# US 10,769,574 B2 · Maximize human resources efficiency by reducing distractions during high productivity periods

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- Number: US 10,769,574 B2
- Document ID: US10769574B2
- Status: Granted
- Kind: B2
- Application number: 15/823,694
- Filed: 2017-11-28
- Published: 2019-05-30
- Granted: 2020-09-08
- Earliest claimed: 2017-11-28
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Uwais Ashraf; Ioannis Georgiou; James Hook; Craig J. Morten
- Web page: https://patentgrep.com/patent/US10769574B2

## CPC

- G06Q 10/06395: {Quality analysis or management}
- G06Q 10/063114: {Status monitoring or status determination for a person or group}
- G06Q 10/06393: {Score-carding, benchmarking or key performance indicator [KPI] analysis}
- G06Q 10/1093: Calendar-based scheduling for persons or groups
- G06Q 10/1097: {using calendar-based scheduling for task assignment}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A method, computer system, and computer program product for reducing one or more distractions during a period of high productivity are provided. The embodiment may include receiving a plurality of user metadata. The embodiment may also include, in response to determining a user is in a high productivity state, analyzing the plurality of received user metadata for potential distractions to the high productivity state. The embodiment may further include, in response to identifying one or more potential distractions based on the plurality of analyzed user metadata, determining an appropriate modification to the one or more identified potential distractions. The embodiment may also include performing the determined appropriate modification.

## Claims

### Claim 1

A processor-implemented method for reducing one or more distractions during a period of high productivity, the method comprising:
receiving, by a processor, a plurality of user metadata;
in response to determining a user is in a high productivity state, analyzing the plurality of received user metadata for potential distractions to the high productivity state;
in response to identifying one or more potential distractions based on the plurality of analyzed user metadata, determining an appropriate modification to the one or more identified potential distractions; and
performing the determined appropriate modification, wherein the appropriate modification is based on a comparison of a usefulness of the identified potential distraction to the user and an amount of distraction of the identified potential distraction to the user.

### Claim 2 (depends on 1)

The method of claim 1, wherein the plurality of user metadata is selected from a group consisting of a current open software application, a successful completion of one or more scheduled tasks, a plurality of calendar details, a chat history, a user typing speed, a user heart rate, a user perspiration level, a user blood pressure, one or more user facial expressions, a user pupil dilation level, a user focus direction, and one or more user hand movements.

### Claim 3 (depends on 1)

The method of claim 1, wherein determining the user is in a high productivity state further comprises:
calculating a productivity score relative to a user baseline using a weighted sum of a plurality of concentration measures, wherein the plurality of concentration measures assign a positive score to items that indicate a higher than average productivity and assign a negative score to items that indicate a lower than average productivity.

### Claim 4 (depends on 3)

The method of claim 3, wherein the user baseline productivity state is calculated based on an initial batch of user metadata.

### Claim 5 (depends on 3)

The method of claim 3, wherein the plurality of concentration measures to which a positive score is assigned are selected from a group consisting of a plurality of well written source code identified through a code analysis tool, a plurality of functional and sample tests for a plurality of new code are successful, and a plurality of recent window focus data, and wherein the plurality of concentration measures to which a negative score is assigned are selected from a group consisting of a low quantity of lines of code produced, a slow consistency rate of work produced, a plurality of historical productivity data, a plurality of historical schedule data, a plurality of hormonal physiological effects, a plurality of brain physiological effects, a plurality of recent device focus data, a plurality of track micro/macro-expressions, a plurality of general health data, and a current noise level.

### Claim 6 (depends on 1)

The method of claim 1, wherein the appropriate modification is selected from a group consisting of reducing a volume level of a sound notification, shifting a notification or window to a side of a graphical user interface, selecting and displaying only a plurality of useful information from a message, preventing a noise from playing until a later time, preventing a notification from executing until a later time, automatically setting a user status to away, and rescheduling a meeting to a later time.

### Claim 7

A computer system for reducing one or more distractions during a period of high productivity, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
receiving a plurality of user metadata;
in response to determining a user is in a high productivity state, analyzing the plurality of received user metadata for potential distractions to the high productivity state;
in response to identifying one or more potential distractions based on the plurality of analyzed user metadata, determining an appropriate modification to the one or more identified potential distractions; and
performing the determined appropriate modification, wherein the appropriate modification is based on a comparison of a usefulness of the identified potential distraction to the user and an amount of distraction of the identified potential distraction to the user.

### Claim 8 (depends on 7)

The computer system of claim 7, wherein the plurality of user metadata is selected from a group consisting of a current open software application, a successful completion of one or more scheduled tasks, a plurality of calendar details, a chat history, a user typing speed, a user heart rate, a user perspiration level, a user blood pressure, one or more user facial expressions, a user pupil dilation level, a user focus direction, and one or more user hand movements.

### Claim 9 (depends on 7)

The computer system of claim 7, wherein determining the user is in a high productivity state further comprises:
calculating a productivity score relative to a user baseline using a weighted sum of a plurality of concentration measures, wherein the plurality of concentration measures assign a positive score to items that indicate a higher than average productivity and assign a negative score to items that indicate a lower than average productivity.

### Claim 10 (depends on 9)

The computer system of claim 9, wherein the user baseline productivity state is calculated based on an initial batch of user metadata.

### Claim 11 (depends on 9)

The computer system of claim 9, wherein the plurality of concentration measures to which a positive score is assigned are selected from a group consisting of a plurality of well written source code identified through a code analysis tool, a plurality of functional and sample tests for a plurality of new code are successful, and a plurality of recent window focus data, and wherein the plurality of concentration measures to which a negative score is assigned are selected from a group consisting of a low quantity of lines of code produced, a slow consistency rate of work produced, a plurality of historical productivity data, a plurality of historical schedule data, a plurality of hormonal physiological effects, a plurality of brain physiological effects, a plurality of recent device focus data, a plurality of track micro/macro-expressions, a plurality of general health data, and a current noise level.

### Claim 12 (depends on 7)

The computer system of claim 7, wherein the appropriate modification is selected from a group consisting of reducing a volume level of a sound notification, shifting a notification or window to a side of a graphical user interface, selecting and displaying only a plurality of useful information from a message, preventing a noise from playing until a later time, preventing a notification from executing until a later time, automatically setting a user status to away, and rescheduling a meeting to a later time.

### Claim 13

A computer program product for reducing one or more distractions during a period of high productivity, the computer program product comprising:
one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor of a computer to perform a method, the method comprising:
receiving a plurality of user metadata;
in response to determining a user is in a high productivity state, analyzing the plurality of received user metadata for potential distractions to the high productivity state;
in response to identifying one or more potential distractions based on the plurality of analyzed user metadata, determining an appropriate modification to the one or more identified potential distractions; and
performing the determined appropriate modification, wherein the appropriate modification is based on a comparison of a usefulness of the identified potential distraction to the user and an amount of distraction of the identified potential distraction to the user.

### Claim 14 (depends on 13)

The computer program product of claim 13, wherein the plurality of user metadata is selected from a group consisting of a current open software application, a successful completion of one or more scheduled tasks, a plurality of calendar details, a chat history, a user typing speed, a user heart rate, a user perspiration level, a user blood pressure, one or more user facial expressions, a user pupil dilation level, a user focus direction, and one or more user hand movements.

### Claim 15 (depends on 13)

The computer program product of claim 13, wherein determining the user is in a high productivity state further comprises:
calculating a productivity score relative to a user baseline using a weighted sum of a plurality of concentration measures, wherein the plurality of concentration measures assign a positive score to items that indicate a higher than average productivity and assign a negative score to items that indicate a lower than average productivity.

### Claim 16 (depends on 15)

The computer program product of claim 15, wherein the user baseline productivity state is calculated based on an initial batch of user metadata.

### Claim 17 (depends on 15)

The computer program product of claim 15, wherein the plurality of concentration measures to which a positive score is assigned are selected from a group consisting of a plurality of well written source code identified through a code analysis tool, a plurality of functional and sample tests for a plurality of new code are successful, and a plurality of recent window focus data, and wherein the plurality of concentration measures to which a negative score is assigned are selected from a group consisting of a low quantity of lines of code produced, a slow consistency rate of work produced, a plurality of historical productivity data, a plurality of historical schedule data, a plurality of hormonal physiological effects, a plurality of brain physiological effects, a plurality of recent device focus data, a plurality of track micro/macro-expressions, a plurality of general health data, and a current noise level.