Systems and methods for maintaining a time series of data points

• US 10,521,443 B2
• Filed: 03/21/2019
• Issued: 12/31/2019
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Brunets, Yurii, Rice, Devin, Rogynskyy, Oleg
• Primary Examiner(s): Nguyen, Thu N
• Application Number:
  US16/360,960
• Publication Number:
  US 20190361896A1
• Time in Patent Office: 285 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

The systems and methods described herein can maintain time series of data points. The system can access at least one of a plurality of electronic activities transmitted or received via electronic accounts of one or more data source providers or a plurality of record objects of one or more systems of record of the one or more data source providers; maintain an association between each electronic activity or record object and a timestamp corresponding to when the electronic activity was sent or received by an or when the record object was last updated; maintain a plurality of node profiles; and generate, for a particular time, a confidence score of a value of a field-value pair of a node profile only using the data of the plurality of electronic activities or the plurality of record objects that correspond to the value and have a corresponding timestamp prior to or contemporaneous with the particular time.

Claims

1. A method, comprising:

accessing, by one or more processors, at least one of a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or a plurality of record objects of one or more systems of record associated with the one or more data source providers;

maintaining, by the one or more processors, an association between each electronic activity or record object and a timestamp respectively corresponding to when the electronic activity was sent or received by an electronic activity account or when the record object was last updated;

maintaining, by the one or more processors a plurality of node profiles, each node profile of the plurality of node profiles including one or more field-value pairs, each field-value pair including a value determined from data of one or more of the plurality of electronic activities or the plurality of record objects; and

generating, by the one or more processors, for a particular time, a confidence score of a particular value of a field-value pair of a node profile only using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time.

2. The method of claim 1, wherein the confidence score is a first confidence score and the field-value pair is a first field-value pair, the method further comprising:

receiving, by the one or more processors, a query for a status of a field corresponding to the first field-value pair of the particular value;

identifying, by the one or more processors, at least one second field-value pair corresponding to the field;

determining, by the one or more processors, a second confidence score of a second value of each of the at least one second field-value pair; and

outputting, by the one or more processors, at least one of the particular value or at least one second value based on the first confidence score and each second confidence score.

3. The method of claim 1, further comprising:

identifying, by the one or more processors, for a field of at least one field-value pair of at least one node profile of the plurality of node profiles, at each of a plurality of points in time, the value corresponding to the field that has a highest confidence score; and

maintaining, by the one or more processors, a time series associating each value corresponding to the field that has the highest confidence score to the respective point in time.

4. The method of claim 3, further comprising generating, by the one or more processors, the plurality of points in time based on timestamps corresponding to the plurality of electronic activities or the plurality of record objects.

5. The method of claim 1, further comprising:

selecting, by the one or more processors, a first node profile of the plurality of node profiles;

identifying, by the one or more processors, a first field of the one or more field-value pairs of the first node profile;

determining, by the one or more processors, a plurality of first values corresponding to the first field for each of a plurality of points in time; and

maintaining, by the one or more processors, a timeline data structure mapping each determined first value to the corresponding point in time.

6. The method of claim 1, wherein the plurality of electronic activities is a plurality of first electronic activities and the plurality of record objects is a plurality of first records objects, the association is a first association, and the timestamp is a first timestamp, the method further comprising:

accessing, by the one or more processors, at least one of a plurality of second electronic activities transmitted or received via the electronic accounts associated with the one or more data source providers or a plurality of second record objects of the one or more systems of record associated with the one or more data source providers;

maintaining, by the one or more processors, a second association between each second electronic activity or second record object and a second timestamp respectively corresponding to when the second electronic activity was sent or received or when the second record object was last updated; and

calculating, by the one or more processors, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using second data of the plurality of second electronic activities or the plurality of second record objects that correspond to the field-value pair of the particular value and have a corresponding second timestamp prior to or contemporaneous with the particular time.

7. The method of claim 1, further comprising:

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

23. The method of claim 1, wherein generating, for the particular time, the confidence score of the particular value of the field-value pair of the node profile only using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time comprises generating, by the one or more processors, for the particular time, the confidence score of the particular value of the field-value pair of the node profile not using data of one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp after the particular time.

8. A system, comprising:

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

access at least one of a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or a plurality of record objects of one or more systems of record associated with the one or more data source providers;

maintain an association between each electronic activity or record object and a timestamp respectively corresponding to when the electronic activity was sent or received by an electronic activity account or when the record object was last updated;

maintain a plurality of node profiles, each node profile of the plurality of node profiles including one or more field-value pairs, each field-value pair including a value determined from data of one or more of the plurality of electronic activities or the plurality of record objects; and

generate, for a particular time, a confidence score of a particular value of a field-value pair of a node profile using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time.

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

receive a query for a status of a field corresponding to the first field-value pair of the particular value;

identify at least one second field-value pair corresponding to the field;

determine a second confidence score of a second value of each of the at least one second field-value pair; and

output at least one of the particular value or at least one second value based on the first confidence score and each second confidence score.

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

identify, for a field of at least one field-value pair of at least one node profile of the plurality of node profiles, at each of a plurality of points in time, the value corresponding to the field that has a highest confidence score; and

maintain a time series associating each value corresponding to the field that has the highest confidence score to the respective point in time.

11. The system of claim 10, wherein the one or more hardware processors are further configured by machine-readable instructions to generate the plurality of points in time based on timestamps corresponding to the plurality of electronic activities or the plurality of record objects.

12. The system of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to:

select a first node profile of the plurality of node profiles;

identify a first field of the one or more field-value pairs of the first node profile;

determine a plurality of first values corresponding to the first field for each of a plurality of points in time;

maintain a timeline data structure mapping each determined first value to the corresponding point in time.

13. The system of claim 8, wherein the plurality of electronic activities is a plurality of first electronic activities and the plurality of record objects is a plurality of first records objects, the association is a first association, and the timestamp is a first timestamp, wherein the one or more hardware processors are further configured by machine-readable instructions to:

access at least one of a plurality of second electronic activities transmitted or received via the electronic accounts associated with the one or more data source providers or a plurality of second record objects of the one or more systems of record associated with the one or more data source providers;

maintain a second association between each second electronic activity or second record object and a second timestamp respectively corresponding to when the second electronic activity was sent or received or when the second record object was last updated; and

calculate, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using second data of the plurality of second electronic activities or the plurality of second record objects that correspond to the field-value pair of the particular value and have a corresponding second timestamp prior to or contemporaneous with the particular time.

14. The system of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to maintain the node profile to include a plurality of fields, each field of the plurality of fields including one or more value data structures, each value data structure of the one or more value data structures corresponding to a value and further including one or more entries, each entry of the one or more entries corresponding to respective one or more data points that include a string that matches the value of the value data structure, each data point of the one or more data points identifying a respective electronic activity of the plurality of electronic activities or a respective record object of the plurality of record objects.

24. The system of claim 8, wherein to generate, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time, the one or more hardware processors are further configured to generate, by the one or more processors, for the particular time, the confidence score of the particular value of the field-value pair of the node profile not using data of one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp after the particular time.

15. 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 at least one of a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or a plurality of record objects of one or more systems of record associated with the one or more data source providers;

maintain an association between each electronic activity or record object and a timestamp respectively corresponding to when the electronic activity was sent or received by an electronic activity account or when the record object was last updated;

maintain a plurality of node profiles, each node profile of the plurality of node profiles including one or more field-value pairs, each field-value pair including a value determined from data of one or more of the plurality of electronic activities or the plurality of record objects; and

generate, for a particular time, a confidence score of a particular value of a field-value pair of a node profile only using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time.

16. The computer-readable storage medium of claim 15, wherein the confidence score is a first confidence score and the field-value pair is a first field-value pair, wherein the one or more processors are further configured to:

receive a query for a status of a field corresponding to the first field-value pair of the particular value;

identify at least one second field-value pair corresponding to the field;

determine a second confidence score of a second value of each of the at least one second field-value pair; and

output at least one of the particular value or at least one second value based on the first confidence score and each second confidence score.

17. The computer-readable storage medium of claim 15, wherein the one or more processors are further configured to:

identify, for a field of at least one field-value pair of at least one node profile of the plurality of node profiles, at each of a plurality of points in time, the value corresponding to the field that has a highest confidence score; and

maintain a time series associating each value corresponding to the field that has the highest confidence score to the respective point in time.

18. The computer-readable storage medium of claim 17, wherein the one or more processors are further configured to generate the plurality of points in time based on timestamps corresponding to the plurality of electronic activities or the plurality of record objects.

19. The computer-readable storage medium of claim 18, wherein the one or more processors are further configured to:

select a first node profile of the plurality of node profiles;

identify a first field of the one or more field-value pairs of the first node profile;

determine a plurality of first values corresponding to the first field for each of the plurality of points in time; and

maintain a timeline data structure mapping each determined first value to the corresponding point in time.

20. The computer-readable storage medium of claim 15, wherein the plurality of electronic activities is a plurality of first electronic activities and the plurality of record objects is a plurality of first records objects, the association is a first association, and the timestamp is a first timestamp, wherein the one or more processors are further configured to:

access at least one of a plurality of second electronic activities transmitted or received via the electronic accounts associated with the one or more data source providers or a plurality of second record objects of the one or more systems of record associated with the one or more data source providers;

maintain a second association between each second electronic activity or second record object and a second timestamp respectively corresponding to when the second electronic activity was sent or received or when the second record object was last updated; and

calculate, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using second data of the plurality of second electronic activities or the plurality of second record objects that correspond to the field-value pair of the particular value and have a corresponding second timestamp prior to or contemporaneous with the particular time.

25. The computer-readable storage medium of claim 15, wherein to generate, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time, the one or more processors are further configured to generate, by the one or more processors, for the particular time, the confidence score of the particular value of the field-value pair of the node profile not using data of one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp after the particular time.

21. A method, comprising:

accessing, by one or more processors, at least one of a plurality of electronic activities transmitted or received via electronic accounts associated with one or more data source providers or a plurality of record objects of one or more systems of record associated with the one or more data source providers;

maintaining, by the one or more processors, an association between each electronic activity or record object and a timestamp respectively corresponding to when the electronic activity was sent or received by an electronic activity account or when the record object was last updated;

maintaining, by the one or more processors a plurality of node profiles, each node profile of the plurality of node profiles including one or more field-value pairs, each field-value pair including a value determined from data of one or more of the plurality of electronic activities or the plurality of record objects; and

generating, by the one or more processors, for a particular time, a confidence score of a particular value of a field-value pair of a node profile using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time.

22. The method of claim 21, wherein generating, for the particular time, the confidence score of the particular value of the field-value pair of the node profile using the data of the one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp prior to or contemporaneous with the particular time comprises generating, by the one or more processors, for the particular time, the confidence score of the particular value of the field-value pair of the node profile not using data of one or more of the plurality of electronic activities or the plurality of record objects that correspond to the field-value pair of the particular value and have a corresponding timestamp after the particular time.