Systems and methods for generating confidence scores of values of fields of node profiles using electronic activities

• US 10,585,880 B2
• Filed: 12/31/2018
• Issued: 03/10/2020
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg
• Primary Examiner(s): Bullock, Joshua
• Application Number:
  US16/237,585
• Publication Number:
  US 20190361865A1
• Time in Patent Office: 435 Days
• Field of Search: 707687
• 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
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• 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 16/245 : Query processing
• CPC Class Codes: G06F 16/24558 : Binary matching operations
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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
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• CPC Class Codes: G06F 16/289 : Object oriented databases
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• 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
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• 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
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• 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
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• 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 dynamically generating and updating confidence scores of values of fields based on data points. A system having access to a plurality of data points corresponding to electronic activities and record objects can identify a node profile of the plurality of node profiles including a plurality of fields, determine, for at least one data point of the one or more data points included in a respective value data structure of a value of a field of the plurality of fields of the node profile, a contribution score of the data point based on a time corresponding to when the data point was generated or updated. The system can then generate a confidence score of the value of the field of the node profile based on the contribution score of the at least one data point.

Claims

1. A method, comprising:

accessing, by one or more processors, at least one of i) data from a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or ii) a plurality of record objects of one or more systems of record associated with the one or more data source providers, the one or more processors maintaining a plurality of node profiles and configured to update the plurality of node profiles using at least one of the data from the plurality of electronic activities or the plurality of record objects;

identifying, by the one or more processors, a node profile of the plurality of node profiles including a plurality of fields, each field of the plurality of fields including one or more value data structures, each value data structure of the one or more value data structures corresponding to a value and further including one or more entries, each entry of the one or more entries corresponding to respective one or more data points that include a string that matches the value of the value data structure, each data point of the one or more data points corresponding to a respective electronic activity of the plurality of electronic activities or a respective record object of the plurality of record objects;

determining, for at least one data point of the one or more data points included in a respective value data structure of a value of a field of the plurality of fields of the node profile, a contribution score of the at least one data point based on a time corresponding to when the at least one data point was generated or updated and a trust score assigned to a system of record corresponding to the at least one data point, the trust score based on comparing field values of object fields included in record objects of the system of record to values of fields of one or more node profiles having respective confidence scores above a threshold; and

generating, by the one or more processors, a confidence score of the value of the field of the node profile based on the contribution score of the at least one data point.

2. The method of claim 1, wherein the at least one data point identifies an electronic activity of the plurality of electronic activities or a record object of the system of record of the one or more systems of record accessible by the one or more processors.

3. The method of claim 1, wherein determining, for at least one data point of the one or more data points that includes the value of the field of the node profile, the contribution score of the at least one data point comprises determining, for each data point of the one or more data points that support the value of the field of the node profile, a respective contribution score of the data point based on a respective time corresponding to when the data point was generated or updated.

4. The method of claim 3, wherein generating, by the one or more processors, the confidence score of the value of the field of the node profile based on the contribution score of the at least one data point includes generating, by the one or more processors, the confidence score of the value of the field of the node profile based on the respective contribution score of each of the one or more data points that support the value of the field of the node profile.

5. The method of claim 1, 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 includes the value of the field of the node profile;

generating, by the one or more processors, a second contribution score of the second electronic activity for the value of the field of the node profile; and

updating, by the one or more processors, the confidence score of the value based on the second contribution score of the second electronic activity.

6. The method of claim 1, further comprising:

identifying, by the one or more processors, a record object of a system of record previously not matched to the value of the field of the node profile;

determining, by the one or more processors, that the record object includes the value of the field of the node profile;

generating, by the one or more processors, a contribution score of the record object; and

updating, by the one or more processors, the confidence score of the value of the field of the node profile based on the contribution score of the record object.

7. The method of claim 1, wherein the data point identifies an electronic activity that is an automatically generated bounce back electronic activity.

8. The method of claim 1, further comprising maintaining, for the value of the field of the node profile, an occurrence metric indicating a number of data points used to support the value.

9. The method of claim 1, wherein the node profile includes a first field having a first value data structure identifying a first value, wherein the first value is assigned to the first field by linking a first electronic activity to the node profile.

10. The method of claim 9, wherein the first value data structure includes a first entry identifying the first electronic activity and wherein the first electronic activity is linked to the first node profile by

identifying, by the one or more processors, from data included in the first electronic activity, a plurality of strings;

identifying, by the one or more processors, a plurality of candidate node profiles to which to link the electronic activity by comparing one or more strings of the plurality of strings to values of fields of respective candidate node profiles;

generating, by the one or more processors, for each candidate node profile, a match score indicating a likelihood that the electronic activity is transmitted or received by an account corresponding to the candidate node profile based on comparing the plurality of strings included in the electronic activity to values of fields included in the candidate node profile; and

linking, by the one or more processors, the first electronic activity to the first node profile based on the match score of the first node profile.

11. The method of claim 1, wherein the value of the field of the node profile includes a first value of a first field of the node profile and wherein the contribution score of the data point is a first contribution score of a first data point and the confidence score is a first confidence score of a first value and wherein the method further comprises:

identifying a second value data structure of a second field of the node profile, the second value data structure corresponding to a second value of the second field and further including one or more second entries corresponding to respective one or more second data points that support the second value of the second value data structure;

determining, for at least one second data point of the one or more second data points of the second value of the second field of the node profile, a second contribution score of the second data point based on a time corresponding to when the second data point was generated or updated; and

generating, by the one or more processors, a second confidence score of the second value of the second field of the node profile based on the second contribution score of the at least one second data point.

12. The method of claim 1, wherein the field of the plurality of fields of the node profile comprises a second value and a corresponding second value data structure including at least one second entry identifying a second electronic activity or at least one second record object that includes a second string that matches the second value.

13. The method of claim 1, wherein the field of the plurality of fields is a first field and wherein a second field of the plurality of fields of the node profile comprises a second value and a corresponding second value data structure including at least one second entry identifying the first electronic activity or the first record object that also includes a second string that matches the second value.

14. The method of claim 1, further comprising:

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

linking, by the one or more processors, the subsequent electronic activity to the node profile by including one or more entries identifying the subsequent electronic activity to one or more value data structures corresponding to one or more values of one or more fields;

generating, by the one or more processors, for each entry identifying the subsequent electronic activity, a contribution score of the subsequent electronic activity, the entry corresponding to a respective value data structure of a respective value of a respective field; and

generating, by the one or more processors, respective confidence scores for the values based on the respective contribution scores of the subsequent electronic activity.

15. The method of claim 14, wherein the subsequent electronic activity includes a signature block in the subsequent electronic activity, and wherein linking the subsequent electronic activity to the node profile comprises:

extracting, by the one or more processors, from the signature block of the subsequent electronic activity, a plurality of strings; and

determining, by the one or more processors, using the plurality of strings extracted from the signature block, that the node profile of the plurality of node profiles includes one or more values that match respective strings of the plurality of strings.

16. A system, comprising:

one or more processors; and

a memory coupled to the one or more processors, the one or more processors configured to;

access at least one of i) data from a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or ii) a plurality of record objects of one or more systems of record associated with the one or more data source providers, the one or more processors maintaining a plurality of node profiles and configured to update the plurality of node profiles using at least one of the data from the plurality of electronic activities or the plurality of record objects;

identify a node profile of the plurality of node profiles including a plurality of fields, each field of the plurality of fields including one or more value data structures, each value data structure of the one or more value data structures corresponding to a value and further including one or more entries, each entry of the one or more entries corresponding to respective one or more data points that include a string that matches the value of the value data structure, each data point of the one or more data points corresponding to a respective electronic activity of the plurality of electronic activities or a respective record object of the plurality of record objects;

determine, for at least one data point of the one or more data points included in a respective value data structure of a value of a field of the plurality of fields of the node profile, a contribution score of the at least one data point based on a time corresponding to when the at least one data point was generated or updated and a trust score assigned to a system of record corresponding to the at least one data point, the trust score based on comparing field values of object fields included in record objects of the system of record to values of fields of one or more node profiles having respective confidence scores above a threshold; and

generate a confidence score of the value of the field of the node profile based on the contribution score of the at least one data point.

17. The system of claim 16, wherein to determine, for at least one data point of the one or more data points that includes the value of the field of the node profile, the contribution score of the at least one data point, the one or more processors are configured to determine, for each data point of the one or more data points that support the value of the field of the node profile, a respective contribution score of the data point based on a respective time corresponding to when the data point was generated or updated.

18. The system of claim 16, wherein the one more processors are configured to generate the confidence score of the value of the field of the node profile based on the contribution score of the at least one data point, the one or more processors are configured to generate, the confidence score of the value of the field of the node profile based on the respective contribution score of each of the one or more data points that support the value of the field of the node profile.

19. The system of claim 16, further comprising the one or more processors configured to:

receive a second electronic activity;

determine that the second electronic activity includes the value of the field of the node profile;

generate a second contribution score of the second electronic activity for the value of the field of the node profile; and

update the confidence score of the value based on the second contribution score of the second electronic activity.

20. The system of claim 16, further comprising the one or more processors configured to:

identify a record object of a system of record previously not matched to the value of the field of the node profile;

determine that the record object includes the value of the field of the node profile;

generate a contribution score of the record object; and

update the confidence score of the value of the field of the node profile based on the contribution score of the record object.

21. The system of claim 16, wherein the node profile includes a first field having a first value data structure identifying a first value, wherein the first value is assigned to the first field by linking a first electronic activity to the node profile.

22. The system of claim 21, wherein the first value data structure includes a first entry identifying the first electronic activity and wherein the first electronic activity is linked to the first node profile by the one or more processors configured to:

identify, from data included in the first electronic activity, a plurality of strings;

identify a plurality of candidate node profiles to which to link the electronic activity by comparing one or more strings of the plurality of strings to values of fields of respective candidate node profiles;

generate, for each candidate node profile, a match score indicating a likelihood that the electronic activity is transmitted or received by an account corresponding to the candidate node profile based on comparing the plurality of strings included in the electronic activity to values of fields included in the candidate node profile; and

link the first electronic activity to the first node profile based on the match score of the first node profile.

23. The system of claim 16, wherein the value of the field of the node profile includes a first value of a first field of the node profile and wherein the contribution score of the data point is a first contribution score of a first data point and the confidence score is a first confidence score of a first value and wherein the system further comprises the one or more processors configured to:

identify a second value data structure of a second field of the node profile, the second value data structure corresponding to a second value of the second field and further including one or more second entries corresponding to respective one or more second data points that support the second value of the second value data structure;

determine, for at least one second data point of the one or more second data points of the second value of the second field of the node profile, a second contribution score of the second data point based on a time corresponding to when the second data point was generated or updated; and

generate a second confidence score of the second value of the second field of the node profile based on the second contribution score of the at least one second data point.

24. The system of claim 16, wherein the field of the plurality of fields of the node profile comprises a second value and a corresponding second value data structure including at least one second entry identifying a second electronic activity or at least one second record object that includes a second string that matches the second value.

25. The system of claim 16, wherein the field of the plurality of fields is a first field and wherein a second field of the plurality of fields of the node profile comprises a second value and a corresponding second value data structure including at least one second entry identifying the first electronic activity or the first record object that also includes a second string that matches the second value.

26. The system of claim 16, further comprising the one or more processors configured to:

receive a subsequent electronic activity;

link the subsequent electronic activity to the node profile by including one or more entries identifying the subsequent electronic activity to one or more value data structures corresponding to one or more values of one or more fields;

generate for each entry identifying the subsequent electronic activity, a contribution score of the subsequent electronic activity, the entry corresponding to a respective value data structure of a respective value of a respective field; and

generate respective confidence scores for the values based on the respective contribution scores of the subsequent electronic activity.

27. A non-transitory computer readable storage medium including processor executable instructions, which when executed by one or more processors, causes the one or more processors to:

access at least one of i) data from a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or ii) a plurality of record objects of one or more systems of record associated with the one or more data source providers, the one or more processors maintaining a plurality of node profiles and configured to update the plurality of node profiles using at least one of the data from the plurality of electronic activities or the plurality of record objects;

identify a node profile of the plurality of node profiles including a plurality of fields, each field of the plurality of fields including one or more value data structures, each value data structure of the one or more value data structures corresponding to a value and further including one or more entries, each entry of the one or more entries corresponding to respective one or more data points that include a string that matches the value of the value data structure, each data point of the one or more data points corresponding to a respective electronic activity of the plurality of electronic activities or a respective record object of the plurality of record objects;

determine, for at least one data point of the one or more data points included in a respective value data structure of a value of a field of the plurality of fields of the node profile, a contribution score of the at least one data point based on a time corresponding to when the at least one data point was generated or updated and a trust score assigned to a system of record corresponding to the at least one data point, the trust score based on comparing field values of object fields included in record objects of the system of record to values of fields of one or more node profiles having respective confidence scores above a threshold; and

generate a confidence score of the value of the field of the node profile based on the contribution score of the at least one data point.