May 31, 2024 Release: Add document highlights to Storybuilder as evidence, exclude selections in Data Visualizer, and more!

Expected AU release: May 29, 2024 (ACT)

Expected release for all others: May 31, 2024 (PST)

Knowledge Base updates: May 31, 2024 (PST)

With this release, we’ve added the option to add document highlights to Storybuilder as evidence, exclude selections in Data Visualizer, and more! — read on for more information about the features coming out this month! If you would like to learn more about the features in this release, join us for a live training session.

User-facing features in this release:

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Add document highlights as Storybuilder evidence

You can now add document highlights to Storybuilder as evidence. Previously, each document could be represented only once as evidence in Storybuilder Timeline, Drafts, or Depositions. With this release, you can add multiple document highlights from the same document to your Story — each with different dates, descriptions, and titles. 

Exclude selections in Data Visualizer

You can now exclude your selections from Data Visualizer and the results table. This adds functionality to explicitly and quickly filter out documents that you're not interested in when you're exploring your data using the Data Visualizer.

As with the Any of and All of options, you can make multiple selections with the Exclude option to exclude more than one selection at once.

Export text highlights to CSV

You can now export highlighted text on a set of documents with CSV export.  This lets you choose to export only specific, highlighted information from a document, rather than the entire document. From the results table, select Export > CSV and include Text Highlights as a field. 

This workflow will be particularly helpful if you are looking to carry out DSAR or FOIA projects, where there is a need to provide selected extracts (for example, personal data, or part of an agency record) from documents, as opposed to the whole document with redactions. 

Group, view, and act on chat conversations

Data from a single chat conversation can be divided across multiple documents, for example in 24-hr segments. Now, Everlaw allows you to group, view, and act on chat conversation families. 

Note: Chat conversation grouping is only available for data uploaded after Release 104 (February 27, 2024 for AU customers and March 1, 2024 for all others). Native chat data uploaded prior to this release can be reprocessed to work with grouping functionality. The original container file for a chat must be reprocessed, not the individual chat files. Processed chat data can be mapped to the fields used for grouping following the steps outlined in this article.

Note: The chat conversation context in the review window only works for chat-typed documents (ie. the "Mime Type" is "application/vnd.chat") and their attachments. Chat documents natively processed by Everlaw will be properly typed as chats, but processed documents may not be. For example, they may be typed as PDFs or HTML files.

  • If your processed data is not properly typed as chats, you can consider overlying the Mime Type field to take advantage of the review window chat context panel.
  • Mapping analogous processed data fields to the corresponding fields used for chat grouping does not re-type documents. So, for example, based on your mappings you can still take advantage of search grouping and auto-code functionality based on chat conversations for your processed chat data, but you will not be able to view the groupings in the review window if the documents are not themselves typed as chats. 

Group by chats from the results table

The grouping settings for search results now includes chat conversation options. You can  group results by:

  • Chat conversations, which pull in and group by chat conversations, including attachments
  • Email and chat conversations, which pull in and group by email and chat conversation families, including attachments

You can also remove attachments when search results are grouped by email threads, chat conversations, or email and chat conversations. 

Viewing chat conversation families in the review window

A new chat conversation context option has been added to the review window context panel. You can use this context to:

  • See and navigate to all chat segments and attachments in a given chat conversation family
  • Batch code these documents

Auto-code by chat conversation 

When creating auto-code rules, you can now select Chat conversation or Email and chat conversations as the document family that the selected code should be propagated to. 

Chat metadata settings

To support these grouping and auto-code options, Everlaw will be adding new metadata fields – and corresponding metadata settings — that will be generated upon native processing:

  • ChatConversationId: Identifies chat segments that belong to the same conversation
  • ChatConversationIndex: Identifies the order that segments should appear in a conversation
  • ParentId: Identifies the container file that was processed to generate the chat documents

Project Admins will be able to modify the underlying fields under Project Settings > Metadata, which will be useful for mapping analogous fields from processed chat data to the fields we use to identify chat conversations. 

Collect attachments to Asana objects

Everlaw now fetches attachments to objects collected through the Asana connector (e.g.  images attached to messages). Only attachments hosted by Asana will be collected. This helps ensure more complete and contextual collections of Asana data.

Learn more about the Asana connector.

Visual improvements

  • Icon buttons now show a gray hover state when a user is hovering over them. This makes it easier to distinguish actionable icons from visual aid icons in the platform.
Before After
  • If a user on a results table selects Edit and then does not make any changes to their search query, there will now be a more discoverable Cancel button to take the user back to the results table. This replaces the Return to results table button.

Before

After

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