WO2023028702A1 - Étiquetage dynamique et génération d'indications de sessions de communication en temps réel - Google Patents

Étiquetage dynamique et génération d'indications de sessions de communication en temps réel Download PDF

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Publication number
WO2023028702A1
WO2023028702A1 PCT/CA2022/051312 CA2022051312W WO2023028702A1 WO 2023028702 A1 WO2023028702 A1 WO 2023028702A1 CA 2022051312 W CA2022051312 W CA 2022051312W WO 2023028702 A1 WO2023028702 A1 WO 2023028702A1
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WO
WIPO (PCT)
Prior art keywords
communication session
relevancy
communications
words
user
Prior art date
Application number
PCT/CA2022/051312
Other languages
English (en)
Inventor
Christopher Landry
Angela Chen
Nancy Cao
Andrew Ni
Jacob Adolphe
Joaquin Fuenzalida Nunez
Original Assignee
Shopify Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/462,979 external-priority patent/US20230062177A1/en
Priority claimed from US17/462,754 external-priority patent/US11934432B2/en
Application filed by Shopify Inc. filed Critical Shopify Inc.
Priority to AU2022341038A priority Critical patent/AU2022341038A1/en
Priority to CA3228869A priority patent/CA3228869A1/fr
Priority to GB2402093.5A priority patent/GB2623285A/en
Publication of WO2023028702A1 publication Critical patent/WO2023028702A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/14—Session management
    • H04L67/147—Signalling methods or messages providing extensions to protocols defined by standardisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/14—Session management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/14—Session management
    • H04L67/143—Termination or inactivation of sessions, e.g. event-controlled end of session
    • H04L67/145—Termination or inactivation of sessions, e.g. event-controlled end of session avoiding end of session, e.g. keep-alive, heartbeats, resumption message or wake-up for inactive or interrupted session
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/2866—Architectures; Arrangements
    • H04L67/30—Profiles
    • H04L67/306—User profiles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/535—Tracking the activity of the user

Definitions

  • the present disclosure relates to real-time communication sessions on a digital communication platform, and, more particularly to dynamic labeling of and generating indications of such communications sessions based on relevant words extracted from the communication session in real-time.
  • a digital communication platform may support (or host) a plurality of concurrent communication sessions (including audio session, video session, text session, etc.) taking place in real-time.
  • Such communication sessions are often assigned predefined labels by a human moderator or administrator.
  • predefined labels are often descriptive of the planned topic of discussion, intended audience, etc.
  • a user on the digital communication platform may have the opportunity to participate in any one or more of the communication sessions.
  • a user may decide whether to join a session based on the predefined label that has been assigned to that session. Accordingly, it is important that the label provides accurate and useful information.
  • Some technologies attempt to address this problem by automating generation of a collection of words (e.g., a word cloud) that represents the words used in the communications within the session.
  • a collection of words e.g., a word cloud
  • Such an approach results in bloating of the collection of words and/or irrelevance of words in the collection as the communication session increases in time duration and/or number of communications.
  • the present disclosure describes systems and methods that enable generation of a dynamic label for a communication session, which uses a dynamically sized sliding window to extract relevant words.
  • the extracted relevant words are analyzed in real-time or near real-time, to generate a dynamic collection of words that can be used as a dynamic label representing the real-time content of the communications in the communication session.
  • Examples of the present disclosure may address the problem of how to dynamically generate labels for communication sessions that are occurring in real-time, in a digital and/or online platform. Due to the real-time and rapid nature of digital communications, such a problem is not a trivial matter of automating regular human activity.
  • Some examples of the present disclosure provide a technical solution that dynamically analyzes the content of the communication session and dynamically sizes a sliding window.
  • the sliding window is used to define a subset of communications from the communication session that is used to extract relevant words for generating the dynamic label for the communication session.
  • Dynamically adjusting the size of the sliding window provides the technical advantage that the amount of data that is analyzed for extracting relevant words is flexible and adapted to the content characteristics of the communication session (thus helping to avoid bloating or irrelevancy of the label).
  • Examples of the present disclosure may also address the problem of how to dynamically notify a user of a communication session that might be of interest to the user. Since there can be multiple real-time communication sessions simultaneously taking place on a digital communication platform, it is not realistic or even possible for a human user to monitor all communication sessions in order to identify a communication session covering a topic of interest. Even with the assistance of dynamically generated labels as disclosed herein, it is not a trivial task for a human user to monitor the ever-changing labels in real-time.
  • Some examples of the present disclosure provide a technical solution that determines user-specific relevancy of words, and uses the user-specific relevancy to identify and notify the user of communication sessions that may be of interest. Further, not only is the user-specific relevancy determined in real-time, but also a user-specific relevancy dictionary (which is used to determine user-specific relevancy of words) may be updated in real-time to reflect the user’s changing interests.
  • examples of the present disclosure provide technical improvements to digital communications, in particular how communication sessions are managed on a digital communication platform.
  • the present disclosure describes a computer system including a processing unit configured to execute instructions to cause the computer system to: monitor an ongoing communication session comprising communications between two or more participants to identify a content characteristic of the communication session; determine a size of a sliding window based on the content characteristic of the communication session for analyzing a most recent subset of communications from the communication session, wherein the size of the sliding window defines a segment of the communication session to include in the most recent subset of communications; analyze the most recent subset of communications to identify one or more relevant words based on one or more relevancy criteria; and generate and provide a dynamic label associated with the communication session, the dynamic label including at least a selected one of the one or more relevant words.
  • the processing unit may be further configured to execute instructions to cause the computer system to: subsequent to generating and providing the dynamic label, identify in real-time a change in the content characteristic of the communication session; determine an adjusted size of the sliding window, based on the changed content characteristic, the adjusted size of the sliding window defining an updated segment of the communication session to include in an updated most recent subset of communications; analyze the updated most recent subset of communications to identify an updated set of one or more relevant words based on the one or more relevancy criteria; and generate and provide in real-time an updated dynamic label associated with the communication session, the updated dynamic label including at least a selected one word from the updated set of one or more relevant words.
  • the content characteristic of the communication session may be: a rate at which words or phrases are communicated in the communication session, and wherein the size of the sliding window determined to be inversely proportional to the rate at which words or phrases are communicated; a rate of change of active participants in the communication session, and wherein the size of the sliding window is determined to be inversely proportional to the rate of change of active participants; a number of relevant words identified in the most recent subset of communications, and wherein the size of the sliding window is determined to maintain the number of relevant words within a target numerical range; a current topic of the communication session, and wherein the size of the sliding window is determined to limit the most recent subset of communications to the current topic; or an average entropy of information communicated in the most recent subset of communications, and wherein the size of the sliding window is determined to maintain the average entropy within a target range.
  • the processing unit may be further configured to execute instructions to cause the computer system to: encode words in the most recent subset of communications into respective embedding vectors using a trained language model.
  • the one or more relevancy criteria may include: relevancy of a word based on semantic relevancy to a topic of the communication session; or relevancy of a word based on semantic relevancy to a userspecific topic indicated in a user profile of a given participant of the communication session. Semantic relevancy may be determined using the respective embedding vectors.
  • the one or more relevancy criteria may include: relevancy of a word based on a relevancy score associated with the word exceeding a predefined threshold, wherein the relevancy score associated with the word is determined using a selected relevancy dictionary; or relevancy of a word based on statistical occurrence of the word in the most recent subset of communications.
  • the size of the sliding window may define the segment of the communication session based on a number of words, or based on a time duration.
  • the dynamic label may be provided to be outputted by a user device.
  • the communication session may include audio communications, and the processing unit may be further configured to execute instructions to cause the computer system to: convert the audio communications to text data.
  • the most recent subset of communications may be a most recent subset of the text data, and the content characteristic of the communication session may be determined from the most recent subset of the text data.
  • the processing unit may be further configured to execute instructions to cause the computer system to generate the dynamic label by: ranking the one or more relevant words based on the one or more relevancy criteria; and generating the dynamic label as a visual representation of the at least some of the one or more relevant words based on the ranking.
  • the present disclosure describes a method including: monitoring an ongoing communication session comprising communications between two or more participants to identify a content characteristic of the communication session; determining a size of a sliding window based on the content characteristic of the communication session for analyzing a most recent subset of communications from the communication session, wherein the size of the sliding window defines a segment of the communication session to include in the most recent subset of communications; analyzing the most recent subset of communications to identify one or more relevant words based on one or more relevancy criteria; and generating and providing a dynamic label associated with the communication session, the dynamic label including at least a selected one of the one or more relevant words.
  • the method may further include: subsequent to generating and providing the dynamic label, identifying in real-time a change in the content characteristic of the communication session; determining an adjusted size of the sliding window, based on the changed content characteristic, the adjusted size of the sliding window defining an updated segment of the communication session to include in an updated most recent subset of communications; analyzing the updated most recent subset of communications to identify an updated set of one or more relevant words based on the one or more relevancy criteria; and generating and providing in real-time an updated dynamic label associated with the communication session, the updated dynamic label including at least a selected one word from the updated set of one or more relevant words.
  • the content characteristic of the communication session may be: a rate at which words or phrases are communicated in the communication session, and wherein the size of the sliding window determined to be inversely proportional to the rate at which words or phrases are communicated; a rate of change of active participants in the communication session, and wherein the size of the sliding window is determined to be inversely proportional to the rate of change of active participants; a number of relevant words identified in the most recent subset of communications, and wherein the size of the sliding window is determined to maintain the number of relevant words within a target numerical range; a current topic of the communication session, and wherein the size of the sliding window is determined to limit the most recent subset of communications to the current topic; or an average entropy of information communicated in the most recent subset of communications, and wherein the size of the sliding window is determined to maintain the average entropy within a target range.
  • the method may further include: encoding words in the most recent subset of communications into respective embedding vectors using a trained language model.
  • the one or more relevancy criteria may include: relevancy of a word based on semantic relevancy to a topic of the communication session; or relevancy of a word based on semantic relevancy to a user-specific topic indicated in a user profile of a given participant of the communication session. Semantic relevancy may be determined using the respective embedding vectors.
  • the one or more relevancy criteria may include: relevancy of a word based on a relevancy score associated with the word exceeding a predefined threshold, wherein the relevancy score associated with the word is determined using a selected relevancy dictionary; or relevancy of a word based on statistical occurrence of the word in the most recent subset of communications.
  • the size of the sliding window may define the segment of the communication session based on a number of words, or based on a time duration.
  • the dynamic label may be provided to be outputted by a user device.
  • the communication session may include audio communications
  • the method may further include: converting the audio communications to text data
  • the most recent subset of communications may be a most recent subset of the text data, and the content characteristic of the communication session may be determined from the most recent subset of the text data.
  • generating the dynamic label may include: ranking the one or more relevant words based on the one or more relevancy criteria; and generating the dynamic label as a visual representation of the at least some of the one or more relevant words based on the ranking.
  • the present disclosure describes a non-transitory computer readable medium having instructions encoded thereon.
  • the instructions when executed by a processing unit of a computer system, cause the computer system to: monitor an ongoing communication session comprising communications between two or more participants to identify a content characteristic of the communication session; determine a size of a sliding window based on the content characteristic of the communication session for analyzing a most recent subset of communications from the communication session, wherein the size of the sliding window defines a segment of the communication session to include in the most recent subset of communications; analyze the most recent subset of communications to identify one or more relevant words based on one or more relevancy criteria; and generate and provide a dynamic label associated with the communication session, the dynamic label including at least a selected one of the one or more relevant words.
  • the present disclosure describes a computer system including a processing unit configured to execute instructions to cause the system to: monitor an ongoing communication session to identify a most recent subset of communications, the most recent subset of communications being defined by a sliding window; analyze the most recent subset of communications to identify one or more relevant words, based on at least a user-specific relevancy criterion, the user-specific relevancy criterion being relevant to a user-specific topic associated with a given user profile; and responsive to identifying the one or more relevant words, provide an indication of the ongoing communication session to a user device associated with the given user profile.
  • the processing unit may be further configured to execute instructions to cause the system to determine the user-specific topic by: tracking online activity associated with the given user profile on one or more online platforms; identifying one or more words associated with the online activity; computing a respective metric for each respective identified word, wherein the metric represents a user engagement with each respective identified word; and determining the user-specific topic by ranking the one or more identified words based on the respective one or more computed metrics.
  • the processing unit may be further configured to execute instructions to cause the system to compute the respective metric for each respective identified word by: extracting, from the tracked online activity, a time duration associated with each respective identified word; and computing the respective metric using a weight value based on the time duration associated with each respective identified word.
  • the tracked online activity may include a plurality of types of online activity
  • the processing unit may be further configured to execute instructions to cause the system to compute the respective metric for each respective identified word by: determining a weight for each of the plurality of types of online activity; and computing the respective metric using a weighted sum of all types of online activity associated with each respective identified word, wherein each type of online activity is weighted by the corresponding determined weight.
  • the processing unit may be further configured to execute instructions to track the online activity by: determining the online activity on the one or more online platforms is associated with the given user profile; and extracting data about the online activity from the one or more online platforms.
  • the user-specific relevancy criterion may be relevant to any of a plurality of user-specific topics associated with the given user profile, and the indication may be provided to the user device responsive to identifying one or more relevant words based on relevancy to any of the plurality of user-specific topics associated with the given user profile.
  • the processing unit may be further configured to execute instructions to cause the system to: generate and provide a label associated with the communication session, the label including at least one of the one or more relevant words.
  • the processing unit may be further configured to execute instructions to cause the system to: encode words in the most recent subset of communications into respective embedding vectors using a trained language model.
  • the one or more relevant words may be identified based on semantic relevancy to the user-specific topic, and semantic relevancy to the user-specific topic may be determined using the respective embedding vectors.
  • the processing unit may be further configured to execute instructions to cause the system to identify the one or more relevant words based on the one or more relevancy words being associated with respective relevancy scores exceeding a predefined threshold.
  • the relevancy scores may be determined using a relevancy dictionary that is selected based on the user-specific topic.
  • the sliding window may be a dynamically sized sliding window, and a size of the sliding window may be determined based on an identified content characteristic of the communication session.
  • the present disclosure describes a method including: monitoring an ongoing communication session to identify a most recent subset of communications, the most recent subset of communications being defined by a sliding window; analyzing the most recent subset of communications to identify one or more relevant words, based on at least a user-specific relevancy criterion, the user-specific relevancy criterion being relevant to a user-specific topic associated with a given user profile; and responsive to identifying the one or more relevant words, providing an indication of the ongoing communication session to a user device associated with the given user profile.
  • the method may further include determining the userspecific topic by: tracking online activity associated with the given user profile on one or more online platforms; identifying one or more words associated with the online activity; computing a respective metric for each respective identified word, wherein the metric represents a user engagement with each respective identified word; and determining the user-specific topic by ranking the one or more identified words based on the respective one or more computed metrics.
  • the respective metric for each respective identified word may be computed by: extracting, from the tracked online activity, a time duration associated with each respective identified word; and computing the respective metric using a weight value based on the time duration associated with each respective identified word.
  • the tracked online activity may include a plurality of types of online activity
  • the respective metric for each respective identified word may be computed by: determining a weight for each of the plurality of types of online activity; and computing the respective metric using a weighted sum of all types of online activity associated with each respective identified word, wherein each type of online activity is weighted by the corresponding determined weight.
  • the user-specific relevancy criterion may be relevant to any of a plurality of user-specific topics associated with the given user profile, and the indication may be provided to the user device responsive to identifying one or more relevant words based on relevancy to any of the plurality of user-specific topics associated with the given user profile.
  • the method may further include: generating and providing a label associated with the communication session, the label including at least one of the one or more relevant words.
  • the method may further include: encoding words in the most recent subset of communications into respective embedding vectors using a trained language model.
  • the one or more relevant words may be identified based on semantic relevancy to the user-specific topic, and semantic relevancy to the user-specific topic is determined using the respective embedding vectors.
  • the one or more relevant words may be identified based on the one or more relevancy words being associated with respective relevancy scores exceeding a predefined threshold.
  • the relevancy scores may be determined using a relevancy dictionary that is selected based on the user-specific topic.
  • the sliding window may be a dynamically sized sliding window, and a size of the sliding window may be determined based on an identified content characteristic of the communication session.
  • the present disclosure describes a non-transitory computer readable medium having instructions encoded thereon.
  • the instructions when executed by a processing unit of a computer system, cause the computer system to: monitor an ongoing communication session to identify a most recent subset of communications, the most recent subset of communications being defined by a sliding window; analyze the most recent subset of communications to identify one or more relevant words, based on at least a user-specific relevancy criterion, the user-specific relevancy criterion being relevant to a user-specific topic associated with a given user profile; and responsive to identifying the one or more relevant words, provide an indication of the ongoing communication session to a user device associated with the given user profile.
  • the present disclosure describes a method including: monitoring an ongoing communication session comprising communications between two or more participants to identify a most recent subset of communications, the most recent subset of communications being defined by a sliding window; selecting a relevancy dictionary from amongst a plurality of available relevancy dictionaries; identifying one or more relevant words in the most recent subset of communications using the relevancy dictionary; and responsive to the identification of the one or more relevant words, providing an indication of the communication session. Identifying one or more relevant words in the most recent subset of communications using the relevancy dictionary may include determining relevancies of words in the most recent subset of communications using the relevancy dictionary.
  • Providing the indication of the communication session may include generating a label associated with the communication session, the label including at least some of the one or more relevant words, wherein the provided indication of the communication session is based on the label.
  • the selected relevancy dictionary may be associated with a particular language and may be selected based on a language of the communication session.
  • the selected relevancy dictionary may be associated with a particular context and may be selected based on the particular context, the particular context corresponding to the communication session.
  • the particular context may pertain to one of a particular industry, a particular company, a particular geographic region, a particular demographic or a particular conference.
  • the selected relevancy dictionary may be associated with a particular user.
  • the particular user may be one of the two or more participants.
  • the selected relevancy dictionary may be selected based on the recent subset of communications.
  • the selected relevancy dictionary may be selected based on a user-specific subset of the recent subset of communications, the user-specific subset including communications transmitted by the particular user.
  • the selected relevancy dictionary may be associated with at least one of a user profile associated with the particular user or a user identifier associated with the particular user.
  • a computer system may include a processing unit configured to execute instructions to cause the computer system to carry out any of the methods described above.
  • a computer program may, when executed by a processor, be configured to cause the processor to carry out any of the methods described above.
  • a computer readable medium may have instructions encoded thereon that, when executed by a processing unit of a computer system, cause the computer system to carry out any of the methods described above.
  • FIG. 1 is a block diagram of an example digital communication platform, in which examples described herein may be implemented;
  • FIG. 2 is a block diagram illustrating an example hardware implementation of the digital communication platform of FIG. 1;
  • FIG. 3 is a flowchart illustrating an example method for generating a dynamic label for a real-time communication session
  • FIGS. 4A and 4B are charts illustrating examples of how the size of a sliding window may be dynamically adjusted in the example method of FIG. 3;
  • FIG. 5 is a flowchart illustrating an example method for generating an indication of a communication session, based on a user-specific relevancy criterion
  • FIG. 6 is a flowchart illustrating an example method for selecting a relevancy dictionary.
  • Examples of the present disclosure are described in the context of a digital communication platform. It should be understood that the communications supported by the digital communication platform may be of any suitable format (e.g., audio, video, text, etc.) and multiple formats may be supported by the same digital communication platform. Although an example of the digital communication platform, this is not intended to be limiting.
  • a communication session refers to a logical framework within which communications can be transmitted and received between a group of participants who belong to the session.
  • the participants of a communication session can change throughout the duration of the session (e.g., new participants can join and existing participants can leave).
  • a user who is not part of the communication session may not transmit or receive communications in the session, but may be able to transmit and receive communications (and possibly view a history of previous communications in the session) after joining the session (i.e., after becoming a participant in the session).
  • a participant in a communication session may be solely a recipient of communications, without transmitting any communications.
  • FIG. 1 is a block diagram of an example digital communication platform 100, in accordance with some examples of the present disclosure.
  • the digital communication platform 100 may be any online platform that supports communication sessions between groups of participants.
  • the digital communication platform 100 may not primarily be used for digital communications but may support communication sessions as a secondary function.
  • the digital communication platform 100 may have e-commerce as a primary lunction (i.e., may be an e-commerce platform) and may support communication sessions as a secondary function; for the purpose of the present disclosure, such a platform may be referred to as a digital communication platform 100.
  • the digital communication platform 100 may cooperate with other online platforms to provide digital communication services to users of the other online platforms.
  • the digital communication platform 100 may communicate with an e-commerce platform 200 over an online network.
  • the digital communication platform 100 may cooperate with an e-commerce platform 200 to provide digital communication services to users of the e-commerce platform 200.
  • the e-commerce platform 200 may cooperate with the digital communication platform 100 to enable users of the digital communication platform 100 to access services (e.g., make purchases at online stores 202, view advertisement objects 204, etc.) provided by the e-commerce platform 200.
  • the digital communication platform 100 may be wholly integrated into another online platform.
  • the present disclosure is not intended to be limited by the example configuration of the digital communication platform 100 as shown in FIG. 1.
  • a user may access the digital communication platform 100 via a user device 150, for example over an online network.
  • the user device 150 may be any suitable computing device having network communication capabilities, such as a desktop computer, a laptop, a tablet, a smartphone, a smart appliance, a network-connected vehicle, an Internet of Things (loT) device, or any portable or non-portable computing device.
  • the digital communication platform 100 hosts one or more communication sessions 102. Only one communication session 102 is illustrated for simplicity, but there may be a plurality of communication sessions 102 hosted by the digital communication platform 100. In some examples, two or more communication sessions 102 may be grouped together in a common context. For example, multiple communication sessions 102 may be associated with a common conference, a common company, a common working group, etc.
  • Each communication session 102 may be associated with one or more session attributes, such as format of communications (e.g., video, audio, text, or combination thereof), level of privacy (e.g., open to the public, limited to a particular context, by invitation only, etc.), maximum number of participants, session identifier (e.g., an alphanumeric identifier that is unique to each communication session 102 on the digital communication platform 100), associated context (e.g., a label indicating the communication session 102 is associated with a particular conference, company, working group, etc.), etc.
  • session attributes such as format of communications (e.g., video, audio, text, or combination thereof), level of privacy (e.g., open to the public, limited to a particular context, by invitation only, etc.), maximum number of participants, session identifier (e.g., an alphanumeric identifier that is unique to each communication session 102 on the digital communication platform 100), associated context (e.g., a label indicating the communication session 102 is associated with a
  • each communication session 102 may be associated with a predefined label, which may be assigned by a human administrator or moderator at the time the communication session 102 is created, or may be a default label assigned to the communication session 102.
  • the predefined label may be relatively static (e.g., may be changed only manually).
  • each communication session 102 may be associated with a dynamically generated label that may change in real-time, without manual intervention.
  • the digital communication platform 100 may store one or more user profiles 104, associated with respective one or more users of the digital communication platform 100. Only one user profile 104 is illustrated for simplicity, but there may be a plurality of user profiles 104 stored by the digital communication platform 100.
  • a user profile 104 may store user-specific information, such as a user identifier (e.g., an alphanumeric identifier that is unique within the digital communication platform 100), historical activity of the user on the digital communication platform 100 (e.g., data about communication sessions 102 that the user has hosted/participated, historical searches for communication sessions 102 on the digital communication platform 100, etc.), preferred language, membership in any particular context (e.g., registered member of a particular conference, employee of a particular company, contributor to a particular working group, etc.), etc.
  • the user profile 104 may also store a user-specific topic (e.g., a topic of interest).
  • a user-specific topic may be a word or phrase that is manually inputted into the user profile 104 by the user associated with the user profile 104, for example.
  • the user-specific topic may, instead of being manually inputted, be automatically populated by the digital communication platform 100 based on the historical activity of the user on the digital platform 100.
  • the digital communication platform 100 may track the time that a given user participates in various communication sessions 102 on the digital communication platform 100, and the topic of discussion in those communication sessions 102 (e.g., based on the topic indicated in the predefined label associated with each communication session 102, or based on relevant words included in a dynamically generated label associated with the communication session 102).
  • the activity of the given user in the various communication sessions 102 may indicate that the topic of discussion is a user-specific topic of interest for the given user, depending on the user’s activity. For example, if the user is an active participant (i.e., actively transmiting communications) about a given topic in a communication session 102, this may indicate that the given topic is a user-specific topic of interest for the user.
  • the amount of time that the user is a participant (e.g., active or passive participant) in a communication session 102 about a given topic may be used as an indicator of the degree of interest.
  • the given topic may be considered a user-specific topic only if the user spends at least a threshold amount of time (e.g., at least 10 minutes) in a communication session 102 about the given topic.
  • the given topic may be considered a user-specific topic if the user is an active participant for at least another threshold amount of time (e.g., at least 20 minutes) in a communication session 102 about the given topic, or if the user is a host or designated speaker in a communication session 102 about the given topic.
  • the digital communication platform 100 may track queries for a particular word by the user (e.g., tracking queries entered into a search fiinctionality provided by the digital communication platform 100), and such queries may be aggregated with the user’s participant history to determine the user-specific topic for the user.
  • the digital communication platform 100 may access information about the user from other external online platforms (e.g., by performing data scraping, or by crossreferencing user profiles, etc.) to identify a user-specific topic. For example, the digital communication platform 100 may cross-reference a given user profile 104 on the digital communication platform 100 with another user profile on the other online platform (e.g., based on common user information such as a common email address, using cookies, using browser fingerprinting, based on data sharing between platforms, etc.), and thus identify online activity (e.g., search queries, page views, transmitted/viewed communications, online purchases, etc.) on other online platforms that is associated with a given user profile 104. Data about the online activity on the other online platform may be extracted (e.g., through data scraping of publicly available information, through user-permitted data sharing, etc.). Relevant word(s) associated with the online activity may be identified.
  • other online platform e.g., by performing data scraping, or by crossreferencing user profiles, etc.
  • the online activity may be aggregated to compute a metric representing user engagement with respective words (which may represent respective topics).
  • the metric may be a weighted aggregate, where the time duration of the online activity associated with each respective word is used as a weight. Additionally or alternatively, different types of online activity may be weighted differently (e.g., online activity that requires active participation, such as being an active participant in a communication session 102 or entering a search query, may have greater weight than online activity that is more passive, such as only viewing a communication or a page).
  • the computed metric may be used to rank the respective words, and a predefined number of highest- ranked words (e.g., top 3 or top 5 ranked words) may be identified as user-specific topics and entered into the user profile 104.
  • a user associated with a user profile 104 may be a participant of a communication session 102.
  • a user may log onto their user profile 104 on the digital communication platform 100, and may be uniquely identified in the communication session 102 using the user identifier associated with their user profile 104.
  • a participant of a communication session 102 may not necessarily be associated with a user profile 104 (e.g., a participant may simply be anonymous or identified as a “guest”).
  • Such an anonymous user may be identified in the communication session 102 by a randomly generated identifier that is assigned to the anonymous user (e.g., associated with the network connection that is established between the anonymous user’s user device 150 and the digital communication platform 100).
  • the randomly generated identifier may be persistent for the duration that the anonymous user is connected to the digital communication platform 100, or may be persistent only for the duration that the anonymous user is a participant in a given communication session 102.
  • the randomly generated identifier may be sufficient to uniquely identify the anonymous user for the duration of the user’s participation in the given communication session 102, but may not be used for tracking historical activity of the anonymous user on the digital communication platform 100.
  • FIG. 2 illustrates an example hardware implementation of the digital communication platform 100. It should be noted that different components of the digital communication platform 100 may be implemented in separate hardware or software components, on a common hardware component or server or configured as a common (integrated) service or engine in the digital communication platform 100. In the example of FIG. 2, the digital communication platform 100 is depicted as being implemented using a single core server 410; however it should be understood that the digital communication platform 100 may be implemented using multiple servers (e.g., using a server cluster), or may be implemented using a virtual machine accessing a virtual pool of computing resources (e.g., as a cloud-based platform), among other possibilities.
  • the core server 410 includes a respective processing unit 412 (e.g., a microprocessor, graphical processing unit, digital signal processor or other computational element), a memory 414 (e.g., random access memory (RAM), read only memory (ROM), hard disk, optical disc, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, and the like, and may include non-transitory or transient memory), and a communications interface 416 (e.g., transmitter, receiver and/or transceiver for wired and/or wireless communications).
  • the memory 414 may store instructions that are executable by the processing unit 412, to implement functions and capabilities of the digital communication platform 100, including functions and capabilities disclosed herein (e.g., the example methods discussed herein), among others.
  • Users using respective user devices 102a, 102b... 102n (generally referred to as user devices 102) may access the digital communication platform 100 via one or more networks 440 (e.g., wired and/or wireless networks, including a virtual private network (VPN), the Internet, and the like).
  • networks 440 e.g., wired and/or wireless networks, including a virtual private network (VPN), the Internet, and the like.
  • the digital communication platform 100 may be in communication with an e-commerce platform 200, as well as other online platforms and online resources (not shown), via the network(s) 440.
  • FIG. 2 illustrates an example hardware implementation of the digital communication platform 100
  • the digital communication platform 100 may be implemented in a distributed manner, or the memory 414 may be replaced with external storage or cloud-based storage, among other possible modifications.
  • the digital communication platform 100 includes a dynamic label generator 300, which dynamically generates and associates labels with respective communication sessions 102 in real-time or near real-time.
  • operations that occur real-time or near real-time include computer operations that process data at the time (or within a short period of time, e.g., within 100ms or within Is) that the data is generated.
  • real-time analysis of a communication session 102 may involve analysis of the content being transmitted/received during the communication session 102 as the content is transmitted/received by participants.
  • the dynamic label generator 300 instead of being a component of the digital communication platform 100, may be an external service that is provided to the digital communication platform 100.
  • the dynamic label generator 300 includes a plurality of modules, including a speech to text converter 302, a trained language module 304, a dictionary manager 306, a sliding window adjuster 310 and a relevancy analyzer 312.
  • the modules may interact with each other, to enable the dynamic label generator 300 to analyze a communication session 102 in real-time or near real-time and generate a dynamic label for the communication session 102. It should be understood that these modules are illustrated as an example implementation of the dynamic label generator 300, and are not intended to be limiting.
  • lunctions that are described as being performed by any one module may be performed by a different module; there may be a greater or fewer number of modules belonging to the dynamic label generator 300; and/or lunctions described as being performed by any one module may instead be a general (i.e., not module-specific) lunction of the dynamic label generation 300.
  • the dynamic label generation 300 rather than analyzing all communications generated by participants during the communication session 102, uses a sliding window to analyze only a most recent subset of the communication session 102, as the basis for generating a dynamic label for the communication session 102.
  • the sliding window is sized dynamically, meaning that the amount of communications contained in the subset that is analyzed may be varied, in response to the content characteristics of the communication session
  • the speech to text converter 302 performs operations to convert audio communications of a communication session 102 to text.
  • the speech to text converter 302 may perform speech to text conversion in real-time or near real-time, as audio communications are transmitted in the communication session 102.
  • the speech to text converter 302 may first extract the audio component from the video communications, to obtain audio communications.
  • Various real-time speech to text conversion algorithms may be used to implement the speech to text converter 302.
  • the speech to text converter 302 may call on an application programming interface (API) to access a third-party speech to text conversion algorithm provided by a third-party service provider (e.g., a cloudbased service provider).
  • API application programming interface
  • the speech to text converter 302 serves to convert audio data from the communication session 102 into text data If a communication session 102 is a text only communication session 102, the speech to text converter 302 may not be used (since the communications in the text only communication session 102 are already in text format).
  • the text data generated by the speech to text converter 302 may be used by other modules of the dynamic label generator 300, as discussed below.
  • the trained language model 304 is a pre-trained neural network model that has been previously trained (e.g., using a human language corpus) to encode text into an embedding vector (i.e., a vectorized feature representation in a latent vector space) that numerically represents the semantic meaning of the text.
  • a preprocessor (not shown) may be used to first convert text data into a set of tokens, using any suitable tokenization technique. For example, each word in the text data may be converted to a respective token (e.g., the text data “What about this?” may be converted to the set of tokens [What], [about], [this] and [?]).
  • the preprocessor may perform stemming or lemmatization to reduce each token to represent a root word (e.g., the token [faster] may be reduced to the lemmatized token [fast]). Other such preprocessing techniques may be used to tokenize the text data.
  • the trained language model 304 then encodes the tokenized text into embedding vectors that represent the semantic meaning of the text.
  • the trained language model 304 may be trained to generate embedding vectors that represent the semantic meaning of each word in the tokenized text.
  • the proximity of two embedding vectors to each other in the latent vector space represents the semantic similarity of the two words represented by the two embedding vectors.
  • An example of the trained language model 304 may be the Bidirectional Encoder Representations from Transformers (BERT) language model, word2vec language model, or Global Vectors (GloVe) language model, among other possibilities.
  • the trained language model 304 may process text data on a word-by-word basis, in realtime or near real-time, as the text data is converted from audio communications by the speech to text converter 302, or as text communications are transmitted in the communication session 102.
  • the trained language model 304 may process text data on a phrase-by-phrase or sentence-by-sentence basis, in real-time or near real-time, where each phrase or sentence may be demarcated by a punctuation mark.
  • the embedding vectors that are generated by the trained language model 304 may be used by other modules (e.g., the relevancy analyzer 312) to determine relevancy of the words represented by the embedding vectors, for example.
  • a clustering algorithm may be used to identify clusters of embedding vectors in the vector space, which represents word clusters.
  • a semantic tree e.g., a semantic tree that has been developed using WordNet or other lexical database, in which each node or leaf of the tree corresponds to a respective word
  • a word that semantically represents a given word cluster may be identified (e.g., a word that corresponds to a node in the semantic tree that is common to all words in the word cluster), and that word may be identified as the topic of the word cluster.
  • the dictionary manager 306 stores and manages one or more relevancy dictionaries 308.
  • a relevancy dictionary 308 may be a resource (e.g., a lookup table, or hash table) that may be accessed (e.g., by the relevancy analyzer 312) to determine the relevancy of respective words.
  • a relevancy dictionary 308 that is specific to/associated with a particular language may be applicable to all communication sessions 102 that use that particular language.
  • Another relevancy dictionary 308 that is specific to/associated with a particular context e.g., a particular industry, a particular company, a particular geographic region, a particular demographic, a particular conference, etc.
  • a particular context e.g., a particular industry, a particular company, a particular geographic region, a particular demographic, a particular conference, etc.
  • a communication session 102 falls within the scope of two or more contexts (e.g., within the context of a particular conference, in a particular geographic region), then respective two or more different relevancy dictionaries 308 may be applicable.
  • Another relevancy dictionary 308 that is specific to/associated with a particular user may be applicable to only communication sessions 102 in which that particular user is a participant, or may be applicable to only communications transmitted by that particular user.
  • a relevancy dictionary 308 may store each word associated with a respective relevancy score.
  • a relevancy score may be a numerical representation of the probability that a given word, found in a communication transmitted in a communication session 102, is relevant to the topic being discussed in the communication session 102. For example, a relevancy score of zero may indicate the word is completely irrelevant to the topic; whereas a relevancy score of one may indicate the word is absolutely relevant to the topic.
  • a relevancy dictionary 308 may be used to identify words that are irrelevant (e.g., having a relevancy score of zero), with all other words assumed to be relevant; or may be used to identify words that are relevant (e.g., having a nonzero positive relevancy score), with all other words assumed to be irrelevant.
  • Words and relevancy scores that are stored in a relevancy dictionary may be predetermined.
  • a language-specific relevancy dictionary 308 may store words that are considered to be irrelevant based on the particular language of the communication session 102 (e.g., common conjunctions or articles in that language may be irrelevant words).
  • a context-specific relevancy dictionary 308 may store words that are considered to be irrelevant in a particular context (e.g., the name of a conference may be irrelevant in the context of communication sessions 102 that belong to that conference; the name of a company or the names of employees may be irrelevant in the context of communication sessions 102 within that company; or slang words may be considered irrelevant in the context of a particular geographic region or demographic).
  • a context-specific relevancy dictionary 308 may store words that are considered to be relevant in a particular context (e.g., industry-specific jargon, abbreviations and acronyms that are nonsensical and/or irrelevant in general may be considered relevant in the context of a particular industry).
  • a session-specific relevancy dictionary 308 may store words that are considered to be relevant to that particular communication session 102 (e.g., if the communication session 102 has a predefined label indicating a topic, then all words that are semantically related to that topic may be considered relevant to that session).
  • a user-specific relevancy dictionary 308 may store words that are considered to be irrelevant for a particular user (e.g., due to user-specific vocabulary choices, such as statistical overrepresentation of certain words or phrases in communications by a particular user). Relevancy scores may be determined empirically (e.g., based on manual rating of words used in a particular language or context), or may be determined automatically by a NLP algorithm (e.g., based on semantic relevancy to atopic of the communication session 102, for example based on semantic word clustering), among other possibilities.
  • words and relevancy scores stored in a relevancy dictionary 308 may be updated dynamically.
  • a session-specific relevancy dictionary 308 may be updated dynamically to store the names of the current participants in a communication session 102 (i.e., add/remove names as participants join/leave the communication session 102), in order to indicate the participants’ names as irrelevant words.
  • the sliding window adjuster 310 performs operations to dynamically adjust the size of the sliding window that is used to analyze a communication session 102.
  • the sliding window defines a most recent subset of communications from the communication session 102 that is to be analyzed by the relevancy analyzer 312.
  • the sliding window may define the segment of the communication session 120 to include in the most recent subset of communications, based on a number of words to consider (e.g., the most recent 100 words or the most recent 50 words in the communication session 102) or a time duration to consider (e.g., the most recent five minutes or the most recent one minute of communications in the communication session 102).
  • the size of the sliding window may be determined based on the content characteristic of the communication session 102.
  • the content characteristic of a communication session 102 refers to characteristics of the content of the communications in the communication session 102, such as the spoken rate of words, rate of change (i.e., turn-taking) of active participants, gaps in speech, and information density and/or level of entropy in content.
  • Content characteristic does not refer to transmission or channel characteristics, such as audio levels, bandwidth, or latency.
  • the sliding window adjuster 310 may compute one or more content characteristics of the communication session 102 and adjust the size of the sliding window in accordance with the computed content characteristic.
  • the size of the sliding window may be dynamically adjusted such that the segment of the communication session 120 that is considered to be the most recent subset of communications to be analyzed is intended to capture a target measure of content (e.g., a target number of words, a target number of topics, a target number of active participants, etc.).
  • a target measure of content e.g., a target number of words, a target number of topics, a target number of active participants, etc.
  • a computed content characteristic may be a rate at which words or phrases are communicated in the communication session 102 (e.g., words per minute).
  • the word rate may be computed by parsing the text data (e.g., text communications, or audio communications that have been converted to text) into words, and computing the number of words per minute.
  • the word rate may be computed by using signal processing to identify pauses or gaps (i.e., a time duration with no audio data) in audio communication, and computing the number of gaps per minute (e.g., based on the assumption that each gap demarks a phrase).
  • the sliding window may be a temporal window that is adjusted inversely proportional to the rate at which words or phrases are communicated (i.e., the time duration that defines the most recent subset of communications is decreased with increasing rate). This adjustment may be made such that the most recent subset of communications corresponds to a target number of words (e.g., a target range of 50-70 words).
  • a target number of words e.g., a target range of 50-70 words.
  • a computed content characteristic may be a rate at which the active participants (i.e., participants who are transmitting communications in the communication session 102) change (e.g., the rate at which different participants are contributing to the communication session 102).
  • the sliding window may be a temporal window that is adjusted inversely proportional to the rate at which the active participants change (i.e., the time duration that defines the most recent subset of communications is decreased with increasing rate). This adjustment may be made such that the most recent subset of communications corresponds to a target number of active participants (e.g., a target range of 1-3 active participants).
  • a computed content characteristic may be the number of relevant words identified in the most recent subset of communications (e.g., the number of relevant words determined using the relevancy analyzer 312, using the current size of the sliding window).
  • the sliding window may be a word count window that is adjusted to maintain the number of relevant words within a defined numerical range (i.e., if the number of relevant words found in the most recent subset of communications is higher than the defined numerical range, the size of the sliding window is decreased). This adjustment may be made such that the most recent subset of communications corresponds to a target number of relevant words (e.g., a target range of 15-20 relevant words).
  • a computed content characteristic may be the topic discussed in the communication session 102.
  • atopic in the communication session 102 may be identified using the trained language model 304 (e.g., based on word clusters, as discussed above).
  • the sliding window may be a temporal or word count window that is adjusted to limit the most recent subset of communications to only the most recent topic in the communication session 102.
  • a computed content characteristic may be the information density contained in the communications in the communication session 102.
  • Information density of human communications may be computed based on the concept of entropy as defined in information theory.
  • the entropy in a sentence may be computed using the formula: where H (X) is the entropy of a sentence X, where Xi is the i-th word in the sentence X, and where ?(%;) is the probability of occurrence of the word Xi .
  • the value of ?(%;) may be approximated as the inverse of the relevancy score of the word Xi (i.e., the higher the relevancy score, the lower the probability of occurrence). The higher the entropy H (X).
  • the sliding window may be a temporal or word count window that is adjusted to maintain the average entropy of the most recent subset of communications to a target range (e.g., a target range of 0.5-1.0). Other measures of information density may be used to similarly adjust the size of the sliding window.
  • the sliding window adjuster 310 may adjust the sliding window for each communication session 102 individually and independently of each other. Further, the sliding window adjuster 310 may use different techniques for determining the size of the sliding window for different communication sessions 102. In some examples, an administrator or moderation of the communication session 102 may have the ability to select the technique by which the sliding window adjuster 310 determines the size of the sliding window. For example, for a communication session 102 that is in the context of an industry-specific conference, it may be expected that topics in the communication session 102 would stay relevant to the particular industry.
  • the sliding window adjuster 310 may dynamically determine the size of the sliding window so that the number of industry-specific relevant words (e.g., determined using an industry-specific relevancy dictionary 308) in the subset of communications defined by the sliding window is within a target range (e.g., 10-20 industry-specific relevant words).
  • a target range e.g. 10-20 industry-specific relevant words.
  • the sliding window adjuster 310 may use the word clustering to identify the most recent topic of discussion and determine the size of the sliding window so that the subset of communications defined by the sliding window corresponds to communications relevant to the most recent topic of discussion.
  • the sliding window adjuster 310 may be used to dynamically determine the size of the sliding window at various points in the processing of the communications. For example, the sliding window adjuster 310 may adjust the size of the sliding window based on the change of active participant in the communication session 102 (i.e., the sliding window adjuster 310 may adjust the size of the sliding window with minimal processing of the communications). In another example, the sliding window adjuster 310 may adjust the size of the sliding window based on gaps in audio communications (i.e., the sliding window adjuster 310 may adjust the size of the sliding window prior to conversion of speech to text).
  • the sliding window adjuster 310 may adjust the size of the sliding window based on the rate/number of words/phrases (i.e., the sliding window adjuster 310 may adjust the size of the sliding window prior to processing by the trained language model 304 and prior to relevancy analysis). In another example, the sliding window adjuster 310 may adjust the size of the sliding window based on the number of relevant words, based on a change of topic, or based on information density, which may require more extensive processing of the communications.
  • the sliding window adjuster 310 may implement an approach to dynamically adjust the size of the sliding window, where the content characteristic can be computed with little or no processing of the communications in the communication session 102 (e.g., where the size of the sliding window is adjusted based on change of active participants, or based on gaps in audio communications). This may be advantageous because the size of the sliding window can be dynamically determined with less use of computing resources. Further processing (e.g., speech to text conversion, execution of the trained language model 304, etc.) of the communications may then be performed on only the segment of the communication session 102 that has been defined by the sliding window. Thus, resource-intensive processing may be limited to a smaller set of data. Such an approach may be particularly advantageous in scenarios where there are a large number of simultaneous communication sessions 102 on the digital communication platform 100 (e.g., 100 communication sessions 102 or more), each of which require dynamic sizing of the sliding window.
  • the content characteristic can be computed with little or no processing of the communications in the communication session 102 (e.g., where the size
  • the sliding window adjuster 310 may implement an approach that requires more processing of the communications (e.g., where the size of the sliding window is adjusted based on the rate/number of words/phrases, or based on the number of relevant words), with the advantage that the size of the sliding window may be more precisely tailored to the content characteristics of each communication session 102 (which may result in the dynamic label being a more accurate and precise representation of the communication session 102).
  • the relevancy analyzer 312 performs operations to determine the relevancy of words communicated in a communication session 102, based on one or more relevancy criteria. In particular, the relevancy analyzer 312 identifies one or more relevant words from the most recent subset of communications (defined by the sliding window), which are used to dynamically generate a label for the communication session 102.
  • the relevancy analyzer 312 may communicate with other modules of the dynamic label generator 300. For example, the relevancy analyzer 312 may process text data (e.g., converted from the most recent subset of audio communications, using the speech to text converter 302; or the most recent subset of text communications) to identify the one or more relevant words.
  • the relevancy analyzer 312 may process embedding vectors (e.g., generated by the trained language model 314) to identify the one or more relevant words.
  • the relevancy analyzer 312 may also refer to one or more relevancy dictionaries 308 managed by the dictionary manage 306, to identify the one or more relevant words.
  • the relevancy analyzer 312 may also implement various statistical techniques to identify the one or more relevant words.
  • the relevancy analyzer 312 may use various relevancy criteria to determine the relevancy of words in the subset of communications defined by the sliding window. [109] In an example, the relevancy analyzer 312 may determine relevant words based on semantic relevancy to a topic (e.g., a predefined topic, or a current topic) of the communication session 102.
  • a topic e.g., a predefined topic, or a current topic
  • the relevancy analyzer 312 may compute the Euclidean distance of each embedding vector to atopic embedding vector representing a topic of the communication session 102 (e.g., an embedding vector representing a predefined topic of the communication session 102, or an embedding vector representing the current topic of the communication session 102). The smaller the Euclidean distance between a given embedding vector and the topic embedding vector, the greater the semantic relevancy to the topic and hence the greater the relevancy of the word represented by the given embedding vector.
  • the relevancy analyzer 312 may identify a predefined number of relevant words (e.g., top 10 or top 20 semantically relevant words) in this way.
  • a current topic of the communication session 102 may be identified through word clustering, as discussed above.
  • the word cluster may be determined to be a topic of the communication session 102 only if the size of the word cluster (e.g., number of words in the word cluster) exceeds a predefined threshold (e.g., over 20 words or over 40 words in the word cluster).
  • the relevancy analyzer 312 may identify a user-specific topic (e.g., a topic of interest or topic of expertise) indicated in a user profile 104 of a given participant in the communication session 102. It may be expected that if a given participant has particular userspecific topic(s) in the user profile 104, then words used by that given participant related to the particular user-specific topic(s) would have greater relevancy (e.g., the given participant may have expertise in the topic). Thus, communications by the given participant may be analyzed for semantic relevancy to a user-specific topic (e.g., based on similarity of embedding vectors, as discussed above) indicated in the user profile 104 associated with the given participant. A predefined number of words that have the highest semantic relevancy to the user-specific topic (e.g., the top 5 or top 10 words) may be identified as relevant words.
  • a user-specific topic e.g., a topic of interest or topic of expertise
  • the relevancy analyzer 312 may use one or more relevancy dictionaries 308 to determine the relevancy score for each word in the most recent subset of communications. Words having a relevancy score exceeding a predefined threshold (e.g., over a threshold of 0.7) may be identified as relevant words. The relevancy analyzer 312 may identify a predefined number of relevant words in this way (e.g., 10 or 20 words with the highest relevancy scores). [113] In some examples, the relevancy analyzer 312 may use one or more relevancy dictionaries 308 to determine irrelevant words in the most recent subset of communications. Then, from the remaining words, a predefined number of words that occur the greatest number of times (e.g., the top 10 or top 20 occurring words) may be identified as relevant words.
  • a predefined number of words that occur the greatest number of times e.g., the top 10 or top 20 occurring words
  • relevancy scores may be determined for the remaining words, using a different relevancy dictionary 308.
  • a relevancy dictionary 308 may be used to determine relevant words in the most recent subset of communications.
  • relevancy scores may be determined for the remaining words, using a different relevancy dictionary 308.
  • a given communication session 102 that is being conducted in a given language (which may be a content characteristic associated with the communication session 102, and which may be explicitly indicated in the session attributes or may be identified using suitable NLP techniques), is in a given context (e.
  • three different relevancy dictionaries 308 may be applicable: a first relevancy dictionary 308 that is specific to/associated with the given language (e.g., containing words that are considered irrelevant in the given language); a second relevancy dictionary 308 that is specific to/associated with the given context (e.g., containing words associated with relevancy scores specific to the given conference); and a third relevancy dictionary 308 that is specific to/associated with the given participant (e.g., containing words that are considered irrelevant when used by the specific participant, for example words that are statistically overrepresented in communications by the specific participant, compared to communications by other participants).
  • a first relevancy dictionary 308 that is specific to/associated with the given language (e.g., containing words that are considered irrelevant in the given language)
  • a second relevancy dictionary 308 that is specific to/associated with the given context (e.g., containing words associated with relevancy scores specific to the given conference)
  • a third relevancy dictionary 308 that is specific to/associated with the given participant (e.g.,
  • the relevancy analyzer 312 may use statistical analysis to determine relevant words in the most recent subset of communications. For example, the relevancy analyzer 312 may analyze the words captured in respective sliding windows across multiple communication sessions 102 (e.g., across all communication sessions 102 belonging to a common context, such as all communication sessions 102 belonging to the same conference, or belonging to the same company). The relevancy analyzer 312 may identify statistically common words (e.g., having a high number of occurrences, or having a high probability in a probability distribution) as irrelevant words in the common context. The relevancy analyzer 312 may exclude the irrelevant words and identify relevant words from the remaining words in the most recent subset of communications, as discussed above.
  • the relevancy analyzer 312 may exclude the irrelevant words and identify relevant words from the remaining words in the most recent subset of communications, as discussed above.
  • the dynamic label generator 300 may generate and provide the dynamic label to be associated with the communication session 102, where the dynamic label includes at least one of the determined relevant words.
  • the generated dynamic label may be outputted to a user device 150, for example.
  • FIG. 3 is a flowchart illustrating an example method 3000 that may be performed by the digital communication platform 100, for example using the dynamic label generator 300, to dynamically generate a label for a communication session 102 hosted on the digital communication platform 100.
  • a current, real-time communication session 102 is monitored to identify a content characteristic of the communication session 102.
  • a content characteristic of a communication session 102 may include characteristics such as the rate of words or phrases communicated, rate of change of active participants, number of relevant words, topic of discussion, gaps in speech, or information density of the communications, among others.
  • Modules of the dynamic label generator 300 such as the speech to text converter 302 and the trained language model 304, may be used to identify the content characteristic, as discussed above.
  • a sliding window is determined with a size based on the content characteristic of the communication session 102.
  • the size of the sliding window may be determined based on the content characteristic of the communication session 102.
  • the segment of the communication session to include in the most recent subset of communications (which will be analyzed for relevant words) may be determined based on the sliding window.
  • the determined sliding window may be used to define the segment of the communication session to include in the most recent subset of communications.
  • the sliding window itself may define the segment of the communication session to include in the most recent subset of communications.
  • the sliding window may be a temporal window, which defines the segment of the communication session to include in the most recent subset of communications in terms of a time duration (e.g., the most recent one minute or the most recent five minutes).
  • the sliding window may be a word count window, which defines the segment of the communication session to include in the most recent subset of communications in terms of a number of words (e.g., the most recent 100 words).
  • the sliding window adjuster 310 may perform operations as discussed above to dynamically determine the size of the sliding window based on the content characteristic. For example, the sliding window adjuster 310 may determine the size of the sliding window to be inversely proportional to the rate at which words or phrases are communicated.
  • the sliding window adjuster 310 may determine the size of the sliding window to be inversely proportional to the rate of change of active participants in the communication session 102. In another example, the sliding window adjuster 310 may determine the size of the sliding window such that the number of relevant words in the most recent subset of communications is within a target range. In another example, the sliding window adjuster 310 may determine the size of the sliding window to limit the most recent subset of communications to the most recent topic in the communication session 102. In another example, the sliding window adjuster 310 may determine the size of the sliding window such that the average entropy (or other measure of information density) of the most recent subset of communications is within a target range.
  • FIG. 4A is a chart that illustrates a simple example in which the size of the sliding window is dynamically adjusted to be inversely proportional to the rate at which words are communicated in the communication session 102.
  • the cumulative number of words in the communication session 102 over time is indicated by a dashed line.
  • the sliding window in this example defines a number of words to include in the most recent subset of communications, and the size of the sliding window over time is indicated by a solid line.
  • the rate of words increases, the size of the sliding window decreases, and vice versa.
  • FIG. 4B is a chart that illustrates another simple example in which the size of the sliding window is dynamically adjusted to limit the most recent subset of communications to only the most recent topic in the communication session 102.
  • the cumulative number of words in the communication session 102 over time is indicated by a dashed line.
  • the sliding window in this example defines a time duration of the most recent subset of communications, and the size of the sliding window over time is indicated by a solid line.
  • a new topic of discussion is detected (e.g., through the use of the trained language model 304 and word clustering techniques) at certain times as indicated. As shown in FIG.
  • the size of the sliding window is decreased when a new topic is detected (to exclude older communications that do not relate to the new topic) and steadily increases until another new topic is detected. Further, in this example, it can be seen that the size of the sliding window is capped at a maximum time duration.
  • the most recent subset of communications e.g., as determined based on the determined sliding window or, potentially, as defined by the sliding window as discussed above
  • the relevancy analyzer 312 may identify relevant word(s) in the most recent subset of communications using various relevancy criteria such as semantic relevancy, relevancy score, statistical occurrence, etc.
  • the relevancy analyzer 312 may identify a predefined number of words that are most semantically relevant to a predefined topic or a current topic in the communication session 102. In another example, the relevancy analyzer 312 may identify a predefined number of words that are most semantically relevant to a user-specific topic indicated in the user profile 104 of a participant in the communication session 102. In another example, the relevancy analyzer 312 may identify a predefined number of words that have the highest relevancy score, based on one or more relevancy dictionaries 308 that are applicable to the communication session 102.
  • the relevancy analyzer 312 may identify a predefined number of words that have the highest statistical occurrence in the most recent subset of communications, after excluding irrelevant words (e.g., using a relevancy dictionary 308, or by excluding words that are statistically common across multiple communication sessions 102).
  • a dynamic label is generated and associated with the communication session 102.
  • the dynamic label includes at least one word selected from the relevant word(s) identified at the operation 3006.
  • the generated dynamic label may be provided as output to a user device 150, for example.
  • the dynamic label may be generated by first ranking the relevant word(s) based on the relevancy criteria (e.g., ranked based on semantic relevancy to the predefined or current topic of the communication session 102, ranked based on relevancy score, or ranked based on highest statistical occurrence).
  • a maximum number of top-ranked relevant words which may be fewer than the number of relevant words identified at the operation 3006, may then be selected to include in the dynamic label. For example, if 20 relevant words are identified, only five or only one top-ranked relevant word may be selected to include in the dynamic label.
  • a relevancy threshold may be used. Only relevant word(s) that pass the relevancy threshold (which may be defined based on the relevancy criteria, such as a threshold relevancy score, a threshold semantic similarity to atopic, or a threshold number of occurrences) may be selected for inclusion in the dynamic label.
  • the dynamic label may be generated as a visual representation of the selected word(s).
  • the dynamic label may be a visual list of the selected word(s) or a word cloud (e.g., where the size of each word in the word cloud indicates the relative relevancy ranking of the respective word) that may be displayed by the user device 150.
  • the dynamic label may be generated as an audio representation of the selected word(s).
  • a text to speech converter may be used to generate an audio output listing the selected word(s), which may be outputted by the user device 150.
  • the dynamic label may be provided to supplement a predefined label that was assigned to the communication session 102 (e.g., by a human administrator or moderator). In other examples, the dynamic label may replace the predefined label.
  • the method 3000 may be performed continuously and in real-time (i.e., concurrent with the ongoing communication session 102).
  • the communication session 102 may continue to be monitored.
  • a change in the content characteristic e.g., a change in the rate of communications, a change in topic, a change in information density, etc.
  • the size of the sliding window is adjusted based on the changed content characteristic (e.g., similar to the operation 3004) and the most recent subset of communications is updated in accordance with the sliding window.
  • the relevancy analysis and dynamic label generating is also repeated, using the updated most recent subset of communications, such that an updated dynamic label is generated and provided.
  • These operations may be performed in real-time with the ongoing communication session, such that the size of the sliding window may be continuously updated dynamically, and the dynamic label is also updated continuously in real-time.
  • the segment of the communication session 102 that is included in the most recent subset of communications is updated.
  • the relevancy analysis may result in an updated set of identified relevant word(s), and updated relevancy ranking. Accordingly, the word(s) selected to be included in the dynamic label may be updated.
  • the dynamic label associated with the communication session 102 may continuously reflect the most up-to-date discussion in the communication session 102, without requiring manual intervention.
  • a user may use the dynamic label associated with a given communication session 102 as a basis for deciding whether or not to join in as a participant in the communication session 102.
  • the digital communication platform 100 may use a notification generator 320 (as shown in FIG. 1) to generate real-time notifications (to be provided to the user via the user’s user device 150), to notify the user when a dynamic label includes a word that is likely to interest the user.
  • FIG. 5 is a flowchart illustrating an example method 5000 that may be performed by the digital communication platform 100, for example using the notification generator 320, to generate and provide an indication of a communication session 102 to a user device 150.
  • a current, real-time communication session 102 is monitored to identify a most recent subset of communications, defined by a sliding window.
  • the sliding window may be a dynamically sized sliding window. For example, operations 3002 and 3004 described above may be performed to dynamically determine the size of the sliding window.
  • the most recent subset of communications (as defined by the sliding window) is analyzed to identify one or more relevant words based on one or more relevancy criteria.
  • the operation 5004 may be similar to the operation 3006.
  • the operation 5004 may include at least one relevancy criterion that is user-specific.
  • a relevancy criterion may be relevancy to a user-specific topic (e.g., a topic of interest) associated with a given user profile 104.
  • a user profile 104 may include a plurality of user-specific topics.
  • the operation 5004 may identify any relevant words in the most recent subset of communications that is relevant to any of the user-specific topics.
  • a user-specific relevancy criterion may be semantic relevancy to a user-specific topic indicated in the user profile 104.
  • the trained language model 304 may be used to generate word embedding vectors that represent respective words in the most recent subset of communications.
  • Topic embedding vectors may be similarly generated to represent each user-specific topic indicated in the user profde 104. Then, using Euclidean distance (or other measure of similarity between vectors), the similarity (and hence semantic relevancy) between the topic embedding vectors and the word embedding vectors may be determined. In this way, words that are semantically relevant to any user-specific topic may be identified from the most recent subset of communications.
  • Another user-specific relevancy criterion may be a relevancy score exceeding a predefined threshold, where the relevancy score is determined using a relevancy dictionary 308 that is selected based on the user-specific topic. For example, if the user-specific topic indicates interest in a particular context-specific topic (e.g., a topic in a specific industry), then the corresponding context-specific relevancy dictionary 308 may be used to determine the relevancy score for the words in the most recent subset of communications.
  • a particular context-specific topic e.g., a topic in a specific industry
  • the relevant words that are identified at the operation 5004 may include words that are specifically relevant to the user-specific topic identified in the user profile 104 (in addition to relevant words that are identified using other relevancy criteria). In other examples, identification of the relevant words may not consider any user-specific relevancy criterion.
  • a dynamic label associated with the communication session 102 is generated.
  • the dynamic label includes at least one word selected from the relevant word(s) identified at the operation 5004.
  • the generated dynamic label may be provided as output to a user device 150, for example.
  • the operation 5006 may be similar to the operation 3008 described above.
  • the word(s) that are selected for inclusion in the dynamic label may include word(s) that are selected based on the user-specific relevancy criterion (e.g., based on relevancy to the user-specific topic).
  • the relevant words identified at the operation 5004 may be ranked by relevancy to any user-specific topic indicated in the user profile 104, and a predefined number of top-ranked words may be selected to include in the dynamic label.
  • the dynamic label may be tailored to the user-specific topics indicated in the user profile 104 (e.g., tailored to the user’s topics of interest). In other examples, the dynamic label may not be tailored to the user-specific topic.
  • an indication of the communication session 102 is generated, based on the user-specific relevancy criterion.
  • the indication may be provided (e.g., as a push notification) to the user device 150 associated with the user profile 104.
  • the indication may be similar to the dynamic label generated at the operation 5006 (e.g., the indication may be a visual representation, such as a word cloud, or an audio output).
  • the user-specific relevancy criterion may be, as discussed above, semantic relevancy to a user-specific topic indicated in the user profile 104, or a relevancy score that is above a predefined threshold, where the relevancy score is determined using a relevancy dictionary 308 that is selected based on the user-specific topic.
  • the user-specific relevancy criterion may be applied to the relevant words identified at the operation 5004, or to the selected words included in the dynamic label at the operation 5006, regardless of whether the user-specific relevancy criterion was used at the operations 5004 or 5006.
  • the user-specific relevancy criterion it may be determined whether the communication session 102 is relevant to the user-specific topic indicated in the user profile 104 (and thus whether the communication session 102 is likely to be of interest to the user associated with the user profile 104).
  • the user-specific relevancy criterion is satisfied (e.g., the identified relevant words in the most recent subset of communications in the communication session 102 have a relevancy score above the predefined threshold, based on a relevancy dictionary 308 that is selected according to the user-specific topic), the indication is generated and provided to the user device 150 associated with the user profile 104.
  • the method 5000 may be used across multiple concurrent communication sessions 102, to identify and notify a user of any communication session 102 (among all concurrent communication sessions 102) that may be of interest to the user.
  • the method 5000 may be used to monitor for usage of words that are relevant for a moderator (e.g., use of inappropriate language), and to notify the moderator of the communication session 102 in which such words have been recently used in communications.
  • the method 5000 may not include generation of the dynamic label (i.e., at the operation 5006).
  • the method 5000 may be used to generate and provide indication of the communication session 102 (i.e., at the operation 5008), based on the user-specific relevancy criterion, without generating the dynamic label associated with the communication session 102.
  • the method 5000 addresses a technical problem using a real-time technical solution. Considering the fast-changing nature of online digital communications, and the fact that there can be a large number of concurrent communication sessions 102 on the digital communication platform 100, a manual approach to identify communication sessions 102 of interest would not be practical or possible.
  • a relevancy dictionary 308 may be used to determine relevancy scores for words in the most recent subset of communications, in order to identify relevant words. Accordingly, it may be important to automatically detect if a relevancy dictionary 308, which is currently being used for a given communication session 102, is no longer applicable.
  • a first relevancy dictionary 308 may have been selected to be used for determining relevancy for a given communication session 102, based on a particular context of the communication session 102. However, over time the communications in the communication session 102 may drift away from the particular context, so that the first relevancy dictionary 308 may no longer be suitable. For example, consider a communication session 102 that is part of a conference for the oil and gas industry. A first industry-specific relevancy dictionary 308 (e.g., containing relevancy scores for words relevant to the oil and gas industry) may have been selected to identify relevant words in the communication session 102.
  • the digital communication platform 100 may perform operations (e.g., using the dynamic label generator 300, in particular the dictionary manager 306) to automatically replace the first relevancy dictionary 308 with a second relevancy dictionary 308 that contains relevancy scores for words relevant to the new topic of discussion, namely renewable energy.
  • FIG. 6 is a flowchart illustrating an example method 6000 that may be performed by the digital communication platform 100, for example using the dynamic label generator 300, in particular the dictionary manager 306, to select a relevancy dictionary 308 for determining relevancy of words in a communication session 102.
  • a current, real-time communication session 102 is monitored to identify a most recent subset of communications, defined by a sliding window.
  • the sliding window may be a dynamically sized sliding window.
  • operations 3002 and 3004 described above may be performed to dynamically determine the size of the sliding window.
  • a relevancy dictionary 308 is selected from among a plurality of available relevancy dictionaries 308.
  • the relevancy dictionary 308 may be selected by replacing a first relevancy dictionary 308 that is currently being used with a second relevancy dictionary 308.
  • the replacement of the first relevancy dictionary 308 with the second relevancy dictionary 308 may be in response to a determination that the first relevancy dictionary 308 is no longer suitable and the second relevancy dictionary 308 is more suitable.
  • the first relevancy dictionary 308 may be considered unsuitable.
  • the first relevancy dictionary 308 may be considered to be unsuitable only if most or all of the words in the most recent subset of communications are found to be irrelevant for at least a predefined time duration (e.g., for at least 5 minutes or at least 2 minutes).
  • the second relevancy dictionary 308 may be identified from the plurality of available relevancy dictionaries 308 by identifying the relevancy dictionary 308 that gives the highest relevancy score to the words in the most recent subset of communications.
  • the relevancy dictionary 308 may be selected at the operation 6004 based on the participants in the communication session 102. For example, as participants join/leave the communication session 102, corresponding user-specific relevancy dictionaries 308 may be selected/unselected for use with the communication session 102.
  • the most recent subset of communications (as defined by the sliding window) is analyzed to identify one or more relevant words using the selected relevancy dictionary 308, for example as discussed above with reference to the operation 3006.
  • a dynamic label associated with the communication session 102 is generated.
  • the dynamic label includes at least one word selected from the relevant word(s) identified at the operation 6006.
  • the generated dynamic label may be provided as output to a user device 150, for example.
  • the operation 6008 may be similar to the operation 3008 described above.
  • an indication of the communication session 102 is generated, based on a user-specific relevancy criterion (e.g., if a relevancy dictionary 308 was selected based on a user-specific topic indicated in a user profile 104).
  • the indication may be provided (e.g., as a push notification) to the user device 150, as discussed above with reference to the operation 5008.
  • the method 6000 addresses a technical problem using a real-time technical solution, which may not be practical or possible to perform manually.
  • the methods 3000, 5000 and 6000 have been described separately, it should be understood that the methods 3000, 5000 and/or 6000 may be used in combination.
  • the size of the sliding window for a communication session 102 may be determined dynamically (e.g., as described in the method 3000), a relevancy dictionary 308 may be selected (e.g., as described in the method 6000) to identify relevant words in the most recent subset of communications defined by the sliding window, and an indication of the communication session 102 may be generated based on a user-specific relevancy criterion (e.g., as described in the method 5000).
  • Other combinations of the methods 3000, 5000 and/or 6000 may be possible.
  • a portion of the communication session 102 may also be stored and optionally provided to the user device 150.
  • a portion of the communication session 102 e.g., a portion that is 10 seconds in length
  • the portion of the communication session 102 may be stored as text data, and may be included in a visual indication provided to the user device 150. This may provide the user with more information about the communication session 102, to enable the user to decide whether or not to participate in the communication session 102.
  • two communication sessions 102 that have overlapping dynamic labels may be dynamically merged together by the digital communication platform 100.
  • the dynamic label associated with a communication session 102 differs significantly from a predefined label associated with the communication session 102
  • an administrator, moderator or a main participant (e.g., main speaker) of the communication session 102 may be notified (e.g., a notification may be provided to the user device 150 associated with the administrator, moderator or main participant).
  • the dynamic label may be considered to differ significantly from the predefined label, and the administrator, moderator or main participant of the communication session 102 may be notified.
  • This notification may be provided in real-time or near real-time (e.g., as soon as the dynamic label differs significantly from the predefined label, or if the dynamic label does not include any words that are semantically relevant to the predefined label for a predetermined period of time), to enable the administrator, moderator or main participant to take appropriate actions (e.g., to redirect the communication session 102 back to the topic indicated in the predefined label).
  • the digital communication platform 100 may communicate with an e- commerce platform 200, as shown in FIG. 1.
  • relevant words identified in a most recent subset of communications in a communication session 102 may be communicated to the e-commerce platform 200.
  • the e-commerce platform 200 may use the identified relevant words to identify advertisement objects 204 (e.g., advertisement objects 204 that contain the relevant words, or that describe products semantically relevant to the relevant words) that may be provided (e.g., displayed in or beside) in association with the communication session 102.
  • advertisement objects 204 e.g., advertisement objects 204 that contain the relevant words, or that describe products semantically relevant to the relevant words
  • the e-commerce platform 200 may provide the identified advertisement objects 204 to the digital communication platform 100, and the digital communication platform 100 may insert advertisement objects 204 into the communication session 102.
  • the identified relevant words may be used by the e-commerce platform 200 to identify online stores 202 that may be relevant (e.g., online stores 202 that contain the relevant words in the store name or store profile, or that sell products that are semantically relevant to the relevant words).
  • the e-commerce platform 200 may generate notifications to the merchants of the identified online stores 202, to indicate that there are possibly discussions about the online store 202 and/or its products in a communication session 102 on the digital communication platform 100.
  • the present disclosure is described, at least in part, in terms of methods, a person of ordinary skill in the art will understand that the present disclosure is also directed to the various components for performing at least some of the aspects and features of the described methods, be it by way of hardware components, software or any combination of the two. Accordingly, the technical solution of the present disclosure may be embodied in the form of a software product.
  • a suitable software product may be stored in a pre-recorded storage device or other similar non-volatile or non-transitory computer readable medium, including DVDs, CD- ROMs, USB flash disk, a removable hard disk, or other storage media, for example.
  • the software product includes instructions tangibly stored thereon that enable a processing device (e.g., a personal computer, a server, or a network device) to execute examples of the methods disclosed herein.

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Abstract

Des systèmes et des procédés sont décrits, permettant de générer une étiquette dynamique pour une session de communication en temps réel. Une session de communication en cours est surveillée afin d'identifier une caractéristique de contenu de la session de communication. Une taille d'une fenêtre coulissante est déterminée sur la base de la caractéristique de contenu, la taille de la fenêtre coulissante définissant un segment de la session de communication à inclure dans le sous-ensemble de communications le plus récent. Le sous-ensemble de communications le plus récent est analysé afin d'identifier des mots pertinents sur la base d'un ou de plusieurs critères de pertinence. Une étiquette dynamique associée à la session de communication est générée, l'étiquette dynamique comprenant au moins un mot sélectionné parmi les mots pertinents.
PCT/CA2022/051312 2021-08-31 2022-08-30 Étiquetage dynamique et génération d'indications de sessions de communication en temps réel WO2023028702A1 (fr)

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