Systems and methods for electronic activity classification

• US 10,496,681 B1
• Filed: 05/21/2019
• Issued: 12/03/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Hermanek, Stefan, Markovsky, Yury
• Primary Examiner(s): Lin, Shew Fen
• Application Number:
  US16/418,891
• Publication Number:
  US 20190361918A1
• Time in Patent Office: 196 Days
• CPC Class Codes: G06F 11/3024 where the computing system component is a central processing unit [CPU]
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
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• CPC Class Codes: G06F 16/1734 Details of monitoring file system events, e.g. by the use of hooks, filter drivers, logs
• CPC Class Codes: G06F 16/178 Techniques for file synchronisation in file systems
• CPC Class Codes: G06F 16/182 Distributed file systems
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/219 Managing data history or versioning querying versioned data G06F16/2474; querying temporal data G06F16/2477
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2358 Change logging, detection, and notification replication G06F16/27
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2386 : Bulk updating operations data conversion details G06F16/258
• CPC Class Codes: G06F 16/245 : Query processing
• CPC Class Codes: G06F 16/24558 : Binary matching operations
• CPC Class Codes: G06F 16/24564 : Applying rules; Deductive queries
• CPC Class Codes: G06F 16/2457 : with adaptation to user needs
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/24578 : using ranking
• CPC Class Codes: G06F 16/2477 : Temporal data queries
• CPC Class Codes: G06F 16/254 : Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/256 : in federated or virtual databases
• CPC Class Codes: G06F 16/26 : Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/27 : Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/273 : Asynchronous replication or reconciliation
• CPC Class Codes: G06F 16/28 : Databases characterised by their database models, e.g. relational or object models
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/288 : Entity relationship models
• CPC Class Codes: G06F 16/289 : Object oriented databases
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06F 16/313 : Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/337 : Profile generation, learning or modification
• CPC Class Codes: G06F 16/355 : Class or cluster creation or modification
• CPC Class Codes: G06F 16/901 : Indexing; Data structures therefor; Storage structures for retrieval from the web G06F16/951
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90344 : by using string matching techniques
• CPC Class Codes: G06F 16/9035 : Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/906 : Clustering; Classification
• CPC Class Codes: G06F 16/93 : Document management systems
• CPC Class Codes: G06F 16/9535 : Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/6218 : to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6245 : Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 40/20 : Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 40/205 : Parsing
• CPC Class Codes: G06F 40/237 : Lexical tools
• CPC Class Codes: G06F 40/295 : Named entity recognition
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: G06F 7/14 : Merging, i.e. combining at least two sets of record carriers each arranged in the same ordered sequence to produce a single set having the same ordered sequence
• CPC Class Codes: G06F 9/542 : Event management; Broadcasting; Multicasting; Notifications
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/025 : Extracting rules from data
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/02 : using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06Q 10/04 : Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063114 : Status monitoring or status determination for a person or group
• CPC Class Codes: G06Q 10/06312 : Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06315 : Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06393 : Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/06398 : Performance of employee with respect to a job function
• CPC Class Codes: G06Q 10/10 : Office automation; Time management
• CPC Class Codes: G06Q 10/107 : Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 10/109 : Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 10/1091 : Recording time for administrative or management purposes
• CPC Class Codes: G06Q 10/1095 : Meeting or appointment
• CPC Class Codes: G06Q 30/0201 : Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: H04L 12/14 : Charging , metering or billing arrangements for data wireline or wireless communications
• CPC Class Codes: H04L 12/1407 : Policy-and-charging control [PCC] architecture
• CPC Class Codes: H04L 12/1831 : Tracking arrangements for later retrieval, e.g. recording contents, participants activities or behavior, network status
• CPC Class Codes: H04L 2101/00 : Indexing scheme associated with group H04L61/00
• CPC Class Codes: H04L 2101/35 : containing special prefixes
• CPC Class Codes: H04L 2101/37 : E-mail addresses
• CPC Class Codes: H04L 41/082 : the condition being updates or upgrades of network functionality
• CPC Class Codes: H04L 41/14 : Network analysis or design
• CPC Class Codes: H04L 43/026 : using flow identification
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/065 : related to network devices
• CPC Class Codes: H04L 43/067 : using time frame reporting
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0876 : Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/14 : using software, i.e. software packages network security related monitoring H04L63/1408
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/212 : using filtering or selective blocking
• CPC Class Codes: H04L 51/234 : for tracking messages
• CPC Class Codes: H04L 51/42 : Mailbox-related aspects, e.g. synchronisation of mailboxes
• CPC Class Codes: H04L 61/45 : Network directories; Name-to-address mapping
• CPC Class Codes: H04L 61/4594 : Address books, i.e. directories containing contact information about correspondents telephone directories in user terminals H04M1/27453
• CPC Class Codes: H04L 67/125 : involving control of end-device applications over a network
• CPC Class Codes: H04L 67/30 : Profiles
• CPC Class Codes: H04L 67/303 : Terminal profiles
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04M 15/00 : Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
• CPC Class Codes: H04M 15/41 : Billing record details, i.e. parameters, identifiers, structure of call data record [CDR]
• CPC Class Codes: H04M 15/50 : for cross-charging network operators
• CPC Class Codes: H04M 15/58 : based on statistics of usage or network monitoring
• CPC Class Codes: H04M 15/60 : based on actual use of network resources
• CPC Class Codes: H04M 15/66 : Policy and charging system
• CPC Class Codes: H04M 15/755 : Account identification
• CPC Class Codes: H04M 2203/556 : Statistical analysis and interpretation
• CPC Class Codes: H04M 3/2218 : Call detail recording
• CPC Class Codes: H04M 3/436 : Arrangements for screening incoming calls , i.e. evaluating the characteristics of a call before deciding whether to answer it based on the calling party profile H04M3/42059; based on location H04M3/42348; based on presence H04M3/42365; diversion H04M3/54
• CPC Class Codes: H04M 3/5175 : Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/56 : Arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities video conference systems H04N7/15
• CPC Class Codes: H04W 4/24 : Accounting or billing

Abstract

The present disclosure relates to electronic activity classification. A plurality of node profiles corresponding to a plurality of unique entities is maintained. A plurality of electronic activities is accessed from electronic accounts of one or more data source providers. Features are extracted from the electronic activities to determine a classification of the first electronic activity. A confidence score is determined indicating a likelihood that the first electronic activity of a classification type. An association is stored between the first electronic activity and a tag corresponding to the first classification type. The confidence score is updated based on a second electronic activity.

Claims

1. A method, comprising:

maintaining, by one or more processors, a plurality of node profiles corresponding to a plurality of unique entities, each node profile including a plurality of field-value pairs;

accessing, by the one or more processors, data corresponding to a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers, the electronic activities used to update the plurality of node profiles;

extracting, by the one or more processors, from data corresponding to a first electronic activity of the plurality of electronic activities, one or more features used to determine a classification of the first electronic activity;

identifying, by the one or more processors, from the data corresponding to the first electronic activity, a sender and one or more recipients of the first electronic activity and a type of an electronic account identifier corresponding to the sender or a recipient of the one or more recipients;

determining, by the one or more processors, a confidence score indicating a likelihood that the first electronic activity is of a first classification type based on the sender and the one or more recipients of the first electronic activity, the type of the electronic account identifier and a connection profile between at least two of the sender and the one or more recipients, the connection profile based on at least one second electronic activity including the at least two of the sender and the one or more recipients as participants of the at least one second electronic activity;

storing, by the one or more processors, in one or more data structures, an association between the first electronic activity and a tag corresponding to the first classification type and the determined confidence score;

accessing, by the one or more processors, data corresponding to a second electronic activity of the plurality of electronic activities;

determining, by the one or more processors, that a sender and one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity; and

updating, by the one or more processors, the determined confidence score of the first electronic activity based on a corresponding confidence score of the second electronic activity indicating that the second electronic activity is of the first classification type responsive to determining that the sender and the one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity corresponding to the first classification type.

2. The method of claim 1, further comprising:

identifying, by the one or more processors, a first node profile corresponding to a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity and a second node profile corresponding to a second participant corresponding to another of the sender or the one or more recipients of the first electronic activity; and

identifying, by the one or more processors and based on the first node profile and second node profile, a connection profile between the first participant and the second participant of the first electronic activity;

wherein determining the confidence score includes determining the confidence score based on the connection profile.

3. The method of claim 1, further comprising:

identifying, by the one or more processors, from a plurality of node field-value pairs of a first node profile corresponding to a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity, a first electronic account identifier and a second electronic account identifier; and

matching, by the one or more processors, the electronic account via which the first electronic activity was transmitted or received to the first electronic account identifier or the second electronic account identifier;

wherein determining the confidence score includes determining the confidence score based on matching the first electronic activity to the first electronic account identifier or the second electronic account identifier.

4. The method of claim 3, wherein the first electronic account identifier corresponds to a company value of the plurality of node field-value pairs.

5. The method of claim 1, further comprising:

determining, by the one or more processors, a context of the first electronic activity by applying natural language processing to the data corresponding to the first electronic activity to determine at least one of a tone or a formality of the first electronic activity;

wherein determining the confidence score includes determining, by the one or more processors, the confidence score based on the context.

6. The method of claim 1, wherein:

determining, by the one or more processors, the confidence score includes determining the confidence score by determining a context of a plurality of electronic activities associated with the first electronic activity.

7. The method of claim 1, further comprising:

determining, by the one or more processors, a connection profile based on the first classification type; and

assigning, by the one or more processors, the connection profile to a first node profile of a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity and a second node profile of a second participant corresponding to another of the sender or the one or more recipients of the first electronic activity.

8. The method of claim 1, wherein the confidence score is a first confidence score, the method further comprising:

identifying, by the one or more processors, at least one second electronic activity having a second timestamp different than a first timestamp of the first electronic activity;

determining, by the one or more processors, a second confidence score indicating that the at least one second electronic activity is of the first classification type; and

storing, by the one or more processors, in the one or more data structures, a temporal association between a metric corresponding to a difference between the first confidence score and the second confidence score and at least one of the first electronic activity or the at least one second electronic activity.

9. The method of claim 1, further comprising:

identifying, by the one or more processors, a first last name of a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity and a second last name of a second participant corresponding to another of the sender or the one or more recipients of the first electronic activity; and

determining, by the one or more processors, whether the first last name and second last name match;

wherein determining the confidence score includes determining the confidence score based on the first last name and the second last name matching.

10. The method of claim 1, further comprising:

identifying, by the one or more processors, a plurality of time stamps indicated when a plurality of electronic activities associated with the first electronic activity are received or transmitted; and

determining, by the one or more processors, a portion of time during which the plurality of electronic activities are received or transmitted based on the plurality of time stamps;

wherein determining the confidence score is based at least on the portion of time.

11. The method of claim 1, further comprising:

identifying, by the one or more processors, an address of the first electronic activity; and

determining, by the one or more processors, whether the address is a home address of a first participant of the participants of the first electronic activity or a second participant of the participants of the first electronic activity;

wherein determining the confidence score includes determining the confidence score based on the address being a home address of the first participant or the second participant.

12. A system comprising:

one or more hardware processors configured by machine-readable instructions to;

maintain a plurality of node profiles corresponding to a plurality of unique entities, each node profile including a plurality of field-value pairs;

access data corresponding to a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers, the electronic activities used to update the plurality of node profiles;

extract, from data corresponding to a first electronic activity of the plurality of electronic activities, one or more features used to determine a classification of the first electronic activity;

identify, from the data corresponding to the first electronic activity, a sender and one or more recipients of the first electronic activity and a type of an electronic account identifier corresponding to the sender or a recipient of the one or more recipients;

determine a confidence score indicating a likelihood that the first electronic activity is of a first classification type based on the sender and the one or more recipients of the first electronic activity, the type of the electronic account identifier and a connection profile between at least two of the sender and the one or more recipients, the connection profile based on at least one second electronic activity including the at least two of the sender and the one or more recipients as participants of the at least one second electronic activity;

store, in one or more data structures, an association between the first electronic activity and a tag corresponding to the first classification type and the determined confidence score;

access data corresponding to a second electronic activity of the plurality of electronic activities;

determine that a sender and one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity; and

update the determined confidence score of the first electronic activity based on a corresponding confidence score of the second electronic activity indicating that the second electronic activity is of the first classification type responsive to determining that the sender and the one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity corresponding to the first classification type.

13. The system of claim 12, wherein the one or more processors are further configured to:

identify a first node profile corresponding to a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity and a second node profile corresponding to a second participant corresponding to another of the sender or the one or more recipients of the first electronic activity; and

identify, based on the first node profile and second node profile, a connection profile between the first participant and the second participant of the first electronic activity;

wherein determining the confidence score includes determining the confidence score based on the connection profile.

14. The system of claim 12, wherein the one or more processors are further configured to:

identify from a plurality of node field-value pairs of a first node profile corresponding to a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity, a first electronic account identifier and a second electronic account identifier; and

match the electronic account via which the first electronic activity was transmitted or received to the first electronic account identifier or the second electronic account identifier;

wherein the one or more processors are configured to determine the confidence score by determining the confidence score based on matching the first electronic activity to the first electronic account identifier or the second electronic account identifier.

15. The system of claim 14, wherein the first electronic account identifier corresponds to a company value of the plurality of node field-value pairs.

16. The system of claim 12, wherein the one or more processors are further configured to:

determine a context of the first electronic activity by applying natural language processing to the data corresponding to the first electronic activity to determine at least one of a tone or a formality of the first electronic activity;

wherein the one or more processors are configured to determine the confidence score by determining the confidence score based on the context.

17. The system of claim 12, wherein the one or more processors are configured to determine the confidence score by determining a context of a plurality of electronic activities associated with the first electronic activity.

18. The system of claim 12, wherein the one or more processors are further configured to determine a connection profile based on the first classification type;

and

assign the connection profile to a first node profile of a first participant corresponding to one of the sender or the one or more recipients of the first electronic activity and a second node profile of a second participant corresponding to another of the sender or the one or more recipients of the first electronic activity.

19. The system of claim 12, wherein the confidence score is a first confidence score, and wherein the one or more processors are further configured to:

identify at least one second electronic activity having a second timestamp different than a first timestamp of the first electronic activity;

determine a second confidence score indicating that the at least one second electronic activity is of the first classification type; and

store, in the one or more data structures, a temporal association between a metric corresponding to a difference between the first confidence score and the second confidence score and at least one of the first electronic activity or the at least one second electronic activity.

20. A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method comprising:

maintaining a plurality of node profiles corresponding to a plurality of unique entities, each node profile including a plurality of field-value pairs;

accessing data corresponding to a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers, the electronic activities used to update the plurality of node profiles;

extracting, from data corresponding to a first electronic activity of the plurality of electronic activities, one or more features used to determine a classification of the first electronic activity;

identifying, from the data corresponding to the first electronic activity, a sender and one or more recipients of the first electronic activity and a type of an electronic account identifier corresponding to the sender or a recipient of the one or more recipients;

determining a confidence score indicating a likelihood that the first electronic activity is of a first classification type based on the sender and the one or more recipients of the first electronic activity, the type of the electronic account identifier and a connection profile between at least two of the sender and the one or more recipients, the connection profile based on at least one second electronic activity including the at least two of the sender and the one or more recipients as participants of the at least one second electronic activity;

storing, in one or more data structures, an association between the first electronic activity and a tag corresponding to the first classification type and the determined confidence score;

accessing data corresponding to a second electronic activity of the plurality of electronic activities;

determining that a sender and one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity; and

updating the determined confidence score of the first electronic activity based on a corresponding confidence score of the second electronic activity indicating that the second electronic activity is of the first classification type responsive to determining that the sender and the one or more recipients of the second electronic activity match the sender and the one or more recipients of the first electronic activity corresponding to the first classification type.