Contact Pro Real Time
Gerarda Zmuda
Amazon Connect Contact Lens now provides real-time conversational analytics for Amazon Connect Chat, extending the machine learning-powered post-contact analytics (e.g., sentiment analysis, automated contact categorization, etc.) to real-time contact scenarios. These capabilities enable contact center managers to detect customer issues during in-progress chat contacts, and help them resolve customer issues faster. For example, managers can now get a real-time email alert when customer sentiment for a chat contact turns negative, allowing them to join the in-progress contact and help resolve the customer issue.
Real-time conversational analytics for chat is supported in all the AWS Regions where real-time conversational analytics for voice is already available. To learn more, please see our help documentation and visit our webpage. For information about Contact Lens pricing, please visit our pricing page.
While there is an integration with LinkedIn Sales Navigator and an extension for Office 365 and Outlook desktop, it's not possible to import contacts from LinkedIn and Outlook or to set up a real-time synchronization.
Your best option would be to export Outlook contacts as a CSV file and import them into HubSpot, as explained here. Similarly, for LinkedIn you would first export your connections and then follow HubSpot's import process.
Wanted to add to this thread that there is an upcoming "HubSpot and LinkedIn CRM Sync" beta that may help with the LinkedIn data moving forward!
You can learn more and sign up for updates here.
Create the data stream on the same account and Region where your Amazon Connect instance resides. For instructions, see Step 1: Create a Data Stream in the Amazon Kinesis Data Streams Developer Guide.
We recommend creating a separate stream for each type of data. While it's possible to use the same stream for real-time contact analysis segment streams, agent events, and contact records, it is much easier to manage and get data from the stream when you use a separate stream for each one. For more information, see the Amazon Kinesis Data Streams Developer Guide.
Enable encryption, and use a customer managed key for call recordings, chat transcripts, or exported reports in your Amazon Connect instance. Then choose the same KMS key for your Kinesis data stream. This key already has the permission (grant) required to be used.
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Here we demonstrate a combined common-path FFOCT/SDOCT microscope (FF/SD OCT), which tracks the axial position of the eye and matches the optical arm lengths of FFOCT in real time to allow consistent imaging of the entire ocular surface (central, peripheral and limbal cornea, limbus, sclera, tear film). We demonstrate that real-time, millimeter-field movies of central and peripheral in vivo corneas consistently reveal cells and nerves, which can be quantified according to the existing medical protocols used for IVCM. This makes FF/SD OCT ready for clinical research and translation into clinical practice as a non-contact OCT-based alternative to IVCM with higher resolution than conventional OCT, and capable of detecting changes in the entire ocular surface. Moreover, we show that our setup also provides the possibility of monitoring tear film evolution, opening a door to quantitative and non-contact assessment of dry eye conditions. Beyond the cornea, the instrument visualizes the scleral and limbal regions, important to stem cell storage and regeneration. FF/SD OCT can also directly view blood flow with high contrast and without fluorescein injection at a high frame rate of 275 frames per second (fps), which, paired with its high-resolution capabilities, allows individual blood cells to be followed. Furthermore, we demonstrate a method to reveal high-resolution blood flow velocity and orientation maps, which may lead the way to new localized diagnostic methodologies of scleral inflammation and approaches to monitor therapeutic effects locally.
The central idea in a common-path FF/SD OCT interferometer is to detect the axial position of the cornea in real-time with SDOCT and use this information to optically match the arms of the FFOCT interferometer by moving the reference arm, leading to consistent FFOCT imaging of a moving in vivo cornea (Fig. 1 and Supplementary Movies 1, 2). SDOCT, coupled to the microscope objectives, displays the locations of the corneal surface and other reflecting surfaces in the XZ plane with high axial resolution (
We also looked at the appearance of stroma in the central and peripheral cornea, and sclera. The dark background of the central corneal stroma (Fig. 6d), becomes bright at the periphery (Fig. 6e, f), which is explained by the increased light scattering from the stromal fibrils, irregular in diameter and arrangement, known from electron microscopy studies32. The aforementioned scattering bright glare of the background makes it more difficult to resolve keratocyte cell nuclei at the periphery, comparing to those in the central cornea, easily visible against the dark background, in agreement with previous confocal microscopy data33. Blood vessels (Fig. 6g) were perforating the conjunctiva and episclera and produced shadows (Fig. 6h) in the upper sections of the sclera.
A complete evaluation of corneal and retinal safety, involving scenarios of real-time imaging (used to visualize static corneal structures) and single prolonged LED exposure (used to visualize blood flow dynamics), was performed. The results are summarized below.
Custom programs written in Labview 2014 and Thorlabs SpectralRadar SDK were used for FFOCT and SDOCT image acquisition and display, peak detection and motor control. MATLAB R2017a was used for plots and calculation of angiography maps. We utilized ImageJ 1.51p for image display and measurements of cell and nerve densities. ZEMAX Optics Studio was used to simulate the light propagation inside the cornea and measure aberrations.
Semi-automated nerve segmentation and density analysis (Fig. 5c) was performed with ImageJ47 using the NeuronJ plugin24. Manual endothelial cell counting (Fig. 5d) was done with the Multi-point Tool in ImageJ. A Manual Tracking ImageJ plugin enabled manual blood cell tracking and the subsequent particle velocity analysis (Supplementary Movie 12).
Ex vivo macaque corneas were obtained from a partner research institution as recuperated waste tissue from an unrelated experiment. Corneas were dissected from the ocular globes within two hours post-mortem and fixed in 2% paraformaldehyde prior to transfer to the imaging lab. Some edematous swelling occurred, causing enhanced visibility of stromal striae49, indicative of tissue stress.
V.M., M.F., and A.C.B. conceptualized the general idea. V.M., P.X., and A.C.B conceived and developed the optical design. V.M., J.S., and A.C.B. conceived the software and hardware architecture. V.M. wrote the software and performed the imaging experiments. K.I. and K.G. provided the corneal samples and guidance on usage and experiments. V.M., K.I., K.G., and A.C.B. analyzed the acquired data. J.S. conceived and developed algorithms for obtaining quantitative blood flow velocity and orientation maps. K.G. (native English speaker) edited for language. V.M. wrote the manuscript, and all the authors contributed with edits and revisions.
Peer review information Nature Communications thanks Kostadinka Bizheva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. We show that these data cover broad sections of the US population and exhibit movement patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We use these indices to document how pandemic-induced reductions in activity vary across people and places.
We are very grateful to Hayden Parsley and Serena Xu for outstanding research assistance under extraordinary circumstances. We thank Drew Breunig, Nicholas Sheilas, Stephanie Smiley, Elizabeth Cutrone, and the team at PlaceIQ for data access and helpful conversations. The views expressed herein are those of the authors and do not necessarily reflect the views of PlaceIQ, the National Bureau of Economic Research, nor CEPR. This research was approved by the University of California, Berkeley Office for Protection of Human Subjects under CPHS Protocol No 2018-05-11122. This material is based upon work supported by the National Science Foundation under Grant No. 2030056, the Tobin Center for Economic Policy at Yale, the Fisher Center for Real Estate and Urban Economics at UC Berkeley, the Zell-Lurie Real Estate Center at Wharton, and the Initiative on Global Markets at Chicago Booth.
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