Systems and methods for matching electronic activities with record objects based on entity relationships

• US 10,509,786 B1
• Filed: 04/30/2019
• Issued: 12/17/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Hermanek, Stefan, Halladay, Dylan, Korkuna, Ostap, Navarro, Jose Alberto Muniz
• Primary Examiner(s): Pyo, Monica M
• Application Number:
  US16/399,706
• Publication Number:
  US 20190361900A1
• Time in Patent Office: 231 Days
• Field of Search: 707755
• 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
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
• 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 systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Claims

1. A method comprising:

accessing, by one or more processors, a plurality of electronic activities transmitted or received via electronic accounts of one or more data source providers;

identifying, by the one or more processors, an electronic activity of the plurality of electronic activities accessed from one or more servers of a first entity;

identifying, by the one or more processors, a first participant of the electronic activity associated with the first entity and a second participant of the electronic activity associated with a second entity;

parsing, by the one or more processors, the electronic activity to extract data for matching the electronic activity to one or more record objects of a system of record of the first entity;

responsive to parsing the electronic activity;

determining, by the one or more processors, whether a record object identifier indicating a relationship between the first entity and the second entity is included in the electronic activity;

responsive to determining that the record object identifier is included in the electronic activity;

identifying, by the one or more processors, using the record object identifier, a first record object of the system of record that includes an instance of the record object identifier, the first record object of a first record object type; and

storing, by the one or more processors, in one or more data structures, a first association between the electronic activity and the first record object; and

responsive to determining that the record object identifier is not included in the electronic activity;

determining, by the one or more processors, a second record object of a second record object type corresponding to the second entity;

identifying, by the one or more processors, text of a body of the electronic activity;

determining, by the one or more processors, from the text of the body, an entity identifier identifying a third entity;

identifying, by the one or more processors, using a matching policy based on the entity identifier identifying the third entity, a third record object that is

i) of the first record object type,

ii) linked to the second record object, and

iii) identifying the third entity; and

storing, by the one or more processors, in the one or more data structures, a second association between the electronic activity and the third record object.

2. The method of claim 1, wherein determining the second record object further comprises:

parsing, by the one or more processors, the electronic activity to identify a domain associated with at least one participant of the electronic activity; and

selecting, by the one or more processors, the second record object based on the domain associated with the at least one participant of the electronic activity.

3. The method of claim 1, further comprising:

identifying, by the one or more processors, a plurality of candidate record objects of the first record object type linked to the second record object;

selecting, by the one or more processors, the third record object from the plurality of candidate record objects based on a match score between the third record object and the electronic activity.

4. The method of claim 1, further comprising:

identifying, by the one or more processors, a plurality of candidate record objects of the first record object type linked to the second record object;

and wherein parsing the electronic activity to extract data includes parsing, by the one or more processors, the electronic activity to generate activity field-value pairs; and

wherein identifying the third record object comprises selecting, by the one or more processors, the third record object based on the activity field-value pairs.

5. The method of claim 1, wherein determining the entity identifier identifying the third entity comprising:

identifying, by the one or more processors, a plurality of entity identifiers, each entity identifier of the plurality of entity identifiers associated with a respective record object associated with the second record object; and

selecting, by the one or more processors, from the plurality of entity identifiers, the entity identifier identifying the third entity based on a similarity score between the selected entity identifier and one or more strings included in the body.

6. The method of claim 1, further comprising:

determining the entity identifier based on an embedding vector generated based on text of a body of the electronic activity.

7. The method of claim 1, further comprising:

maintaining, by the one or more processors, a node graph linking a plurality of nodes using the plurality of electronic activities or a plurality of systems of record, each system of record including one or more record objects;

identifying, by the one or more processors, from the node graph, a first group node corresponding to the first entity;

identifying, by the one or more processors, from the node graph, a second group node corresponding to the second entity;

identifying, by the one or more processors, from the node graph, a third group node of the third entity based on the third group node linking to both the first group node and the second group node.

8. The method of claim 1, wherein the electronic activity is a first electronic activity and the method further comprising:

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

determining, by the one or more processors, that the second electronic activity is related to the first electronic activity; and

selecting, by the one or more processors, the third record object based on determining that the second electronic activity is related to the first electronic activity.

9. A system comprising:

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

access a plurality of electronic activities transmitted or received via electronic accounts of one or more data source providers;

identify an electronic activity of the plurality of electronic activities accessed from one or more servers of a first entity;

identify a first participant of the electronic activity associated with the first entity and a second participant of the electronic activity associated with a second entity;

parse the electronic activity to extract data for matching the electronic activity to one or more record objects of a system of record of the first entity;

responsive to parsing the electronic activity;

determine whether a record object identifier indicating a relationship between the first entity and the second entity is included in the electronic activity;

responsive to determining that the record object identifier is included in the electronic activity;

identify, using the record object identifier, a first record object of the system of record that includes an instance of the record object identifier, the first record object of a first record object type; and

store, in one or more data structures, a first association between the electronic activity and the first record object; and

responsive to determining that the record object identifier is not included in the electronic activity;

determine a second record object of a second record object type corresponding to the second entity;

identify text of a body of the electronic activity;

determine, from the text of the body, an entity identifier identifying a third entity;

identify using a matching policy based on the entity identifier identifying the third entity,

i) a third record object that is

ii) of the first record object type,

ii) linked to the second record object, and

iii) identifying the third entity; and

store, in the one or more data structures, a second association between the electronic activity and the third record object.

10. The system of claim 9, wherein the one or more hardware processors are further configured by machine-readable instructions to:

parse the electronic activity to identify a domain associated with at least one participant of the electronic activity; and

select the second record object based on the domain associated with the at least one participant of the electronic activity.

11. The system of claim 9, wherein the one or more hardware processors are further configured by machine-readable instructions to:

identify a plurality of candidate record objects of the first record object type linked to the second record object;

select the third record object from the plurality of candidate record objects based on a match score between the third record object and the electronic activity.

12. The system of claim 9, wherein the one or more hardware processors are further configured by machine-readable instructions to:

identify a plurality of candidate record objects of the first record object type linked to the second record object;

parse the electronic activity to generate activity field-value pairs; and

select the third record object based on the activity field-value pairs.

13. The system of claim 9, wherein determining the entity identifier identifying the third entity comprising:

identify a plurality of entity identifiers, each entity identifier of the plurality of entity identifiers associated with a respective record object associated with the second record object; and

select, from the plurality of entity identifiers, the entity identifier identifying the third entity based on a similarity score between the selected entity identifier and one or more strings included in the body.

14. The system of claim 9, wherein the one or more hardware processors are further configured by machine-readable instructions to:

determine the entity identifier based on an embedding vector generated based on text of a body of the electronic activity.

15. The system of claim 9, wherein the one or more hardware processors are further configured by machine-readable instructions to:

maintain a node graph linking a plurality of nodes using the plurality of electronic activities or a plurality of systems of record, each system of record including one or more record objects;

identify a first group node corresponding to the first entity;

identify, from the node graph, a second group node corresponding to the second entity;

identify, from the node graph, a third group node of the third entity based on the third group node linking to both the first group node and the second group node.

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

access a plurality of electronic activities transmitted or received via electronic accounts of one or more data source providers;

identify an electronic activity of the plurality of electronic activities accessed from one or more servers of a first entity;

identify a first participant of the electronic activity associated with the first entity and a second participant of the electronic activity associated with a second entity;

parse the electronic activity to extract data for matching the electronic activity to one or more record objects of a system of record of the first entity;

responsive to parsing the electronic activity;

determine whether a record object identifier indicating a relationship between the first entity and the second entity is included in the electronic activity;

responsive to determining that the record object identifier is included in the electronic activity;

identify, using the record object identifier, a first record object of the system of record that includes an instance of the record object identifier, the first record object of a first record object type; and

store, in one or more data structures, a first association between the electronic activity and the first record object; and

responsive to determining that the record object identifier is not included in the electronic activity;

determine a second record object of a second record object type corresponding to the second entity;

identify text of a body of the electronic activity;

determine, from the text of the body, an entity identifier identifying a third entity;

identify using a matching policy based on the entity identifier identifying the third entity, a third record object that is

i) of the first record object type,

ii) linked to the second record object, and

iii) identifying the third entity; and

store, in the one or more data structures, a second association between the electronic activity and the third record object.