Patent Publication Number: US-2023140747-A1

Title: Automated Rearrangement Of Digital Whiteboard Content

Description:
FIELD 
     This disclosure relates to content arrangement, and, more specifically, to the automated rearrangement of content input, detected, or otherwise introduced within a digital space such as a digital whiteboard based on category metadata determined for the content. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. 
         FIG.  1    is a block diagram of an example of an electronic computing and communications system. 
         FIG.  2    is a block diagram of an example internal configuration of a computing device of an electronic computing and communications system. 
         FIG.  3    is a block diagram of an example of a software platform implemented by an electronic computing and communications system. 
         FIG.  4    is a block diagram of an example of a system for automated content rearrangement. 
         FIG.  5    is a block diagram of an example of automated rearrangement of content within a digital whiteboard. 
         FIG.  6    is a block diagram of an example of arranging content detected from images. 
         FIG.  7    is a flowchart of an example of a technique for automated content rearrangement. 
         FIG.  8    is a flowchart of an example of a technique for training a learning model for automated content rearrangement. 
         FIG.  9    is a flowchart of an example of a technique for arranging content detected from images. 
     
    
    
     DETAILED DESCRIPTION 
     Many projects start with a brainstorm meeting, which typically involves a group of people identifying and vetting ideas of various quality and collaborating to generate an expansive list of ideas with which to move forward. Brainstorm meetings may move at a rapid pace in a manner that makes it difficult to thoroughly document ideas without disrupting the important dialogue between the group. For example, a group of people may share prompts, thoughts, and the like with one another to stimulate idea generation. While the discussion may be unstructured to keep it organic, the lack of structure and back and forth between group members may result in ideas being documented with minimal information, with the goal of eventually returning to those ideas later on and hoping that whoever documented the minimal information recalls the ideas in fuller detail. For example, some or all of the people in a brainstorm group may each be given a notepad (e.g., a pad of sticky notes) and the direction to fill out the pages of that notepad with ideas as quickly as possible. The notepad pages may be left in piles or affixed to walls without being organized given the quick pace of the brainstorm exercise. 
     After the idea generation component of the brainstorm meeting is completed, it then becomes the job of one or more group members to manually review the notepad pages, deduce relevant category information for the notepad pages, and organize the notepad pages into those categories. However, this is a very time consuming process and is subject to considerable error where the person organizing the notepad pages does not properly understand the content written thereon. Furthermore, while the typical output of this idea review and organization process generally identifies one or more action items to be addressed, it may be very challenging to understand which of the ideas in a given category are the best, what they relate to, and so on. For example, ideas may be miscategorized or commonalities between ideas within a category or across categories may not be accurately identified. The group participating in the brainstorm must thus expend considerable effort to arrive at these action items, which may be demanded by a stakeholder seeking to ensure the brainstorm meeting was an effective use of time. 
     Recently, digital collaboration software, such as digital whiteboard software, has been adopted by brainstorming groups and others as an efficient medium for documenting ideas and sorting them. Conventional digital collaboration software allows users to add content, such as sticky notes, to a digital collaboration space to document ideas during a brainstorm meeting, and the ability to type ideas out may in some cases enable more detail to be captured in a short period of time. Digital collaboration spaces generally update changes in real-time to show the same content to all connected users at any given time. In this way, ideas may become visible to all users responsive to content documenting the ideas being added to a digital collaboration space. However, conventional digital collaboration software nonetheless suffers from certain drawbacks in that it does not provide a way to automatically categorize content entered therein. Thus, a user of conventional digital collaboration software participating in a brainstorm meeting must still manually sort through, categorize, and organize the content added within the digital collaboration space. One solution involves collecting manually-entered category information from users of the digital collaboration software and sorting digital collaboration content based on that manually-entered category information; however, this solution suffers from certain drawbacks, namely, in that the software relies entirely upon the accuracy and completeness of the manually-entered category information, which, given it is manually-entered, is susceptible to error. 
     Implementations of this disclosure address problems such as these by the automated rearrangement of content within software based on category metadata determined by software for the content. In particular, content items are rearranged within a digital collaboration space implemented by digital collaboration software (e.g., digital whiteboarding software) based on metadata criteria (e.g., category metadata) determined for the content items using one or more learning models. The content items are added to the digital whiteboard by one or more users. For each of the content items, the metadata criteria is determined using a learning model that processes information associated with the content item. A rearrangement of the content items is determined based on the metadata criteria determined for each of the content items. The content items, rearranged according to the rearrangement, are then output to a layer of the digital collaboration space. Other rearrangements of the content items can be determined based on other metadata criteria (e.g., non-category metadata) associated with the content items, and the content items, rearranged according to those other rearrangements, can be output to other layers of the digital collaboration space. 
     To describe some implementations in greater detail, reference is first made to examples of hardware and software structures used to implement a system for automated content rearrangement.  FIG.  1    is a block diagram of an example of an electronic computing and communications system  100 , which can be or include a distributed computing system (e.g., a client-server computing system), a cloud computing system, a clustered computing system, or the like. 
     The system  100  includes one or more customers, such as customers  102 A through  102 B, which may each be a public entity, private entity, or another corporate entity or individual that purchases or otherwise uses software services, such as of a unified communications as a service (UCaaS) platform or other software platform provider. Each customer can include one or more clients. For example, as shown and without limitation, the customer  102 A can include clients  104 A through  104 B, and the customer  102 B can include clients  104 C through  104 D. A customer can include a customer network or domain. For example, and without limitation, the clients  104 A through  104 B can be associated or communicate with a customer network or domain for the customer  102 A and the clients  104 C through  104 D can be associated or communicate with a customer network or domain for the customer  102 B. 
     A client, such as one of the clients  104 A through  104 D, may be or otherwise refer to one or both of a client device or a client application. Where a client is or refers to a client device, the client can comprise a computing system, which can include one or more computing devices, such as a mobile phone, a tablet computer, a laptop computer, a notebook computer, a desktop computer, or another suitable computing device or combination of computing devices. Where a client instead is or refers to a client application, the client can be an instance of software running on a customer device (e.g., a client device or another device). In some implementations, a client can be implemented as a single physical unit or as a combination of physical units. In some implementations, a single physical unit can include multiple clients. 
     The system  100  can include a number of customers and/or clients or can have a configuration of customers or clients different from that generally illustrated in  FIG.  1   . For example, and without limitation, the system  100  can include hundreds or thousands of customers, and at least some of the customers can include or be associated with a number of clients. 
     The system  100  includes a datacenter  106 , which may include one or more servers. The datacenter  106  can represent a geographic location, which can include a facility, where the one or more servers are located. The system  100  can include a number of datacenters and servers or can include a configuration of datacenters and servers different from that generally illustrated in  FIG.  1   . For example, and without limitation, the system  100  can include tens of datacenters, and at least some of the datacenters can include hundreds or another suitable number of servers. In some implementations, the datacenter  106  can be associated or communicate with one or more datacenter networks or domains, which can include domains other than the customer domains for the customers  102 A through  102 B. 
     The datacenter  106  includes servers used for implementing software services of a UCaaS platform. The datacenter  106  as generally illustrated includes an application server  108 , a database server  110 , and a telephony server  112 . The servers  108  through  112  can each be a computing system, which can include one or more computing devices, such as a desktop computer, a server computer, or another computer capable of operating as a server, or a combination thereof. A suitable number of each of the servers  108  through  112  can be implemented at the datacenter  106 . The UCaaS platform uses a multi-tenant architecture in which installations or instantiations of the servers  108  through  112  is shared amongst the customers  102 A through  102 B. 
     In some implementations, one or more of the servers  108  through  112  can be a non-hardware server implemented on a physical device, such as a hardware server. In some implementations, a combination of two or more of the application server  108 , the database server  110 , and the telephony server  112  can be implemented as a single hardware server or as a single non-hardware server implemented on a single hardware server (e.g., a virtual machine). In some implementations, the datacenter  106  can include servers other than or in addition to the servers  108  through  112 , for example, a media server, a proxy server, or a web server. 
     The application server  108  runs web-based software services deliverable to a client, such as one of the clients  104 A through  104 D. As described above, the software services may be of a UCaaS platform. For example, the application server  108  can implement all or a portion of a UCaaS platform, including conferencing software, messaging software, and/or other intra-party or inter-party communications software. The application server  108  may, for example, be or include a unitary Java Virtual Machine (JVM). 
     In some implementations, the application server  108  can include an application node, which can be a process executed on the application server  108 . For example, and without limitation, the application node can be executed in order to deliver software services to a client, such as one of the clients  104 A through  104 D, as part of a software application. The application node can be implemented using processing threads, virtual machine instantiations, or other computing features of the application server  108 . In some such implementations, the application server  108  can include a suitable number of application nodes, depending upon a system load or other characteristics associated with the application server  108 . For example, and without limitation, the application server  108  can include two or more nodes forming a node cluster. In some such implementations, the application nodes implemented on a single application server  108  can run on different hardware servers. 
     The database server  110  stores, manages, or otherwise provides data for delivering software services of the application server  108  to a client, such as one of the clients  104 A through  104 D. In particular, the database server  110  may implement one or more databases, tables, or other information sources suitable for use with a software application implemented using the application server  108 . The database server  110  may include a data storage unit accessible by software executed on the application server  108 . A database implemented by the database server  110  may be a relational database management system (RDBMS), an object database, an XML database, a configuration management database (CMDB), a management information base (MIB), one or more flat files, other suitable non-transient storage mechanisms, or a combination thereof. The system  100  can include one or more database servers, in which each database server can include one, two, three, or another suitable number of databases configured as or comprising a suitable database type or combination thereof. 
     In some implementations, one or more databases, tables, other suitable information sources, or portions or combinations thereof may be stored, managed, or otherwise provided by one or more of the elements of the system  100  other than the database server  110 , for example, the client  104  or the application server  108 . 
     The telephony server  112  enables network-based telephony and web communications from and to clients of a customer, such as the clients  104 A through  104 B for the customer  102 A or the clients  104 C through  104 D for the customer  102 B. Some or all of the clients  104 A through  104 D may be voice over internet protocol (VOIP)-enabled devices configured to send and receive calls over a network  114 . In particular, the telephony server  112  includes a session initiation protocol (SIP) zone and a web zone. The SIP zone enables a client of a customer, such as the customer  102 A or  102 B, to send and receive calls over the network  114  using SIP requests and responses. The web zone integrates telephony data with the application server  108  to enable telephony-based traffic access to software services run by the application server  108 . Given the combined functionality of the SIP zone and the web zone, the telephony server  112  may be or include a cloud-based private branch exchange (PBX) system. 
     The SIP zone receives telephony traffic from a client of a customer and directs same to a destination device. The SIP zone may include one or more call switches for routing the telephony traffic. For example, to route a VOIP call from a first VOIP-enabled client of a customer to a second VOIP-enabled client of the same customer, the telephony server  112  may initiate a SIP transaction between a first client and the second client using a PBX for the customer. However, in another example, to route a VOIP call from a VOIP-enabled client of a customer to a client or non-client device (e.g., a desktop phone which is not configured for VOIP communication) which is not VOIP-enabled, the telephony server  112  may initiate a SIP transaction via a VOIP gateway that transmits the SIP signal to a public switched telephone network (PSTN) system for outbound communication to the non-VOIP-enabled client or non-client phone. Hence, the telephony server  112  may include a PSTN system and may in some cases access an external PSTN system. 
     The telephony server  112  includes one or more session border controllers (SBCs) for interfacing the SIP zone with one or more aspects external to the telephony server  112 . In particular, an SBC can act as an intermediary to transmit and receive SIP requests and responses between clients or non-client devices of a given customer with clients or non-client devices external to that customer. When incoming telephony traffic for delivery to a client of a customer, such as one of the clients  104 A through  104 D, originating from outside the telephony server  112  is received, a SBC receives the traffic and forwards it to a call switch for routing to the client. 
     In some implementations, the telephony server  112 , via the SIP zone, may enable one or more forms of peering to a carrier or customer premise. For example, Internet peering to a customer premise may be enabled to ease the migration of the customer from a legacy provider to a service provider operating the telephony server  112 . In another example, private peering to a customer premise may be enabled to leverage a private connection terminating at one end at the telephony server  112  and at the other end at a computing aspect of the customer environment. In yet another example, carrier peering may be enabled to leverage a connection of a peered carrier to the telephony server  112 . 
     In some such implementations, a SBC or telephony gateway within the customer environment may operate as an intermediary between the SBC of the telephony server  112  and a PSTN for a peered carrier. When an external SBC is first registered with the telephony server  112 , a call from a client can be routed through the SBC to a load balancer of the SIP zone, which directs the traffic to a call switch of the telephony server  112 . Thereafter, the SBC may be configured to communicate directly with the call switch. 
     The web zone receives telephony traffic from a client of a customer, via the SIP zone, and directs same to the application server  108  via one or more Domain Name System (DNS) resolutions. For example, a first DNS within the web zone may process a request received via the SIP zone and then deliver the processed request to a web service which connects to a second DNS at or otherwise associated with the application server  108 . Once the second DNS resolves the request, it is delivered to the destination service at the application server  108 . The web zone may also include a database for authenticating access to a software application for telephony traffic processed within the SIP zone, for example, a softphone. 
     The clients  104 A through  104 D communicate with the servers  108  through  112  of the datacenter  106  via the network  114 . The network  114  can be or include, for example, the Internet, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), or another public or private means of electronic computer communication capable of transferring data between a client and one or more servers. In some implementations, a client can connect to the network  114  via a communal connection point, link, or path, or using a distinct connection point, link, or path. For example, a connection point, link, or path can be wired, wireless, use other communications technologies, or a combination thereof. 
     The network  114 , the datacenter  106 , or another element, or combination of elements, of the system  100  can include network hardware such as routers, switches, other network devices, or combinations thereof. For example, the datacenter  106  can include a load balancer  116  for routing traffic from the network  114  to various servers associated with the datacenter  106 . The load balancer  116  can route, or direct, computing communications traffic, such as signals or messages, to respective elements of the datacenter  106 . 
     For example, the load balancer  116  can operate as a proxy, or reverse proxy, for a service, such as a service provided to one or more remote clients, such as one or more of the clients  104 A through  104 D, by the application server  108 , the telephony server  112 , and/or another server. Routing functions of the load balancer  116  can be configured directly or via a DNS. The load balancer  116  can coordinate requests from remote clients and can simplify client access by masking the internal configuration of the datacenter  106  from the remote clients. 
     In some implementations, the load balancer  116  can operate as a firewall, allowing or preventing communications based on configuration settings. Although the load balancer  116  is depicted in  FIG.  1    as being within the datacenter  106 , in some implementations, the load balancer  116  can instead be located outside of the datacenter  106 , for example, when providing global routing for multiple datacenters. In some implementations, load balancers can be included both within and outside of the datacenter  106 . In some implementations, the load balancer  116  can be omitted. 
       FIG.  2    is a block diagram of an example internal configuration of a computing device  200  of an electronic computing and communications system. In one configuration, the computing device  200  may implement one or more of the client  104 , the application server  108 , the database server  110 , or the telephony server  112  of the system  100  shown in  FIG.  1   . 
     The computing device  200  includes components or units, such as a processor  202 , a memory  204 , a bus  206 , a power source  208 , peripherals  210 , a user interface  212 , a network interface  214 , other suitable components, or a combination thereof. One or more of the memory  204 , the power source  208 , the peripherals  210 , the user interface  212 , or the network interface  214  can communicate with the processor  202  via the bus  206 . 
     The processor  202  is a central processing unit, such as a microprocessor, and can include single or multiple processors having single or multiple processing cores. Alternatively, the processor  202  can include another type of device, or multiple devices, configured for manipulating or processing information. For example, the processor  202  can include multiple processors interconnected in one or more manners, including hardwired or networked. The operations of the processor  202  can be distributed across multiple devices or units that can be coupled directly or across a local area or other suitable type of network. The processor  202  can include a cache, or cache memory, for local storage of operating data or instructions. 
     The memory  204  includes one or more memory components, which may each be volatile memory or non-volatile memory. For example, the volatile memory can be random access memory (RAM) (e.g., a DRAM module, such as DDR SDRAM). In another example, the non-volatile memory of the memory  204  can be a disk drive, a solid state drive, flash memory, or phase-change memory. In some implementations, the memory  204  can be distributed across multiple devices. For example, the memory  204  can include network-based memory or memory in multiple clients or servers performing the operations of those multiple devices. 
     The memory  204  can include data for immediate access by the processor  202 . For example, the memory  204  can include executable instructions  216 , application data  218 , and an operating system  220 . The executable instructions  216  can include one or more application programs, which can be loaded or copied, in whole or in part, from non-volatile memory to volatile memory to be executed by the processor  202 . For example, the executable instructions  216  can include instructions for performing some or all of the techniques of this disclosure. The application data  218  can include user data, database data (e.g., database catalogs or dictionaries), or the like. In some implementations, the application data  218  can include functional programs, such as a web browser, a web server, a database server, another program, or a combination thereof. The operating system  220  can be, for example, Microsoft Windows®, Mac OS X®, or Linux®; an operating system for a mobile device, such as a smartphone or tablet device; or an operating system for a non-mobile device, such as a mainframe computer. 
     The power source  208  provides power to the computing device  200 . For example, the power source  208  can be an interface to an external power distribution system. In another example, the power source  208  can be a battery, such as where the computing device  200  is a mobile device or is otherwise configured to operate independently of an external power distribution system. In some implementations, the computing device  200  may include or otherwise use multiple power sources. In some such implementations, the power source  208  can be a backup battery. 
     The peripherals  210  includes one or more sensors, detectors, or other devices configured for monitoring the computing device  200  or the environment around the computing device  200 . For example, the peripherals  210  can include a geolocation component, such as a global positioning system location unit. In another example, the peripherals can include a temperature sensor for measuring temperatures of components of the computing device  200 , such as the processor  202 . In some implementations, the computing device  200  can omit the peripherals  210 . 
     The user interface  212  includes one or more input interfaces and/or output interfaces. An input interface may, for example, be a positional input device, such as a mouse, touchpad, touchscreen, or the like; a keyboard; or another suitable human or machine interface device. An output interface may, for example, be a display, such as a liquid crystal display, a cathode-ray tube, a light emitting diode display, or other suitable display. 
     The network interface  214  provides a connection or link to a network (e.g., the network  114  shown in  FIG.  1   ). The network interface  214  can be a wired network interface or a wireless network interface. The computing device  200  can communicate with other devices via the network interface  214  using one or more network protocols, such as using Ethernet, transmission control protocol (TCP), internet protocol (IP), power line communication, an IEEE 802.X protocol (e.g., Wi-Fi, Bluetooth, or ZigBee), infrared, visible light, general packet radio service (GPRS), global system for mobile communications (GSM), code-division multiple access (CDMA), Z-Wave, another protocol, or a combination thereof. 
       FIG.  3    is a block diagram of an example of a software platform  300  implemented by an electronic computing and communications system, for example, the system  100  shown in  FIG.  1   . The software platform  300  is a UCaaS platform accessible by clients of a customer of a UCaaS platform provider, for example, the clients  104 A through  104 B of the customer  102 A or the clients  104 C through  104 D of the customer  102 B shown in  FIG.  1   . The software platform  300  may be a multi-tenant platform instantiated using one or more servers at one or more datacenters including, for example, the application server  108 , the database server  110 , and the telephony server  112  of the datacenter  106  shown in  FIG.  1   . 
     The software platform  300  includes software services accessible using one or more clients. For example, a customer  302  as shown includes four clients—a desk phone  304 , a computer  306 , a mobile device  308 , and a shared device  310 . The desk phone  304  is a desktop unit configured to at least send and receive calls and includes an input device for receiving a telephone number or extension to dial to and an output device for outputting audio and/or video for a call in progress. The computer  306  is a desktop, laptop, or tablet computer including an input device for receiving some form of user input and an output device for outputting information in an audio and/or visual format. The mobile device  308  is a smartphone, wearable device, or other mobile computing aspect including an input device for receiving some form of user input and an output device for outputting information in an audio and/or visual format. The desk phone  304 , the computer  306 , and the mobile device  308  may generally be considered personal devices configured for use by a single user. The shared device  310  is a desk phone, a computer, a mobile device, or a different device which may instead be configured for use by multiple specified or unspecified users. 
     Each of the clients  304  through  310  includes or runs on a computing device configured to access at least a portion of the software platform  300 . In some implementations, the customer  302  may include additional clients not shown. For example, the customer  302  may include multiple clients of one or more client types (e.g., multiple desk phones or multiple computers) and/or one or more clients of a client type not shown in  FIG.  3    (e.g., wearable devices or televisions other than as shared devices). For example, the customer  302  may have tens or hundreds of desk phones, computers, mobile devices, and/or shared devices. 
     The software services of the software platform  300  generally relate to communications tools, but are in no way limited in scope. As shown, the software services of the software platform  300  include telephony software  312 , conferencing software  314 , messaging software  316 , and other software  318 . Some or all of the software  312  through  318  uses customer configurations  320  specific to the customer  302 . The customer configurations  320  may, for example, be data stored within a database or other data store at a database server, such as the database server  110  shown in  FIG.  1   . 
     The telephony software  312  enables telephony traffic between ones of the clients  304  through  310  and other telephony-enabled devices, which may be other ones of the clients  304  through  310 , other VOIP-enabled clients of the customer  302 , non-VOIP-enabled devices of the customer  302 , VOIP-enabled clients of another customer, non-VOIP-enabled devices of another customer, or other VOIP-enabled clients or non-VOIP-enabled devices. Calls sent or received using the telephony software  312  may, for example, be sent or received using the desk phone  304 , a softphone running on the computer  306 , a mobile application running on the mobile device  308 , or using the shared device  310  that includes telephony features. 
     The telephony software  312  further enables phones that do not include a client application to connect to other software services of the software platform  300 . For example, the telephony software  312  may receive and process calls from phones not associated with the customer  302  to route that telephony traffic to one or more of the conferencing software  314 , the messaging software  316 , or the other software  318 . 
     The conferencing software  314  enables audio, video, and/or other forms of conferences between multiple participants, such as to facilitate a conference between those participants. In some cases, the participants may all be physically present within a single location, for example, a conference room, in which the conferencing software  314  may facilitate a conference between only those participants and using one or more clients within the conference room. In some cases, one or more participants may be physically present within a single location and one or more other participants may be remote, in which the conferencing software  314  may facilitate a conference between all of those participants using one or more clients within the conference room and one or more remote clients. In some cases, the participants may all be remote, in which the conferencing software  314  may facilitate a conference between the participants using different clients for the participants. The conferencing software  314  can include functionality for hosting, presenting scheduling, joining, or otherwise participating in a conference. The conferencing software  314  may further include functionality for recording some or all of a conference and/or documenting a transcript for the conference. 
     The messaging software  316  enables instant messaging, unified messaging, and other types of messaging communications between multiple devices, such as to facilitate a chat or other virtual conversation between users of those devices. The unified messaging functionality of the messaging software  316  may, for example, refer to email messaging which includes a voicemail transcription service delivered in email format. 
     The other software  318  enables other functionality of the software platform  300 . Examples of the other software  318  include, but are not limited to, device management software, resource provisioning and deployment software, administrative software, third party integration software, and the like. In one particular example, the other software  318  can include digital collaboration software, such as digital whiteboard software, for facilitating collaborations between users of the software platform  300  and/or automated content rearrangement software for automated rearrangement of content within a digital collaboration space implemented by digital collaboration software, such as a digital whiteboard. 
     The software  312  through  318  may be implemented using one or more servers, for example, of a datacenter such as the datacenter  106  shown in  FIG.  1   . For example, one or more of the software  312  through  318  may be implemented using an application server, a database server, and/or a telephony server, such as the servers  108  through  112  shown in  FIG.  1   . In another example, one or more of the software  312  through  318  may be implemented using servers not shown in  FIG.  1   , for example, a meeting server, a web server, or another server. In yet another example, one or more of the software  312  through  318  may be implemented using one or more of the servers  108  through  112  and one or more other servers. The software  312  through  318  may be implemented by different servers or by the same server. 
     Features of the software services of the software platform  300  may be integrated with one another to provide a unified experience for users. For example, the messaging software  316  may include a user interface element configured to initiate a call with another user of the customer  302 . In another example, the telephony software  312  may include functionality for elevating a telephone call to a conference. In yet another example, the conferencing software  314  may include functionality for sending and receiving instant messages between participants and/or other users of the customer  302 . In yet another example, the conferencing software  314  may include functionality for file sharing between participants and/or other users of the customer  302 . In some implementations, some or all of the software  312  through  318  may be combined into a single software application run on clients of the customer, such as one or more of the clients  304  through  310 . 
       FIG.  4    is a block diagram of an example of a system  400  for automated content rearrangement. The system  400  includes a server  402  which runs digital collaboration software  404  for facilitating interactions within a digital collaboration space between users of user devices  406 . The user devices  406  may be client devices, for example, the clients  304  through  310  shown in  FIG.  3   , or non-client devices. 
     A digital collaboration space instantiated or otherwise facilitated using the digital collaboration software  404  may be accessed and interacted with by the user devices  406 . For example, one or more of the user devices  406  may be used to add content items to the digital collaboration space, modify content items within the digital collaboration space, and/or remove content items from the digital collaboration space. As used herein, a content item is, includes, or otherwise refers to content which may be visually represented in some way within a digital collaboration space. Examples of content items include, but are not limited to, text objects, such as text boxes and digital sticky notes, and non-text objects, such as illustrations and flowcharts. 
     The digital collaboration software  404  may be implemented as a software service of a software platform, for example, the software platform  300  shown in  FIG.  3   . Alternatively, the digital collaboration software  404  may be implemented as a web service accessible through web browsers running at the user devices  406 . In some implementations, the digital collaboration software  404  and the digital collaboration space may respectively be digital whiteboard software and a digital whiteboard. In some implementations, the digital collaboration software  404  and the digital collaboration space may respectively be workplace productivity software and a workplace productivity space such as a document, spreadsheet, or slideshow presentation. 
     In some implementations, the digital collaboration space may be instantiated during a conference implemented using conferencing software, such as the conferencing software  314  shown in  FIG.  3   . For example, the digital collaboration space may be instantiated during a screen share operation within the conference. The digital collaboration space may then be accessed by devices connected to the conference, such as the user devices  406 . In some such implementations, the digital collaboration software  404  or a portion thereof may be included in the conferencing software. 
     The user devices  406  operate and connect in a shared session with the digital collaboration space so that changes to the digital collaboration space made by one of the user devices  406  (e.g., an addition, modification, or removal of one or more content items) are visible, generally in real-time, to all of the user devices  406 . In some cases, however, permissions for selectively limiting visibility into group edits to the digital collaboration space may be defined and customized. Sharing permissions for the digital collaboration space may be maintained on a paradigm basis. For example, where the digital collaboration space is accessed during a conference, the host of the conference may be recognized as the owner of the digital collaboration space with elevated permissions and control. In another example, outside of a conference setting, the digital collaboration space may be shared without a named host. 
     The digital collaboration software  404  includes automated content rearrangement software  408 . The automated content rearrangement software  408  categorizes content items within a digital collaboration space initialized or otherwise facilitated by the digital collaboration software  404  according to category metadata determined for those content items and outputs rearrangements  410  of those content items according to the determined category metadata. A rearrangement  410  is or otherwise refers to a reordering or other relocation of some or all of the content items within the digital collaboration space. 
     The rearrangements  410  may refer to the original content items or copies of the content items. For example, the rearrangements  410  may be represented by the content items being rearranged within the same part (e.g., layer, as described below) of the digital collaboration space as they were added, such as by the content items themselves being rearranged within that same part. In such a case, the rearrangements  410  may represent or otherwise include data usable by the digital collaboration software  404  to relocate the subject content items on one or more existing layers of the digital collaboration space. In another example, the rearrangements  410  may be represented by the content items being rearranged within a different part (e.g., layer) of the digital collaboration space from the one in which they were added, such as by copies of the content items being rearranged within that different part. In such a case, the rearrangements  410  may represent or otherwise include new data for the digital collaboration software  404  to populate within one or more new layers of the digital collaboration space. 
     The rearrangements  410  may or may be used to represent the content items in one or more formats on one or more layers of the digital collaboration space. Examples of formats which may be used include tables (e.g., in which each column represents a different type of data or metadata by which to arrange the content items), graphs (e.g., in which different axes, bars, or the like represent different types of data or metadata by which to arrange the content items), flowcharts (e.g., in which different blocks or other objects within a flowchart represent different types of data or metadata by which to arrange the content items), and lists (e.g., in which each entry or section of entries represents a different type of data or metadata by which to arrange the content items). As used herein, a layer of a digital collaboration space generally includes or otherwise refers to a document, sheet, space, workbook, page, or other aspect configured to include the content items in one form or another. Layers of the digital collaboration space may be navigable by users of the user devices  406 . For example, a user may freely move between a first layer on which various digital sticky notes are added to the digital collaboration space and a second layer within which those digital sticky notes are rearranged in some new format, such as a table or like format. Thus, by rearranging the content items according to their respective category metadata, the rearrangements  410  of the content items may improve visualization of the content items, which may initially be added to the digital collaboration space in arbitrary or suboptimal locations, according to the category metadata. 
     In some implementations, where the content items are rearranged into a new layer of the digital collaboration space, the automated content rearrangement software  408  may visually represent the content items for user ease in identifying the source locations for the rearranged content items (e.g., the original locations of those content items within the digital collaboration space). For example, the automated content rearrangement software  408  may add color coding information, alphanumeric coding information, or other trackable information to each of the content item in the source location and the content item in the rearrangement. A user of a user device  406  may match the subject information for a content item to verify its source location. 
     In some implementations, not all of the content items within a digital collaboration space may be rearranged according to the rearrangements  410 . For example, the automated content rearrangement software  408  may in some cases determine that two or more of the content items are syntactically and/or semantically identical. To avoid duplication of content items in the rearrangements  410 , the automated content rearrangement software  408  may thus determine the rearrangements  410  using only one of the two or more content items. In such a case, a visualization of the rearranged content items may include only one of the two or more content items. 
     As mentioned above, the content items may be rearranged based on category metadata determined for the content items. The category metadata generally indicates a category of content with which a subject content item corresponds. For example, where a content item includes the text “Options: blue, red, green,” the category may be “Colors” and the category metadata may thus indicate that the content item corresponds to the “Colors” category. In another example, where a content item includes the text “Teams led by Joe Smith and Mary Sue,” the category may be “Team Leaders” and the category metadata may thus indicate that the content item corresponds to the “Team Leaders” category. Categories may be generic, such as in the examples described above, or specific to a given digital collaboration space or meeting which uses the digital collaboration space. 
     The category metadata may in some cases be determined based on user input received from one or more of the user devices  406 . In such a case, the category metadata may be deemed to be manually determined based on that user input. Alternatively, the automated content rearrangement software  408  may use one or more learning models  412  to determine the category metadata for the content items. The one or more learning models  412  may, for example, each be a machine learning model, such as one or more of a neural network (e.g., a convolutional neural network, recurrent neural network, or other neural network), decision tree, vector machine, Bayesian network, genetic algorithm, deep learning system separate from a neural network, or other machine learning model. 
     The one or more learning models  412  process metadata associated with the content items to determine the category metadata for the content items. The metadata associated with a content item includes information identifying one or more of a name or identifier of a user who added and/or modified the content item, text within the content item, positive and/or negative feedback on the content item (e.g., expressed as user votes or emojis), and other aspects usable to determine category metadata for the content item. The content item is tagged with the various metadata. For example, one or more users of the user devices  406  may input the metadata for the purpose of tagging the content item with it. Inputting the metadata may thus include the user of a subject user device  406  entering information into a field associated with the content item and that information being used as metadata with which the content item is tagged. 
     The one or more learning models  412  may be trained for processing different types of content items. For example, a first learning model  412  may be trained for text detection and recognition, such as to understand the text of a content item. In another example, a second learning model  412  may be trained for object detection and recognition, such as for non-text objects like illustrations entered into the digital collaboration space by freehand drawing, insertion from a source file or address, or the like. In some cases, such as where all of the content items are of a same type (e.g., text), a single learning model  412  may be used to determine category metadata for the content items. In other cases, such as where some of the content items are of one type (e.g., text) and others of the content items are of another type (e.g., illustration), multiple learning models  412  may be used to determine category metadata for the content items. 
     In particular, a learning model  412  trained for text detection and recognition may use natural language processing (NLP) to interpret the text metadata of a content item, such as based on the words of the text themselves and/or a semantic neighborhood of those words, and determine peripherally-related keyword phrases based on both the core language that is used and increasing areas of potential relativity. For example, the learning model  412  trained for text detection and recognition may be trained to ontologically recognize relationships between keywords, such as based on publicly available definitions and uses of those keywords and/or based on definitions and uses of those keywords specific to an entity (e.g., a customer of the software platform). 
     The learning model  412  determines text which is strongly or weakly correlated with one another using ontological relationships as a foundation and training data obtained from one or more digital collaboration spaces. The training data may refer to information identifying manual user rearrangements of content items based on the initial rearrangements determined using the automated content rearrangement software  408  being inaccurate. For example, a digital collaboration space may include a first content item with the text “Zoom Rooms Controller” and a second content item with the text “ZRC.” The learning model  412  may initially not recognize that the second content item is an acronym of or otherwise related to the first content item, and in the absence of other metadata it may determine different category metadata for each of the first content item and the second content item. A user of a user device  406  may then present input manually rearranging the first content item and the second content item into the same category to the digital collaboration software  404 . The digital collaboration software  404  can pass that input to the automated content rearrangement software  408  for use as training data for updating the learning model  412 , or the digital collaboration software  404  can package that input as training data and provide it directly to the learning model  412  by bypassing the automated content rearrangement software  408 . In particular, the learning model  412  may use that training data to determine a relationship between the first content item and the second content item. In a later digital collaboration space into which those first and second content items are added, the learning model  412  may thus use its updated understanding to determine same category metadata for the first content item and the second content item. 
     The automated content rearrangement software  408  may begin processing content items to determine the rearrangements  410  in response to input received from a user device  406 . For example, at some point during a brainstorm meeting in which a digital collaboration space implemented using the digital collaboration software  404  is used to document content items representing brainstorm ideas, a user may cause the automated content rearrangement software  408  to determine the rearrangements  410  by interacting with a user interface element (e.g., a button on a graphical user interface) of the digital collaboration software  404 . The automated content rearrangement software  408  may thus determine the rearrangements  410  responsive to or otherwise based on that user interface element interaction by the user. Alternatively, the automated content rearrangement software  408  may periodically (e.g., on a discrete time interval basis) or continuously process content items within a digital collaboration space to determine the rearrangements  410 . For example, new rearrangements may be determined and/or previously determined rearrangements may be updated responsive to or otherwise based on changes within the digital collaboration space without specific input indicating to process the content items for rearrangement being received. 
     As described above, the automated content rearrangement software  408  generally determines the rearrangements  410  based on the category metadata determined for the content items. For example, the rearrangements  410  may be output at a layer of the digital collaboration space in a table or other format such as where each column of the table or aspect of the other format corresponds to a different category. In such a case, a single layer may be used to visually represent all of the content items which were rearranged in their rearranged locations according to their respective category metadata. However, in some cases, the automated content rearrangement software  408  may determine the rearrangements  410  based on metadata other than or in addition to the category metadata. For example, the rearrangements  410  may be output at a layer of the digital collaboration space in a table or other format such as where each column of the table or aspect of the other format corresponds to a different user who added the subject content items, a different portion of a timeline for completing one or more tasks related to the subject content items, or a score or other measurement of positive/negative feedback attributed to the subject content items by one or more users. In another example, the rearrangements  410  may be output at multiple layers of the digital collaboration space in which each layer includes a different table or other format representing rearranged content items according to the above examples. In such a case, a user may freely switch between those multiple layers to view the different rearrangements  410  of the content items as they like. 
     The metadata criteria which are used to determine the particular format and number of layers for the rearrangements  410  may be determined based on user input received from one or more of the user devices  406 . For example, a user may specify the types of metadata over which the automated content rearrangement software  408  determines the rearrangements  410  and upon which those rearrangements  410  are visually represented to the users of the user devices  406 . Alternatively, the metadata criteria may be automatically determined by the automated content rearrangement software  408 . For example, the automated content rearrangement software  408  may determine a rearrangement  410  for each type of metadata. In another example, the automated content rearrangement software  408  may determine a rearrangement  410  for only those types of metadata with which a threshold number of content items correspond. In one such case, where only a couple of the content items have been given positive/negative feedback (e.g., in the form of upvotes/downvotes or likes/dislikes), the automated content rearrangement software  408  may not determine a rearrangement  410  according to positive/negative feedback metadata for the content items (i.e., not perform a rearrangement). 
     As mentioned above, the rearrangements  410  are generally visible to the users of the user devices  406  within the digital collaboration space implemented by the digital collaboration software  404 . However, in some cases, the rearrangements  410  may be made available for viewing external to that digital collaboration space. For example, the digital collaboration software  404  may produce output representing the rearrangements  410  in a document format which can be shared with others beyond the users of the user devices  406 . In one such case, a summary of the rearrangements  410  may be presented in a document file and emailed or otherwise made accessible to one or more stakeholders who wish to review the output of a brainstorm meeting conducted using the digital collaboration software  404 . In another such case, where one or more of the rearrangements  410  are associated with a person, a summary of the content items represented by those rearrangements  410  may be presented to that person for actioning (e.g., as a form of task assignment). For example, that person may be determined using organizational charts which associate a task corresponding to the subject content items with one or more people within an organization. 
     In some implementations, information representing the content items within different digital collaboration spaces and/or rearrangements of those content items may be linked in some way. For example, the digital collaboration software  404 , the automated content rearrangement software  408 , or other software may track which users are most frequently contributing content items to digital collaboration spaces, which users tend to contribute content items to which categories of content, and/or categories of content which tend to have relatively high numbers of content items arranged therein across digital collaboration spaces. In another example, the content items and/or rearrangements across digital collaboration spaces may be mapped to one another based on some perceived commonality of metadata (e.g., category metadata). In one such case, where one digital collaboration space is associated with a first user group (e.g., a first team) and another digital collaboration space is associated with a second user group (e.g., a second team) and those digital collaboration spaces include a commonality of metadata, the digital collaboration software  404 , the automated content rearrangement software  408 , or other software may transmit a suggestion to one or more users of the first user group and/or the second user group to suggest a collaboration between those user groups and/or, where sharing is enabled, to share the content items and/or rearrangements of those respective digital collaboration spaces with the other user group. 
     In some implementations, one or more content items may be automatically added to the digital collaboration space, such as by the digital collaboration software  404 , the automated content rearrangement software  408 , or other software. For example, where the digital collaboration space is being used during a telephone call or a conference, a real-time transcription of the telephone call or conference can be generated, such as using an automatic speech recognition (ASR) or like tool included in or external to the software implementing the telephone call or conference (e.g., respectively, the telephony software  312  or the conferencing software  314  shown in  FIG.  3   ). In this way, the real-time transcription can be processed to identify the content items which were discussed over the telephone call or conference, such as to increase efficiency of a brainstorm or like meeting by not requiring users to add those content items manually into the digital collaboration space. In some such implementations, content items added automatically may be added to a designated location within the digital collaboration space while awaiting rearrangement using the automated content rearrangement software  408 . In other such implementations, content items added automatically may not be added within the digital collaboration space but later used by the automated content rearrangement software  408  and included in the rearrangements  410 . 
     As shown and described, the digital collaboration software  404  includes the automated content rearrangement software  408 . However, in some implementations, the automated content rearrangement software  408  may be external to the digital collaboration software  404 . In some such implementations, the digital collaboration software  404  and the automated content rearrangement software  408  may be run on different servers. 
     The digital collaboration software  404  and the automated content rearrangement software  408  are usable as described above in various use cases. In one example use case, the digital collaboration software  404  and the automated content rearrangement software  408  may be used for brainstorming. For example, the users of the user devices  406  may be brainstorming ideas for something and documenting those ideas in one form or another as content items within a digital whiteboard. The ideas may be jotted down in random or otherwise arbitrary locations on a first layer of the digital whiteboard (e.g., wherever there is room). At some point during the brainstorm meeting, a user causes the automated content rearrangement software  408  to begin processing the content items. The automated content rearrangement software  408  determines the rearrangements  410  and outputs the content items on one or more layers of the digital whiteboard according to the rearrangements  410 . 
     In another example use case, the digital collaboration software  404  and the automated content rearrangement software  408  may be used for competitions, such as innovation competitions in which competitors are tasked with conceiving of ideas for something. For example, teams of one or more users of the user devices  406  may use their own digital collaboration space to write down and discuss ideas as content items. As with the brainstorming example, the ideas may be jotted down in random or otherwise arbitrary locations on a first layer of the digital collaboration space. At the end of the innovation competition, or before moving to a next segment thereof, a user of a given team causes the automated content rearrangement software  408  to begin processing the content items. The automated content rearrangement software  408  determines the rearrangements  410  and outputs the content items on one or more layers of the digital collaboration space according to the rearrangements  410 . 
     In yet another example use case, the digital collaboration software  404  and the automated content rearrangement software  408  may be used for contact center interactions between a software user and a contact center agent. For example, the contact center agent may record his or her notes regarding the contact center interaction with the software user as content items in a document or other file implemented by the digital collaboration software  404 . The notes may be recorded in order with the discussion between the software user and the contact center agent. To efficiently analyze those content items, such as to rearrange them by category, the contact center agent at some point during the contact center interaction may cause the automated content rearrangement software  408  to begin processing the content items. The automated content rearrangement software  408  determines the rearrangements  410  and outputs the content items on one or more layers of the document or other file according to the rearrangements  410 . In some such cases, the rearrangements  410  may persist such as for use by the same contact center agent or another contact center agent during subsequent contact center interactions with the same software user or with a different software user. For example, during a subsequent contact center interaction, the rearrangements  410  may be updated based on further notes, as content items, documented by a contact center agent. 
       FIG.  5    is a block diagram of an example of automated rearrangement of content within a digital whiteboard  500 . The digital whiteboard  500  is implemented by digital whiteboard software  502 . For example, the digital whiteboard software  502  may be the digital collaboration software  404  shown in  FIG.  4   . A user of a user device  504 , which may, for example, be one of the user devices  406  shown in  FIG.  4   , accesses the digital whiteboard  500  using a client application  506  to add one or more content items into a first layer  508  of the digital whiteboard  500 . The content items are considered to be in their source locations within the first layer  508 . The user then interacts with a user interface element of the digital whiteboard software  502  to cause automated content rearrangement software  510 , such as the automated content rearrangement software  408  shown in  FIG.  4   , to determine a rearrangement of the content items within the first layer  508 . The automated content rearrangement software  510  outputs the content items according to the rearrangement to a second layer  512  of the digital whiteboard  500 . The user may then view the rearranged content items within the second layer  512 . 
       FIG.  6    is a block diagram of an example of arranging content detected from images. Implementations of content arrangement described with respect to  FIG.  6    refer to approaches in which content items are determined using images representing those content items within a physical space  600 , such as a conference room, office, or other space. In particular, one or more physical content items  602  representing the content items are located within the physical space  600 . For example, the physical content items  602  may be sticky notes in a pad formation, affixed to a table or surface, or otherwise located somewhere in the physical space  600 . The physical content items  602  include content, such as text written thereon. The physical content items  602  are tangible matter which can be seen and held by a person within the physical space  600 . As such, the physical content items  602  are capable of being photographed. 
     One or more image capture devices  604 , such as cameras of mobile devices, cameras of appliances installed within the physical space  600 , or the like, can be used to capture images of the physical content items  602 . The images may then transmitted from the image capture devices  604  to a server  606 , which may, for example, be the server  402  shown in  FIG.  4   . A first learning model  608  at the server  606  processes the images to detect and recognize the physical content items  602  as objects within the images. A second learning model  610  uses the object detection output from the first learning model  608  to detect and recognize the content of the physical content items  602 , such as the text thereon. For example, the first learning model  608  may be a machine learning model trained for object detection and recognition, such as to detect and recognize common objects and/or objects commonly located within the physical space  600  based on shape, color, and/or other visual attributes. In another example, the second learning model  610  may be a machine learning model trained for text detection and recognition, such as to detect and recognize handwritten text on the objects detected and recognized by the first learning model  608 . In some cases, the second learning model  610  may be trained based on the handwriting of one or more specific users. 
     The output of the second learning model  610 , the text of the physical content items  602 , is processed at digital collaboration software  612 , such as to add content items representative of the text of the physical content items  602  into a digital collaboration space implemented by the digital collaboration software  612 . For example, the digital collaboration software  612  may be the digital collaboration software  404  shown in  FIG.  4   . The digital collaboration software  612  includes automated content rearrangement software  614 , which may, for example, be the automated content rearrangement software  408  shown in  FIG.  4   . The automated content rearrangement software  614  processes the content items as described above with respect to  FIG.  4    to determine a rearrangement  616  of those content items. For example, the content items may be output to a layer of the digital collaboration space according to the rearrangement  616 . 
     In some implementations, the digital collaboration software  612  may be omitted. For example, the automated content rearrangement software  614  may output the rearrangement  616  in a form and to a software aspect other than the digital collaboration software  612 . In one such case, the automated content rearrangement software  614  can produce a document including the rearrangement  616  and store the document within a data store, transmit the document to one or more people (e.g., via email), or otherwise output the document. 
     To further describe some implementations in greater detail, reference is next made to examples of techniques which may be performed by or using a system for automated content rearrangement.  FIG.  7    is a flowchart of an example of a technique  700  for automated content rearrangement.  FIG.  8    is a flowchart of an example of a technique  800  for training a learning model for automated content rearrangement.  FIG.  9    is a flowchart of an example of a technique  900  for arranging content detected from images. 
     The technique  700 , the technique  800 , and/or the technique  900  can be executed using computing devices, such as the systems, hardware, and software described with respect to  FIGS.  1 - 6   . The technique  700 , the technique  800 , and/or the technique  900  can be performed, for example, by executing a machine-readable program or other computer-executable instructions, such as routines, instructions, programs, or other code. The steps, or operations, of the technique  700 , the technique  800 , and/or the technique  900  or another technique, method, process, or algorithm described in connection with the implementations disclosed herein can be implemented directly in hardware, firmware, software executed by hardware, circuitry, or a combination thereof. 
     For simplicity of explanation, the technique  700 , the technique  800 , and the technique  900  each depicted and described herein as a series of steps or operations. However, the steps or operations in accordance with this disclosure can occur in various orders and/or concurrently. Additionally, other steps or operations not presented and described herein may be used. Furthermore, not all illustrated steps or operations may be required to implement a technique in accordance with the disclosed subject matter. 
     Referring first to  FIG.  7   , the technique  700  for automated content rearrangement is shown. At  702 , a digital collaboration space is initialized. The digital collaboration space is implemented using digital collaboration software. For example, the digital collaboration space may be a digital whiteboard implemented using digital whiteboard software. Initializing the digital collaboration space can include the digital collaboration software instantiating the digital collaboration space using server resources for access by one or more user devices. 
     At  704 , content items are identified within the digital collaboration space. The content items may be added by one or more users of the user devices connected to the digital collaboration software. The content items include text objects and non-text objects. In some implementations, one or more of the content items may be added to the digital collaboration space based on a real-time transcription of a conference or a telephone call. For example, the digital collaboration space may be used during a conference or telephone call, and at least one of the content items may be automatically added to the digital whiteboard based on the real-time transcription of the conference or telephone call. 
     At  706 , category metadata is determined for each content item. The category metadata is determined using one or more learning models that process information associated with the respective content items. The information associated with a content item includes metadata, such as metadata identifying a user who added the content item to the digital collaboration space, metadata identifying text or other content of the content item, and/or metadata identifying positive/negative feedback to the content item. The one or more learning models used to process the information associated with a content item is based on the type of content item. For example, where the content item is a text object, a first learning model trained for text recognition can be used to determine the category metadata for the content item. In another example, where the content item is a non-text object, a second learning model trained for object recognition can be used to determine category metadata for the content item. 
     At  708 , a rearrangement of the content items is determined based on the category metadata determined for the content items. In some cases, the rearrangement of the content items is determined responsive to input received from a user device. In other cases, the rearrangement is determined by automatically processing the content items on a periodic basis. The rearrangement represents a reordering or other relocation of the content items from their source locations within the digital collaboration space relative to one another based on the category metadata. In particular, determining the rearrangement of the content items includes grouping ones of the content items based on categories represented by the category metadata determined for each of the content items. In some cases, a determination can be made that two or more of the content items correspond to common content, such as by those two or more common items being syntactically and/or semantically identical. Determining the rearrangement of the content items in such a case can include using only one of the two or more of the content items within the rearrangement of the content items. 
     In some implementations, determining the rearrangement of the content items can include determining a sequence of operations associated with two or more of the content items having common category metadata and rearranging the two or more of the content items to represent a flowchart indicative of the sequence of operations. For example, a sequence of operations may be inferred using a learning model (e.g., a machine learning model trained for text recognition) based on context of the two or more of the content items. Those content items may thus be arranged, according to a determined rearrangement, based on that inferred sequence of operations, such as in a flowchart format. 
     At  710 , the content items rearranged according to the rearrangement are output to a layer of the digital collaboration space. Outputting the content items to the layer of the digital collaboration space includes rearranging the content items according to the determined rearrangement. The layer of the digital collaboration space to which the rearranged content items are output may be a layer of the digital collaboration space within which the content items are identified. Alternatively, the layer of the digital whiteboard to which the rearranged content items are output may be different from a layer of the digital whiteboard within which the content items are identified. The layer of the digital collaboration space to which the rearranged content items are output may then be accessed by one or more user devices. In some implementations, a document indicating the content items rearranged according to the rearrangement, such as is output within the layer of the digital collaboration space, may be produced. The document may, for example, be shared outside of the digital collaboration space. 
     In some implementations, the technique  700  may be performed to determine a rearrangement of the content items other than based on category metadata or to determine one or more rearrangements of the content items other than based on category metadata in addition to the rearrangement determined based on the category metadata. For example, the rearrangement may instead be determined based on user metadata or other non-category metadata. In another example, a second rearrangement based on non-category metadata may be determined and the content items rearranged according to the second rearrangement may be output to a second layer of the digital collaboration space. In such a case, both layers of the digital collaboration space are accessible to the one or more user devices. 
     In some implementations, rearrangement information obtained from a user device after the determination of the rearrangement of the content items can be used to update the one or more learning models. For example, the determined rearrangement may be inaccurate such as based on insufficient training data used to train a learning model used to determine the category information. In such a case, input from a user device specifying that the rearrangement was inaccurate, such as by the input indicating to move a given content item from one category to another, can be used to update, or further train, the learning model. In particular, that input can be used to tune the learning model to recognize that the content item and others like it should correspond to the category to which it was moved and not the earlier-determined category. 
     Referring next to  FIG.  8   , the technique  800  for training a learning model for automated content rearrangement is shown. At  802 , category metadata is determined for content items using one or more learning models. For example, the category metadata can be determined as described above with respect to  FIG.  7   . 
     At  804 , a rearrangement of the content items is determined based on the category metadata. For example, the rearrangement of the content items can be determined as described above with respect to  FIG.  7   . 
     At  806 , input indicating a change to the rearrangement of the content items is received. The input in particular indicates that one or more of the content items has been manually moved, by a user of a user device, from one part of the rearrangement of the content items to another so as to indicate that the one or more of the content items correspond to categories other than the categories earlier-determined therefor. For example, a content item with the text “Green energy” may initially be determined to have category metadata corresponding to the category “Colors.” A user may notice the content item as having been arranged in a table column corresponding to the “Colors” category. For example, the user may believe that the content item actually corresponds to the “Climate change” category. The user may thus move the content item from the column corresponding to the “Colors” category to a column corresponding to the “Climate change” category. The input indicates this change. 
     At  808 , content item information associated with the input is determined. Based on the input indicating the change to the arrangement of the content items, information, such as various metadata associated with the subject content items, is determined. The information is used to determine new understandings for the content items, such as by causing one or more learning models to re-map their understandings of the content items from an original one (e.g., associated with an earlier-determined category) to a new one (e.g., associated with the new category determined based on the input). 
     At  810 , the one or more learning models are updated based on the content item information. For example, updating the one or more learning models can include using the content item information to update, or train, the one or more learning models to recognize that further instances of the subject content items should be categorized according to the new category corresponding to the received input. 
     Referring last to  FIG.  9   , the technique  900  for arranging content detected from images is shown. At  902 , one or more images are captured within a physical space. The images may be captured using a camera of a mobile device, a camera of an appliance installed within the physical space, or another camera. The images may be captured at one time during a conference within the physical space, at periodic times throughout that conference, or continuously throughout that conference. 
     At  904 , one or more physical content items (e.g., sticky notes) are detected within the one or more images using a first learning model. The first learning model may, for example, be a machine learning model trained for object detection and recognition. The first learning model processes the images to detect and recognize objects representing the one or more physical content items within the images. 
     At  906 , content of the physical content items is detected using a second learning model. The second learning model may, for example, be a machine learning model trained for text detection and recognition. The second learning model processes the output of the first learning model to detect and recognize content of the physical content items, such as text (e.g., handwritten text) or illustrations included in the physical content items. 
     At  908 , category metadata for each of the physical content items is determined based on the content corresponding to the content items. For example, the category metadata can be determined as described above with respect to  FIG.  7   . 
     At  910 , digital representations of the physical content items are output in an arrangement that is based on the category metadata. The digital representations are content items added within a digital collaboration space to represent the content of the physical content items. The arrangement of those digital representations may be determined as described above with respect to the rearrangements determined as described with respect to  FIG.  7   . 
     The implementations of this disclosure describe methods, systems, devices, apparatuses, and non-transitory computer readable media for automated rearrangement of content within a digital collaboration space. In some implementations, a method comprises, a non-transitory computer readable medium stores instructions operable to cause one or more processors to perform operations comprising, and/or an apparatus comprises a memory and a processor configured to execute instructions stored in the memory for identifying content items within a digital collaboration space (e.g., a digital whiteboard); for each of the content items, determining category metadata using one or more learning models that process information associated with the content item; determining a rearrangement of the content items based on the category metadata determined for each of the content items; and outputting the content items rearranged according to the rearrangement to a layer of the digital collaboration space (e.g., the digital whiteboard). 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the rearrangement of the content items is determined responsive to input received from a user device. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the layer of the digital collaboration space (e.g., the digital whiteboard) is a first layer of the digital collaboration space (e.g., the digital whiteboard), and the method comprises, the operations comprise, and/or the instructions include instructions for determining a second rearrangement of the content items according to metadata other than the category metadata; and outputting the content items rearranged according to the second rearrangement to a second layer of the digital collaboration space (e.g., the digital whiteboard), wherein the first layer of the digital collaboration space (e.g., the digital whiteboard) and the second layer of the digital collaboration space (e.g., the digital whiteboard) are both accessible to one or more user devices. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the method comprises, the operations comprise, and/or the instructions include instructions for determining that two or more of the content items correspond to common content, and determining the rearrangement of the content items comprises using only one of the two or more of the content items within the rearrangement of the content items. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the content items include a first content item comprising a text object and a second content item comprising a non-text object, and determining the category metadata comprises: using a first learning model trained for text recognition to determine category metadata for the first content item; and using a second learning model trained for object recognition to determine category metadata for the second content item. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the method comprises, the operations comprise, and/or the instructions include instructions for using rearrangement information obtained from a user device after the determination of the rearrangement of the content items to update the one or more learning models. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, determining the rearrangement of the content items comprises: determining a sequence of operations associated with two or more of the content items having common category metadata; and rearranging the two or more of the content items to represent a flowchart indicative of the sequence of operations. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, one or more of the content items is added to the digital collaboration space (e.g., the digital whiteboard) by processing an image representing a physical content item within a physical space. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the layer of the digital collaboration space (e.g., the digital whiteboard) to which the rearranged content items are output is different from a layer of the digital collaboration space (e.g., the digital whiteboard) within which the content items are identified. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the information processed to determine the category metadata for each content item includes non-category metadata associated with the content item. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the digital collaboration space (e.g., the digital whiteboard) is used during a conference or telephone call and at least one of the content items is automatically added to the digital collaboration space (e.g., the digital whiteboard) based on a real-time transcription of the conference or telephone call. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the method comprises, the operations comprise, and/or the instructions include instructions for determining a second rearrangement of the content items based on non-category metadata associated with the content items; and outputting the content items rearranged according to the second rearrangement to a second layer of the digital collaboration space (e.g., the digital whiteboard). 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, determining the rearrangement of the content items comprises automatically processing the content items on a periodic basis. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the content items are color coded to identify source locations of the content items within the digital collaboration space (e.g., the digital whiteboard). 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the method comprises, the operations comprise, and/or the instructions include instructions for producing a document indicating the content items rearranged according to the rearrangement. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the content items include text objects and non-text objects. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the layer of the digital collaboration space (e.g., the digital whiteboard) to which the rearranged content items are output is a layer of the digital collaboration space (e.g., the digital whiteboard) within which the content items are identified. 
     In some implementations of the method, the non-transitory computer readable medium, and/or the apparatus, the layer is a first layer, and the method comprises, the operations comprise, and/or the instructions include instructions for outputting the content items rearranged according to a second rearrangement determined based on non-category metadata associated with the content items to a second layer of the digital collaboration space (e.g., the digital whiteboard). 
     The implementations of this disclosure can be described in terms of functional block components and various processing operations. Such functional block components can be realized by a number of hardware or software components that perform the specified functions. For example, the disclosed implementations can employ various integrated circuit components (e.g., memory elements, processing elements, logic elements, look-up tables, and the like), which can carry out a variety of functions under the control of one or more microprocessors or other control devices. Similarly, where the elements of the disclosed implementations are implemented using software programming or software elements, the systems and techniques can be implemented with a programming or scripting language, such as C, C++, Java, JavaScript, assembler, or the like, with the various algorithms being implemented with a combination of data structures, objects, processes, routines, or other programming elements. 
     Functional aspects can be implemented in algorithms that execute on one or more processors. Furthermore, the implementations of the systems and techniques disclosed herein could employ a number of conventional techniques for electronics configuration, signal processing or control, data processing, and the like. The words “mechanism” and “component” are used broadly and are not limited to mechanical or physical implementations, but can include software routines in conjunction with processors, etc. Likewise, the terms “system” or “tool” as used herein and in the figures, but in any event based on their context, may be understood as corresponding to a functional unit implemented using software, hardware (e.g., an integrated circuit, such as an ASIC), or a combination of software and hardware. In certain contexts, such systems or mechanisms may be understood to be a processor-implemented software system or processor-implemented software mechanism that is part of or callable by an executable program, which may itself be wholly or partly composed of such linked systems or mechanisms. 
     Implementations or portions of implementations of the above disclosure can take the form of a computer program product accessible from, for example, a computer-usable or computer-readable medium. A computer-usable or computer-readable medium can be a device that can, for example, tangibly contain, store, communicate, or transport a program or data structure for use by or in connection with a processor. The medium can be, for example, an electronic, magnetic, optical, electromagnetic, or semiconductor device. 
     Other suitable mediums are also available. Such computer-usable or computer-readable media can be referred to as non-transitory memory or media, and can include volatile memory or non-volatile memory that can change over time. The quality of memory or media being non-transitory refers to such memory or media storing data for some period of time or otherwise based on device power or a device power cycle. A memory of an apparatus described herein, unless otherwise specified, does not have to be physically contained by the apparatus, but is one that can be accessed remotely by the apparatus, and does not have to be contiguous with other memory that might be physically contained by the apparatus. 
     While the disclosure has been described in connection with certain implementations, it is to be understood that the disclosure is not to be limited to the disclosed implementations but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as is permitted under the law.