Systems and methods for classifying phone numbers based on node profile data

• US 10,516,784 B2
• Filed: 03/31/2019
• Issued: 12/24/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Hermanek, Stefan, Markovsky, Yury, Brunets, Yurii, Rogynskyy, Oleg
• Primary Examiner(s): Patel, Hemant S
• Application Number:
  US16/371,044
• Publication Number:
  US 20190364154A1
• Time in Patent Office: 268 Days
• Field of Search: 379354
• 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
• 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
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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
• 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 methods, systems, and storage media for classifying phone numbers based on node profile data. Exemplary embodiments for classifying phone numbers based on node profile data may maintain a plurality of node profiles and generate a plurality of activity field-value pairs from an electronic activity. Each activity field-value pair of the plurality of activity field-value pairs corresponding to at least one participant of the electronic activity. Exemplary embodiments may further parse the electronic activity to identify a string corresponding to an electronic activity phone number, determine a type of phone number to which the electronic activity phone number corresponds using a data structure, identify a node profile of the plurality of node profiles corresponding to a participant of the electronic activity to which the electronic activity phone number corresponds, and update the identified node profile by the determined phone number type.

Claims

1. A method comprising:

maintaining, by one or more processors, a plurality of node profiles including one or more field-value pairs, at least one of the field-value pairs of each node profile identifying a phone number corresponding to the node profile;

generating, by the one or more processors, a plurality of activity field-value pairs from data of an electronic activity accessed by the one or more processors, each activity field-value pair of the plurality of activity field-value pairs corresponding to at least one participant of the electronic activity;

parsing, by the one or more processors, the electronic activity to identify a string including at least a predetermined number of numerical characters satisfying one or more predetermined regular expression (regex) patterns corresponding to an electronic activity phone number;

identifying, by the one or more processors, a first node profile of the plurality of node profiles corresponding to a sender or one or more recipients of the electronic activity to which the electronic activity phone number corresponds;

determining, by the one or more processors, a phone number type to which the electronic activity phone number corresponds, the phone number type determined using at least one data structure storing respective associations between each respective second phone number of a plurality of second phone numbers and a corresponding field type identifying a phone number type of the respective second phone number, wherein each second phone number is extracted from a respective node profile of the plurality of node profiles that includes a second field-value pair that matches a corresponding second field-value pair of the first node profile, the second field-value pair identifying a group entity, the phone number type determined based on comparing at least a portion of the electronic activity phone number to data from a corresponding portion of each of the plurality of the extracted second phone numbers included in the at least one data structure; and

updating, by the one or more processors, the identified first node profile by adding the electronic activity phone number as a new value of a field corresponding to the determined phone number type or updating a confidence score of an existing field-value pair corresponding to the electronic activity phone number.

2. The method of claim 1, further comprising determining, by the one or more processors, from content included in the electronic activity, that the phone number type of the electronic activity phone number cannot be determined based on the content included in the electronic activity.

3. The method of claim 1, wherein the at least one data structure includes a count for each second phone number of the plurality of second phone numbers corresponding to a number of the node profiles that include a field-value pair having a value that matches the second phone number.

4. The method of claim 1, wherein the phone number type is one of a home phone number, a cell phone number, a group entity general number, or a group entity direct number.

5. The method of claim 1, further comprising:

accessing, by the one or more processors, information of a plurality of electronic activities; and

generating, by the one or more processors, the node profiles based on the information of the plurality of electronic activities.

6. The method of claim 1, further comprising generating, by the one or more processors, the at least one data structure to include, for each second phone number included in the at least one data structure, an aggregate confidence score based on respective individual confidence scores of values of field-value pairs of node profiles that match the second phone number.

7. The method of claim 1, further comprising generating, by the one or more processors, the at least one data structure to include, for each phone number included in the at least one data structure, a frequency of the phone number within the at least one data structure.

8. The method of claim 1, further comprising generating, by the one or more processors, the at least one data structure to include, for a first set of phone numbers included in the at least one data structure, a value corresponding to a group entity name.

9. The method of claim 1, wherein the plurality of node profiles includes a plurality of member node profiles and further comprising:

accessing, by the one or more processors, a publicly available database to determine a phone number of a group entity associated with a group node profile that is linked to the plurality of node profiles;

comparing the electronic activity phone number to the phone number of the group entity to determine a match; and

determining, by the one or more processors, that the phone number type of the electronic activity phone number is a group entity phone number based on the match.

10. The method of claim 1, wherein determining, by the one or more processors, a type of phone number to which the electronic activity phone number corresponds further comprises using a probabilistic model to determine a probability score indicating a likelihood that the electronic activity phone number is the type of phone number.

11. The method of claim 10, wherein the at least one data structure includes, for each second phone number included in the at least one data structure, a frequency of the second phone number within the at least one data structure or a phone number type of the second phone number;

and

wherein determining the phone number type of the electronic activity phone number using the probabilistic model includes determining the probability score based on the frequency of the phone number.

12. The method of claim 1, further comprising:

determining, by the one or more processors, a first field value identifying a residential address of the participant;

determining, by the one or more processors, a prefix of the electronic activity phone number;

determining, by the one or more processors, a location corresponding to the prefix of the electronic activity phone number; and

classifying, by the one or more processors, the electronic activity phone number as a personal phone number responsive to determining that the location corresponding to the prefix matches a portion of the residential address.

13. The method of claim 1, further comprising:

determining, by the one or more processors, a first phone number prefix corresponding to a residential address of the at least one participant;

determining, by the one or more processors, a second phone number prefix of the electronic activity phone number;

determining, by the one or more processors, that second prefix doesn'"'"'t match the first prefix; and

classifying, by the one or more processors, the electronic activity phone number as a cell phone number responsive to determining that the second phone number prefix of the electronic activity phone number does not match the first phone number prefix corresponding to a residential address of the at least one participant.

14. The method of claim 1, further comprising:

parsing, by the one or more processors, the electronic activity to identify an extension number associated with the electronic activity phone number; and

updating, by the one or more processors, the identified node profile by adding the electronic activity phone number and the extension number as the new value of the field corresponding to the determined phone number type.

15. A system comprising:

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

maintain a plurality of node profiles including one or more field-value pairs, at least one of the field-value pairs of each node profile identifying a phone number corresponding to the node profile;

generate a plurality of activity field-value pairs from data of an electronic activity accessed by the one or more processors, each activity field-value pair of the plurality of activity field-value pairs corresponding to at least one participant of the electronic activity;

parse the electronic activity to identify a string including at least a predetermined number of numerical characters corresponding to an electronic activity phone number;

identify a first node profile of the plurality of node profiles corresponding to a sender or one or more recipients of the electronic activity to which the electronic activity phone number corresponds;

determine a phone number type to which the electronic activity phone number corresponds, the phone number type determined using at least one data structure storing respective associations between each respective second phone number of a plurality of second phone numbers and a corresponding field type identifying a phone number type of the respective second phone number, wherein each second phone number is extracted from a respective node profile of the plurality of node profiles that includes a second field-value pair that matches a corresponding second field-value pair of the first node profile, the second field-value pair identifying a group entity, the phone number type determined based on comparing at least a portion of the electronic activity phone number to data from a corresponding portion of each of the plurality of the extracted second phone numbers included in the at least one data structure; and

update the identified first node profile by adding the electronic activity phone number as a new value of a field corresponding to the determined phone number type or updating a confidence score of an existing field-value pair corresponding to the electronic activity phone number.

16. The system of claim 15, wherein the one or more hardware processors are further configured by machine-readable instructions to determine from content included in the electronic activity, that the phone number type of the electronic activity phone number cannot be determined based on the content included in the electronic activity.

17. The system of claim 15, wherein the at least one data structure includes a count for each second phone number of the plurality of second phone numbers corresponding to a number of the node profiles that include a field-value pair having a value that matches the second phone number.

18. The system of claim 15, wherein the phone number type is one of a home phone number, a cell phone number, a group entity general number, or a group entity direct number.

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

access information of a plurality of electronic activities; and

generate the node profiles based on the information of the plurality of electronic activities.

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

maintain a plurality of node profiles including one or more field-value pairs, at least one of the field-value pairs of each node profile identifying a phone number corresponding to the node profile;

generate a plurality of activity field-value pairs from data of an electronic activity accessed by the one or more processors, each activity field-value pair of the plurality of activity field-value pairs corresponding to at least one participant of the electronic activity;

parse the electronic activity to identify a string including at least a predetermined number of numerical characters corresponding to an electronic activity phone number;

identify a first node profile of the plurality of node profiles corresponding to a sender or one or more recipients of the electronic activity to which the electronic activity phone number corresponds;

determine a phone number type to which the electronic activity phone number corresponds, the phone number type determined using at least one data structure storing respective associations between each respective second phone number of a plurality of second phone numbers and a corresponding field type identifying a phone number type of the respective second phone number, wherein each second phone number is extracted from a respective node profile of the plurality of node profiles that includes a second field-value pair that matches a corresponding second field-value pair of the first node profile, the second field-value pair identifying a group entity, the phone number type determined based on comparing at least a portion of the electronic activity phone number to data from a corresponding portion of each of the plurality of the extracted second phone numbers included in the at least one data structure; and

update the identified first node profile by adding the electronic activity phone number as a new value of a field corresponding to the determined phone number type or updating a confidence score of an existing field-value pair corresponding to the electronic activity phone number.