Systems and methods for generating a performance profile of a node profile including field-value pairs using electronic activities

• US 10,657,130 B2
• Filed: 03/31/2019
• Issued: 05/19/2020
• Est. Priority Date: 05/24/2018
• Status: Active Grant

• Current Assignee: People.ai, Inc.
• Original Assignee: People.ai, Inc.
• Inventors: Rice, Devin, Shah, Kavita, Draper, Adam, Hai, Wei, Navarro, Jose Alberto Muniz, Rogynskyy, Oleg
• Primary Examiner(s): Zhang, Shirley X
• Application Number:
  US16/371,048
• Publication Number:
  US 20190363959A1
• Time in Patent Office: 415 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

This disclosure relates to methods, systems, and storage media for generating a performance profile of a node profile. First electronic activities linked to a first node profile can be accessed. For each first electronic activity, a type of the first electronic activity can be determined. A feature extraction policy can be selected to generate a first feature array for the first electronic activity. The first feature array for the first electronic activity can be generated based on the type of the first electronic activity. A first performance profile of the first node profile can be generated for the time period by providing the generated first feature array for each first electronic activity to one or more models trained using second feature arrays corresponding to second electronic activities of second node profiles. An association between the first node profile and the first performance profile can be stored.

Claims

1. A method comprising:

accessing, by one or more processors, data of a plurality of first electronic activities linked to a first node profile maintained by the one or more processors and having an associated timestamp within a time period;

for each first electronic activity of the plurality of first electronic activities;

determining, by the one or more processors, a type of the first electronic activity;

selecting, by the one or more processors, a feature extraction policy to generate a first feature array for the first electronic activity based on the type of the first electronic activity;

parsing, by the one or more processors, the data of the first electronic activity;

generating, by the one or more processors, using a language complexity determination engine, a language complexity score indicating a level of language complexity of the first electronic activity;

determining, by the one or more processors, a character count or a word count of the first electronic activity;

determining, by the one or more processors, an estimated amount of time to generate the first electronic activity using the language complexity score and the character count or the word count; and

generating, by the one or more processors, the first feature array for the first electronic activity based on the type of the first electronic activity and the estimated amount of time to generate the first electronic activity;

generating, by the one or more processors, a first performance profile of the first node profile for the time period by providing the generated first feature array for each first electronic activity to one or more models trained using second feature arrays corresponding to second electronic activities of second node profiles, each second node profile assigned a respective second performance profile; and

storing, by the one or more processors, an association between the first node profile and the first performance profile.

2. The method of claim 1, further comprising:

for each first electronic activity of the plurality of first electronic activities, categorizing, by the one or more processors, the first electronic activity into one of at least a first type or a second type, based on a data format of the first electronic activity;

determining, by the one or more processors, a first number of the plurality of first electronic activities categorized as the first type;

determining, by the one or more processors, a second number of the plurality of first electronic activities categorized as the second type; and

generating, by the one or more processors, the first performance profile based on the first number and the second number.

3. The method of claim 1, wherein, for at least one of the first electronic activities, generating the first feature array comprises:

extracting, by the one or more processors, one or more activity field-values from the at least one electronic activity to match the at least one electronic activity to a third node profile of at least one participant of the at least one electronic activity;

determining, by the one or more processors, from the third node profile to which the at least one electronic activity is matched, an importance metric of the at least one participant of the at least one electronic activity based on at least one field-value pair of the third node profile satisfying an importance determination policy; and

generating, by the one or more processors, the first feature array for the at least one electronic activity based on the importance metric of the at least one participant.

4. The method of claim 1, wherein, for at least one electronic activity of the plurality of first electronic activities, generating the first feature array comprises:

comparing, by the one or more processors, the at least one electronic activity with at least a second electronic activity of the plurality of first electronic activities to determine a difference in the data between the at least one electronic activity and the second electronic activity;

determining, by the one or more processors, that the difference in data is below a threshold; and

generating, by the one or more processors, the first feature array for the at least one electronic activity based on determining that the difference in data is below the threshold.

5. The method of claim 1, wherein, for at least one of the first electronic activities, generating the first feature array comprises:

identifying, by the one or more processors, a recipient of the first electronic activity;

determining, by the one or more processors, that none of the plurality of first electronic activities were sent from the identified recipient; and

generating, by the one or more processors, the first feature array for the at least one electronic activity based on the determination that none of the plurality of first electronic activities were sent from the identified recipient.

6. The method of claim 1, further comprising:

identifying, by the one or more processors, for at least one electronic activity of the plurality of first electronic activities, a third node profile corresponding to a recipient of the at least one electronic activity;

determining, by the one or more processors, a connection type of a connection between the first node profile and the third node profile; and

discarding, from the plurality of first electronic activities, the at least one electronic activity prior to generating the first performance profile, based on the connection type of the connection between the first node profile and the third node profile.

7. The method of claim 1, wherein determining the type of at least one electronic activity of the plurality of first electronic activities comprises determining, by the one or more processors, that the at least one electronic activity is one of an electronic mail activity, an electronic calendar activity or a phone activity.

8. The method of claim 1, further comprising:

determining, by the one or more processors, from the plurality of first electronic activities, a vacation period for the first node profile using a vacation detection policy;

determining, by the one or more processors, that the time period overlaps with at least a portion of the vacation period; and

adjusting, by the one or more processors, the first performance profile responsive to determining that the time period overlaps with at least the portion of the vacation period.

9. The method of claim 1, further comprising selecting, by the one or more processors, at least one of the second node profiles based on a field-value pair of the second node profile matching a corresponding field-value pair of the first node profile.

10. A system comprising:

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

access data of a plurality of first electronic activities linked to a first node profile maintained by the one or more processors and having an associated timestamp within a time period;

for each first electronic activity of the plurality of first electronic activities;

determine a type of the first electronic activity;

select a feature extraction policy to generate a first feature array for the first electronic activity based on the type of the first electronic activity;

parsing the data of the first electronic activity;

generating using a language complexity determination engine, a language complexity score indicating a level of language complexity of the first electronic activity;

determining a character count or a word count of the at least one electronic activity;

determining an estimated amount of time to generate the at least one electronic activity using the language complexity score and the character count or the word count; and

generate the first feature array for the first electronic activity based on the type of the first electronic activity and the estimated amount of time to generate the at least one electronic activity;

generate a first performance profile of the first node profile for the time period by providing the generated first feature array for each first electronic activity to one or more models trained using second feature arrays corresponding to second electronic activities of second node profiles, each second node profile assigned a respective second performance profile; and

store an association between the first node profile and the first performance profile.

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

for each first electronic activity of the plurality of first electronic activities, categorize the first electronic activity into one of at least a first type or a second type, based on a data format of the first electronic activity;

determine a first number of the plurality of first electronic activities categorized as the first type;

determine a second number of the plurality of first electronic activities categorized as the second type; and

generate the first performance profile based on the first number and the second number.

12. The system of claim 10, wherein, for at least one electronic activity of the plurality of first electronic activities, the one or more processors are further configured by the machine-readable instructions to generate the first feature array by:

extracting one or more activity field-values from the at least one electronic activity to match the at least one electronic activity to a third node profile of at least one participant of the at least one electronic activity;

determining from the third node profile to which the at least one electronic activity is matched, an importance metric of the at least one participant of the at least one electronic activity based on at least one field-value pair of the third node profile satisfying an importance determination policy; and

generating the first feature array for the at least one electronic activity based on the importance metric of the at least one participant.

13. The system of claim 10, wherein, for at least one electronic activity of the plurality of first electronic activities, the one or more processors are further configured by the machine-readable instructions to generate the first feature array by:

comparing the at least one electronic activity with at least a second electronic activity of the plurality of first electronic activities to determine a difference in the data between the at least one electronic activity and the second electronic activity;

determining that the difference in the data is below a threshold; and

generating the first feature array for the at least one electronic activity based on determining that the difference in the data is below the threshold.

14. The system of claim 10, wherein, for at least one electronic activity of the plurality of first electronic activities, the one or more processors are further configured by the machine-readable instructions to generate the first feature array by:

identifying a recipient of the at least one electronic activity;

determining that none of the plurality of first electronic activities were sent from the identified recipient; and

generating the first feature array for the at least one electronic activity based on the determination that none of the plurality of first electronic activities were sent from the identified recipient.

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

identify, for at least one electronic activity of the plurality of first electronic activities, a third node profile corresponding to a recipient of the at least one electronic activity;

determine a connection type of a connection between the first node profile and the third node profile; and

discard, from the plurality of first electronic activities, the at least one electronic activity prior to generating the first performance profile, based on the connection type of the connection between the first node profile and the third node profile.

16. The system of claim 10, wherein the one or more processors are further configured by the machine-readable instructions to determine the type of at least one electronic activity of the plurality of first electronic activities by determining that the at least one electronic activity is one of an electronic mail activity, an electronic calendar activity or a phone activity.

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

determine, from the plurality of first electronic activities, a vacation period for the first node profile using a vacation detection policy;

determine that the time period overlaps with at least a portion of the vacation period; and

adjust the first performance profile responsive to determining that the time period overlaps with at least the portion of the vacation period.

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 a plurality of first electronic activities linked to a first node profile maintained by the one or more processors and having an associated timestamp within a time period;

for each first electronic activity of the plurality of first electronic activities;

determining a type of the first electronic activity;

selecting a feature extraction policy to generate a first feature array for the first electronic activity based on the type of the first electronic activity;

parsing the data of the first electronic activity;

generating using a language complexity determination engine, a language complexity score indicating a level of language complexity of the at least one electronic activity;

determining a character count or a word count of the at least one electronic activity;

determining an estimated amount of time to generate the at least one electronic activity using the language complexity score and the character count or the word count; and

generating the first feature array for the first electronic activity based on the type of the first electronic activity and the estimated amount of time to generate the at least one electronic activity;

generating a first performance profile of the first node profile for the time period by providing the generated first feature array for each first electronic activity to one or more models trained using second feature arrays corresponding to second electronic activities of second node profiles, each second node profile assigned a respective second performance profile; and

storing an association between the first node profile and the first performance profile.