SYSTEMS AND METHODS FOR IDENTIFYING A SEQUENCE OF EVENTS AND PARTICIPANTS FOR RECORD OBJECTS

• US 20200133953A1
• Filed: 12/23/2019
• Published: 04/30/2020
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Markovsky, Yury
• Granted Patent: US 11,153,396 B2
• Time in Patent Office: Days
• 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

Methods, systems, and storage media for identifying a sequence of events and participants for record objects are disclosed. Exemplary implementations may: access record objects of a system of record; identify a subset of record objects associated with a group entity and having a first record object status; identify one or more electronic activities linked to the record objects; determine an event-participant pattern based on the electronic activities linked to the record object; identify electronic activities linked with a second record object; determine that a first event is performed by the a participant type and a second event is not yet performed by a second participant type; generate a content item identifying an action to trigger a performance of the second event; and transmit the content item to a device of a participant of at least one electronic activity linked with the second record object.

Claims

1. A method, comprising:

accessing, by one or more processors, a plurality of record objects of a system of record of a data source provider, each record object of the plurality of record objects corresponding to a respective group entity, each record object comprising one or more object field-value pairs and linked to one or more electronic activities;

identifying, by the one or more processors, a subset of record objects of the plurality of record objects associated with a group entity and having a first record object status;

identifying, by the one or more processors, for each record object of the subset of record objects, one or more electronic activities linked to the record object, each electronic activity identifying one or more participants and corresponding to at least one event;

determining, by the one or more processors, for each record object of the subset of record objects, an event-participant pattern based on the electronic activities linked to the record object, the event-participant pattern including at least a first event performed by a first participant type and a second event performed by a second participant type;

identifying, by the one or more processors, electronic activities linked with a second record object of the plurality of record objects;

determining, by the one or more processors, from the identified electronic activities linked with the second record object, that the first event is performed by the first participant type and the second event is not yet performed by the second participant type;

generating, by the one or more processors, a content item identifying an action to trigger a performance of the second event by at least one participant of the second participant type to update a record object status of the second record object to the record object status of the first record object; and

transmitting, by the one or more processors, the content item to a device of a participant of at least one electronic activity linked with the second record object.

2. The method of claim 1, wherein the first event corresponds to an update to a value for an object field-value pair of the record object and wherein the second event corresponds to an occurrence of a type of electronic activity with one or more participants having the second participant type.

3. The method of claim 1, further comprising:

detecting, by the one or more processors, based on subsequent electronic activity corresponding to the second record object, occurrence of the second event; and

updating, by the one or more processors responsive to detecting the occurrence of the second event, the record object status of the second record object status to match the record object status of the first record object.

4. The method of claim 1, further comprising determining, by the one or more processors, the first participant type of a participant of at least one of the identified electronic activities corresponding to the second record object using node field-value pairs of a node profile corresponding to the participant.

5. The method of claim 1, further comprising determining, by the one or more processors, the first participant type of a participant of at least one of the identified electronic activities corresponding to the second record object using an object field-value pair of the second record object identifying the participant.

6. The method of claim 1, further comprising:

identifying, by the one or more processors, the participant to which to transmit the content item comprises;

identifying, by the one or more processors, a group of participants associated with one another and corresponding to the second record object; and

selecting, by the one or more processors from the group of participants, the participant based on at least one of a field-value pair of a node profile corresponding to the participant or a connection profile between the participant and the at least one participant linked to the second record object.

7. The method of claim 6, wherein the content item identifies a second participant of the at least one participant, and the second participant is identified using electronic activities linked with other record objects of the group entity, the second participant having the second participant type and being included in electronic activities corresponding to the second event of the other record objects.

8. The method of claim 1, wherein determining that the first event is performed by the first participant type comprises:

identifying, by the one or more processors, a thread of the identified electronic activities linked with the second record object;

identifying, by the one or more processors, from the thread of the identified electronic activities, an electronic activity corresponding to the first event;

determining, by the one or more processors, a context of the identified electronic activity based on one or more other electronic activities within the thread; and

detecting, by the one or more processors, performance of the first event based on the context of the identified electronic activity.

9. The method of claim 1, wherein determining that the first event is performed by the first participant type comprises:

identifying, by the one or more processors, from the identified electronic activities linked with the second record object, an electronic activity including a new participant;

determining, by the one or more processors, a node field-value pair of a node profile linked to the new participant; and

detecting, by the one or more processors, performance of the first event based on the node field-value pair of the node profile of the new participant satisfying an event detection policy corresponding to the first event.

10. The method of claim 1, wherein determining that the first event is performed by the first participant type comprises:

identifying, by the one or more processors, from the identified electronic activities linked with the second record object, an electronic activity including a new participant;

identifying, by the one or more processors from a record object of the subset of record objects having the first record object status, an object field-value pair indicating the new participant; and

detecting, by the one or more processors, performance of the first event based on the identified electronic activity including the new participant and the at least one record object of the subset of record objects having the first completion status.

11. The method of claim 1, further comprising:

receiving, by the one or more processors, the event-participant pattern from a computing device associated with the group entity.

12. The method of claim 1, wherein the event-participant pattern comprises a predetermined set of events including the first event and the second event.

13. The method of claim 1, wherein determining that the first event is performed by the first participant type comprises:

determining, by the one or more processors, that the first event is performed by the first participant type responsive to inputting the identified electronic activities linked to the second record object in a machine learning model that is trained using electronic activities of previous record objects having the first record object status and electronic activities of previous record objects having the second record object status, wherein the machine learning model is configured to provide an output indicating performance of the first event.

14. The method of claim 1, wherein determining that the first event is performed by the first participant type comprises:

assigning, by the one or more processors, an importance score to each of the identified electronic activities linked to the second record object based on a change in a value for an object field-value pair of the second record object; and

detecting, by the one or more processors, performance of the first event based on the importance scores for the identified electronic activities satisfying an event detection threshold corresponding to the first event.

15. A system comprising:

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

access a plurality of record objects of a system of record of a data source provider, each record object of the plurality of record objects corresponding to a respective group entity, each record object comprising one or more object field-value pairs and linked to one or more electronic activities;

identify a subset of record objects of the plurality of record objects associated with a group entity and having a first record object status;

identify, for each record object of the subset of record objects, one or more electronic activities linked to the record object, each electronic activity identifying one or more participants and corresponding to at least one event;

determine, for each record object of the subset of record objects, an event-participant pattern based on the electronic activities linked to the record object, the event-participant pattern including at least a first event performed by a first participant type and a second event performed by a second participant type;

identify electronic activities linked with a second record object of the plurality of record objects;

determine, from the identified electronic activities linked with the second record object, that the first event is performed by the first participant type and the second event is not yet performed by the second participant type;

generate a content item identifying an action to trigger a performance of the second event by at least one participant of the second participant type to update a record object status of the second record object to the record object status of the first record object; and

transmit the content item to a device of a participant of at least one electronic activity linked with the second record object.

16. The system of claim 15, wherein determining that the first event is performed by the first participant type comprises:

identify a thread of the identified electronic activities linked with the second record object;

identify, from the thread of the identified electronic activities, an electronic activity corresponding to the first event;

determine a context of the identified electronic activity based on one or more other electronic activities within the thread; and

detect performance of the first event based on the context of the identified electronic activity.

17. The system of claim 15, wherein determining that the first event is performed by the first participant type comprises:

identify, from the identified electronic activities linked with the second record object, an electronic activity including a new participant;

determine a node field-value pair of a node profile linked to the new participant; and

detect performance of the first event based on the node field-value pair of the node profile of the new participant satisfying an event detection policy corresponding to the first event.

18. The system of claim 15, wherein determining that the first event is performed by the first participant type comprises:

identify, from the identified electronic activities linked with the second record object, an electronic activity including a new participant;

identify, from a record object of the subset of record objects having the first record object status, an object field-value pair indicating the new participant; and

detect performance of the first event based on the identified electronic activity including the new participant and the at least one record object of the subset of record objects having the first completion status.

19. The system of claim 15, wherein determining that the first event is performed by the first participant type comprises:

determine that the first event is performed by the first participant type responsive to inputting the identified electronic activities linked to the second record object in a machine learning model that is trained using electronic activities of previous record objects having the first record object status and electronic activities of previous record objects having the second record object status, wherein the machine learning model is configured to provide an output indicating performance of the first event.

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

accessing a plurality of record objects of a system of record of a data source provider, each record object of the plurality of record objects corresponding to a respective group entity, each record object comprising one or more object field-value pairs and linked to one or more electronic activities;

identifying a subset of record objects of the plurality of record objects associated with a group entity and having a first record object status;

identifying, for each record object of the subset of record objects, one or more electronic activities linked to the record object, each electronic activity identifying one or more participants and corresponding to at least one event;

determining, for each record object of the subset of record objects, an event-participant pattern based on the electronic activities linked to the record object, the event-participant pattern including at least a first event performed by a first participant type and a second event performed by a second participant type;

identifying electronic activities linked with a second record object of the plurality of record objects;

determining, from the identified electronic activities linked with the second record object, that the first event is performed by the first participant type and the second event is not yet performed by the second participant type;

generating a content item identifying an action to trigger a performance of the second event by at least one participant of the second participant type to update a record object status of the second record object to the record object status of the first record object; and

transmitting the content item to a device of a participant of at least one electronic activity linked with the second record object.