Systems and methods for linking record objects to node profiles

• US 10,528,601 B2
• Filed: 12/31/2018
• Issued: 01/07/2020
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Joseph, Rajit Kurien, Petrov, Volodymyr, Brunets, Yurii
• Primary Examiner(s): Chau, Dung K
• Application Number:
  US16/237,582
• Publication Number:
  US 20190361922A1
• Time in Patent Office: 372 Days
• Field of Search: 707634, 707738, 707748, 707749
• 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 system described herein can automatically match, link, or otherwise associate record objects with one or more node profiles. For a record object that is eligible or qualifies to be matched with one or more node profiles, the system can selectively apply rules to match the record object to a subset of the one or more node profiles based on values of fields extracted from the record object and values of the node profiles. The system can then store an association between the record object and the subset of the one or more node profiles.

Claims

1. A method comprising:

accessing, by one or more processors, a plurality of record objects of one or more systems of record, each record object of the plurality of record objects corresponding to a record object type and the each record object comprising one or more object field-value pairs associating an object field value to a corresponding field of the each record object, the systems of record corresponding to one or more data source providers;

maintaining, by the one or more processors, a plurality of node profiles corresponding to a plurality of unique entities, each node profile including one or more node field-value pairs associating a node field value to a corresponding field of the each node profile;

identifying, by the one or more processors, a record object of the plurality of record objects to match to at least one node profile of the plurality of node profiles;

comparing, by the one or more processors, the object field values of the one or more object field-value pairs of the record object to the corresponding node field values of the corresponding fields of a node profile of the plurality of node profiles;

generating, by the one or more processors based on the comparing, a match score of the node profile indicating a likelihood that the record object corresponds to the node profile;

matching, by the one or more processors, the record object to the node profile of the plurality of node profiles responsive to determining that the match score of the node profile satisfies a threshold;

updating, by the one or more processors, a first value data structure of a first node field-value of the node profile by adding a first entry identifying the record object;

updating, by the one or more processors, a confidence score of the first node field value based on the first entry identifying the record object;

updating, by the one or more processors, responsive to determining that a second object field-value pair matches a second node field-value pair of the node profile, a second value data structure of the second node field-value pair by adding a second entry identifying the record object; and

updating, by the one or more processors, a second confidence score of the second node field value based on the second entry identifying the record object.

2. The method of claim 1, wherein the record object is a first record object of a first subset, the method comprising:

matching, by the one or more processors, a second record object to a second subset of the plurality of node profiles;

identifying, by the one or more processors, a link between the first record object and the second record object; and

at least one of (i) adding the second subset to the first subset or (ii) increasing a match score of comparisons of the node field-value pairs of node profiles of the second subset to the object field-value pairs of the first record object.

3. The method of claim 1, further comprising generating a contribution score of the first entry identifying the record object based on a trust score assigned to the system of record associated with the record object, the contribution score contributing to the confidence score of the first node field value.

4. The method of claim 3, further comprising generating the trust score of the system of record based on comparing the values of object fields of record objects of the system of record to values of node profiles having a confidence score above a predetermined threshold.

5. The method of claim 3, wherein the contribution score is based on a time at which the record object was last updated or modified.

6. The method of claim 1, wherein matching the record object to the node profile includes matching the record object to the node profile based on a matching policy, the matching policy including a first rule having a first weight and a second rule having a second weight.

7. The method of claim 1, further comprising:

maintaining, by the one or more processors, a shadow record object corresponding to the record object;

adding, by the one or more processors, to the shadow record object, one or more values to one or more shadow object fields of the shadow record object determined from the node profile to which the record object is matched.

8. The method of claim 7, further comprising providing a notification to a device to update a value of the object field of the record object based on the one or more values added to the one or more shadow object fields of the shadow record object.

9. The method of claim 1, further comprising:

determining, by the one or more processors, that a third object field value of a third object field of the record object is different from a third node field value of a corresponding field of the node profile;

determining, by the one or more processors, that the third node field value of the corresponding field has a confidence score above a predetermined threshold; and

generating, by the one or more processors, a request to update the third object field value to match the third node field value in the third object field of the record object responsive to determining that the third node field value of the corresponding field has a confidence score above a predetermined threshold.

10. The method of claim 1, comprising determining, by the one or more processors, the match score as a weighted average based on a uniqueness score of each object field value.

11. A method comprising:

accessing, by one or more processors, a plurality of record objects of one or more systems of record, each record object of the plurality of record objects corresponding to a record object type and the each record object comprising one or more object fields having one or more object field values, the one or more systems of record corresponding to one or more data source providers;

maintaining, by the one or more processors, a plurality of node profiles corresponding to a plurality of unique entities, each node profile including a plurality of node profile fields, each node profile field of the plurality of node profile fields including one or more value data structures, each value data structure of the one or more value data structures including a node field value and one or more entries corresponding to respective one or more data points that support the node field value of the value data structure;

identifying, by the one or more processors, a record object of the plurality of record objects to match to at least one node profile of the plurality of node profiles;

matching, by the one or more processors, the record object to a first node profile of the plurality of node profiles based on a first object field value matching a first node field value of the first node profile or a second object field value matching a second node field value of the first node profile;

updating, by the one or more processors, a first value data structure of the first node field value by adding a first entry identifying the record object;

updating, by the one or more processors, a first confidence score of the first node field value based on the first entry identifying the record object;

updating, by the one or more processors, a second value data structure of the second node field value by adding a second entry identifying the record object; and

updating, by the one or more processors, a second confidence score of the second node field value based on the second entry identifying the record object.

12. The method of claim 11, further comprising generating a contribution score of the first entry identifying the record object based on a trust score assigned to a system of record of the one or more systems of record associated with the record object, the contribution score contributing to the confidence score of the first node field value.

13. The method of claim 12, further comprises generating the trust score of the system of record based on comparing values of object fields of record objects of the system of record to values of node profiles having confidence scores above a predetermined threshold.

14. The method of claim 12, wherein the contribution score is based on a time at which the record object was last updated or modified.

15. The method of claim 11, wherein the matching the record object to the first node profile includes matching the record object to the first node profile based on a matching policy, the matching policy including a first rule having a first weight and a second rule having a second weight.

16. The method of claim 11, further comprising:

maintaining, by the one or more processors, a shadow record object corresponding to the record object;

adding, by the one or more processors, to the shadow record object, one or more values to one or more shadow object fields of the shadow record object determined from the first node profile to which the record object is matched.

17. The method of claim 16, further comprising providing a notification to a device to update a value of the object field of the record object based on the one or more values added to the one or more shadow object fields of the shadow record object.

18. The method of claim 11, further comprising:

determining, by the one or more processors, that a third object field value of a third object field of the record object is different from a third node field value of a corresponding field of the node profile;

determining, by the one or more processors, that the third node field value of the corresponding field has a confidence score above a predetermined threshold; and

generating, by the one or more processors, a request to update the third object field value to match the third node field value in the third object field of the record object responsive to determining that the third node field value of the corresponding field has a confidence score above a predetermined threshold.