Automated assessment of organization mood

Patent metadata

Field	Value
Publication number	US-9716599-B1
Application number	US-201313829550-A
Country	US
Kind code	B1
Filing date	Mar 14, 2013
Priority date	Mar 14, 2013
Publication date	Jul 25, 2017
Grant date	Jul 25, 2017

Abstract

Methods for preventing the transmission of sensitive information to locations outside of a secure network by a person who has legitimate access to the sensitive information are described. In some embodiments, in order for an end user of a computing device to establish a secure connection with a secure network and access data stored on the secure network, a client application running on the computing device may be required by the secure network. The client application may monitor visual cues (e.g., facial expressions and gestures) associated with the end user, detect suspicious activity performed by the end user based on the visual cues, and in response to detecting suspicious activity may perform mitigating actions to prevent the transmission of sensitive information such as alerting human resources personnel or requiring authorization prior to sending information to locations outside of the secure network.

First claim

What is claimed is:

1. A method, comprising:
determining a baseline group mood classification associated with a plurality of people, the baseline group mood classification corresponds with a first time period;
transmitting an electronic message to a plurality of target addresses associated with the plurality of people subsequent to the first time period and prior to a second time period;
determining a group mood classification associated with the plurality of people in response to transmitting the electronic message, the determining a group mood classification comprises determining a plurality of individual mood classifications associated with the plurality of people reading the electronic message during the second time period and determining a most frequent classification of the plurality of individual mood classifications, wherein determining the plurality of individual mood classifications comprises detecting that a first person corresponding with a first email address of the plurality of target addresses has caused the electronic message to be displayed and determining a first individual mood classification of the plurality of individual mood classifications associated with the first person while the first person is reading the electronic message;
detecting that the group mood classification has deviated from the baseline group mood classification by more than a threshold amount and that the deviation comprises a positive deviation or a negative deviation;
transmitting the electronic message to a second set of addresses different from the plurality of target addresses in response to detecting that the group mood classification has deviated from the baseline group mood classification by more than the threshold amount and that the deviation comprises the positive deviation; and
preventing transmission of the electronic message to the second set of addresses in response to detecting that the group mood classification has deviated from the baseline group mood classification by more than the threshold amount and that the deviation comprises the negative deviation.

2. The method of claim 1 , wherein:
the determining a plurality of individual mood classifications comprises applying facial expression and mood detection techniques to images of the plurality of people captured during the second time period; and
the determining the plurality of individual mood classifications comprises determining the first individual mood classification of the plurality of individual mood classifications associated with the first person while the first person is reading the electronic message at a first point in time and determining a second individual mood classification of the plurality of individual mood classifications associated with a second person while the second person is reading the electronic message at a second point in time different from the first point in time.

3. The method of claim 1 , wherein:
the detecting that the group mood classification has deviated from the baseline group mood classification comprises determining that a first numerical value associated with the group mood classification is less than a second numerical value associated with the baseline group mood classification by more than the threshold amount.

4. The method of claim 1 , wherein:
the detecting that the group mood classification has deviated from the baseline group mood classification comprises determining that a first numerical value associated with the group mood classification is more than a second numerical value associated with the baseline group mood classification by more than the threshold amount.

5. The method of claim 1 , wherein:
the determining a baseline group mood classification comprises applying facial expression and mood detection techniques to images of the plurality of people captured during the first time period.

6. The method of claim 1 , wherein:
the second time period is less than the first time period.

7. The method of claim 1 , wherein:
the determining a group mood classification is performed by a server; and
the detecting that the group mood classification has deviated is performed by the server.

8. A system, comprising:
a storage device configured to store a baseline group mood classification associated with a plurality of people, the baseline group mood classification corresponds with a first time period; and
a processor configured to transmit an electronic message to a plurality of target addresses associated with the plurality of people subsequent to the first time period and prior to a second time period and determine a group mood classification associated with the plurality of people in response to transmitting the electronic message, the processor configured to determine a plurality of individual mood classifications associated with the plurality of people asynchronously reading the electronic message during the second time period and determine a most frequent classification of the plurality of individual mood classifications, the processor configured to detect that a first person corresponding with a first email address of the plurality of target addresses has caused the electronic message to be displayed and identify a first individual mood classification of the plurality of individual mood classifications associated with the first person while the first person is reading the electronic message, the processor configured to detect that the group mood classification has deviated from the baseline group mood classification by more than a threshold amount and that the deviation comprises a positive deviation or a negative deviation, the processor configured to transmit the electronic message to a second set of addresses different from the plurality of target addresses in response to detecting that the group mood classification has deviated from the baseline group mood classification by more than the threshold amount and that the deviation comprises the positive deviation, the processor configured to prevent transmission of the electronic message to the second set of addresses in response to detecting that the group mood classification has deviated from the baseline group mood classification by more than the threshold amount and that the deviation comprises the negative deviation.

9. The system of claim 8 , wherein:
the processor is configured to determine the group mood classification by acquiring images of the plurality of people captured during the second time period and applying mood detection techniques to the images.

10. The system of claim 8 , wherein:
the processor is configured to detect that the group mood classification has deviated from the baseline group mood classification by determining that a first numerical value associated with the group mood classification is less than a second numerical value associated with the baseline group mood classification by more than the threshold amount.

11. A computer program product, comprising:
a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising:
computer readable program code configured to determine a baseline group mood classification associated with a plurality of people, the baseline group mood classification corresponds with a first time period;
computer readable program code configured to transmit an electronic message to a plurality of target addresses associated with the plurality of people subsequent to the first time period and prior to a second time period;
computer readable program code configured to determine a group mood classification associated with the plurality of people in response to transmitting the electronic m

Assignees

• Ca Inc

Inventors

• Gates Carrie E
• Silberman Gabriel M
• Velez-Rojas Maria C
• Mankovskii Serguei
• Greenspan Steven L

Classifications

• G06Q10/063 Primary

  Operations research, analysis or management · CPC title

• H04L12/58 Primary

  Electricity · mapped topic

• G06K9/00335

  Physics · mapped topic

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06Q10/105

  Human resources · CPC title