Systems and methods for node resolution using multiple fields with dynamically determined priorities based on field values

• US 10,516,587 B2
• Filed: 03/21/2019
• Issued: 12/24/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Brunets, Yurii, Rice, Devin, Rogynskyy, Oleg, Wilson, James
• Primary Examiner(s): Lai, Michael C
• Application Number:
  US16/360,884
• Publication Number:
  US 20190363958A1
• Time in Patent Office: 278 Days
• Field of Search: 709223
• 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
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• 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
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• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/235 Update request formulation
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• 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
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• 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 16/24578 : using ranking
• CPC Class Codes: G06F 16/2477 : Temporal data queries
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• 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 systems and methods described herein can use multiple fields with dynamically determined priorities based on field values for node resolution. The system can generate activity field-value pairs including an activity value associated with an activity field from an electronic activity. The system can determine a frequency score based on a first count of node field-value pairs that match the activity value. The system can assign a weight to the activity value based on the frequency score. The system can generate a match score of a candidate node profile indicating a likelihood that the electronic activity is transmitted or received by an account corresponding to the candidate node profile. The system can store an association between the electronic activity and the node profile selected based on the match score.

Claims

1. A method, comprising:

maintaining, by one or more processors, a plurality of node profiles, each node profile of the plurality of node profiles corresponding to a respective entity and including node field-value pairs;

accessing, by the one or more processors, data of a plurality of electronic activities transmitted or received via at least one electronic account associated with at least one data source provider;

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

determining, by the one or more processors, for a first node field-value pair that matches a first activity field-value pair of the plurality of activity field-value pairs, a first count corresponding to a first number of node profiles of the plurality of node profiles that includes the first node field-value pair;

determining, by the one or more processors, for a second node field-value pair that matches a second activity field-value pair of the plurality of activity field-value pairs, a second count corresponding to a second number of node profiles of the plurality of node profiles that includes the second node field-value pair;

determining, by the one or more processors, a first frequency score of the first activity field-value pair based on the first count and a second frequency score of the second activity field-value pair based on the second count;

assigning, by the one or more processors, a first weight to the first activity field-value pair based on the first frequency score and a second weight to the second activity field-value pair based on the second frequency score, the first weight greater than the second weight responsive to the first frequency score being less than the second frequency score;

generating, by the one or more processors, for each candidate node profile of a plurality of candidate node profiles of the plurality of node profiles, a respective match score of the candidate node profile indicating a likelihood that the electronic activity is transmitted or received by an electronic account corresponding to the candidate node profile based on the first weight assigned to the first activity field-value pair and the second weight assigned to the second activity field-value pair;

selecting, by the one or more processors, from the plurality of candidate node profiles, a node profile based on the respective match score of the node profile; and

storing, by the one or more processors, in one or more data structures, an association between the electronic activity and the node profile.

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

accessing, by the one or more processors, data of a second electronic activity;

determining, by the one or more processors, for a third node field-value pair that matches a third activity field-value pair generated from the second electronic activity, a third count corresponding to a third number of node profiles of the plurality of node profiles that includes the third node field-value pair;

determining, by the one or more processors, for a fourth node field-value pair that matches a fourth activity field-value pair generated from the second electronic activity, a fourth count corresponding to a fourth number of node profiles of the plurality of node profiles that includes the fourth node field-value pair;

determining, by the one or more processors, a third frequency score of the third activity field-value pair of the second electronic activity based on the third count and a fourth frequency score of the fourth activity field-value pair of the second electronic activity based on the fourth count, the third activity field-value pair associated with the first activity field-value pair, the fourth activity field-value pair associated with the second activity field-value pair; and

assigning, by the one or more processors, a third weight to the third activity field-value and a fourth weight to the fourth activity field-value pair, the third weight less than the fourth weight responsive to the third frequency score being greater than the fourth frequency score.

3. The method of claim 1, further comprising:

determining, by the one or more processors, at least one of a first location or a first time zone of a participant of the electronic activity; and

wherein generating, for each candidate node profile, the respective match score comprises generating, by the one or more processors, the match score of the node profile based on comparing the at least one of the first location or the first time zone to at least one of a second location or a second time zone associated with the node profile.

4. The method of claim 1, further comprising:

maintaining, by the one or more processors, at least one of a first name distribution based on a count of each node value of the plurality of node field-value pairs that is associated with a first name field or a last name distribution based on a count of each node value of the plurality of node field-value pairs that is associated with a last name field.

5. The method of claim 4, further comprising:

determining, by the one or more processors, the first frequency score or the second frequency score based on the at least one of the first name distribution or the last name distribution.

6. The method of claim 4, further comprising:

generating, by the one or more processors, the plurality of activity field-value pairs using at least one of the first name distribution or the last name distribution.

7. The method of claim 1, further comprising:

determining, by the one or more processors, at least one of (i) a location associated with a particular node profile of the plurality of node profiles based on location data associated with second electronic activities linked to the particular node profile or (ii) a time zone of the particular node profile based on timestamp data associated with the second electronic activities linked to the particular node profile; and

wherein generating, for each candidate node profile, the respective match score comprises generating, by the one or more processors, the match score for the particular node profile based on the at least one of the location or the time zone of the particular node profile.

8. A system comprising:

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

maintain a plurality of node profiles, each node profile of the plurality of node profiles corresponding to a respective entity and including node field-value pairs;

access data of a plurality of electronic activities transmitted or received via at least one electronic account associated with at least one data source provider;

generate a plurality of activity field-value pairs from data of an electronic activity of the plurality of electronic activities, each activity field-value pair of the plurality of activity field-value pairs including a respective activity value associated with a respective activity field;

determine, for a first node field-value pair that matches a first activity field-value pair of the plurality of activity field-value pairs, a first count corresponding to a first number of node profiles of the plurality of node profiles that includes the first node field-value pair;

determine, for a second node field-value pair that matches a second activity field-value pair of the plurality of activity field-value pairs, a second count corresponding to a second number of node profiles of the plurality of node profiles that includes the second node field-value pair;

determine a first frequency score of the first activity field-value pair based on the first count and a second frequency score of the second activity field-value pair based on the second count;

assign a first weight to the first activity field-value pair based on the first frequency score and a second weight to the second activity field-value pair based on the second frequency score, the first weight greater than the second weight responsive to the first frequency score being less than the second frequency score;

generate for each candidate node profile of a plurality of candidate node profiles of the plurality of node profiles, a respective match score of the candidate node profile indicating a likelihood that the electronic activity is transmitted or received by an electronic account corresponding to the candidate node profile based on the first weight assigned to the first activity field-value pair and the second weight assigned to the second activity field-value pair;

select, from the plurality of candidate node profiles, a node profile based on the respective match score of the node profile; and

store in one or more data structures, an association between the electronic activity and the node profile.

9. The system of claim 8, wherein the electronic activity is a first electronic activity;

wherein the one or more hardware processors are further configured by machine-readable instructions to

access data of a second electronic activity;

determine, for a third node field-value pair that matches a third activity field-value pair generated from the second electronic activity, a third count corresponding to a third number of node profiles of the plurality of node profiles that includes the third node field-value pair;

determine, for a fourth node field-value pair that matches a fourth activity field-value pair generated from the second electronic activity, a fourth count corresponding to a fourth number of node profiles of the plurality of node profiles that includes the fourth node field-value pair;

determine a third frequency score of the third activity field-value pair pair of the second electronic activity based on the third count and a fourth frequency score of the fourth activity field-value pair of the second electronic activity based on the fourth count, the third activity field-value pair associated with the first activity field-value pair, the fourth activity field-value pair associated with the second activity field-value pair; and

assign a third weight to the third activity field-value pair and a fourth weight to the fourth activity field-value pair, the third weight less than the fourth weight responsive to the third frequency score being greater than the fourth frequency score.

10. The system of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to:

determine at least one of a first location or a first time zone of a participant of the electronic activity;

wherein to generate, for each candidate node profile, the respective match score, the one or more hardware processors are further configured by machine-readable instructions to generate the match score of the node profile based on comparing the at least one of the first location or the first time zone to at least one of a second location or a second time zone associated with the node profile.

11. The system of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to:

maintain at least one of a first name distribution based on a count of each node value of the plurality of node field-value pairs that is associated with a first name field or a last name distribution based on a count of each node value of the plurality of node field-value pairs that is associated with a last name field.

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

determine the first frequency score or the second frequency score based on the at least one of the first name distribution or the last name distribution.

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

generate the plurality of activity field-value pairs using at least one of the first name distribution or the last name distribution.

14. The system of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to:

determine at least one of a location associated with a particular node profile of the plurality of node profiles based on location data associated with second electronic activities linked to the particular node profile or a time zone of the particular node profile based on timestamp data associated with the second electronic activities linked to the particular node profile; and

wherein to generate, for each candidate node profile, the respective match score, the one or more hardware processors are further configured by machine-readable instructions to generate the match score for the particular node profile based on the at least one of the location or the time zone of the particular node profile.

15. 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, each node profile of the plurality of node profiles corresponding to a respective entity and including node field-value pairs;

accessing data of a plurality of electronic activities transmitted or received via at least one electronic account associated with at least one data source provider;

generating a plurality of activity field-value pairs from data of an electronic activity of the plurality of electronic activities, each activity field-value pair of the plurality of activity field-value pairs including a respective activity value associated with a respective activity field;

determining, for a first node field-value pair that matches a first activity field-value pair of the plurality of activity field-value pairs, a first count corresponding to a first number of node profiles of the plurality of node profiles that includes the first node field-value pair;

determining, for a second node field-value pair that matches a second activity field-value pair of the plurality of activity field-value pairs, a second count corresponding to a second number of node profiles of the plurality of node profiles that includes the second node field-value pair;

determining a first frequency score of the first activity field-value pair based on the first count and a second frequency score of the second activity field-value pair based on the second count;

assigning a first weight to the first activity field-value pair based on the first frequency score and a second weight to the second activity field-value pair based on the second frequency score, the first weight greater than the second weight responsive to the first frequency score being less than the second frequency score;

generating for each candidate node profile of a plurality of candidate node profiles of the plurality of node profiles, a respective match score of the candidate node profile indicating a likelihood that the electronic activity is transmitted or received by an electronic account corresponding to the candidate node profile based on the first weight assigned to the first activity field-value pair and the second weight assigned to the second activity field-value pair;

selecting, from the plurality of candidate node profiles, a node profile based on the respective match score of the node profile; and

storing in one or more data structures, an association between the electronic activity and the node profile.

16. The non-transitory computer-readable storage medium of claim 15, wherein the electronic activity is a first electronic activity;

wherein the method further comprises

accessing data of a second electronic activity;

determining, for a third node field-value pair that matches a third activity field-value pair generated from the second electronic activity, a third count corresponding to a third number of node profiles of the plurality of node profiles that includes the third node field-value pair;

determining, for a fourth node field-value pair that matches a fourth activity field-value pair generated from the second electronic activity, a fourth count corresponding to a fourth number of node profiles of the plurality of node profiles that includes the fourth node field-value pair;

determining a third frequency score of the third activity field-value pair of the second electronic activity based on the third count and a fourth frequency score of the fourth activity field-value pair of the second electronic activity based on the fourth count, the third activity field-value pair associated with the first activity field-value pair, the fourth activity field-value pair associated with the second activity field-value pair; and

assigning a third weight to the third activity field-value pair and a fourth weight to the fourth activity field-value pair, the third weight less than the fourth weight responsive to the third frequency score being greater than the fourth frequency score.