Systems and methods for matching electronic activities to record objects using feedback based match policies

• US 10,489,430 B1
• Filed: 05/21/2019
• Issued: 11/26/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Nykytiuk, Volodymyr, Hermanek, Stefan, Korkuna, Ostap
• Primary Examiner(s): Meng, Jau Shya
• Application Number:
  US16/418,629
• Publication Number:
  US 20190361924A1
• Time in Patent Office: 189 Days
• Field of Search: None
• 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

Systems and methods for matching electronic activities to record objects using feedback based match policies can include accessing a plurality of electronic activities and record objects. The systems and method can include identifying candidate record objects by applying a matching model. The systems and methods can include selecting a record object based on a match score. The systems and methods can include configuring the matching model in a first configuration responsive to a first feedback type or configuring the matching model in a second configuration responsive to a second feedback type.

Claims

1. A method, comprising:

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

accessing, by the 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 comprising one or more object fields having one or more object field values, the systems of record corresponding to the one or more data source providers;

identifying, by the one or more processors, responsive to applying a matching model for identifying candidate record objects, a plurality of candidate record objects with which to match an electronic activity of the plurality of electronic activities, the matching model used to generate a respective match score between the electronic activity and a respective candidate record object of the plurality of candidate record objects;

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

monitoring, by the one or more processors, the match between the electronic activity and the first record object for feedback regarding the match;

responsive to not receiving a first feedback regarding the selection of the first record object,

identifying, by the one or more processors, a first data structure including an electronic activity identifier identifying the electronic activity, a record object identifier indicating the first record object and a first label indicating that the electronic activity matches the first record object;

configuring, by the one or more processors, the matching model in a first configuration by training the matching model using the first data structure, the matching model configured to output a second match score between the electronic activity and the first record object in the first configuration greater than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a first input to the matching model; and

responsive to receiving a second feedback corresponding to the electronic activity not matching the first record object,

modifying, by the one or more processors, the first label of the first data structure to a second label indicating that the electronic activity does not match the first record object or generating, by the one or more processors, a second data structure including the electronic activity identifier, the record object identifier indicating the first record object, and the second label indicating that the electronic activity does not match the first record object; and

configuring, by the one or more processors, the matching model in a second configuration by training the matching model using the second data structure or the modified first data structure, the matching model configured to output a third match score between the electronic activity and the first record object in the second configuration less than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a second input to the matching model.

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

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

applying, the one or more processors, the matching model in the first configuration or in the second configuration to the second electronic activity to generate a fourth match score between the second electronic activity and the first record object; and

removing, by the one or more processors, in the one or more data structures, the association between the second electronic activity and the first record object responsive to the fourth match score satisfying a removal condition.

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

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

applying, the one or more processors, the matching model in the first configuration or the second configuration to the third electronic activity to generate a fifth match score between the third electronic activity and the first record object; and

maintaining, by the one or more processors, in the one or more data structures, the association between the third electronic activity and the first record object responsive to the fifth match score not satisfying a removal condition.

4. The method of claim 1, further comprising:

identifying, by the one or more processors, the second feedback comprises identifying an indication that the electronic activity matches a second record object of the plurality of candidate record objects;

wherein configuring the matching model in the second configuration comprises configuring, by the one or more processors, responsive to the indication, the matching model to generate a fourth match score between the electronic activity and the second record object greater than the first match score between the electronic activity and the first record object.

5. The method of claim 4, further comprising:

generating, by the one or more processors, a third data structure including the electronic activity identifier, the record object identifier, and a third label indicating that the electronic activity matches the second record object as a third input to the matching model; and

providing, by the one or more processors, the third data structure as an input to the matching model to cause the matching model to generate the fourth match score between the electronic activity and the second record object greater than the first match score between the electronic activity and the first record object.

6. The method of claim 1, wherein applying the matching model to generate the respective match score comprises generating, by the one or more processors from the electronic activity, a plurality of activity field-value pairs to which to compare to the one or more object field values of the one or more object fields of the first record object.

7. The method of claim 1, wherein applying the matching model to generate the respective match scores comprises:

generating, by the one or more processors, a plurality of structured data tags for the electronic activity by identifying one or more keywords of the electronic activity, one or more character strings of the electronic activity satisfying a predetermined regular expression pattern, or one or more participants of the electronic activity; and

providing, by the one or more processors, the plurality of structured data tags and the one or more object field values of the one or more object fields of the first record object as a model input to the matching model.

8. A system, the system comprising:

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

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

access 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 comprising one or more object fields having one or more object field values, the systems of record corresponding to the one or more data source providers;

identify, responsive to applying a matching model for identifying candidate record objects, a plurality of candidate record objects with which to match an electronic activity of the plurality of electronic activities, the matching model used to generate a respective match score between the electronic activity and a respective candidate record object of the plurality of candidate record objects;

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

monitor the match between the electronic activity and the first record object for feedback regarding the match;

responsive to not receiving a first feedback regarding the selection of the first record object,

identify a first data structure including an electronic activity identifier identifying the electronic activity, a record object identifier indicating the first record object and a first label indicating that the electronic activity matches the first record object;

configure the matching model in a first configuration by training the matching model using the first data structure, the matching model configured to output a second match score between the electronic activity and the first record object in the first configuration greater than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a first input to the matching model; and

responsive to receiving a second feedback corresponding to the electronic activity not matching the first record object,

modify the first label of the first data structure to a second label indicating that the electronic activity does not match the first record object or generating, by the one or more processors, a second data structure including the electronic activity identifier, the record object identifier indicating the first record object, and the second label indicating that the electronic activity does not match the first record object; and

configure the matching model in a second configuration by training the matching model using the second data structure or the modified first data structure, the matching model configured to output a third match score between the electronic activity and the first record object in the second configuration less than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a second input to the matching model.

9. The system of claim 8, wherein the electronic activity is a first electronic activity and wherein the one or more hardware processors are further configured by machine-readable instructions to:

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

apply the matching model in the first configuration or in the second configuration to the second electronic activity to generate a fourth match score between the second electronic activity and the first record object; and

remove, in the one or more data structures, the association between the second electronic activity and the first record object responsive to the fourth match score satisfying a removal condition.

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

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

apply the matching model in the first configuration or the second configuration to the third electronic activity to generate a fifth match score between the third electronic activity and the first record object; and

maintain, in the one or more data structures, the association between the third electronic activity and the first record object responsive to the fifth match score not satisfying a removal condition.

11. The method of claim 8, further comprising:

identify the second feedback comprises identifying an indication that the electronic activity matches a second record object of the plurality of candidate record objects;

wherein configuring the matching model in the second configuration comprises configuring, by the one or more processors, responsive to the indication, the matching model to generate a fourth match score between the electronic activity and the second record object greater than the first match score between the electronic activity and the first record object.

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

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

access 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 comprising one or more object fields having one or more object field values, the systems of record corresponding to the one or more data source providers;

identify, responsive to applying a matching model for identifying candidate record objects, a plurality of candidate record objects with which to match an electronic activity of the plurality of electronic activities, the matching model used to generate a respective match score between the electronic activity and a respective candidate record object of the plurality of candidate record objects;

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

monitor the match between the electronic activity and the first record object for feedback regarding the match;

responsive to not receiving a first feedback regarding the selection of the first record object

identify a first data structure including an electronic activity identifier identifying the electronic activity, a record object identifier indicating the first record object and a first label indicating that the electronic activity matches the first record object;

configure the matching model in a first configuration by training the matching model using the first data structure, the matching model configured to output a second match score between the electronic activity and the first record object in the first configuration greater than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a first input to the matching model; and

responsive to receiving a second feedback corresponding to the electronic activity not matching the first record object,

modify the first label of the first data structure to a second label indicating that the electronic activity does not match the first record object or generating, by the one or more processors, a second data structure including the electronic activity identifier, the record object identifier indicating the first record object, and the second label indicating that the electronic activity does not match the first record object; and

configure the matching model in a second configuration by training the matching model using the second data structure or the modified first data structure, the matching model configured to output a third match score between the electronic activity and the first record object in the second configuration less than the first match score responsive to receiving data corresponding to the electronic activity and the first record object as a second input to the matching model.