responding members who responded to the request to the user. In some embodiments, the user identifier data can be an email address. In some embodiments, the user identifier data can be automatically anonymized so that the user does not know who the responder member is until 24 responder members give consent to the user identifier data to be revealed. In some embodiments, the server sends an aggregate response to the request, which includes a summary of the response provided by the responding user.

[0099] Figure 4 A flowchart 400 is provided that illustrates yet another embodiment of a method of dynamically matching a request to a group of members. The flowchart is similar to the previous figures and is presented to provide the steps in a visual manner. Figure 4 The steps provided in correspond to Figure 3 Some steps are provided in the , and the details of these steps can be found in Figure 3 obtained in the above detailed description.

[0100] Step A corresponds to Figure 3 Step 310. In step A, the application receives a request from a user for dynamically matching the request to a member group. The request may include at least one group profile identifier, a sample size (N) of the user group to which the request is sent, and a deadline (T).

[0101] Step B corresponds to Figure 3Step 320. In step B, based on criteria provided by the user or default values ​​placed by the application (e.g., maximum sample size or maximum cutoff time), the application searches a member database including both paying members and non-paying members within a reference, and generates an initial member population including one or more candidates from the members of the database. Candidates are generated based on their similarity, relevance, or association with one or more population profile identifiers predetermined by the user.

[0102] Step C corresponds to Figure 3 Step 330. In step C, the application generates a final member group including a subset of the initial member group based on the candidate preferences and the candidate profile identities. In some embodiments, the candidate preferences refer to the preferences of each candidate, for example, the candidate's time availability. For example, a candidate may specify that he prefers to request a response only once a week, and another candidate may specify that she does not want to be contacted at all. The candidate profile identity is an indicator of the candidate's qualifications, profession, or ranking in a member group or group or profession. In another embodiment, the candidate profile standing attributes can be determined by the server through artificial intelligence and assigned to members based on their responsiveness, their qualifications, status, activity, credibility, member profile, etc.

[0103] The application then proceeds to the dynamic request phase and the dynamic response phase. The dynamic request phase corresponds to Figure 3 The steps 340 and 360 correspond to the dynamic response phase. Figure 3 Steps 350 and 370 of . Each dynamic request phase is followed by a dynamic response phase until a termination condition is reached. In the dynamic request phase, the application sends a first request to a subset of the final member population. In some embodiments, as shown in the above formula, the subset is determined by applying an oversampling amount. The first request is sent to a subset of the final user population in a random manner. The application then monitors the number of responses obtained in the dynamic response phase. The dynamic response is adjusted by the number of responses obtained from the responders and the time elapsed since the first request was sent. Depending on which condition is met, the dynamic response is activated. The application will continue the termination process or continue the dynamic request phase again. If the number of responses obtained from the responders is equal to or exceeds the sample size, the application will continue the termination process and send a visual representation of the response to the user. If not, the application will continue to send subsequent requests to non-responders within the subset of the final user population until the termination condition is reached. Once the termination condition is reached, the application will continue to send a visual representation of the response obtained from the responder to the user. The termination condition is reached when the elapsed time (starting from the time the first request was sent to the user population) is equal to or exceeds the deadline for the response, or when the number of responders is equal to or exceeds the sample size.

[0104] Step D corresponds to Figure 3Step 380. In step D, once the termination condition is reached, the application sends a visual representation of the results of the responses obtained from the responders of the request to the user. The visual representation can be in the form of statistics, tables, graphs, charts, etc., so that the results can be visually understood and read by the user. In some embodiments, user identifier data of the responders can be provided to the user. In some embodiments, the server will also send one or more user identifier data of the responding users who responded to the request to the user. In some embodiments, the user identifier data can be an email address. In some embodiments, the user identifier data can be automatically anonymized so that the user does not know who the responder member is until the responder member gives consent for the user identifier data to be revealed. In some embodiments, the server sends an aggregate response to the request, which includes a summary of the responses provided by the responding members.

[0105] Figure 5 A flowchart 500 is provided showing another embodiment of a method for dynamically matching a request with a member population. The flowchart is similar to the previous FIG. Figure 3 and 4 ), and the steps are provided in a visual way. Figure 5 Steps AD correspond to Figure 4 Steps AD and Figure 3 Some of the steps in the Figure 3 and 4 obtained in the above detailed description.

[0106] Frame 510 shows another embodiment of the method for dynamically matching a request with a member population. At the end of step C, step 511 is activated. Step 511 involves issuing a request based on a fixed sample size number that is automatically repeated. In this step, a request is sent to a selected subset of the final member population. In some embodiments, the request is based on a request previously configured by the user or a historical request of the user, which is saved and stored in the user account or server. The user only needs to retrieve the previously configured request or historical request and configure the new request according to the fixed sample size number that is the same as the historical request or a new fixed sample size number. In some embodiments, the fixed sample size can be the same selected subset of the final member population, i.e., the same member for the previous request, or it can be a different set of selected subsets of the final member population. In some embodiments, the selected subset of the final member population is determined by the user or application and is determined by the sample size configured by the user or application. In some embodiments, the user can also configure the application to issue a request at a predetermined time interval in step 512. For example, if the user has previously issued a poll request related to a product, service, event, or incidence, he can configure the survey to be issued at predetermined time intervals, such as on a weekly, monthly, or annual basis, to select a subset of the ultimate member population. In this step, the application sends requests to the selected subset of the ultimate member population at predetermined time intervals, such as once a month. The application similarly monitors the responses obtained by the subset of the ultimate member population, and only issues subsequent requests to non-responders within the subset of the ultimate member population. In some embodiments, the application monitors the responses obtained by the subset of the ultimate member population, and only issues subsequent requests to non-responders within the subset of the ultimate member population. In this way, the application provides the user with the convenience of tracking any changes or trends in the opinions, attitudes, sentiments on services, products, events, incidences, etc. of the member population.

[0107] Frame 520 shows another embodiment of the method for dynamically matching a request with a member group. At the end of step A, step 521 is activated. Step 521 generates an initial member group based on the selected group profile identifier. For example, a location. The location can include the current real-time location of the member, the current residence of the member, the country of residence, etc. This provides another method for the user to determine the priority and pay attention to certain standards of the request. If the user wants to focus on a predetermined geographic location due to the needs of the request, for example, the request can involve a real-time survey at a specific meeting, and the survey only hopes to poll the attendees of the meeting, the initial member group can be generated based on the real-time location of the attendees of the meeting. In some examples, the user may wish to poll participants, members or crowds at concerts, politics or in a certain geographic location where there are large groups of members, such as at a land crossing or other event between two countries. In other examples, the user can be a corporate entity or retail enterprise with a plan for a special promotion within a limited duration. For example, a retail store may offer free ice cream if you visit the stall or store between 6pm and 8pm, or a restaurant may offer a 1-for-1 lunch special today for the first 10 customers, or a hawker stall may offer $1 chicken rice between 5pm and 6pm today, or a gym may offer a 20% discount if you sign up for a gym membership today. In some embodiments, step 521 generates an initial member population based on the selected geographic location. The application or server searches and identifies its member database for members currently within the selected geographic location and generates an initial member population based on the members within the selected geographic location.

[0108] Block 530 involves Figure 3 and 4 In some embodiments, if the user has chosen to activate steps 511-512 or step 521, the user can configure the request to proceed with the dynamic request and dynamic response phases. It is contemplated by those skilled in the art that if the user chooses to do so, step 521 can be activated first, and then steps 511 and 512 can be activated.

[0109] Figure 6An embodiment of another application of the present invention according to an embodiment of the present invention is shown. In use, the system provides a user interface, such as on an application, for enabling a user to facilitate a connection with a group of members within a predetermined time period based on a request entered by the user. For example, the system 100 allows a user to request data related to a specific search request in the context of a travel companion application. In another embodiment, the system 100 requests a candidate to issue a call to action in response to an advertisement or marketing proposal. The user enters a request based on a specific user profile identifier or preference or an attribute of a travel companion that the user wants to travel with. In the case of an advertisement, the request is based on a user profile identifier, such as a specific preference, attribute or profile of a candidate that a business entity wants to reach. The user profile identifier can be gender, age range, education, resident status, nationality, travel location, preferred date of travel. The user enters and updates the request via auto-fill, input, edit removal, predetermined criteria. The user can also manually enter data about the user, such as social network information, activities, actions as part of the request data.

[0110] The server 200 will search and generate a group of potential candidates among the members of the internal or external database according to the received request to handle the request. The database containing the member data may belong to the server 200 or the third-party server 300, depending on the application access settings, or whether the user data is within the server 200 or belongs to the third-party server 300. The system monitors and tracks the number of responses received by the first group of matching prospective candidates within a predetermined time period. The predetermined time period may be configured and determined by the user or the system. The system 100 will determine whether the number of responses received by the server within the predetermined time period meets or exceeds the threshold target number of responses. If the number of responses does not meet or exceed the threshold target number of responses, the server will continue to send subsequent requests to a second group of prospective candidates in the database that is different from the first group of prospective candidates, and the second group of prospective candidates matches at least one or more criteria of the request data. For example, the second group of prospective candidates may have one or more user attributes or preferences that are similar or the same as the attributes or preferences indicated by the user in the request data. Then, the system will track and monitor the number of responses received by the server within a second predetermined time period, which may be the same, less than, or more than the predetermined time period of the first request. If the total number of responses from the first group and / or the second group of prospective candidates does not equal or exceed the target number of responses, the system 100 will continue to send subsequent requests to another prospective candidate group, which may be the same or different prospective candidate group as the first and second groups of candidates. Once the predetermined time period expires, the system 100 will continue to send subsequent requests until a termination condition is reached, and then terminate the subsequent requests. In other words, the system will continue to repeatedly send subsequent requests until a termination condition is reached. The termination condition is reached when the number of responses from the first and subsequent candidate groups equals or exceeds a threshold number, or when the number of responses from the subsequent candidate group equals or exceeds a threshold number. Once the system determines that the total number of responses from the first request and the second request meets or exceeds the target number of responses, the system will present matching prospective candidates to the user.

[0111] Figure 7An embodiment of the second application of the present invention according to an embodiment of the present invention is shown. In use, the system provides a user interface that enables a user to conduct an online survey within a predetermined time period. For example, a user may urgently need to poll a specific topic before an important meeting, decision or project submission. The system allows a user to create request data by creating a questionnaire, which allows the user to enter specific questions with possible answers, and sends the request data within a predetermined time period based on the request data entered by the user to connect with the expected or matched candidates, and presents the results of the survey to the user. The user interface includes context, domain, field subject, communication utility, and content such as a survey form or presentation that enables the recipient to fill in the form. The user enters and updates the request data via automatic filling, input, editing removal, and predetermined standards.

[0112] The system 100 will process the request data by searching and matching a set of prospective candidates among the members of the database according to the received request data. The database containing the member data may belong to the server 200 or the third-party server 300, depending on the application access settings. The system monitors and tracks the number of responses received by the first group of matched prospective candidates within a predetermined time period. The predetermined time period may be configured and determined by the user or the system. The system 100 will determine whether the number of responses received by the server within the predetermined time period meets or exceeds the threshold target number of responses. If the number of responses does not meet or exceed the threshold target number of responses, the server will continue to send a subsequent or second request that matches at least one or more criteria of the request data to a subsequent or second group of prospective candidates in the database, which is different from the first group of prospective candidates. For example, the second group of prospective candidates may have one or more user attributes or preferences that are similar or the same as the attributes or preferences indicated by the user in the request data. Then, the system will track and monitor the number of responses received by the server within a second predetermined time period, which may be the same, less than, or greater than the predetermined time period of the first request. If the total number of responses from the first expected candidate group and / or the second expected candidate group does not equal or exceed the target number of responses, the system 100 will continue to send subsequent requests to subsequent expected candidate groups, which may be the same or different expected candidate groups as the first candidate group and the second candidate group. Once the predetermined time period expires, the system 100 will continue to send subsequent requests until the termination condition is reached, and then terminate the subsequent requests. In other words, the system will continue to repeatedly send subsequent requests until the termination condition is reached. The termination condition is reached when the number of responses from the first and subsequent candidate groups equals or exceeds the threshold number, or when the number of responses from the subsequent candidate group equals or exceeds the threshold number. Once the system determines that the total number of responses from the first request and the second request meets or exceeds the target number of responses, the system will present the survey results to the user.

[0113] Although the present invention has been specifically shown and described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the concept and scope of the present invention as defined by the appended claims. Therefore, the scope of the present invention is indicated by the appended claims, and it is therefore intended to cover all changes within the meaning and scope of the equivalents of the claims.

Claims

1. A computer-implemented method for dynamically matching a request from a requester with a group of members, characterized in that: include: receiving, from a server, a request from a requestor, wherein the request includes at least one population profile identifier, a sample size, and a predetermined time period for a response; generating, by a processor, an initial group of members comprising one or more candidates, each candidate being associated with at least one group profile identifier; generating, by the processor, a final population of members comprising a subset of the initial population of members based on the candidate preferences and the candidate profile identities; The server sends a first request to a first subset of the ultimate group of members; determining whether a number of responders to the first request within the elapsed time period is greater than or equal to the sample size; If the number of responders to the first request is less than the sample size and if the elapsed time period is less than the predetermined time period for responses, the server sending a subsequent request to a subsequent subset of the ultimate population of members; determining whether a total number of responders to the first request and the subsequent request within the elapsed time period is greater than or equal to the sample size, if the total number of responders to the first request and the subsequent request is less than the sample size and if the elapsed time period is less than the predetermined time period for responding, sending the subsequent request to the subsequent subset of the ultimate population of members; as well as When the total number of responders is equal to or greater than the sample size and the elapsed time period exceeds a predetermined time period for the responses, the server sends a visual representation including responses obtained by the total number of responders to the requester.

2. The computer-implemented method of claim 1, wherein: The first subset of the final population of members is generated by determining an oversampling number, wherein the oversampling number comprises a sum of the sample size and a predetermined percentage of the sample size.

3. The computer-implemented method of claim 1 , wherein: Wherein each of the one or more candidates is selected based on its association with the at least one population profile identifier.

4. The computer-implemented method of claim 1, wherein: The candidate preferences include a time availability for responding to the first request.

5. The computer-implemented method of claim 1, wherein: The candidate profile identity represents the qualifications, reputation or status of each of the one or more candidates within the user group.

6. The computer-implemented method of claim 1, wherein: The elapsed time period represents the time that has passed since the first request was sent.

7. The computer-implemented method of claim 1, wherein: Wherein the visual representation comprises one or more of the following: statistics, graphs, charts and tables.

8. The computer-implemented method of claim 1, wherein: in, The at least one group profile identifier includes one or more of: age group, gender, date of birth, current geographic location, religion, nationality, and country of residence.

9. The computer-implemented method of claim 1, wherein: Wherein the initial group of members includes the one or more candidates located within a selected geographic location.

10. The computer-implemented method of claim 1, wherein: Wherein said first request sent to said first subset of said end-member population is sent at predetermined time intervals.

11. The computer-implemented method of claim 1 , wherein: Also includes: A relevance score is generated for the initial population of members reflecting relevance to the at least one population profile identifier.

12. The computer-implemented method of claim 11, wherein: in, The relevance score is calculated based on an association of each candidate with the at least one population profile identifier.

13. A system for dynamically matching a request from a requester with a member group, characterized in that: The system comprises: at least one processor; and at least one memory including computer program code; Together with the at least one processor, the at least one memory and the computer program code are configured to cause the system to at least: receiving, from a server, a request from a requestor, wherein the request includes at least one population profile identifier, a sample size, and a predetermined time period for a response; generating, by a processor, an initial population of members comprising one or more candidates, each candidate being associated with the at least one population profile identifier; generating, by the processor, a final population of members comprising a subset of the initial population of members based on candidate preferences and candidate profile identities; The server sends a first request to a first subset of the ultimate group of members; determining whether a number of responders to the first request within an elapsed time period is greater than or equal to the sample size; sending, by the server, a subsequent request to a subsequent subset of the ultimate population of members if the number of responders to the first request is less than the sample size and if the elapsed time period is less than the predetermined time period for responses; determining whether a total number of responders to the first request and the subsequent request within the elapsed time period is greater than or equal to the sample size, If the total number of responders to the first request and the subsequent request is less than the sample size and if the elapsed time period is less than the predetermined time period for responding, sending the subsequent request to the subsequent subset of the ultimate population of members; and When the total number of responders is equal to or greater than the sample size and the elapsed time period exceeds the predetermined time period for the response, the server sends a visual representation including responses obtained by the total number of responders to the requester.

14. The system according to claim 13, characterized in that Wherein the first subset of the ultimate population of members is generated by determining an oversampling number, wherein the oversampling number comprises a sum of the sample size and a predetermined percentage of the sample size.

15. The system according to claim 13, characterized in that Wherein each of the one or more candidates is selected based on its association with the at least one population profile identifier.

16. The system according to claim 13, characterized in that The candidate preferences include a time availability for responding to the first request.

17. The system according to claim 13, characterized in that The candidate profile identity represents the qualifications, reputation or status of each of the one or more candidates within the user group.

18. The system according to claim 13, characterized in that The elapsed time period represents the time that has passed since the first request was sent.

19. The system according to claim 13, characterized in that Wherein the visual representation comprises one or more of the following: statistics, graphs, charts and tables.

20. The system according to claim 13, characterized in that in, The at least one group profile identifier includes one or more of: age group, gender, date of birth, current geographic location, religion, nationality, and country of residence.

21. The system according to claim 13, characterized in that Wherein the initial group of members includes the one or more candidates located within a selected geographic location.

22. The system according to claim 13, characterized in that Wherein said first request sent to said first subset of said end-member population is sent at predetermined time intervals.

23. The system according to claim 13, characterized in that Also includes: A relevance score is generated for the initial population of members reflecting relevance to the at least one population profile identifier.

24. The system according to claim 23, characterized in that in, The relevance score is calculated based on an association of each candidate with the at least one population profile identifier.

25. A computer program comprising instructions, characterized in that When the instructions are executed by a computer, the computer is caused to perform the method of any one of claims 1-12.

26. A computer-readable medium comprising instructions, characterized in that: When the instructions are executed by a computer, the computer is caused to perform the method of any one of claims 1-12.

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