Computer implementation methods, systems, and computer programs (improvement of video conference participant sessions)

Abstract

Methods, computer program products, and computer systems for improving video conference participant sessions are provided. [Solution] The method captures parameters of independent features of a participant's video conference session, where the independent features include the physical configuration of the participant's video conference setup. The method obtains a feedback rating for the participant's video conference session and applies a model analysis of the participant's video conference session based on the feedback rating and the parameters of the independent features to obtain a session effectiveness score. The method provides calibration suggestions for the parameters of the independent features to improve the participant's video conference session.

Description

Technical Field

[0001] The present invention relates to video conference sessions, and more particularly to the improvement of video conference participant sessions.

[0002] Video conferencing is an important tool in business for interacting with other people. Due to the low-quality use of the configuration and distribution of video conferencing, communication and influence by participants are often lost. By applying specific techniques to improve the clarity and effectiveness of the interaction, the effectiveness of the participants can be enhanced.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Factors related to meeting effectiveness include the position of the presenter relative to their own camera and microphone, the acoustic and visual quality of the system being used, and the lighting provided. Participants often do not take the time to optimally set up their own environment. It is also difficult for participants to recognize how their session is being received by other participants.

Means for Solving the Problems

[0004] According to one aspect of the present invention, a computer implementation method for improving a video conference participant session is provided, the method comprising the steps of: capturing parameters of an independent feature of the participant video conference session, wherein the independent feature includes the physical configuration of the participant's video conference setup; obtaining a feedback rating for the participant video conference session; applying a model analysis of the participant video conference session based on the feedback rating and the parameters of the independent feature to obtain a session effectiveness score; and providing calibration suggestions for the parameters of the independent feature to improve the participant's video conference session. A system for improving video conferencing participant sessions is provided, comprising a processor and memory, the memory configured to provide the processor with computer program instructions for performing a method including the steps of: capturing parameters of an independent feature of a participant video conferencing session, wherein the independent feature includes the physical configuration of the participant's video conferencing setup; obtaining a feedback rating for the participant video conferencing session; applying a model analysis of the participant video conferencing session based on the feedback rating and the parameters of the independent feature to obtain a session effectiveness score; and providing calibration suggestions for the parameters of the independent feature to improve the participant's video conferencing session.

[0005] A further aspect of the present invention provides a computer program product for improving video conferencing participant sessions, the computer program product comprising a computer-readable storage medium having program instructions embodied therein, the program instructions being executable by a processor, causing the processor to capture parameters of an independent feature of a participant video conferencing session, wherein the independent feature includes the physical configuration of the participant's video conferencing setup; to obtain a feedback rating for the participant video conferencing session; to obtain a session effectiveness score by applying a model analysis of the participant video conferencing session based on the feedback rating and the parameters of the independent feature; and to provide calibration suggestions for the parameters of the independent feature in order to improve the participant's video conferencing session.

[0006] The method has the advantage of using model analysis to provide calibration suggestions to improve meeting effectiveness based on feedback combined with parameters of the physical configuration of the video conferencing setup. The model analysis can utilize logistic regression model analysis, which involves parameter weighting to show the contribution of each parameter to the effectiveness of the participant meeting session.

[0007] The computer-readable storage medium may be a non-temporary computer-readable storage medium, and the computer-readable program code may be executable by a processing circuit.

[0008] The present invention strives to provide one or more concepts for improving video conferencing participant sessions. Such concepts can be computer-implemented. That is, such methods can be implemented in a computer infrastructure having computer-executable code that is tangibly embodied in a computer-readable storage medium having program instructions configured to perform the proposed methods. The present invention further strives to provide a computer program product that includes computer program code for implementing the proposed concepts when executed on a processor. [Brief explanation of the drawing]

[0009] Herein, embodiments of the present invention will be described merely as examples with reference to the attached drawings. [Figure 1] This is a flowchart illustrating an exemplary embodiment of the method according to an embodiment of the present invention. [Figure 2] This is a block schematic diagram illustrating an exemplary embodiment of the method described. [Figure 3] This is a schematic diagram of an exemplary embodiment of a video conference participant session that implements the method described. [Figure 4] This is a block diagram of an exemplary embodiment of a system according to an embodiment of the present invention. [Figure 5] This is a block diagram of an exemplary embodiment of a computing environment for executing at least some of the computer code involved in carrying out the present invention.

[0010] It should be understood that, for the sake of simplicity and clarity, the elements shown in the diagrams are not necessarily drawn to scale. For example, for clarity, the dimensions of some elements may be exaggerated relative to others. Furthermore, where appropriate, reference numbers may be repeated in the diagrams to indicate corresponding or similar features. [Modes for carrying out the invention]

[0011] Methods, systems, and computer program product embodiments for improving video conferencing participant sessions are provided. A session may be a meeting, a series of meetings, or a part of a meeting. Video conferencing sessions may be conducted via web-based or downloaded video conferencing systems. The effectiveness of a video conferencing session for a given participant depends on the physical configuration of the session. The physical configuration may include the acoustic and visual quality of the computing system, as well as components of the physical environment captured within the session.

[0012] The effectiveness of a video conferencing session for a given participant is most accurately assessed by other participants in the session, and the method described is to obtain participant feedback ratings for the video conferencing session, which are used for improvement analysis. Physical system and environmental data are captured using multimodal techniques, and the method described is to combine the captured data with feedback to improve the modeling.

[0013] The described method applies a model analysis of participant video conferencing sessions based on feedback ratings and parameters of physical configuration and, optionally, other input features to obtain a session effectiveness score. Calibration suggestions are provided based on the session effectiveness score to improve participant video conferencing sessions.

[0014] The method can automatically provide a score for video conferencing sessions and identify mitigating factors that impair meeting effectiveness. This can be achieved by determining meeting effectiveness by constructing a meeting effectiveness quotient (MEQ) based on physical data and participant feedback.

[0015] Improvements to video conference participant sessions are provided in the field of video conferencing and online meeting technology.

[0016] Referring to Figure 1, flowchart 100 illustrates an exemplary embodiment of the described method for improving video conference participant sessions.

[0017] The method includes step 101 of capturing parameters of independent features of a participant video conference session. Independent features include the physical configuration of the participant's video conferencing system settings. The physical configuration may include system configurations such as acoustic and visual settings. The physical configuration may include the participant's environmental setup, such as their position, lighting, room configuration, and objects captured within the video conference field of view. Independent features of a participant video conference session may also include independent features of participant input characteristics, such as the participant's physiological signals. Physiological signals may include the intensity or nature of facial expressions, vocal characteristics, gestures, etc.

[0018] Step 101, which captures the parameters of independent features of participant video conference sessions, may use multimodal analysis of physical configuration features. Multimodal analysis analyzes acoustic and visual streams as separate entities (modes).

[0019] The step 101 of capturing parameters of the physical configuration may be performed by an extension to the video conferencing system being used by the participants. In the case of independent features of the form of the physical configuration of the environment, the capturing step may include object detection in the video display area.

[0020] Step 101, which captures environmental setup parameters, may include object detection within the field of view of the video display area. This can detect background objects and foreground objects such as the presenter, and capture and analyze the object's position, lighting, and the resulting effect of the object within the display.

[0021] The method can classify 102 participant video conference sessions for each session type. The session type can include the time of the session, the room type, the number of participants such as a one-on-one session versus a large group session, etc. The classification of the session type may extend to the domain or profile of the participants. The domain or profile may be related to role classification or other participant classifications. As an example, remote employees in a non-office environment may have a different profile from office-based employees.

[0022] The method can obtain 103 a feedback rating for a participant video conference session. The feedback rating can be obtained by one or more other participants in the video conference session. The feedback can be obtained by a feedback form presented to the participants at the end of the session.

[0023] The method applies 104 model analysis of the participant video conference session based on the feedback rating and parameters of independent features to obtain a session effectiveness score. The stage 104 of applying the model analysis can be based on the session type if the sessions are classified by type. The stage 104 of applying the model analysis can span multiple domains or profiles. The session effectiveness score can be provided with respect to the session type, domain, or profile.

[0024] The stage 104 of applying the model analysis can apply logistic regression model analysis using the feedback rating as the dependent variable and the parameters as the independent variables. The model analysis may be performed by a machine learning method, and the machine learning model can be trained across session types including the participant domain or profile.

[0025] The session effectiveness score may include weights of independent features, which indicate their impact on the feedback rating and provide calibration suggestions for the parameters of the independent features based on their weights. The session effectiveness score may use a normalized scale. The session effectiveness score may be referred to herein as the Meeting Effectiveness Index (MEQ).

[0026] The method provides calibration suggestions for independent feature parameters to improve participants' video conferencing sessions 105. The step of providing calibration suggestions 105 may provide a display or prompt for the calibration suggestions within the participants' video conferencing system.

[0027] One or more calibration suggestions can be used to automatically configure one or more video conferencing sessions. For example, based on the analysis performed, acoustic, video, and lighting settings can be automatically configured. Some settings may still need to be configured manually.

[0028] If the stage of capturing the parameters of the physical configuration is performed by an extension to the video conferencing system, the stage of providing calibration suggestions may provide the parameter configuration via the extension.

[0029] If the stage of capturing parameters of independent features applies object detection within the video display area, the stage of providing calibration suggestions can provide object positioning suggestions within the remediated view.

[0030] If the step of capturing the parameters of independent features of a participant video conference session also includes the independent features of participant input characteristics, then the step of providing calibration suggestions for the parameters of independent features also provides participant input suggestions.

[0031] Session effectiveness can be calibrated for improvement across various session types, domains, and profiles. Calibration can reduce factors that impair session effectiveness and promote factors that enhance it.

[0032] Referring to Figure 2, block diagram 200 shows a schematic diagram of the method described.

[0033] A video conference participant session 210 having physical configuration features 220 is shown. The physical configuration features 220 may be video conferencing system features and / or environmental features. Video conferencing system features may be acoustic or visual system control devices. Environmental features may relate to the positioning or lighting of objects in the environment captured by the video conference, such as a room or background. The physical configuration features 220 have physical feature parameters, which may be measures of features such as intensity, position, or other measures. The physical feature parameters may include physical feature system parameters 221 of the video conferencing system in that session. The physical feature parameters may include physical feature environment parameters 222 of the environmental setup of that session.

[0034] A video conference participant session 210 may also have participant input features 230, which are forms of physiological input from the participant. Participant input features 230 have input feature parameters 231, which may be measures of input such as intensity, emotion, or impact.

[0035] Multimodal analysis 240 can measure physical feature parameters 221, 222 and input feature parameters 231 using various acoustic and visual measurement and analysis methods. Multimodal analysis 240 may be performed on the environment (such as a room) to measure features such as speaker placement, call quality, and lighting. Multimodal analysis 240 may also be performed on participant inputs such as facial expressions, vocalizations, gestures, and physiological signals, all of which can be measured as intensity. Input features may be captured by the participant's biological activity, such as gaze direction, eye closure, heart rate, and vocal rhythm or speed.

[0036] Participant feedback 250 is obtained, for example, using a rating form 251 for session feedback 252 about a particular participant. Participant feedback 250 may be provided by one or more other participants or observers of the session.

[0037] A logistic regression model analysis 260 may be performed to generate a session effectiveness score 270, which may include feature parameter weights 271. The weights are provided by the model for each feature and can indicate the importance of the feature parameter to the score. The session effectiveness score 270 may be trained across multiple domains or profiles. Participant feedback as a dependent variable may be combined with physical and input features as independent variables and can be analyzed using logistic regression.

[0038] The session calibration suggestion output 280 may provide feature parameter calibrations 281 for participants to improve the session. Some of the parameter calibrations 281 may be physically applied to the video conferencing system or to the environment. Other parameter calibrations 281 may be suggestions regarding participant input characteristics.

[0039] In logistic regression, the formula is

number

[0040] P ミーティング_有効性 =e 特徴1(重み)+特徴2(重み)+特徴3(重み)+切片 / 1+e 特徴1(重み)+特徴2(重み)+特徴3(重み)+切片

[0041] [Determining weights for logistic regression]

[0042] The weights (or coefficients) of a logistic regression are determined using a method called maximum likelihood estimation (MLE). This method finds the coefficient values ​​that are most likely to occur under the logistic model for the observed data.

[0043] In a logistic regression model, the probability p that a given observation belongs to a particular class is defined as being based on a linear combination of predictor variables. The likelihood of the observed data is calculated. In logistic regression, the likelihood function expresses the probability of a given data being observed as a function of model parameters whose weights have been determined. For each observation, the probability p that the response variable takes that observed value is calculated. In the case of a binary result, the likelihood function can be determined for all N observations, and the logarithm of the likelihood function is taken.

[0044] Since the likelihood function is a product of probabilities, it can become extremely small, leading to numerical instability. This is simplified by taking the natural logarithm of the likelihood, called the log-likelihood, which transforms the product into a sum. The goal is to maximize the log-likelihood to find the values ​​of the coefficients (weights) that maximize it. This is typically done using iterative optimization algorithms such as gradient descent or the Newton-Raphson method. These algorithms step-by-step adjust the coefficients to approach the values ​​that maximize the log-likelihood.

[0045] As the algorithm converges, the resulting coefficients become the weights that maximize the likelihood of observing that data under the logistic regression model. These weights can then be used to interpret the relationship between the predictor variable and the probability of the outcome.

[0046] Each coefficient represents the change in the log odds of the outcome when the predictor variable increases by one unit. In practice, a positive coefficient suggests that the probability of the outcome increases as the predictor increases; and a negative coefficient suggests that the probability of the outcome decreases as the predictor increases.

[0047] The parameters and coefficients of the logistic regression model, as well as an example of the importance of each feature, are shown below for the features of lighting within the field of view of a video conference session and the presenter's height.

[0048] The generalized linear model (GLM) is given by: glm(Equation = Response ~ Predictor (Illumination + Height), Family = Binomial, Data = Data). Deviance residual: [Table 1] coefficient [Table 2] Significance code: 0'***' 0.001'**' 0.01'*' 0.05'.' 0.1' '1 (The variance parameter for the binomial family of distributions is assumed to be 1.) Null deviance: 13.862 for 9 degrees of freedom Residual deviance: 8.424 with 7 degrees of freedom AIC: 14.424 Number of Fisher scoring iterations: 4

[0049] A smaller value of Pr(>|z|) means that the feature is more important. The model's output provides weights that can be used to propose calibrations for future sessions. For example, the p-value for lighting is 0.0523, which is more important than height, which has a p-value of 0.1336. Therefore, calibration of lighting is considered to be more sensitive to enabling a more optimal meeting experience.

[0050] The model can measure independent characteristics of participant sessions from multimodal analysis. Participant feedback can be used to determine whether or not the participant session was effective.

[0051] The participant session effectiveness index can provide a score from 0 to 1 indicating how effective a particular session was, and can also provide a set of weights for the impact of each feature on that session.

[0052] By using a combination of logistic regression and multimodal analysis, it is possible to calibrate participants' physical systems and environments, as well as delivery, thereby improving meeting effectiveness.

[0053] Features can be monitored using video conferencing system plugins or extensions. For example, the following features can be automatically measured: Acoustic quality - Perceptual Evaluation of Speech Quality (PESQ) score, latency, Video Quality - Perceptual Evaluation of Video Quality (PEVQ) score, Lighting quality - lux

[0054] Referring to Figure 3, the schematic diagram shows an exemplary embodiment of a video conferencing system 300, which includes a video conferencing session component 310 that can provide calibration input before the session is started.

[0055] Based on feedback from previous sessions, participants may be provided with calibration inputs. A participant feedback component 350 may be provided at the end of the session to provide feedback to other participants. For example, this may include feedback forms 354, 355, and 356 for different participants 351, 352, and 353.

[0056] Calibration input may be provided using a camera display 320 that shows a preview of the participant's system and environment output. The display 320 may show the participant's position 322 along with a graphic 324 showing the improved position. The display 320 may also show objects 321 in the display that are impairing the output, and a graphic 323 may identify such objects. In this way, calibration can be applied to the participant's environment using an asymmetric position indicator. A post-repair view may be provided.

[0057] Calibration inputs may be provided by automatic configuration settings 330 for the video conferencing system 300, such as acoustic settings 331, video settings 332, and lighting settings 333. The automatic configuration settings 330 may be provided as a file exported to the participants' systems. Distracting background objects may be automatically blurred using the video conferencing system's blurring function. Physical settings, such as improving room lighting by turning on artificial light sources, may also be prompted.

[0058] Calibration inputs may be provided by an input feedback component 340 that can provide calibration for participant inputs such as speech speed 341, emotion input 342, and gesture input 343. These participant inputs are examples, and other prompts may be provided during the session, such as prompting the user to look at the camera if a lack of eye contact is detected.

[0059] Referring to Figure 4, the block diagram shows a computing system 400 in which the described system may be implemented. The computing system 400 may include at least one processor 401, hardware modules, or circuits for performing the functions of the described component, which may be software units running on at least one processor. Multiple processors running parallel processing threads may be provided to enable parallel processing of some or all of the functions of the component. Memory 402 may be configured to provide computer instructions 403 to at least one processor 401 to perform the functions of the component.

[0060] The computing system 400 may include a camera 404 and a microphone 405 for capturing the image and voice of participants. The computing system 400 may include a display 406 for participants to view their own output and input from other participants in the video conferencing session. The computing system 400 can run the video conferencing system 410, for example, as a web-based application or as software downloaded onto the computing system 400.

[0061] The video conferencing improvement system 420 may be provided as an extension to the video conferencing system 410. The video conferencing improvement system 420 may include the following components that provide software instructions.

[0062] A feature parameter capture component 421 may be provided to capture the parameters of independent features of a participant video conferencing session. The feature parameter capture component 421 may include a physical feature component 422 to capture the physical configuration of the participant's video conferencing setup. The feature parameter capture component 421 may include an input feature component 423 to capture the independent features of the participant input characteristics. The feature parameter capture component 421 may include a multimodal analysis component 424 to capture the parameters of independent features using multimodal analysis.

[0063] A classification component 425 may be provided for classifying participant video conference sessions by session type, domain, or profile, and for applying model analysis based on the session type.

[0064] A feedback component 426 may be provided to obtain feedback ratings for participant video conference sessions.

[0065] A modeling component 430 may be provided for applying a model analysis of participant video conferencing sessions based on feedback ratings and parameters of independent features. The modeling component 430 may include a logistic regression component 431 for applying a logistic regression model analysis using feedback ratings as the dependent variable and parameters as independent variables.

[0066] The modeling component 430 may include a scoring component 432 for providing a session effectiveness score. The scoring component 432 may include a weighting component 433 for providing weights for independent features that indicate their influence on the feedback rating.

[0067] A calibration component 440 may be provided to improve participants' video conferencing sessions by offering calibration suggestions for independent feature parameters.

[0068] The calibration component 440 may include a display component 441 for providing a display of calibration suggestions within the participant's video conferencing system, such as object positioning suggestions in the post-repair view.

[0069] The calibration component 440 may include a configuration component 442 for applying a configuration to the video conferencing system, including one based on the weights of independent features.

[0070] The calibration component 440 may include an input prompt component 443 for providing calibration suggestions as participant input suggestions.

[0071] The feature parameter capture component 421 may be provided as an extension 411 to the video conferencing system 410. The calibration component 440 may provide parameter configuration via the extension 411.

[0072] Various aspects of this disclosure are described by explanatory text, flowcharts, block diagrams of computer systems, and / or block diagrams of machine logic included in embodiments of computer program products (CPPs). With respect to any flowchart, operations may be performed in a different order than those shown in a given flowchart, depending on the technology involved. For example, also depending on the technology involved, two operations shown in consecutive blocks of a flowchart may be performed in reverse order, as a single integrated stage, simultaneously, or with at least partial temporal overlap.

[0073] Embodiments of a computer program product ("CPP Embodiment" or "CPP") are terms used in this disclosure to describe any set of one or more storage media (also called "mediums") that are collectively comprised of a set of one or more storage devices that collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations as defined in a given CPP claim. A "storage device" is any tangible device capable of holding and storing instructions for use by a computer processor. Computer-readable storage media may be, but are not limited to, electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, mechanical storage media, or any suitable combination of those described above. Some known types of storage devices, including these media, include diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital purpose discs (DVDs), memory sticks, floppy disks, mechanically encoded devices (such as pits / lands formed on the main surface of a punch card or disk), or any suitable combination of the foregoing. When the term "computer-readable storage medium" is used in this disclosure, it shall not be construed as storage in the form of a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, light pulses passing through optical fiber cables, electrical signals transmitted through wires, and / or other transmission media.As those skilled in the art will understand, data is typically moved at several intermittent points during the normal operation of a storage device, such as during access, defragmentation, or garbage collection; however, data is not transient while it is stored, and therefore the above does not mean that the storage device is transient.

[0074] Referring to Figure 5, the computing environment 500 includes an example of an environment for executing at least some of the computer code involved in carrying out the method of the present invention, for example, the video conferencing improvement system code 550. In addition to block 550, the computing environment 500 includes, for example, a computer 501, a wide area network (WAN) 502, an end-user device (EUD) 503, a remote server 504, a public cloud 605, and a private cloud 506. In this embodiment, the computer 501 includes a processor set 510 (including processing circuits 520 and a cache 521), a communication fabric 511, volatile memory 512, persistent storage 513 (including an operating system 522 and the block 550 identified above), a peripheral device set 514 (including a user interface (UI) device set 523, storage 524, and an Internet of Things (IoT) sensor set 525), and a network module 515. The remote server 504 includes a remote database 530. Public Cloud 605 includes a gateway 540, a cloud orchestration module 541, a host physical machine set 542, a virtual machine set 543, and a container set 544.

[0075] Computer 501 may take the form of a desktop computer, laptop computer, tablet computer, smartphone, smartwatch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device currently known or to be developed in the future that can run programs, access networks, or query databases such as the remote database 530. As is well understood in the field of computer technology, and depending on the technology, the execution of a computer implementation may be distributed among multiple computers and / or multiple locations. On the other hand, in this description of the computing environment 500, in order to keep the explanation as simple as possible, the detailed discussion will focus on a single computer, specifically computer 501. Although computer 501 is not shown in the cloud in Figure 5, it may be located in the cloud. On the other hand, computer 501 is not required to be located in the cloud, except to any extent that may be shown positively.

[0076] The processor set 510 includes one or more computer processors of any type currently known or to be developed in the future. The processing circuitry 520 may be distributed across multiple packages, for example, multiple interconnected integrated circuit chips. The processing circuitry 520 may implement multiple processor threads and / or multiple processor cores. The cache 521 is memory located within the processor chip package and is typically used for data or code that should be available for high-speed access by threads or cores running on the processor set 510. The cache memory is typically organized into multiple levels depending on its relative proximity to the processing circuitry. Alternatively, some or all of the cache for the processor set may be located "off-chip". In some computing environments, the processor set 510 may operate using qubits and be designed to perform quantum computing.

[0077] Computer-readable program instructions are typically loaded onto computer 501 to cause the processor set 510 of computer 501 to perform a series of operational steps, thereby executing the computer implementation method. Instructions thus executed instantiate the method (collectively referred to as the "Method of the Invention") as defined in the flowcharts and / or descriptions of the computer implementation method contained in this document. These computer-readable program instructions are stored in various types of computer-readable storage media, such as the cache 521 and other storage media discussed below. The program instructions and associated data are accessed by the processor set 510 to control and direct the implementation of the Method of the Invention. In computing environment 500, at least some of the instructions for implementing the Method of the Invention may be stored in block 550 of persistent storage 513.

[0078] The communication fabric 511 is a signal conduction path that enables various components of the computer 501 to communicate with one another. Typically, this fabric is made up of switches and conductive paths, such as buses, bridges, physical input / output ports, and similar components. Other types of signal communication paths, such as fiber optic communication paths and / or wireless communication paths, may be used.

[0079] Volatile memory 512 is any type of volatile memory currently known or to be developed in the future. Examples include dynamic random access memory (RAM) or static RAM. Typically, volatile memory 512 is characterized by random access, but this is not required unless explicitly stated. In computer 501, volatile memory 512 is located in a single package and resides inside computer 501, but alternatively or additionally, volatile memory may be distributed across multiple packages and / or located externally to computer 501.

[0080] Persistent storage 513 is any form of non-volatile storage for a computer that is currently known or may be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is supplied to the computer 501 and / or directly to the persistent storage 513. Persistent storage 513 may be read-only memory (ROM), but typically at least a portion of the persistent storage allows for writing, deleting, and rewriting of data. Some well-known forms of persistent storage include magnetic disks and solid-state storage devices. The operating system 522 may take several forms, such as various known proprietary operating systems employing a kernel or open-source portable operating system interface type operating systems. The code contained in block 550 typically includes at least some of the computer code involved in carrying out the methods of the present invention.

[0081] The peripheral device set 514 includes a set of peripheral devices for the computer 501. Data communication connections between the computer 501's peripheral devices and other components may be implemented in various ways, such as Bluetooth® connections, near-field communication (NFC) connections, connections made by cables (such as Universal Serial Bus (USB) type cables), insert-type connections (e.g., Secure Digital (SD) cards), connections made through local area communication networks, and even connections made through wide area networks such as the Internet. In various embodiments, the UI device set 523 may include components such as a display screen, speakers, microphones, wearable devices (such as goggles and smartwatches), keyboards, mice, printers, touchpads, game controllers, and haptic devices. Storage 524 is external storage such as an external hard drive, or insertable storage such as an SD card. Storage 524 may be persistent and / or volatile. In some embodiments, storage 524 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 501 is required to have a large amount of storage (for example, computer 501 locally stores and manages a large database), this storage may be provided by peripheral storage devices designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. The IoT sensor set 525 consists of sensors that may be used in an Internet of Things application. For example, one sensor may be a thermometer and another may be a motion detector.

[0082] The network module 515 is a collection of computer software, hardware, and firmware that enables computer 501 to communicate with other computers via the WAN 502. The network module 515 may include hardware such as a modem or Wi-Fi® signal transceiver, software for packetizing and / or depacketizing data for communication network transmission, and / or web browser software for transmitting data over the Internet. In some embodiments, the network control and network forwarding functions of the network module 515 are performed on the same physical hardware device. In other embodiments (e.g., embodiments utilizing Software-Defined Networking (SDN)), the control and forwarding functions of the network module 515 are performed on physically separate devices so that the control function manages several different network hardware devices. Computer-readable program instructions for carrying out the method of the present invention can typically be downloaded from an external computer or external storage device to computer 501 via a network adapter card or network interface included in the network module 515.

[0083] WAN502 is any wide area network (e.g., the Internet) that can transmit computer data over non-local distances by any technology currently known or to be developed for transmitting computer data. In some embodiments, WAN502 may be replaced and / or complemented by a local area network (LAN), such as a Wi-Fi® network, designed to transmit data between devices located in a local area. WANs and / or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and edge servers.

[0084] The end-user device (EUD) 503 is any computer system used and controlled by an end-user (e.g., a customer of the company operating computer 501), and may take any of the forms discussed above in relation to computer 501. EUD 503 typically receives useful and valuable data from the operation of computer 501. For example, in a hypothetical case where computer 501 is designed to provide recommendations to an end-user, these recommendations would typically be transmitted from computer 501's network module 515 to EUD 503 via WAN 502. Thus, EUD 503 can display or otherwise present the recommendations to the end-user. In some embodiments, EUD 503 may be a client device such as a thin client, heavy client, mainframe computer, or desktop computer.

[0085] The remote server 504 is any computer system that provides at least some data and / or functionality to computer 501. The remote server 504 may be controlled and used by the same entity that operates computer 501. The remote server 504 represents a machine that collects and stores useful and valuable data for use by other computers, such as computer 501. For example, in a hypothetical case where computer 501 is designed and programmed to provide recommendations based on historical data, this historical data may be provided to computer 501 from the remote database 530 of the remote server 504.

[0086] Public Cloud 605 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and / or other computing capabilities, particularly data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages resource sharing to achieve consistency and economies of scale. Direct active management of computing resources in Public Cloud 605 is performed by the computer hardware and / or software of the Cloud Orchestration Module 541. The computing resources provided by Public Cloud 605 are typically implemented by virtual computing environments running on various computers that make up the computers of the host physical machine set 542, which is a universe of physical computers located within and / or available to Public Cloud 605. Virtual computing environments (VCEs) typically take the form of virtual machines from the virtual machine set 543 and / or containers from the container set 544. These VCEs may be stored as images and are understood to be transferable either as images or after instantiation of VCEs, within and between various physical machine hosts. The cloud orchestration module 541 manages the transfer and storage of images, deploys new VCE instances, and manages active instances of VCE deployments. The gateway 540 is a collection of computer software, hardware, and firmware that enables the public cloud 605 to communicate over the WAN 502.

[0087] Here, we will provide some further explanation about virtualized computing environments (VCEs). A VCE can be stored as an "image." A new active instance of a VCE can be instantiated from an image. Two well-known types of VCEs are virtual machines and containers. A container is a VCE that uses operating system-level virtualization. This refers to an operating system feature where the kernel allows for the existence of multiple isolated user-space instances called containers. These isolated user-space instances typically behave like actual computers from the perspective of the programs running within them. Computer programs running on a typical operating system can utilize all the resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and the devices allocated to that container; this feature is known as containerization.

[0088] The private cloud 506 is similar to the public cloud 605, except that the computing resources are available for use only by a single enterprise. While the private cloud 506 is shown as being in communication with the WAN 502, in other embodiments, the private cloud may be completely isolated from the internet and accessible only via a local / private network. A hybrid cloud is a combination of multiple clouds of different types (e.g., private, community, or public cloud types), often implemented by different vendors. Each of the multiple clouds remains a distinct, discrete entity, but the larger hybrid cloud architecture is coupled together by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple configuration clouds. In this embodiment, both the public cloud 605 and the private cloud 506 are part of a larger hybrid cloud.

[0089] The descriptions of various embodiments of the present invention are presented for illustrative purposes only and are not intended to be exhaustive or limitful to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein have been selected to best describe the principles, practical applications, or technical improvements to the technologies available on the market of the embodiments, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

[0090] Improvements and modifications can be made to the foregoing without departing from the scope of the present invention.

Claims

1. A computer implementation method for improving video conference participant sessions, wherein the computer implementation method is A step of capturing parameters of independent features of a participant's video conferencing session, where said independent features include the physical configuration of the participant's video conferencing setup; The step of obtaining feedback ratings for the aforementioned participant video conference session; A step of obtaining a session effectiveness score by applying a model analysis of the participant video conference session based on the feedback rating and the parameters of the independent features; and A step of providing calibration suggestions for the parameters of the independent features in order to improve the video conferencing session of the participant. A computer implementation method comprising the following features.

2. The computer implementation method according to claim 1, wherein the step of capturing the parameters of the independent features of the participant video conference session further comprises the step of using a multimodal analysis of the independent features of the acoustic and visual streams of the participant video conference session.

3. The computer implementation method according to claim 1, wherein the step of capturing the parameters of the independent features of the participant video conferencing session further comprises the step of capturing the independent features of a form of video conferencing system configuration, wherein the step of providing the calibration proposal for the parameters of the independent features comprises the step of providing a video conferencing system configuration.

4. The computer implementation method according to claim 1, wherein the step of capturing the parameters of the independent features of the participant video conference session further comprises the step of capturing the independent features in the form of an environment setup feature, wherein the step of providing the calibration proposal for the parameters of the independent features comprises the step of providing an environment setup prompt.

5. The computer implementation method according to claim 1, wherein the step of capturing the parameters of the independent features of the form of the physical configuration comprises object detection within a video display area, and the step of providing the calibration proposal comprises the step of providing an object positioning proposal within the post-repair view.

6. The computer implementation method according to claim 1, wherein the step of capturing the parameters of the independent feature of the participant video conference session comprises the independent feature having participant input characteristics, and the step of providing the calibration proposal for the parameters of the independent feature comprises the step of providing participant input prompts.

7. The computer implementation method according to claim 1, wherein the step of applying the model analysis further comprises the step of applying a logistic regression model using the feedback rating as the dependent variable and the parameters as independent variables, and the coefficients of the logistic regression model provide weighting indications for the parameters.

8. The computer implementation method according to claim 1, wherein the session effectiveness score includes the weights of the independent features, the weights of the independent features indicate the influence of the independent features on the feedback rating, and the method provides a calibration suggestion for the parameters of the independent features based on the weights of the independent features.

9. The computer implementation method according to claim 1, further comprising the steps of classifying the participant video conference sessions by session type, and applying the model analysis based on the session type.

10. The computer implementation method according to claim 1, wherein the step of providing the calibration suggestion includes the step of providing a display of the calibration suggestion within the participant's video conferencing system.

11. The computer implementation method according to any one of claims 1 to 10, wherein the step of capturing the parameters of the physical configuration is performed by an extension to the video conferencing system, and the step of providing the calibration proposal comprises the step of providing the configuration of the parameters via the extension.

12. The system comprises a processor and memory, and the memory is connected to the processor. A step of capturing parameters of independent features of a participant's video conferencing session, where said independent features include the physical configuration of the participant's video conferencing setup; The step of obtaining feedback ratings for the aforementioned participant video conference session; A step of obtaining a session effectiveness score by applying a model analysis of the participant video conference session based on the feedback rating and the parameters of the independent features; and A step of providing calibration suggestions for the parameters of the independent features in order to improve the video conferencing session of the participant. It is configured to provide computer program instructions for performing a method that includes, A system for improving video conference participant sessions.

13. The system according to claim 12, wherein the step of applying the model analysis involves applying a logistic regression model using the feedback rate as the dependent variable and the parameters as independent variables, and the coefficients of the logistic regression model provide weighted indications for the parameters.

14. The system according to claim 12, wherein the session effectiveness score includes the weights of the independent features, the weights of the independent features indicate the influence of the independent features on the feedback rating, and the system provides the calibration proposal for the parameters of the independent features based on the weights of the independent features.

15. The system according to claim 12, further comprising the steps of classifying the participant video conference sessions by session type, and applying the model analysis based on the session type.

16. The system according to claim 12, wherein the step of providing the calibration suggestion includes the step of providing a display of the calibration suggestion within the participant's video conferencing system.

17. The system according to claim 12, wherein the step of capturing the parameters of the physical configuration is performed by an extension to the video conferencing system, and the step of providing the calibration proposal includes the step of providing the configuration of the parameters via the extension.

18. The system according to claim 12, wherein the step of capturing the parameters of the independent feature of the participant video conference session includes the independent feature which is a participant input characteristic, and the step of providing the calibration suggestion for the parameters of the independent feature includes the step of providing the participant input suggestion.

19. The system according to any one of claims 12 to 18, wherein the step of capturing the parameters of the independent features of the physical configuration comprises object detection within a video display area, and the step of providing the calibration proposal comprises the step of providing an object positioning proposal within the post-repair view.

20. The program instructions are provided, and the program instructions are executable by the processor, A procedure for capturing parameters of independent features of a participant's video conferencing session, wherein the independent features include the physical configuration of the participant's video conferencing setup; Procedure for obtaining feedback ratings for the aforementioned participant video conference session; A procedure for obtaining a session effectiveness score by applying a model analysis of the participant video conference session based on the feedback rating and the parameters of the independent features; and A procedure to provide calibration suggestions for the parameters of the independent features in order to improve the video conferencing session of the participant. A computer program that has [a certain characteristic].

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