Systems and methods for detecting events based on updates to node profiles from electronic activities

• US 10,489,457 B1
• Filed: 03/31/2019
• Issued: 11/26/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Wulf, John, Prakash, Sathya Hariesh, Brunets, Yurii, Rogynskyy, Oleg, Joseph, Rajit Kurien, Markovsky, Yury
• Primary Examiner(s): Rostami, Mohammad S
• Application Number:
  US16/371,042
• Publication Number:
  US 20190361936A1
• Time in Patent Office: 240 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
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• 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
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• 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 methods, systems, and storage media for detecting events based on updates to node profiles from electronic activities. Exemplary implementations may access an electronic activity transmitted or received via an electronic account associated with a data source provider; generate a plurality of activity field-value pairs; maintain a plurality of node profiles; identify a first state of a first node profile of the plurality of node profiles; update the first node profile using the electronic activity; identify a second state of the first node profile subsequent to updating the first node profile using the electronic activity; detect a state change of the first node profile based on the first state and the second state; determine that the state change satisfies an event condition; and store an association between the first node profile and an event type corresponding to the event condition.

Claims

1. A method, comprising:

accessing, by one or more processors, data of a plurality of electronic activities transmitted or received via a plurality of electronic accounts associated with a plurality of data source providers, each electronic activity of the plurality of electronic activities corresponding to at least one of an email, a call, a calendar event or a message;

generating, by the one or more processors, by parsing data of a first subset of electronic activities of the plurality of the electronic activities, a plurality of activity field-value pairs, each activity field-value pair including an activity value associated with an activity field;

maintaining, by the one or more processors, in one or more data structures, a plurality of node profiles, each node profile of the plurality of node profiles including one or more node field-value pairs, each node field-value pair including a node value associated with a node field, each node value associated with a confidence score;

updating, by the one or more processors, a first node profile of the plurality of node profiles based on the first subset of electronic activities by adding a new value corresponding to a field, the field corresponding to a last name field, a title field, a company name field, a location field, an email address field or a phone number field of the first node profile;

maintaining, by the one or more processors, a first confidence score of a first value corresponding to the field and a second confidence score of the new value, the second confidence score of the new value based on respective contribution scores of respective electronic activities of the subset of electronic activities associated with a respective activity field-value pair corresponding to the new value, the first confidence score of the first value based on respective contribution scores of respective electronic activities of a second subset of electronic activities associated with a respective activity field-value pair corresponding to the first value;

comparing, by the one more processors, the second confidence score to the first confidence score or to a threshold corresponding to one or more event detection policies, each event detection policy used for determining a respective event type of a plurality of event types, each event type corresponding to one or more of a last name field, a title field, a company name field, a location field, an email address field or a phone number field of a respective node profile;

determining, by the one or more processors, based on the second confidence score of the new value exceeding the first confidence score or satisfying the threshold, a first event of a first event type of the plurality of event types based on the field of the first node profile to which the new value corresponds; and

storing, by the one or more processors, responsive to determining the first event, an association between the first node profile and the first event type corresponding to the first event.

2. The method of claim 1, further comprising:

selecting, by the one or more processors, the first node profile to update using the electronic activity based on a match score of the first node profile indicating a likelihood that the electronic activity is transmitted or received by the electronic account corresponding to the first node profile or a server corresponding to the electronic account.

3. The method of claim 1, wherein updating the first node profile includes updating a second field-value pair corresponding to the first field using the electronic activity, the method further comprising:

identifying, by the one or more processors, a first field-value pair corresponding to the first field having a first highest confidence score in the first state;

identifying, by the one or more processors, that a confidence score of the second field-value pair is greater than a corresponding confidence score of the first field-value pair in the second state; and

wherein determining that the state change satisfies the event condition comprises determining that the confidence score of the second field-value pair is greater than the corresponding confidence score of the first field-value pair in the second state.

4. The method of claim 1, further comprising:

receiving, by the one or more processors, a second electronic activity;

identifying, by the one or more processors, the first node profile and at least one candidate node profile of the plurality of node profiles to which to link the second electronic activity; and

selecting, by the one or more processors, the first node profile to which to link the second electronic activity responsive to detecting the state change to include the tag assigned to the first node profile.

5. The method of claim 1, further comprising:

updating, by the one or more processors, responsive to the state change satisfying the event condition, a hierarchy data structure that includes the first node profile.

6. The method of claim 1, wherein the tag is indicative of the electronic activity being a bounce-back electronic activity.

7. The method of claim 1, further comprising:

determining, by the one or more processors, from the electronic activity, at least one of a time or date at which the first electronic activity was transmitted or received; and

wherein

storing, by the one or more processors, the association between the first node profile and an event type corresponding to the event condition comprises storing the association between the first node profile and the event type corresponding to the event condition and the at least one of the time or the date.

8. The method of claim 1, wherein determining that the state change satisfies the event condition comprises determining that the first node field is at least one of a job title field, a company field, or a last name field, and the update indicates that the one or more node values include a new value associated with the first field in the second state and not in the first state.

9. The method of claim 1, wherein determining that the state change satisfies the event condition comprises determining that the first node field includes at least two of a city field, a state field, or a phone number field, and the update indicates the one or more node values to include a new value associated with the first field in the second state and not in the first state.

10. A system, the system comprising:

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

access data of a plurality of electronic activities transmitted or received via a plurality of electronic account associated with a plurality of data source providers, each electronic activity of the plurality of electronic activities corresponding to at least one of an email, a call, a calendar event or a messager;

generate by parsing data of a first subset of electronic activities of the plurality of the electronic activities, a plurality of activity field-value pairs, each activity field-value pair including an activity value associated with an activity field;

maintain in one or more data structures, a plurality of node profiles, each node profile of the plurality of node profiles including one or more node field-value pairs, each node field-value pair including a node value associated with a node field, each node value associated with a confidence score;

update a first node profile of the plurality of node profiles based on the first subset of electronic activities by adding a new value corresponding to a field, the field corresponding to a last name field, a title field, a company name field, a location field, an email address field or a phone number field of the first node profile;

maintaining, by the one or more processors, a first confidence score of a first value corresponding to the field and a second confidence score of the new value, the second confidence score of the new value based on respective contribution scores of respective electronic activities of the subset of electronic activities associated with a respective activity field-value pair corresponding to the new value, the first confidence score of the first value based on respective contribution scores of respective electronic activities of a second subset of electronic activities associated with a respective activity field-value pair corresponding to the first value;

comparing, by the one more processors, the second confidence score to the first confidence score or to a threshold corresponding to one or more event detection policies, each event detection policy used for determining a respective event type of a plurality of event types, each event type corresponding to one or more of a last name field, a title field, a company name field, a location field, an email address field or a phone number field of a respective node profile;

determining, by the one or more processors, based on the second confidence score of the new value exceeding the first confidence score or satisfying the threshold, a first event of a first event type of the plurality of event types based on the field of the first node profile to which the new value corresponds; and

store responsive to determining the first event, an association between the first node profile and the first event type corresponding to the first event.

11. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to:

select the first node profile to update using the electronic activity based on a match score of the first node profile indicating a likelihood that the electronic activity is transmitted or received by the electronic account corresponding to the first node profile or a server corresponding to the electronic account.

12. The system of claim 10, wherein updating the first node profile includes updating a second field-value pair corresponding to the first field using the electronic activity;

wherein the one or more hardware processors are further configured by machine-readable instructions to identify a first field-value pair corresponding to the first field having a first highest confidence score in the first state;

wherein the one or more hardware processors are further configured by machine-readable instructions to identify that a confidence score of the second field-value pair is greater than a corresponding confidence score of the first field-value pair in the second state;

wherein determining that the state change satisfies the event condition comprises determining that the confidence score of the second field-value pair is greater than the corresponding confidence score of the first field-value pair in the second state.

13. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions b:

receive a second electronic activity;

identify the first node profile and at least one candidate node profile of the plurality of node profiles to which to link the second electronic activity;

select the first node profile to which to link the second electronic activity responsive to detecting the state change to include the tag assigned to the first node profile.

14. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to:

update responsive to the state change satisfying the event condition, a hierarchy data structure that includes the first node profile.

15. The system of claim 10, wherein the tag is indicative of the electronic activity being a bounce-back electronic activity.

16. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to:

determine from the electronic activity, at least one of a time or date at which the first electronic activity was transmitted or received;

wherein storing the association between the first node profile and an event type corresponding to the event condition comprises storing the association between the first node profile and the event type corresponding to the event condition and the at least one of the time or the date.

17. The system of claim 10, wherein determining that the state change satisfies the event condition comprises determining that the first node field is at least one of a job title field, a company field, or a last name field, and wherein the update indicates that the one or more node values include a new value associated with the first field in the second state and not in the first state.

18. The system of claim 10, wherein determining that the state change satisfies the event condition comprises determining that the first node field includes at least two of a city field, a state field, or a phone number field, and wherein the update indicates the one or more node values to include a new value associated with the first field in the second state and not in the first state.

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

accessing data of a plurality of electronic activities transmitted or received via a plurality of electronic accounts associated with a plurality of data source providers, each electronic activity of the plurality of electronic activities corresponding to at least one of an email, a call, a calendar event or a message;

generating by parsing data of a first subset of electronic activities of the plurality of the electronic activities, a plurality of activity field-value pairs, each activity field-value pair including an activity value associated with an activity field;

maintaining in one or more data structures, a plurality of node profiles, each node profile of the plurality of node profiles including one or more node field-value pairs, each node field-value pair including a node value associated with a node field, each node value associated with a confidence score;

updating a first node profile of the plurality of node profiles based on the first subset of electronic activities by adding a new value corresponding to a field, the field corresponding to a last name field, a title field, a company name field, a location field, an email address field or a phone number field of the first node profile;

maintaining a first confidence score of a first value corresponding to the field and a second confidence score of the new value, the second confidence score of the new value based on respective contribution scores of respective electronic activities of the subset of electronic activities associated with a respective activity field-value pair corresponding to the new value, the first confidence score of the first value based on respective contribution scores of respective electronic activities of a second subset of electronic activities associated with a respective activity field-value pair corresponding to the first value;

comparing the second confidence score to the first confidence score or to a threshold corresponding to one or more event detection policies, each event detection policy used for determining a respective event type of a plurality of event types, each event type corresponding to one or more of a last name field, a title field, a company name field, a location field, an email address field or a phone number field of a respective node profile;

determining based on the second confidence score of the new value exceeding the first confidence score or satisfying the threshold, a first event of a first event type of the plurality of event types based on the field of the first node profile to which the new value corresponds; and

storing responsive to determining the first event, an association between the first node profile and the first event type corresponding to the first event.

20. The computer-readable storage medium of claim 19, wherein the method further comprises:

selecting the first node profile to update using the electronic activity based on a match score of the first node profile indicating a likelihood that the electronic activity is transmitted or received by the electronic account corresponding to the first node profile or a server corresponding to the electronic account.