Abstract: Embodiments are directed to managing the provisioning of healthcare services. A visit profile that includes a reason for the visit and patient demographic information may be provided by a patient requesting a visit to obtain healthcare services. Match scores for providers may be generated based on matching models and the visit profile. A provider may be assigned to the visit based on the match scores. A virtual waiting room may be provided for the patient based on the visit profile. The visit profile may be updated with supplemental information based on the interactions between the patient and the virtual waiting room, patient information, or visit information. Updated match scores may be provided for the providers based on the updated visit profile. If an updated match score exceeds a previously determined higher match score, another provider associated with a higher match score may be assigned to the visit.

Abstract: Embodiments are directed to verifying media stream quality for multiparty video conferences. A verification video may be generated based on verification goals for a video provided by a video service. A marker may be embedded in the verification video. A video conference may be established using video stations such that the video conference may be provided by a video service. The verification video may be streamed to a video input of each video station. The video may be streamed to a video output buffer of each video station such that the video provides a view of the video conference and such that the marker that corresponds to each video station may be included in the video. Video information may be captured from the video output buffer of the video stations. The video service may be classified based on the video information from each video station.

Abstract: Embodiments are directed to verifying media stream quality for multiparty video conferences. A verification video may be generated based on verification goals for a video provided by a video service. A marker may be embedded in the verification video. A video conference may be established using video stations such that the video conference may be provided by a video service. The verification video may be streamed to a video input of each video station. The video may be streamed to a video output buffer of each video station such that the video provides a view of the video conference and such that the marker that corresponds to each video station may be included in the video. Video information may be captured from the video output buffer of the video stations. The video service may be classified based on the video information from each video station.

Abstract: Embodiments are directed to managing the provisioning of healthcare services. A visit profile that includes a reason for the visit and patient demographic information may be provided by a patient requesting a visit to obtain healthcare services. Match scores for providers may be generated based on matching models and the visit profile. A provider may be assigned to the visit based on the match scores. A virtual waiting room may be provided for the patient based on the visit profile. The visit profile may be updated with supplemental information based on the interactions between the patient and the virtual waiting room, patient information, or visit information. Updated match scores may be provided for the providers based on the updated visit profile. If an updated match score exceeds a previously determined higher match score, another provider associated with a higher match score may be assigned to the visit.

Abstract: Embodiments are directed to verifying media stream quality for multiparty video conferences. A verification video may be generated based on verification goals for a video provided by a video service. A marker may be embedded in the verification video. A video conference may be established using video stations such that the video conference may be provided by a video service. The verification video may be streamed to a video input of each video station. The video may be streamed to a video output buffer of each video station such that the video provides a view of the video conference and such that the marker that corresponds to each video station may be included in the video. Video information may be captured from the video output buffer of the video stations. The video service may be classified based on the video information from each video station.

Abstract: Embodiments are directed to assessing performances of physical activities. Assessment engines may collect performance content that includes information associated with performances of activities. The assessment engines may employ classifiers to classify the performance content to determine occurrences of features of the performance content and classify assessment content to determine scores that are associated with features included in the performances. The assessment engines may provide correlation values associated with the performances based on historical performance content, assessment content, or scores. The assessment engine may provide a report that includes a localized evaluation of the performances based on the correlation values, performance content, assessment content, and scores. In some cases, the performance content may include videos of the performances.

Abstract: Embodiments are directed to assessing performances of physical activities. Assessment engines may collect performance content that includes information associated with performances of activities. The assessment engines may employ classifiers to classify the performance content to determine occurrences of features of the performance content and classify assessment content to determine scores that are associated with features included in the performances. The assessment engines may provide correlation values associated with the performances based on historical performance content, assessment content, or scores. The assessment engine may provide a report that includes a localized evaluation of the performances based on the correlation values, performance content, assessment content, and scores. In some cases, the performance content may include videos of the performances.

Abstract: Embodiments are directed to assessing performances of physical activities. Assessment engines may collect performance content that includes information associated with performances of activities. The assessment engines may employ classifiers to classify the performance content to determine occurrences of features of the performance content and classify assessment content to determine scores that are associated with features included in the performances. The assessment engines may provide correlation values associated with the performances based on historical performance content, assessment content, or scores. The assessment engine may provide a report that includes a localized evaluation of the performances based on the correlation values, performance content, assessment content, and scores. In some cases, the performance content may include videos of the performances.

Abstract: Embodiments are directed to deploying a crowd to assess the performance of human-related activities. Content, such as video, audio, and/or textual content is captured. The content documents a subject's performance of the subject activity. The content, as well as an associated assessment tool (AT), are provided to reviewers. The AT includes questions that are directed to assessing domains of the performance. The reviewers review the content and assess the performance of the subject activity by providing assessment data. The assessment data includes answers to the questions of the AT. After a statistically significant number of independent reviewers have provided a statistically significant volume of assessment data, the assessment data is collated to generate statistical reviewer distributions of the independent assessments of various domains of the performance. A report is generated based on the collated assessment data. The report includes an overview of the crowd-sourced assessment of the performance.

Abstract: Enabling the selection, modification, and subsequent generation of online advertisements for either a one time use or as part of an advertising campaign. The performance of an advertisement is iteratively reviewed based on heuristics, statistics, metrics, real-time feedback, and the like, either singly or in some combination. Iterative monitoring of performance information for related, but different, online advertisements is analyzed and employed to recommend and/or automatically generate changes to an online advertisement, or advertising campaign. An editing facility is also provided to enable a user to modify a plurality of elements in an online advertisement, and at least some of these editable/modifiable elements are monitored for performance information. The editing facility can enable editing of different file formats for online advertisements, including, Flash, SVG, and Gnash.

Abstract: Enabling the selection, modification, and subsequent generation of online advertisements for either a one time use or as part of an advertising campaign. The performance of an advertisement is iteratively reviewed based on heuristics, statistics, metrics, real-time feedback, and the like, either singly or in some combination. Iterative monitoring of performance information for related, but different, online advertisements is analyzed and employed to recommend and/or automatically generate changes to an online advertisement, or advertising campaign. An editing facility is also provided to enable a user to modify a plurality of elements in an online advertisement, and at least some of these editable/modifiable elements are monitored for performance information. The editing facility can enable editing of different file formats for online advertisements, including, Flash, SVG, and Gnash.