Patent Publication Number: US-2022222629-A1

Title: Computer-implemented systems configured for automated electronic calendar item predictions for calendar item rescheduling and methods of use thereof

Description:
COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the software and data as described below and in drawings that form a part of this document: Copyright, Capital One Service, LLC, All Rights Reserved. 
     FIELD OF TECHNOLOGY 
     The present disclosure generally relates to improved computer-based platforms/systems, improved computing devices/components and/or improved computing objects configured for one or more novel technological applications of systems for automated electronic calendar management and methods of use thereof. 
     BACKGROUND OF TECHNOLOGY 
     A computer network platform/system may include a group of computers (e.g., clients, servers, smart routers (e.g., trading smart routers)) and other computing hardware devices that are linked together through one or more communication channels to facilitate communication and/or resource-sharing, via one or more specifically programmed graphical user interfaces (GUIs), among a wide range of users. 
     SUMMARY OF DESCRIBED SUBJECT MATTER 
     In some embodiments, the present disclosure provides an exemplary computer-based method/apparatus that includes at least the following steps of receiving, by at least one processor, an out-of-office notification associated with at least one meeting attendee. The at least one processor identifies at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee. where the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification; where the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings includes a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting. The at least one processor utilizes a meeting scheduling machine learning model to predict a plurality of parameters of at least one meeting room object representing at least one respective candidate rescheduled meeting associated with the one or more respective need-to-reschedule meetings; where the meeting scheduling machine learning model is configured to predict the plurality of parameters of the at least one meeting room object based at least in part on schedule information associated with the at least one need-to-reschedule meeting data items and location information associated with the at least one need-to-reschedule meeting data items; where the plurality of parameters of at least one meeting room object includes: i) a meeting location parameter, and ii) a meeting time parameter; where the schedule information includes: i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, where the meeting history data includes: 1) cancellation data identifying meeting cancellations, and 2) rescheduling data identifying meeting rescheduling occurrences; where the location information includes: i) an attendee location data identifying at least one respective location associated with the at least one respective attendee, ii) available meeting room data identifying all available meeting rooms, and iii) a meeting room location associated with each available meeting room. The at least one processor causes to display an indication of the at least one respective candidate rescheduled meeting in response to the at least one out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting. The at least one processor receives a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee, and the at least one processor dynamically secures the at least one respective candidate rescheduled meeting for at least one respective meeting. 
     In some embodiments, the present disclosure provides an exemplary computer-based method that includes at least the following steps of receiving, by at least one processor, an out-of-office notification associated with at least one meeting attendee. The at least one processor identifies at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee; where the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification; where the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings includes a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting; determining, by the at least one processor, an error in a plurality of parameters of at least one meeting room object predicted by a meeting scheduling machine learning model based at least in part on the one or more need-to-reschedule meetings associated with the need-to-reschedule meeting data; where the plurality of parameters of at least one meeting room object includes: i) a meeting location parameter, and ii) a meeting time parameter; where the at least one meeting room object represents at least one candidate meeting room; The at least one processor trains the meeting scheduling machine learning model based at least in part on the error. The at least one processor utilizes the meeting scheduling machine learning model to predict a plurality of new parameters of the at least one meeting room object representing at least one respective candidate rescheduled meeting associated with the one or more respective need-to-reschedule meetings; where the meeting scheduling machine learning model is configured to predict the plurality of new parameters of the at least one meeting room object based at least in part on schedule information associated with the at least one need-to-reschedule meeting data items and location information associated with the at least one need-to-reschedule meeting data items; where the plurality of new parameters of at least one meeting room object includes: i) a new meeting location parameter, and ii) a new meeting time parameter; where the schedule information includes: i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, where the meeting history data includes: 1) cancellation data identifying meeting cancellations, and 2) rescheduling data identifying meeting rescheduling occurrences; where the location information includes: i) an attendee location data identifying at least one respective location associated with the at least one respective attendee, ii) available meeting room data identifying all available meeting rooms, and iii) a meeting room location associated with each available meeting room. The at least one processor causes to display an indication of the at least one respective candidate rescheduled meeting in response to the out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting. The at least one processor receives a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee, and the at least one processor dynamically secures the at least one respective candidate rescheduled meeting for at least one respective meeting. 
     In some embodiments, the present disclosure provides an exemplary computer-based system that includes at least the following components of a calendar database configured to store calendar data associated with each employee of an organization, a meeting room database configured to store meeting room characteristics of possible meeting rooms of the organization; and at least one processor in communication with the calendar database and the meeting room database. The at least one processor is configured to: receive an out-of-office notification associated with at least one meeting attendee; identify at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee; where the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification; where the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings includes a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting; utilize a meeting scheduling machine learning model to predict a plurality of parameters of at least one meeting room object representing at least one respective candidate rescheduled meeting; where the meeting scheduling machine learning model is configured to predict the plurality of parameters of the at least one meeting room object based at least in part on schedule information associated with the need-to-reschedule meeting data and location information associated with the need-to-reschedule meeting data; where the plurality of parameters of at least one meeting room object includes: i) a meeting location parameter, and ii) a meeting time parameter; where the schedule information includes: i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee, where the meeting history data includes: 1) cancellation data identifying meeting cancellations, and 2) rescheduling data identifying meeting rescheduling occurrences; where the location information includes: i) an attendee location data identifying at least one respective location associated with the at least one respective attendee, ii) available meeting room data identifying all available meeting rooms, and iii) a meeting room location associated with each available meeting room; cause to display an indication of the at least one respective candidate rescheduled meeting in response to the at least one out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting; receive a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee; and dynamically secure the at least one respective candidate rescheduled meeting for at least one respective meeting. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ one or more illustrative embodiments. 
         FIGS. 1-7  show one or more schematic flow diagrams, certain computer-based architectures, and/or screenshots of various specialized graphical user interfaces which are illustrative of some exemplary aspects of at least some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Various detailed embodiments of the present disclosure, taken in conjunction with the accompanying figures, are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive. 
     Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same embodiment(s), though it may. Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments may be readily combined, without departing from the scope or spirit of the present disclosure. 
     In addition, 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 meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time and/or dynamically. As used herein, the term “real-time” is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the “real-time processing,” “real-time computation,” and “real-time execution” all pertain to the performance of a computation during the actual time that the related physical process (e.g., a user interacting with an application on a mobile device) occurs, in order that results of the computation can be used in guiding the physical process. 
     As used herein, the term “dynamically” and term “automatically,” and their logical and/or linguistic relatives and/or derivatives, mean that certain events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc. 
     As used herein, the term “runtime” corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application. 
     In some embodiments, exemplary inventive, specially programmed computing systems/platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols/modes such as, without limitation, IPX/SPX, X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes. In some embodiments, the NFC can represent a short-range wireless communications technology in which NFC-enabled devices are “swiped,” “bumped,” “tap” or otherwise moved in close proximity to communicate. In some embodiments, the NFC could include a set of short-range wireless technologies, typically requiring a distance of 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to 424 kbit/s. In some embodiments, the NFC can involve an initiator and a target; the initiator actively generates an RF field that can power a passive target. In some embodiment, this can enable NFC targets to take very simple form factors such as tags, stickers, key fobs, or cards that do not require batteries. In some embodiments, the NFC&#39;s peer-to-peer communication can be conducted when a plurality of NFC-enable devices (e.g., smartphones) within close proximity of each other. 
     The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. 
     As used herein, the terms “computer engine” and “engine” identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.). 
     Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth. 
     Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints. 
     One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.). 
     In some embodiments, one or more of exemplary inventive computer-based systems of the present disclosure may include or be incorporated, partially or entirely into at least one personal computer (PC), laptop computer, ultra-laptop computer, tablet, touch pad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, television, smart device (e.g., smart phone, smart tablet or smart television), wearable device, mobile interne device (MID), messaging device, data communication device, and so forth. 
     As used herein, term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud servers are examples. 
     In some embodiments, as detailed herein, one or more of exemplary inventive computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and/or output any digital object and/or data unit (e.g., from inside and/or outside of a particular application) that can be in any suitable form such as, without limitation, a file, a contact, a task, an email, a tweet, a map, an entire application (e.g., a calculator), etc. In some embodiments, as detailed herein, one or more of exemplary inventive computer-based systems of the present disclosure may be implemented across one or more of various computer platforms such as, but not limited to: (1) AmigaOS, AmigaOS 4, (2) FreeBSD, NetBSD, OpenBSD, (3) Linux, (4) Microsoft Windows, (5) OpenVMS, (6) OS X (Mac OS), (7) OS/2, (8) Solaris, (9) Tru64 UNIX, (10) VM, (11) Android, (12) Bada, (13) BlackBerry OS, (14) Firefox OS, (15) iOS, (16) Embedded Linux, (17) Palm OS, (18) Symbian, (19) Tizen, (20) WebOS, (21) Windows Mobile, (22) Windows Phone, (23) Adobe AIR, (24) Adobe Flash, (25) Adobe Shockwave, (26) Binary Runtime Environment for Wireless (BREW), (27) Cocoa (API), (28) Cocoa Touch, (29) Java Platforms, (30) JavaFX, (31) JavaFX Mobile, (32) Microsoft XNA, (33) Mono, (34) Mozilla Prism, XUL and XULRunner, (35) .NET Framework, (36) Silverlight, (37) Open Web Platform, (38) Oracle Database, (39) Qt, (40) SAP NetWeaver, (41) Smartface, (42) Vexi, and (43) Windows Runtime. 
     In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, a stand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product. 
     For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device. 
     In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to handle numerous concurrent users that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on. 
     In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to output to distinct, specifically programmed graphical user interface implementations of the present disclosure (e.g., a desktop, a web app., etc.). In various implementations of the present disclosure, a final output may be displayed on a displaying screen which may be, without limitation, a screen of a computer, a screen of a mobile device, or the like. In various implementations, the display may be a holographic display. In various implementations, the display may be a transparent surface that may receive a visual projection. Such projections may convey various forms of information, images, and/or objects. For example, such projections may be a visual overlay for a mobile augmented reality (MAR) application. 
     In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming and/or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications. 
     As used herein, the term “mobile electronic device,” or the like, may refer to any portable electronic device that may or may not be enabled with location tracking functionality (e.g., MAC address, Internet Protocol (IP) address, or the like). For example, a mobile electronic device can include, but is not limited to, a mobile phone, Personal Digital Assistant (PDA), Blackberry™, Pager, Smartphone, or any other reasonable mobile electronic device. 
     As used herein, terms “proximity detection,” “locating,” “location data,” “location information,” and “location tracking” refer to any form of location tracking technology or locating method that can be used to provide a location of, for example, a particular computing device/system/platform of the present disclosure and/or any associated computing devices, based at least in part on one or more of the following techniques/devices, without limitation: accelerometer(s), gyroscope(s), Global Positioning Systems (GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonable form of wireless and/or non-wireless communication; WiFi™ server location data; Bluetooth™ based location data; triangulation such as, but not limited to, network based triangulation, WiFiTM server information based triangulation, Bluetooth™ server information based triangulation; Cell Identification based triangulation, Enhanced Cell Identification based triangulation, Uplink-Time difference of arrival (U-TDOA) based triangulation, Time of arrival (TOA) based triangulation, Angle of arrival (AOA) based triangulation; techniques and systems using a geographic coordinate system such as, but not limited to, longitudinal and latitudinal based, geodesic height based, Cartesian coordinates based; Radio Frequency Identification such as, but not limited to, Long range RFID, Short range RFID; using any form of RFID tag such as, but not limited to active RFID tags, passive RFID tags, battery assisted passive RFID tags; or any other reasonable way to determine location. For ease, at times the above variations are not listed or are only partially listed; this is in no way meant to be a limitation. 
     As used herein, terms “cloud,” “Internet cloud,” “cloud computing,” “cloud architecture,” and similar terms correspond to at least one of the following: (1) a large number of computers connected through a real-time communication network (e.g., Internet); (2) providing the ability to run a program or application on many connected computers (e.g., physical machines, virtual machines (VMs)) at the same time; (3) network-based services, which appear to be provided by real server hardware, and are in fact served up by virtual hardware (e.g., virtual servers), simulated by software running on one or more real machines (e.g., allowing to be moved around and scaled up (or down) on the fly without affecting the end user). 
     In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be configured to securely store and/or transmit data by utilizing one or more of encryption techniques (e.g., private/public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH),WHIRLPOOL, RNGs). 
     The aforementioned examples are, of course, illustrative and not restrictive. 
     As used herein, the term “user” shall have a meaning of at least one user. In some embodiments, the terms “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the terms “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data. 
       FIG. 1 through 7  illustrate systems and methods of meeting rescheduling predictions using machine learning techniques and database intercommunication. The following embodiments provide technical solutions and/or technical improvements that overcome technical problems, drawbacks and/or deficiencies in the technical fields involving machine learning, database technologies, networking technologies, dynamic resource management, message coordination, among others. As explained in more detail, below, technical solutions and/or technical improvements herein include aspects of improved database communication and record keeping, dynamic resource management, and machine learning. Based on such technical features, further technical benefits become available to users and operators of these systems and methods. Moreover, various practical applications of the disclosed technology are also described, which provide further practical benefits to users and operators that are also new and useful improvements in the art. 
       FIG. 1  depicts a diagram of an exemplary illustrative collaboration system according to an illustrative embodiment of the present invention. 
     In some embodiments, a collaboration system  100  access schedule and communication data associated with users to facilitate collaboration between and amongst the users by providing collaborative services and functions. In some embodiments, the collaboration services include predictions as to various collaborative interactions, such as, e.g., predicting optimum meeting schedules, email prioritization, task schedules and prioritization, among other collaboration services, and generating electronic calendar and electronic communication items, such as, electronic calendar meeting invites, electronically booking locations such as meeting rooms, and electronically ordering and prioritizing communication. In some embodiments, the collaboration system  100  may automatically convey the prediction to each user involved with the collaborative interaction at a respective user computing device  160 . 
     In some embodiments, the user computing device  160  may include a personal computer, a mobile computing device such as a tablet or smartphone, among other computing devices. In some embodiments, there may be a plurality of user computing devices  160  in communication with the collaboration system  100 , such as, e.g., between about 1 and about 10 computing devices, between about 1 and about 20 computing devices, between about 1 and about 100 computing devices, or any other number of computing devices for providing collaboration services to each user of, e.g., a set of customers, an organization such as a company, corporation, foundation, family, social club, school, university, government agency, or other organization, a set of organizations, or other group of users. 
     In some embodiments, the collaboration system  100  receives data from multiple data sources related to user schedules, relationships and communication to facilitate comprehensive and accurate prediction of collaboration characteristics for automatically initiating collaborative interactions. In some embodiments, the data may include, e.g., user calendar data, organizational personnel data, user location data, meeting room data, user email data, user task data, user work product data, among other task, communication and schedule data. Accordingly, in some embodiments, the collaboration system  100  receives the user calendar data, the organizational personnel data, the user location data, the meeting room data, the user email data, the user task data, and the user work product data from a calendar database  101 , an organization database  102 , a location database  103 , a meeting room database  104 , an email database  105 , a task database  106 , and a work product database  107 , respectively. 
     In some embodiments, the calendar database  101  may include a virtual calendar associated with each user in communication with the collaboration system  100  via a respective user computing device  160 . The virtual calendar may include scheduled meetings and appointments, out-of-office periods, scheduled vacations, working hours, among other calendar-related data of each user in communication with collaboration system  100 . In some embodiments, the virtual calendar may include scheduling information such as, e.g., availability and meeting history (e.g., cancellations, rescheduled meetings, relocated meeting, etc.) that are represented in a calendar program associated with each user. Examples of such calendar programs may include but are not limited to, e.g., Microsoft™ Outlook™, Google™ Calendar, Apple™ Calendar, IBM™ Notes, among other programs having virtual calendaring functions. Information entered into such programs may be stored in the calendar database  101  to aggregate scheduling information for use by the collaboration system  100 . 
     In some embodiments, the organization database  102  may include a virtual organization chart or other representation of position hierarchy associated with each user in communication with the collaboration system  100  via a respective user computing device  160 . The virtual organization chart may include a hierarchy of personnel in an organization and an organization structure, including, e.g., entry-level personnel up through senior management and executives. 
     In some embodiments, the location database  103  may include a representation of a location of each user in communication with the collaboration system  100  via a respective user computing device  160 . The location may include, e.g., a latitude and longitude, a street address, a building identification, a room identification within a building, a floor within a building, among others and combinations thereof. 
     In some embodiments, the meeting room database  104  may include a list of possible meeting rooms and a representation of a location of each room listed as a possible meeting room. The location may include, e.g., a latitude and longitude, a street address, a building identification, a room identification within a building, a floor within a building, among others and combinations thereof. 
     In some embodiments, the email database  105  may include, e.g., an archive of sent and received emails associated with each user in communication with the collaboration system  100 . In some embodiments, the emails may include, e.g., metadata, text, attachments, media, recipients, senders, carbon-copy (CC) recipients, among other data associated with each email. In some embodiments, the emails may be extracted or otherwise received from an email program and/or service associated with each user. Examples of such email programs and/or services may include but are not limited to, e.g., Microsoft™ Outlook™, Google™ Gmail™, Apple™ Mail, IBM™ Notes, among other email programs and services. Information entered into such programs may be stored in the calendar database  101  to aggregate scheduling information for use by the collaboration system  100 . 
     In some embodiments, the task database  106  may include a history of work tasks assigned to each user. In some embodiments, the history may include, e.g., start dates, completion dates, start times, completion times, task subject, task project, collaborators and/or team-mates associated with each task, among other task related data. In some embodiments, the task database  106  may receive the task history from project management, task management and task tracking platforms and programs, such as, e.g., Jira™, Microsoft Dynamics™, NetSuite™, Launchpad™, among others and combinations thereof. 
     In some embodiments, the work product database  107  may include a history of work product produced by each user in communication with the collaboration system  100 . In some embodiments, the work product may include completed projects, such as, e.g., papers, administrative documents, published documents, documents submitted to, e.g., supervisors and/or project management platforms as complete, source code, software releases, among other types of work product. The work product database  107  may include, e.g., a document repository, a document storage, a cloud storage platform, a server database, a distributed database, among others. 
     As used herein, a “database” refers to any suitable type of database or storage system for storing data. A database may include centralized storage devices, a distributed storage system, a blockchain network, and others, including a database managed by a database management system (DBMS). In some embodiments, an exemplary DBMS-managed database may be specifically programmed as an engine that controls organization, storage, management, and/or retrieval of data in the respective database. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to provide the ability to query, backup and replicate, enforce rules, provide security, compute, perform change and access logging, and/or automate optimization. In some embodiments, the exemplary DBMS-managed database may be chosen from Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQL implementation. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to define each respective schema of each database in the exemplary DBMS, according to a particular database model of the present disclosure which may include a hierarchical model, network model, relational model, object model, or some other suitable organization that may result in one or more applicable data structures that may include fields, records, files, and/or objects. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to include metadata about the data that is stored. 
     In some embodiments, the collaboration system  100  may include models for performing multiple collaborative services. In some embodiments, the collaboration system  100  includes a calendaring model  110  to, e.g., automatically schedule, reschedule and cancel meetings, appointments, out-of-office periods, unavailability periods, and other virtual calendar items associated with users based on data from one or more of the calendar database  101 , the organization database  102 , the location database  103 , the meeting room database  104 , the email database  105 , the task database  106 , and the work product database  107 . In some embodiments, the calendaring model  110  may include, e.g., machine learning models, such as, e.g., one or more exemplary AI/machine learning techniques chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary implementation of Neural Network may be executed as follows:
     i) Define Neural Network architecture/model,   ii) Transfer the input data to the exemplary neural network model,   iii) Train the exemplary model incrementally,   iv) determine the accuracy for a specific number of timesteps,   v) apply the exemplary trained model to process the newly-received input data,   vi) optionally and in parallel, continue to train the exemplary trained model with a predetermined periodicity.   

     In some embodiments, the calendaring model  110  may, e.g., employ the AI/machine learning techniques to predict an optimum meeting between attendees based on, e.g., location data, meeting room data, and schedule information including, e.g., calendar data such as availability and meeting history (e.g., cancellations, rescheduling, etc.) among others. Based on such data received from, e.g., the calendar database  101 , the location database  103 , and the meeting room database  104 , the machine learning model may predict a place and time that is least likely to be cancelled or rescheduled. In some embodiments, the machine learning model may incorporate organizational hierarchies from the organization database  102  to prioritize the schedule and location of attendees higher in the hierarchy. 
     In some embodiments, the calendaring model  110  may employ the machine learning model and/or AI/machine learning techniques as described above to reschedule cancelled meetings. Such rescheduled meetings may be rescheduled automatically based on, e.g., location data, meeting room data, and schedule information including, e.g., calendar data such as availability and meeting history (e.g., cancellations, rescheduling, etc.) among others, from the location database  103 , the meeting room database  104  and/or the calendar database  101 . 
     In some embodiments, the collaboration system  100  includes an emailing model  120  to, e.g., determine an order of priority of received emails. In some embodiments, the collaboration system  100  may receive electronic messages, including emails, instant message communications, simple message service (SMS) messages, among other electronic message formats. In some embodiments, the emailing model  120  may receive and/or determine indicators of attributes of the electronic messages, such as, e.g., sender, recipient or recipients, carbon-copied (cc&#39;d) recipients, subject line text, electronic message text, attached files and/or media, hyperlinks, urgency markers, read-receipts, associated conversations for each electronic message, related calendar events, such as a calendar event created with the electronic message or a calendar event for which the electronic message is a response, among other attributes. In some embodiments, the emailing model  120  may include, e.g., AI/machine learning techniques, such as those described above, to form parameters from one or more of the attributes, such as, e.g., subject line text, sender including data from the organization database  102 , and related calendar events including calendar data from the calendar database  101  by correlating each of the attributes with a likelihood of the user viewing, responding to, forwarding, deleting, delaying, or otherwise interacting with the electronic message. 
     In some embodiments, using the parameters, the AI/machine learning techniques of the emailing model  120  may predict a level of priority of each electronic message. In some embodiments, the level of priority may be a priority scale, such as a numeric scale in a range of between about 1 and about 10, between about 1 and about 5, between about 1 and about 100, or other range. In some embodiments, the level of priority may include a relative level of priority, where the emailing model  120  ranks each electronic message according to a relative priority level compared to each other electronic message. Accordingly, in some embodiments, a user may be presented with a list of electronic messages in an order of priority such that the most important and/or actionable electronic messages may be presented first. In some embodiments, the collaboration system  100  includes a locating model  130  to, e.g., optimize a location of a meeting based on, e.g., location data from the location database  103  and meeting room data from the meeting room database  104 . In some embodiments, the locating model  130  employs AI/machine learning techniques, such as those described above, to predict an optimum location for a meeting that will reduce the likelihood of the meeting being cancelled, rescheduled or relocated. In some embodiments, the optimum location depends on a location of each attendee to a meeting as well as a location of each available meeting room for the meeting. In some embodiments, the locating model  130  may also take into account meeting room resources, such as, e.g., video conferencing equipment, technology support, size, furniture, among other features of a meeting room. In some embodiments, the predicted optimum location can be provided to the calendar model  110 , which may, in turn, automatically schedule a meeting using the predicted optimum meeting location. 
     In some embodiments, the collaboration system  100  includes a task model  140  to, e.g., automatically schedule calendar events to perform a task associated with one or more meetings based on, e.g., email data from the email database  105  associated with emails related to the meetings, task data from the task database  106  associated with current and past tasks of each attendee, and work product data from the work product database  107  associated with completed work product of each attendee. In some embodiments, the task model  140  interacts with the calendar model  110  to determine tasks associated with an upcoming meeting, a time to complete the tasks, and attendees associated with the task. To do so, in some embodiments, the task model  140  employs AI/machine learning techniques, such as those described above, to predict a task parameter leading up to the meeting. The task parameter can be used by the calendar model  110  to automatically determine a task time and location for the associated attendees to complete the task prior to the meeting. In an embodiment, the calendar model  110  may then automatically schedule the task time and location each attendee&#39;s respective calendar to facilitate private, uninterrupted work time. 
     In some embodiments, the collaboration system  100  may include additional databases and models, such as, e.g., an email database  205  storing emails associated with each attendee, a task database  206  storing tasks (e.g., work tasks for work assignments) associated with each attendee, a work product database  207  storing work produced (e.g., documents, files, images, etc.) associated with each attendee, among others. Similarly, the collaboration system  100  may include additional models, such as, e.g., an emailing model  220  for recognizing and parsing emails associated with each attendee, a locating model  230  for recognizing and predicting locations of attendees and/or meetings, a task model  240  for recognizing and predicting work tasks, among other models or any combination thereof. 
       FIG. 2  depicts a diagram of a calendaring model for an exemplary illustrative automated calendar management system in accordance with an illustrative embodiment of the present invention. 
     In some embodiments, a collaboration system  200  includes a calendaring model  210  in communication with collaboration databases, such as, a calendar database  201 , an organization database  202 , a location database  203  and a meeting room database  204 , among other suitable databases for providing information for calendar and meeting collaboration. In some embodiments, each of the collaboration databases may include data in a suitable format, such as, e.g., tables, text, tuples, arrays, etc. Each data item in the collaboration databases may also include metadata associated with information such as, e.g., origin of the data, destination, format, time and date, geographic location information, source identifier (ID), among other information. 
     In some embodiments, the calendaring model  210  may leverage the data in the collaboration databases, including associated metadata, to predict optimum meeting times and/or locations, such as optimum meeting times based on scheduling information related to attendees, optimum meeting locations based on location information associated with the attendees as well as available meeting rooms, and meeting room resources. 
     In some embodiments, the calendaring model  210  may predict the meeting times and/or locations in response to a user interaction  214  from a user computing device  214 . In some embodiments, the user interaction  214  includes, e.g., setting an out-of-office status in a calendar or email program. Out-of-office statuses may sometimes include a time period that has meetings scheduled therein with other meeting attendees. Such meetings may be cancelled or rescheduled to accommodate the out-of-office status of the user. Thus, in some embodiments, the calendaring model  210  may detect the out-of-office status via the user interaction  214  and initiate a rescheduling process for each meeting within the out-of-office period. 
     In some embodiments, the calendaring model  210  may predict the rescheduled meetings using the data in the collaboration databases. The calendaring model  210  may receive the data and employ a parse engine  211 , a calendar machine learning engine  212  and an optimizer  213  to deduce a correlation between the data and an optimum meeting schedule, including the meeting location and the meeting time. In some embodiments, each of the parse engine  211 , calendar machine learning engine  212  and the optimizer  213  may include, e.g., software, hardware and/or a combination thereof. For example, in some embodiments, the parse engine  211  may include a processor and a memory, the memory having instructions stored thereon that cause the processor to parse data. In some embodiments, the calendar machine learning engine  212  may include a processor and a memory, the memory having instructions stored thereon that cause the processor to predict optimum meeting times and locations from the parsed data. In some embodiments, the optimizer  213  may include a processor and a memory, the memory having instructions stored thereon that cause the processor to optimize the parse engine  211  and/or the calendar machine learning engine  212  according to, e.g., an error of the predicted meeting time and location. 
     In some embodiments, the parse engine  211  may transform the data as well as the user interaction  214  including the out-of-office status into, e.g., feature vectors or feature maps such that the calendar machine learning engine  212  may generate meeting predictions based on features of the data. Thus, in some embodiments, the parse engine  211  may receive the data, parse the data, and extract features according to a feature extraction algorithm. Data parsing and feature extraction may utilize methods depending on a type of data being received. For example, the parse engine  211  may include language parsing when the data includes text and character strings. Thus, in some embodiments, the parse engine  211  may include, e.g., a classifier for natural language recognition. However, in some embodiments, the data may be a table. In such a case, the parse engine  211  may simply extract features into, e.g., a feature vector directly from the data. However, in some embodiments, the data may include a combination of character strings, as well as structured data, such as tables, tuples, lists, arrays, among other. Thus, in some embodiments, the parse engine  211  may include model or algorithm for parsing the character strings and then extracting feature vectors from the structured data and the parsed character strings. 
     In some embodiments, the feature extraction algorithm may include, e.g., independent component analysis, an isomap, kernel principle component analysis (PCA), latent semantic analysis, partial least squares, principal component analysis, multifactor dimensionality reduction, nonlinear dimensionality reduction, multilinear PCA, multilinear subspace learning, semidefinite embedding, autoencoding, among others and combinations thereof. As a result, the parse engine  211  may generate feature vectors having, e.g., availability and meeting history features, personnel features, attendee location features, available meeting room features, and/or out-of-office features, among other possible features. 
     In some embodiments, the feature vectors produced by the parse engine  211  may be employed by the calendar machine learning engine  212  to develop a prediction for an optimum meeting time and location. In some embodiments, the calendar machine learning engine  212  is configured to make at least two predictions in response to the feature vectors: a rescheduled meeting location and a rescheduled meeting time associated with each meeting affected by the out-of-office status. In some embodiments, the calendar machine learning engine  212  may utilize, e.g., classification machine learning techniques to develop a prediction from the availability and meeting history features, personnel features, attendee location features, and available meeting room features for each meeting affected by the out-of-office features. In some embodiments, the result of this prediction process produces a meeting location parameter and a meeting time parameter for each rescheduled meeting that correspond to an optimal meeting time for all of the attendees of each rescheduled meeting, and an optimal location for all of the attendees, respectively. 
     For example, in some embodiments, the calendar machine learning engine  212  may include, e.g., a convolutional neural network (CNN) having multiple convolutional layers to receive a feature map composed of each of the feature vectors, convolutionally weight each element of the feature map using the convolutional layers, and generate an output representing both the meeting location parameter and the meeting time parameter. However, in some embodiments, the calendar machine learning engine  212  may include two models, a meeting location machine learning model and a meeting time machine learning model. Each of the meeting location machine learning model and the meeting time machine learning model may receive the feature map and generate respective outputs of the meeting location parameter and the meeting time parameter separately. 
     In some embodiments, the calendar machine learning engine  212  may then convert the meeting location parameter and the meeting time parameter into a meeting request to submit to the user and any other attendees of each of the rescheduled meetings. In some embodiments, the meeting request is transmitted to the user computer  260  to produce a meeting request indication for the user to select, decline, or modify. The selection and/or modification may cause the collaboration system  200  to automatically book the meeting room associated with the meeting location parameter at the time associated with the meeting room parameters. 
     In some embodiments, one or more of the user and/or attendees may accept, decline or modify the meeting request with a response. The response or responses may then be returned to an optimizer  213  that evaluates the meeting location parameters and the meeting time parameter against a ground truth. Here, the ground truth may be the responses associated with the meeting request. Thus, in effect, each response to the meeting request may be used as feedback into the optimize  213  to optimize the calendaring model  210  for on-line learning. Thus, in some embodiments, the optimizer  213  may determine an error associated with the predicted meeting location parameter and meeting time parameter as compared to the time and location of the meeting request and whether the meeting request was accepted or not. In some embodiments, the optimizer  213  may backpropagate the error to the parse engine  211 , the calendar machine learning engine  212 , or both to train each engine in an on-line fashion. 
       FIG. 3  depicts a diagram of a calendaring model for an exemplary illustrative automated calendar management system in accordance with an illustrative embodiment of the present invention. 
     In some embodiments, a collaboration system  200  may initiate an automated meeting rescheduling process upon receipt of a user interaction including, e.g., out-of-office data  214 . In some embodiments, the user interaction includes an out-of-office message, status or request provided by, e.g., a user computing device, such as the user computing device  260  or other device, that generates the out-of-office data  214 . In some embodiments, the out-of-office data  214  includes an identification of the associated user and a date range in which the user is expected to be unavailable. In some embodiments, the out-of-office data  214  may include data formatted for the collaboration system  200  and ready to undergo feature extraction. However, in some embodiments, the out-of-office data  214  may include an image, character string, or other unstructured data related to the out-of-office message, status or request. In some embodiments, where the out-of-office data  214  is unstructured, the parse engine  211  may parse the date using, e.g., a parsing algorithm such as a natural language recognition model, an image recognition model, or other algorithm. 
     In some embodiments, based on the out-of-office data  214 , the collaboration system  200  may pull data from collaboration databases  270  including each of the calendar database  201 , the location database  203 , and the meeting room database  204  associated with the data range of the out-of-office period. Accordingly, the parse engine  211  may receive, e.g., availability and meeting history data  221  from the calendar database  201  related to meetings within the out-of-office period. Similarly, the parse engine  211  may receive, e.g., attendee location data  223  and available meeting room data  224  from the location database  203  and the meeting room database  204 , respectively. 
     In some embodiments, collaboration system  200  may direct the out-of-office data  214  to the collaboration databases  270 . In response to the out-of-office data  214 , each of the calendar database  201 , the organization database  202 , the location database  203  and the meeting room database  204  may determine data to provide to the parse engine  211 . For example, in some embodiments, the calendar database  201  may compare the out-of-office data  214  and the associated out-of-office period to determine each meeting scheduled within the out-of-office period for the user. The calendar database  201  may identify the scheduled meetings and determine attendees for each scheduled meeting. In some embodiments, the calendar database  201  may also identify a meeting history associated with each attendee including the user, including scheduled meetings, cancelled meetings, rescheduled meetings, relocated meetings, among other meeting history items. In some embodiments, the calendar database  201  may provide the scheduled meetings, attendees, and meeting histories to the parse engine  211  as availability and meeting history data  221 . 
     In some embodiments, the organization database  202  may utilize the attendees identified by the calendar database  201  to identify attendee hierarchy within an associated organization according to, e.g., an organization chart or other data related to personnel hierarchy for the associated organization. The organization database  202  may provide the attendee hierarchy based on the, e.g., organization chart, to the parse engine  211  as personnel data  222  associated with each attendee of each scheduled meeting. 
     In some embodiments, the location database  203  may utilize the attendees identified by the calendar database  201  to identify locations, such as, e.g., office locations, of each of the attendees of each of the scheduled meetings. The location database  203  may provide the location information to the parse engine  211  as attendee location data  223 . 
     In some embodiments, the meeting room database  204  may utilize the out-of-office data  214  to determine a set of available meeting rooms for a predetermined period near the out-of-office period but not including the out-of-office period. For example, in some embodiments, the meeting room database  204  may track meeting room characteristics, including, e.g., availability, location, size, resources, among other characteristics for each known meeting location. The meeting room database  204  determine a set of available meeting rooms based on the meeting room availability within, e.g, about one week, about two weeks, about one month, or other suitable time of the out-of-office period. The characteristics of the set of available meeting rooms may be provided to the parse engine  211  as available meeting room data  224 . 
     In some embodiments, the parse engine  211  may receive, e.g., each of the availability and meeting history data  221 , the personnel data  222 , the attendee location data  223  and the available meeting room data  224  and extract features. Thus, in some embodiments, the parse engine  211  may transform the data as well as the out-of-office data  214  into, e.g., feature vectors or feature maps. Thus, in some embodiments, the parse engine  211  may receive the data, parse the data, and extract features according to a feature extraction algorithm. Data parsing and feature extraction may utilize methods depending on a type of data being received. For example, the parse engine  211  may include language parsing when the data includes text and character strings. In some embodiments, the parse engine  211  may include, e.g., a classifier for natural language recognition. However, in some embodiments, the data may be a table. In such a case, the parse engine  211  may simply extract features into, e.g., a feature vector directly from the data. However, in some embodiments, the data may include a combination of character strings, as well as structured data, such as tables, tuples, lists, arrays, among other. Thus, in some embodiments, the parse engine  211  may include model or algorithm for parsing the character strings and then extracting feature vectors from the structured data and the parsed character strings. 
     In some embodiments, as a result of the parsing and feature extraction, the parse engine  211  may produce meeting history features  231 , personnel features  232 , attendee location features  233 , available meeting room features  234  and out-of-office features  235  from each of the meeting history data  221 , the personnel data  222 , the attendee location data  223  and the available meeting room data  224 , and the out-of-office data  214 , respectively. 
     In some embodiments, the feature extraction algorithm may include, e.g., independent component analysis, an isomap, kernel principle component analysis (PCA), latent semantic analysis, partial least squares, principal component analysis, multifactor dimensionality reduction, nonlinear dimensionality reduction, multilinear PCA, multilinear subspace learning, semidefinite embedding, autoencoding, among others and combinations thereof. As a result, the parse engine  211  may generate feature vectors having, e.g., meeting history features  231 , the personnel features  232 , the attendee location features  233  and the available meeting room features  234  such that predictive features of scheduling information and location information can be provided to the calendar machine learning engine  212 . In some embodiments, the feature vectors are aggregated into a feature map prior to modeling in the calendar machine learning engine  212 . 
     In some embodiments, the calendar machine learning engine  212  receives the meeting history features  231 , the personnel features  232 , the attendee location features  233 , the available meeting room features  234  and the out-of-office features  235  to model meeting time and location for each meeting affected by the out-of-office status to predict optimum rescheduled meetings. In some embodiments, the calendar machine learning engine  212  includes scheduling models  215  to predict the location and time of a meeting that is most likely to be accepted by all attendees. 
     In some embodiments, the scheduling models  215  may include separate models for predicting meetings to be rescheduled, predict meeting times for the meetings to be rescheduled, and predicting meeting locations for meetings to be rescheduled. In some embodiments, the scheduling models  215  may first determined the meetings to be rescheduled using a scheduling model based on the out-of-office features  235 . The rescheduling model may provide the meetings to be rescheduled to each of a meeting time model and a meeting location model. 
     In some embodiments, the meeting time model may predict a meeting time parameter  241  that represents an optimum meeting time for each attendee of each meeting to be rescheduled. In some embodiments, the meeting time model may predict the meeting time parameter  241  based on scheduling information and personnel information, including, e.g., the meeting history features  231 , the personnel features  232  and the available meeting room features  234 . In some embodiments, using, e.g., a classifier, such as those described above, the meeting time model may transform the meeting history features  231 , the personnel features  232  and the available meeting room features  234  into a feature vector output that may be decoded into the meeting time parameter  241  representing the optimum meeting time based on the scheduling information of the attendees and meeting rooms. 
     In some embodiments, the meeting location model may predict a meeting location parameter  242  that represents an optimum meeting location for each attendee of each meeting to be rescheduled. In some embodiments, the meeting time model may predict the meeting location parameter  242  based on location information and personnel information, including, e.g., the meeting history features  231 , the personnel features  232 , the attendee location features  233  and the available meeting room features  234 . In some embodiments, using, e.g., a classifier, such as those described above, the meeting location model may transform the meeting history features  231 , the personnel features  232 , the attendee location features  233  and the available meeting room features  234  into a feature vector output that may be decoded into the meeting location parameter  242  representing the optimum meeting location based on the location information of the attendees and meeting rooms. 
     While an arrangement of the scheduling models  215  is described above in accordance with some embodiments of the present collaboration system  200 , some embodiments may differ. In some embodiments, the scheduling models  215  may include a single model that predicts both the meeting time parameter  241  and the meeting location parameter  242  based on a feature map formed from the meeting history features  231 , the personnel features  232 , the attendee location features  233  and the available meeting room features  234 . In some embodiments, the rescheduling model, the meeting time model and meeting location model operate in parallel and combine outputs to form the meeting location parameter  242  and the meeting time parameter  241 . In some embodiments, the rescheduling model, the meeting time model and meeting location model operate in series, with the output of the rescheduling model being provided to the meeting location model, and the output of the meeting location model being provided to the meeting time model to generate the meeting time parameter  241  and the meeting location parameter  242 . In some embodiments, the meeting time model performs the predictions of the rescheduling model concurrently with predicting the meeting time parameter  241 . Other combinations of models are contemplated as well. 
     In some embodiments, an event generator  216  of the calendar machine learning engine  212  may receive the meeting time parameter  241  and the meeting location parameter  242  to generate a candidate rescheduled meeting  251 . The candidate rescheduled meeting  251  represents a meeting request for a corresponding meeting to be rescheduled, including a time and location for the meeting and a list of attendees. In some embodiments, the candidate rescheduled meeting  251  may automatically book a meeting a room at the specified time and location. In some embodiments, the candidate rescheduled meeting  251  is first provided to each attendee at a user computing device  260 . Each attendee may then accept, decline or request an alternative time or location according to a user selection  252 . In some embodiments, the user selection  252  may cause the securing of the meeting time at the meeting location according to the candidate rescheduled meeting  251 . 
     In some embodiments, the user selection  252  may be provided to an optimizer  213  of the calendaring model  213 . In some embodiments, the optimizer  213  may compare the user selection  252  to the corresponding predicted meeting time parameter  241  and meeting location parameter  242 . Based on a difference between the user selection  252  to the corresponding predicted meeting time parameter  241  and meeting location parameter  242 , the optimizer  213  may determine an error in the predictions by the calendar machine learning engine  212 . In some embodiments, the optimizer  213  backpropagates the error to the calendar machine learning engine  212  to train the scheduling models  215  in an on-line fashion such that each prediction may be used as a training pair with the corresponding user selection. Thus, the scheduling models  215  may be updated as users provide user selections  252  to continually improve the scheduling models  215 . In some embodiments, the optimizer  213  may employ optimization models including, but not limited to, e.g., gradient descent, regularization, stochastic gradient descent, Nesterov accelerated gradient, Adagrad, AdaDelta, adaptive momentum estimation (AdaM), root-mean-square propagation (RMS Prop), among others and combinations thereof. 
     In some embodiments, the user selection  252  may be a second selection after an initial selection. For example, where a user initially accepts the candidate rescheduled meeting  251  via the user selection  252 , the user may later cancel the meeting by a second user selection  252 . The second user selection may be provided to the optimizer  213  in a similar fashion to the initial user selection  252  to determine an error and train the calendar machine learning engine  212 . Moreover, the user selection  252  and/or the candidate rescheduled meeting  251  may be provided to the meeting history of each attendee in the calendar database  201  to update the calendar database  201  with current information. 
     Similarly, in some embodiments, the user selection  252  and/or the corresponding candidate rescheduled meeting  251  may be provided to each of the location database  203  and the meeting room database  204  to update information related to the location of attendees and availability of meeting rooms at the time of the candidate rescheduled meeting  251 . In some embodiments, the collaboration databases  270  are only updated upon the user selection  251 . 
       FIG. 4  depicts a block diagram of an exemplary computer-based system/platform  400  in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the exemplary inventive computing devices and/or the exemplary inventive computing components of the exemplary computer-based system/platform  400  may be configured to manage a large number of members and/or concurrent transactions, as detailed herein. In some embodiments, the exemplary computer-based system/platform  400  may be based on a scalable computer and/or network architecture that incorporates varies strategies for assessing the data, caching, searching, and/or database connection pooling. An example of the scalable architecture is an architecture that is capable of operating multiple servers. 
     In some embodiments, referring to  FIG. 4 , members  402 - 404  (e.g., clients) of the exemplary computer-based system/platform  400  may include virtually any computing device capable of receiving and sending a message over a network (e.g., cloud network), such as network  405 , to and from another computing device, such as servers  406  and  407 , each other, and the like. In some embodiments, the member devices  402 - 404  may be personal computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, and the like. In some embodiments, one or more member devices within member devices  402 - 404  may include computing devices that typically connect using a wireless communications medium such as cell phones, smart phones, pagers, walkie talkies, radio frequency (RF) devices, infrared (IR) devices, CBs, integrated devices combining one or more of the preceding devices, or virtually any mobile computing device, and the like. In some embodiments, one or more member devices within member devices  402 - 404  may be devices that are capable of connecting using a wired or wireless communication medium such as a PDA, POCKET PC, wearable computer, a laptop, tablet, desktop computer, a netbook, a video game device, a pager, a smart phone, an ultra-mobile personal computer (UMPC), and/or any other device that is equipped to communicate over a wired and/or wireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments, one or more member devices within member devices  402 - 404  may include may run one or more applications, such as Internet browsers, mobile applications, voice calls, video games, videoconferencing, and email, among others. In some embodiments, one or more member devices within member devices  402 - 404   404  may be configured to receive and to send web pages, and the like. In some embodiments, an exemplary specifically programmed browser application of the present disclosure may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web based language, including, but not limited to Standard Generalized Markup Language (SMGL), such as HyperText Markup Language (HTML), a wireless application protocol (WAP), a Handheld Device Markup Language (HDML), such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, and the like. In some embodiments, a member device within member devices  402 - 404  may be specifically programmed by either Java, .Net, QT, C, C++ and/or other suitable programming language. In some embodiments, one or more member devices within member devices  402 - 404  may be specifically programmed include or execute an application to perform a variety of possible tasks, such as, without limitation, messaging functionality, browsing, searching, playing, streaming or displaying various forms of content, including locally stored or uploaded messages, images and/or video, and/or games. 
     In some embodiments, the exemplary network  405  may provide network access, data transport and/or other services to any computing device coupled to it. In some embodiments, the exemplary network  405  may include and implement at least one specialized network architecture that may be based at least in part on one or more standards set by, for example, without limitation, Global System for Mobile communication (GSM) Association, the Internet Engineering Task Force (IETF), and the Worldwide Interoperability for Microwave Access (WiMAX) forum. In some embodiments, the exemplary network  405  may implement one or more of a GSM architecture, a General Packet Radio Service (GPRS) architecture, a Universal Mobile Telecommunications System (UMTS) architecture, and an evolution of UMTS referred to as Long Term Evolution (LTE). In some embodiments, the exemplary network  405  may include and implement, as an alternative or in conjunction with one or more of the above, a WiMAX architecture defined by the WiMAX forum. In some embodiments and, optionally, in combination of any embodiment described above or below, the exemplary network  405  may also include, for instance, at least one of a local area network (LAN), a wide area network (WAN), the Internet, a virtual LAN (VLAN), an enterprise LAN, a layer  3  virtual private network (VPN), an enterprise IP network, or any combination thereof. In some embodiments and, optionally, in combination of any embodiment described above or below, at least one computer network communication over the exemplary network  405  may be transmitted based at least in part on one of more communication modes such as but not limited to: NFC, RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In some embodiments, the exemplary network  405  may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine readable media. 
     In some embodiments, the exemplary server  406  or the exemplary server  407  may be a web server (or a series of servers) running a network operating system, examples of which may include but are not limited to Microsoft Windows Server, Novell NetWare, or Linux. In some embodiments, the exemplary server  406  or the exemplary server  407  may be used for and/or provide cloud and/or network computing. Although not shown in  FIG. 4 , in some embodiments, the exemplary server  406  or the exemplary server  407  may have connections to external systems like email, SMS messaging, text messaging, ad content providers, etc. Any of the features of the exemplary server  406  may be also implemented in the exemplary server  407  and vice versa. 
     In some embodiments, one or more of the exemplary servers  406  and  407  may be specifically programmed to perform, in non-limiting example, as authentication servers, search servers, email servers, social networking services servers, SMS servers, IM servers, MMS servers, exchange servers, photo-sharing services servers, advertisement providing servers, financial/banking-related services servers, travel services servers, or any similarly suitable service-base servers for users of the member computing devices  401 - 404 . 
     In some embodiments and, optionally, in combination of any embodiment described above or below, for example, one or more exemplary computing member devices  402 - 404 , the exemplary server  406 , and/or the exemplary server  407  may include a specifically programmed software module that may be configured to send, process, and receive information using a scripting language, a remote procedure call, an email, a tweet, Short Message Service (SMS), Multimedia Message Service (MMS), instant messaging (IM), internet relay chat (IRC), mIRC, Jabber, an application programming interface, Simple Object Access Protocol (SOAP) methods, Common Object Request Broker Architecture (CORBA), HTTP (Hypertext Transfer Protocol), REST (Representational State Transfer), or any combination thereof. 
       FIG. 5  depicts a block diagram of another exemplary computer-based system/platform  500  in accordance with one or more embodiments of the present disclosure. However, not all of these components may be required to practice one or more embodiments, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of various embodiments of the present disclosure. In some embodiments, the member computing devices  502   a,    502   b  thru  502   n  shown each at least includes a computer-readable medium, such as a random-access memory (RAM)  508  coupled to a processor  510  or FLASH memory. In some embodiments, the processor  510  may execute computer-executable program instructions stored in memory  508 . In some embodiments, the processor  510  may include a microprocessor, an ASIC, and/or a state machine. In some embodiments, the processor  510  may include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor  510 , may cause the processor  510  to perform one or more steps described herein. In some embodiments, examples of computer-readable media may include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as the processor  510  of client  502   a,  with computer-readable instructions. In some embodiments, other examples of suitable media may include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. In some embodiments, the instructions may comprise code from any computer-programming language, including, for example, C, C++, Visual Basic, Java, Python, Perl, JavaScript, and etc. 
     In some embodiments, member computing devices  502   a  through n may also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a physical or virtual keyboard, a display, or other input or output devices. In some embodiments, examples of member computing devices  502   a  through n (e.g., clients) may be any type of processor-based platforms that are connected to a network  506  such as, without limitation, personal computers, digital assistants, personal digital assistants, smart phones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In some embodiments, member computing devices  502   a  through n may be specifically programmed with one or more application programs in accordance with one or more principles/methodologies detailed herein. In some embodiments, member computing devices  502   a  through n may operate on any operating system capable of supporting a browser or browser-enabled application, such as Microsoft™, Windows™, and/or Linux. In some embodiments, member computing devices  502   a  through n shown may include, for example, personal computers executing a browser application program such as Microsoft Corporation&#39;s Internet Explorer™, Apple Computer, Inc.&#39;s Safari™, Mozilla Firefox, and/or Opera. In some embodiments, through the member computing client devices  502   a  through n, users,  512   a  through n, may communicate over the exemplary network  506  with each other and/or with other systems and/or devices coupled to the network  506 . As shown in  FIG. 5 , exemplary server devices  504  and  513  may include processor  505  and processor  514 , respectively, as well as memory  517  and memory  516 , respectively. In some embodiments, exemplary server devices  504  and  513  may be also coupled to the network  506 . In some embodiments, one or more member computing devices  502   a  through n may be mobile clients. 
     In some embodiments, at least one database of exemplary databases  507  and  515  may be any type of database, including a database managed by a database management system (DBMS). In some embodiments, an exemplary DBMS-managed database may be specifically programmed as an engine that controls organization, storage, management, and/or retrieval of data in the respective database. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to provide the ability to query, backup and replicate, enforce rules, provide security, compute, perform change and access logging, and/or automate optimization. In some embodiments, the exemplary DBMS-managed database may be chosen from Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQL implementation. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to define each respective schema of each database in the exemplary DBMS, according to a particular database model of the present disclosure which may include a hierarchical model, network model, relational model, object model, or some other suitable organization that may result in one or more applicable data structures that may include fields, records, files, and/or objects. In some embodiments, the exemplary DBMS-managed database may be specifically programmed to include metadata about the data that is stored. 
     In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate in a cloud computing/architecture  525  such as, but not limiting to: infrastructure a service (IaaS)  710 , platform as a service (PaaS)  708 , and/or software as a service (SaaS)  706  using a web browser, mobile app, thin client, terminal emulator or other endpoint  704 .  FIGS. 6 and 7  illustrate schematics of exemplary implementations of the cloud computing/architecture(s) in which the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be specifically configured to operate. 
     At least some aspects of the present disclosure will now be described with reference to the following numbered clauses.
     1. A method comprising:   

     receiving, by at least one processor, an out-of-office notification associated with at least one meeting attendee; 
     identifying, by the at least one processor, at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee;
         wherein the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification;   wherein the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings comprises a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting;       

     utilizing, by the at least one processor, a meeting scheduling machine learning model to predict a plurality of parameters of at least one meeting room object representing at least one respective candidate rescheduled meeting associated with the one or more respective need-to-reschedule meetings;
         wherein the meeting scheduling machine learning model is configured to predict the plurality of parameters of the at least one meeting room object based at least in part on schedule information associated with the at least one need-to-reschedule meeting data items and location information associated with the at least one need-to-reschedule meeting data items;   wherein the plurality of parameters of at least one meeting room object comprises:
           i) a meeting location parameter, and   ii) a meeting time parameter;   
           wherein the schedule information comprises:
           i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   wherein the meeting history data comprises:
               1) cancellation data identifying meeting cancellations, and   2) rescheduling data identifying meeting rescheduling occurrences;   
               
           wherein the location information comprises:
           i) an attendee location data identifying at least one respective location associated with the at least one respective attendee,   ii) available meeting room data identifying all available meeting rooms, and   iii) a meeting room location associated with each available meeting room;   
               

     causing to display, by the at least one processor, an indication of the at least one respective candidate rescheduled meeting in response to the at least one out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting; 
     receiving, by the at least one processor, a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee; and 
     dynamically securing, by the at least one processor, the at least one respective candidate rescheduled meeting for at least one respective meeting.
     2. The method of clause 1, wherein the location information further comprises meeting room needs associated with each of the scheduled meetings;   

     wherein the meeting room needs comprise:
         i) meeting room resources, and   ii) a meeting room size.       3. The method of clause 2, wherein the attendee location data associated with the at least one respective attendee comprises a real-time location based on tracking an employee badge.   4. The method of clause 2, wherein the attendee location data associated with the at least one respective attendee comprises a real-time location based on global positioning (GPS) data associated with an attendee mobile device.   5. The method of clause 1, further comprising determining, by the at least one processor, traffic data identifying a traffic delay for a transit time associated a transit from each respective attendee location to each meeting room location associated with each available candidate meeting rooms.   6. The method of clause 1, further comprising determining, by the at least one processor, a cancellation prediction using the meeting scheduling machine learning model based at least in part on the cancellation data associate with each of the at least one respective attendee.   7. The method of clause 1, wherein the meeting scheduling machine learning model is further utilized to predict an attendee prioritization parameter to prioritize the availability associated with each of the at least one attendee according to each respective hierarchical position associated with each respective at least one attendee;   

     wherein the attendee prioritization parameter comprises:
         i) a prioritization of the schedule information associated with the at least one respective attendee, and   ii) the location information associated with the at least one respective attendee;       

     wherein the hierarchical position of each of the at least one attendee is based on an organization chart.
     8. The method of clause 1, further comprising training, by the at least one processor, the meeting scheduling machine learning model based on a meeting result.   9. The method of clause 8, wherein the meeting result comprises meeting disposition data identifying a completed meeting at the meeting location and at the meeting time.   10. The method of clause 8, wherein the meeting disposition data comprises one of selection comprising a cancellation indication and a reschedule indication;   

     wherein the cancellation indication identifies:
         i) a cancelling of the meeting location, and   ii) a cancelling of the meeting time;       

     wherein the reschedule indication identifies:
         i) a rescheduling of the meeting location, and   ii) a rescheduling of the meeting time.       11. A method comprising:   

     receiving, by at least one processor, an out-of-office notification associated with at least one meeting attendee; 
     identifying, by the at least one processor, at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee;
         wherein the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification;   wherein the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings comprises a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting;       

     determining, by the at least one processor, an error in a plurality of parameters of at least one meeting room object predicted by a meeting scheduling machine learning model based at least in part on the one or more need-to-reschedule meetings associated with the need-to-reschedule meeting data;
         wherein the plurality of parameters of at least one meeting room object comprises:
           i) a meeting location parameter, and   ii) a meeting time parameter;   
           wherein the at least one meeting room object represents at least one candidate meeting room;       

     training, by the at least one processor, the meeting scheduling machine learning model based at least in part on the error; 
     utilizing, by the at least one processor, the meeting scheduling machine learning model to predict a plurality of new parameters of the at least one meeting room object representing at least one respective candidate rescheduled meeting associated with the one or more respective need-to-reschedule meetings;
         wherein the meeting scheduling machine learning model is configured to predict the plurality of new parameters of the at least one meeting room object based at least in part on schedule information associated with the at least one need-to-reschedule meeting data items and location information associated with the at least one need-to-reschedule meeting data items;   wherein the plurality of new parameters of at least one meeting room object comprises:
           i) a new meeting location parameter, and   ii) a new meeting time parameter;   
           wherein the schedule information comprises:
           i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   wherein the meeting history data comprises:
               1) cancellation data identifying meeting cancellations, and   2) rescheduling data identifying meeting rescheduling occurrences;   
               
           wherein the location information comprises:
           i) an attendee location data identifying at least one respective location associated with the at least one respective attendee,   ii) available meeting room data identifying all available meeting rooms, and   iii) a meeting room location associated with each available meeting room;   
               

     causing to display, by the at least one processor, an indication of the at least one respective candidate rescheduled meeting in response to the out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting; 
     receiving, by the at least one processor, a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee; and 
     dynamically securing, by the at least one processor, the at least one respective candidate rescheduled meeting for at least one respective meeting.
     12. The method of clause 11, wherein the attendee location data associated with the at least one respective attendee comprises a real-time location based on tracking an employee badge.   13. The method of clause 11, wherein the attendee location data associated with the at least one respective attendee comprises a real-time location based on global positioning (GPS) data associated with an attendee mobile device.   14. The method of clause 11, further comprising determining, by the at least one processor, traffic data identifying a traffic delay for a transit time associated a transit from each respective attendee location to each meeting room location associated with each available candidate meeting rooms.   15. The method of clause 11, further comprising determining, by the at least one processor, a cancellation prediction using the meeting scheduling machine learning model based at least in part on the cancellation data associate with each of the at least one respective attendee.   16. The method of clause 11, wherein the meeting scheduling machine learning model is further utilized to predict an attendee prioritization parameter to prioritize the availability associated with each of the at least one attendee according to each respective hierarchical position associated with each respective at least one attendee;   

     wherein the attendee prioritization parameter comprises:
         i) a prioritization of the schedule information associated with the at least one respective attendee, and   ii) the location information associated with the at least one respective attendee; wherein the hierarchical position of each of the at least one attendee is based on an organization chart.       17. The method of clause 11, further comprising training, by the at least one processor, the meeting scheduling machine learning model based on a meeting result.   18. The method of clause 17, wherein the meeting result comprises meeting disposition data identifying a completed meeting at the meeting location and at the meeting time.   19. The method of clause 17, wherein the meeting disposition data comprises one of selection comprising a cancellation indication and a reschedule indication;   

     wherein the cancellation indication identifies:
         i) a cancelling of the meeting location, and   ii) a cancelling of the meeting time;       

     wherein the reschedule indication identifies:
         i) a rescheduling of the meeting location, and   ii) a rescheduling of the meeting time.       20. A system comprising:   

     a calendar database configured to store calendar data associated with each employee of an organization; 
     a meeting room database configured to store meeting room characteristics of possible meeting rooms of the organization; and 
     at least one processor in communication with the calendar database and the meeting room database;
         wherein the at least one processor is configured to:
           receive an out-of-office notification associated with at least one meeting attendee;   identify at least one need-to-reschedule meeting data item of one or more respective need-to-reschedule meetings associated with the at least one meeting attendee from at least one electronic calendar associated with at least one meeting attendee;
               wherein the one or more respective need-to-reschedule meetings are scheduled within an out-of-office time period based on the out-of-office notification;   wherein the at least one need-to-reschedule meeting data items of one or more respective need-to-reschedule meetings comprises a respective attendee data identifying at least one respective attendee of each respective need-to-reschedule meeting;   
               utilize a meeting scheduling machine learning model to predict a plurality of parameters of at least one meeting room object representing at least one respective candidate rescheduled meeting;
               wherein the meeting scheduling machine learning model is configured to predict the plurality of parameters of the at least one meeting room object based at least in part on schedule information associated with the need-to-reschedule meeting data and location information associated with the need-to-reschedule meeting data;   wherein the plurality of parameters of at least one meeting room object comprises:
                   i) a meeting location parameter, and   ii) a meeting time parameter;   
                   wherein the schedule information comprises:
                   i) an availability data identifying availability of each of the at least one respective attendee based on respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   ii) a meeting history data identifying meeting history of the at least one respective attendee based on the respective calendar data obtained from each respective electronic calendar associated with the respective at least one attendee,   wherein the meeting history data comprises:    1) cancellation data identifying meeting cancellations, and    2) rescheduling data identifying meeting rescheduling occurrences;   
                   wherein the location information comprises:
                   i) an attendee location data identifying at least one respective location associated with the at least one respective attendee,   ii) available meeting room data identifying all available meeting rooms, and   iii) a meeting room location associated with each available meeting room;   
                   
               cause to display an indication of the at least one respective candidate rescheduled meeting in response to the at least one out-of-office notification on a screen of at least one computing device associated with the at least one respective attendee based at least in part on the plurality of predicted parameters of the at least one respective meeting room object representing the at least one respective candidate rescheduled meeting;   receive a selection of the at least one respective candidate rescheduled meeting from the at least one respective attendee; and   dynamically secure the at least one respective candidate rescheduled meeting for at least one respective meeting.   
               

     Publications cited throughout this document are hereby incorporated by reference in their entirety. While one or more embodiments of the present disclosure have been described, it is understood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that various embodiments of the inventive methodologies, the inventive systems/platforms, and the inventive devices described herein can be utilized in any combination with each other. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and/or any desired steps may be eliminated).