Systems and methods for inferring schedule patterns using electronic activities of node profiles

• US 10,496,688 B1
• Filed: 04/29/2019
• Issued: 12/03/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rogynskyy, Oleg, Wulf, John, Brunets, Yurii
• Primary Examiner(s): Trujillo, James
• Assistant Examiner(s): Morris, John J
• Application Number:
  US16/398,157
• Publication Number:
  US 20190362248A1
• Time in Patent Office: 218 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
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• 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 inferring schedule patterns using electronic activities of node profiles are disclosed. Exemplary implementations may: access electronic activities via electronic accounts associated with data source providers; identify, for a node profile, a set of electronic activities of each electronic account linked to the node profile within a time period; identify for each electronic activity, a timestamp of the electronic activity; generate for each time interval within the time period, a temporal distribution of electronic activity for the time interval based on respective timestamps of each electronic activity; determine an electronic activity pattern based on the temporal distribution of electronic activity for each time interval within the time period; identify based on the electronic activity pattern, a first region of the electronic activity pattern satisfying a region identification policy; and store, in one or more data structures, an association between the first region and the node profile.

Claims

1. A method comprising:

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

identifying, by the one or more processors, for a node profile maintained by the one or more processors, a first set of electronic activities sent from or received by at least one electronic account linked to the node profile within a first time period;

identifying, by the one or more processors, for each electronic activity of the first set of electronic activities, a timestamp at which the electronic activity was sent or received;

generating, by the one or more processors, for each time interval within the first time period, a first temporal distribution of electronic activity for the time interval based on respective timestamps of each electronic activity of the set of electronic activities within the time interval;

determining, by the one or more processors, an electronic activity pattern based on the temporal distribution of electronic activity for each time interval within the first time period;

identifying, by the one or more processors, based on the electronic activity pattern, a first region of the electronic activity pattern satisfying a region identification policy;

storing, by the one or more processors in one or more data structures, an association between the first region and the node profile;

identifying, by the one or more processors, a second set of electronic activities sent from or received by the at least one electronic account linked with the node profile within a second time period;

generating, by the one or more processors, for a second time interval within the second time period, a second temporal distribution of electronic activity for the second time interval based on respective timestamps of each electronic activity of the second set of electronic activities within the second time interval;

determining, by the one or more processors, responsive to comparing the second temporal distribution to the determined electronic activity pattern for the node profile, that the second temporal distribution satisfies an event condition detection policy for detecting an event condition; and

storing, by the one or more processors, in the one or more data structures, an association between the node profile and the event condition.

2. The method of claim 1, wherein identifying the first region of the temporal distribution of electronic activity satisfying the region identification policy comprises identifying the first region by applying a probabilistic model to the electronic activity pattern.

3. The method of claim 1, further comprising determining a first type of electronic activity and wherein the first set of electronic activities within the time interval corresponds to the first type.

4. The method of claim 1, wherein identifying the first region comprises:

comparing, by the one or more processors, the determined electronic activity pattern to a plurality of stored electronic activity patterns corresponding to respective first regions; and

identifying, by the one or more processors, the first region based on which of the plurality of stored electronic activity patterns most closely matches the determined electronic activity pattern.

5. The method of claim 1, further comprising:

identifying, by the one or more processors, one or more values for a field-value pair of the node profile, and

wherein identifying the first region comprises identifying, by the one or more processors, based on the electronic activity pattern and the identified one or more values, the first region of the electronic activity pattern satisfying the region identification policy.

6. The method of claim 1, wherein the event condition detection policy is used to detect the event condition from a plurality of event conditions based on the determined electronic activity pattern satisfying a corresponding characteristic of a plurality of characteristics associated with a respective event condition.

7. The method of claim 1, wherein the event condition detection policy includes a machine learning model trained to classify temporal distributions of electronic activity;

and

wherein determining that the second temporal distribution satisfies the event condition detection policy comprises determining that the second temporal distribution corresponds to the event condition using the machine learning model.

8. The method of claim 7, further comprising:

identifying, for a first event condition type, a first plurality of training examples including temporal distributions corresponding to the first event condition type and a first label identifying the first event condition type; and

training the machine learning model using the first plurality of training examples to detect event conditions corresponding to the first event condition type.

9. The method of claim 8, further comprising:

identifying, for a second event condition type, a second plurality of training examples including temporal distributions corresponding to the second event condition type and a second label identifying the second event condition type; and

training the machine learning model using the second plurality of training examples to detect event conditions corresponding to the second event condition type.

10. 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 a plurality of electronic accounts associated with a plurality of data source providers;

identify, for a node profile maintained by the one or more processors, a first set of electronic activities sent from or received by at least one electronic account linked to the node profile within a first time period;

identify, for each electronic activity of the first set of electronic activities, a timestamp at which the electronic activity was sent or received;

generate, for each time interval within the first time period, a first temporal distribution of electronic activity for the time interval based on respective timestamps of each electronic activity of the set of electronic activities within the time interval;

determine an electronic activity pattern based on the temporal distribution of electronic activity for each time interval within the first time period;

identify, based on the electronic activity pattern, a first region of the electronic activity pattern satisfying a region identification policy;

store, in one or more data structures, an association between the first region and the node profile;

identify a second set of electronic activities sent from or received by the at least one electronic account linked with the node profile within a second time period;

generate, for a second time interval within the second time period, a second temporal distribution of electronic activity for the second time interval based on respective timestamps of each electronic activity of the second set of electronic activities within the second time interval;

determine, responsive to comparing the second temporal distribution to the determined electronic activity pattern for the node profile, that the second temporal distribution satisfies an event condition detection policy for detecting an event condition; and

store, in the one or more data structures, an association between the node profile and the event condition.

11. The system of claim 10, wherein identifying the first region of the temporal distribution of electronic activity satisfying the region identification policy comprises identifying the first region by applying a probabilistic model to the electronic activity pattern.

12. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to determine a first type of electronic activity, and wherein the first set of electronic activities within the time interval corresponds to the first type.

13. The system of claim 10, wherein identifying the first region comprises comparing the determined electronic activity pattern to a plurality of stored electronic activity patterns corresponding to respective first regions;

and

wherein identifying the first region comprises identifying the first region based on which of the plurality of stored electronic activity patterns most closely matches the determined electronic activity pattern.

14. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to:

identify one or more values for a field-value pair of the node profile;

wherein identifying the first region comprises identifying, based on the electronic activity pattern and the identified one or more values, the first region of the electronic activity pattern satisfying the region identification policy.

15. The system of claim 10, wherein the event condition detection policy is used to detect the event condition from a plurality of event conditions based on the determined electronic activity pattern satisfying a corresponding characteristic of a plurality of characteristics associated with a respective event condition.

16. The system of claim 10, wherein the event condition detection policy includes a machine learning model trained to classify temporal distributions of electronic activity;

and

wherein determining that the second temporal distribution satisfies the event condition detection policy comprises determining that the second temporal distribution corresponds to the event condition using the machine learning model.

17. The system of claim 16, wherein the one or more hardware processors are further configured by machine-readable instructions to:

identify, for a first event condition type, a first plurality of training examples including electronic activity patterns of a respective node profile and a first label identifying the first event condition type;

train the machine learning model using the first plurality of training examples to detect event conditions corresponding to the first event condition type.

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

accessing data of a plurality of electronic activities transmitted or received via a plurality of electronic accounts associated with a plurality of data source providers;

identifying, for a node profile maintained by the one or more processors, a first set of electronic activities sent from or received by at least one electronic account linked to the node profile within a first time period;

identifying, for each electronic activity of the first set of electronic activities, a timestamp at which the electronic activity was sent or received;

generating, for each time interval within the first time period, a first temporal distribution of electronic activity for the time interval based on respective timestamps of each electronic activity of the set of electronic activities within the time interval;

determining, an electronic activity pattern based on the temporal distribution of electronic activity for each time interval within the first time period;

identifying, based on the electronic activity pattern, a first region of the electronic activity pattern satisfying a region identification policy;

storing, in one or more data structures, an association between the first region and the node profile;

identifying a second set of electronic activities sent from or received by the at least one electronic account linked with the node profile within a second time period;

generating, for a second time interval within the second time period, a second temporal distribution of electronic activity for the second time interval based on respective timestamps of each electronic activity of the second set of electronic activities within the second time interval;

determining, responsive to comparing the second temporal distribution to the determined electronic activity pattern for the node profile, that the second temporal distribution satisfies an event condition detection policy for detecting an event condition; and

storing, in the one or more data structures, an association between the node profile and the event condition.