BACKGROUND

Meetings may be unproductive or ineffective. Unproductive meetings may be costly and a waste of time. For example, statistics show that unproductive meetings cost companies and individuals over $37 billion per year.

SUMMARY

The present disclosure provides methods and systems for quantitatively and qualitatively measuring the effectiveness of meetings. Methods and systems of the present disclosure may be capable of evaluating the effectiveness of meetings in a quantitative manner by calculating a meeting score standardized across meetings, groups, departments, companies, or other levels of organizations. Methods and systems provided herein may dynamically generate feedback surveys personalized for each individual meeting for collecting feedback data which is further used for calculating the meeting score. Methods and systems of the present disclosure may be implemented on or seamlessly integrated into a variety of platforms, including existing meeting platforms and/or meeting software. Methods and systems provided herein may dynamically modify the interface or appearance of existing meeting software or provide a standalone software to display processed and analyzed meeting and/or calendar data to help inform of the meetings effectiveness in real time.

The present disclosure provides systems and methods for measuring meeting effectiveness. Meeting effectiveness score can be used to generate recommendations for future meetings or to modify or create a meeting software during the course of a meeting to improve interactions and conversations during that meeting. Systems and methods of the present disclosure may utilize supervised learning, unsupervised learning or semi-supervised learning for generating quantitative and qualitative analysis of meeting effectiveness and/or generating recommendations in an automated fashion. The provided methods and systems may be capable of accounting for the variability among meetings (e.g., different meeting profiles). In some cases, the meeting effectiveness quantification algorithm may continually improve to provide personalized recommendations based on feedback data or meeting data.

In an aspect, a method for quantifying meeting effectiveness is provided. The method may comprise: (a) receiving calendar data related to a meeting; (b) generating a feedback survey based on the calendar data for collecting user feedback data, in which the feedback survey is presented to at least one or more participants in the meeting; (c) generating, using a trained machine learning algorithm, a meeting score indicative of an effectiveness of the meeting based on the meeting data and the user feedback data, and (d) displaying the meeting score within a graphical user interface (GUI) on the electronic device.

In a related yet separate aspect, a system for quantifying meeting effectiveness is provided. The system comprises a computer comprising one or more computer processors that are individually or collectively programmed to: receive calendar data and meeting data related to a meeting; generate a feedback survey based on the calendar data for collecting user feedback data, wherein the feedback survey is presented to at least one or more participants in the meeting; generate, using a machine learning algorithm trained model, a meeting score indicative of an effectiveness of the meeting based on the meeting data and the user feedback data; and display the meeting score within a graphical user interface (GUI) on an electronic device.

In a further related yet separate aspect, a non-transitory computer-readable medium comprising machine-executable instructions that, upon execution by one or more computer processors, implements a method for visualizing digital content of a web-based application. The method comprises: receiving calendar data and meeting data related to a meeting; generating a feedback survey based on the calendar data for collecting user feedback data, wherein the feedback survey is presented to at least one or more participants in the meeting; generating, using a machine learning algorithm trained model, a meeting score indicative of an effectiveness of the meeting based on the meeting data and the user feedback data; and displaying the meeting score within a graphical user interface (GUI) on an electronic device.

In some embodiments, the calendar data comprises calendar metadata. In some embodiments, a content of the feedback survey or a timing of presenting the feedback survey on the user device is determined based on the calendar metadata. In some cases, the content of the feedback survey comprises one or more questions related to a comment about the meeting and options corresponding to at least one question, wherein the options are generated based at least in part on the meeting. In some embodiments, at least a portion of the meeting data is captured by the electronic device. In some embodiments, the meeting data comprises audio data, video data or chat messages. In some embodiments, the user feedback data is provided by the at least one or more participants via the GUI on the electronic device.

In some embodiments, the method further comprises displaying a recommendation on improving the meeting score within the GUI. In some cases, the recommendations are generated using the trained machine learning algorithm. In some cases, the recommendation comprises a recommended meeting size, recommended meeting attendees, a recommended meeting duration, a recommended meeting location, recommended meeting keywords, recommended meeting deliverables, or a recommended meeting agenda.

In some embodiments, the method further comprises providing real-time meeting effectiveness information during the meeting. In some cases, the method further comprises generating one or more visual indicators indicative of the real-time meeting effectiveness information based at least in part on a real-time meeting data stream or the calendar data. In some cases, the method further comprises presenting the one or more visual indicators on a GUI of the meeting. In some cases, the one or more visual indicators comprise a distribution of speaking time among a plurality of participants of the meeting. In some cases, the method further comprises rearranging a plurality of graphical representations of the plurality of participants according to the one or more visual indicators. In some cases, the one or more visual indicators comprise an indication of an interruption of speaking during the meeting. In some cases, the one or more visual indicators comprise a remaining time of the meeting. In some instances, the remaining time of the meeting is estimated based at least in part on the calendar data.

In another aspect, a method for providing real-time meeting effectiveness information is provided. The method comprises: receiving a meeting data stream and calendar data related to a meeting; generating one or more visual indicators indicative of the meeting effectiveness base at least in part on the meeting data stream and calendar data; and presenting the one or more visual indicators on a GUI of the meeting.

Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.

Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

