F O R M 2
THE PATENTS ACT, 1970
(39 of 1970)
The patent Rule, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
TITLE OF THE INVENTION
A SYSTEM FOR CANCELLATION OF NOISE IN A WORKSPACE AND A
METHOD THEREOF
Name and address of the applicant:
a) Name-Zensar Technologies Limited
b) Nationality: Indian
c) Address: Zensar Knowledge Park, Plot#4, MIDC, Kharadi, Off Nagar Road, Pune-411014, Maharashtra, India
The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[0001] The present application claims priority from Indian Provisional Patent Application No. 201821049988 filed on 31st December 2018, the entirety of which is incorporate herein by a reference.
TECHNICAL FIELD [0002] The present subject matter is related in general to a method and system for noise cancellation, and more particularly, to cancelling noise signals in a workspace.
BACKGROUND
[0003] In a work environment a user’s work efficiency may be affected by various noise signals produced by various noise sources present in the vicinity of the user. The noise sources may be, for example, people talking near the user, telephones ringing, typing sound made by other users, noise generated by server rooms/printers, informal discussion other users present in the vicinity, etc. These noise signals may have varying amplitudes and frequencies depending on the sources from which the noise signals are produced.
[0004] Thus, there is a need in the art to attenuate the noise signals generated in the work environment, thereby helping a user work efficiently.
SUMMARY
[0005] In one non-limiting example, a noise cancellation device in a work environment is described, the device comprises a memory, one or more sensors configured to monitor a first set of noise signals, having one or more characteristics, in the work environment, wherein the one or more noise signals are generated from one or more noise sources. The noise cancellation device further comprises a processor configured to determine a current session efficiency score of a user in the work environment, the current session being defined by a predetermined time

period, compare the current session efficiency score with a historical session efficiency score to determine a change in the efficiency score of the user, wherein the historical session efficiency score of the user is calculated for a past session, the past session being defined by a predetermined time period in the past, wherein the change in the efficiency score indicates increase or decrease of an efficiency score of the user in the work environment, identify a second set of noise signals from the first set of noise signals upon detecting the change in the efficiency score of the user is greater than a threshold. The noise cancellation device further comprises a noise cancellation unit coupled to the processor and configured to generate a noise cancellation signal to attenuate the identified second set of noise signals.
[0006] In one non-limiting example, a method for attenuating one or more noise signals in a work environment is described, the method comprises monitoring a first set of noise signals having at least one attribute wherein the first set of noise signals are generated from one or more noise sources, determining a current session efficiency score of a user in the work environment, the current session being defined by a predetermined time period, comparing the current session efficiency score with a historical session efficiency score to determine a change in the efficiency score of the user, wherein the historical session efficiency score of the user is calculated for a past session, the past session being defined by a pre-determined time period in the past, wherein the change in the efficiency score indicates increase or decrease of an efficiency score of the user in the work environment, identifying a second set of noise signals from the first set of noise signals upon detecting the change in the efficiency score of the user is greater than a threshold and generating a noise cancellation signal to attenuate the identified second set of noise signals.
[0007] In one non-limiting example, the current session efficiency score and the past session efficiency score of the user is determined based on set of parameters comprising lines of codes written by the user, number of errors made by the user while writing the codes, reduction in number of keystrokes by the user in the predetermined time period, change in usage of scroll keys by the user, and determining movement of eyeballs of the user.

[0008] In one non-limiting example, the attributes of the first set of noise signals that are monitored are distance, frequency and amplitude.
[0009] In one non-limiting example, the second set of noise signals are identified by correlating at least one attribute of the first set of noise signal with the change in efficiency score of the user.
[0010] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
[0011] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
[0012] BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed embodiments. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:
[0014] Figure 1 depicts an exemplary embodiment of a work environment in accordance with some embodiments of the present disclosure;

[0015] Figure 2 depicts an exemplary embodiment of block diagram in accordance with some embodiments of the present disclosure;
[0016] Figure 3 depicts an exemplary embodiment of a work environment in accordance with some embodiments of the present disclosure;
[0017] Figure 4 depicts a method of noise cancellation in a work environment in accordance with some embodiment of the present disclosure;
[0018] Figure 5 depicts a relationship model in accordance with an embodiment of the present disclosure.
[0019] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0020] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
[0021] The terms “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed

or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[0022] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
[0023] The present disclosure addresses the shortcomings of the conventional art and proposes a method and an apparatus for cancelling one or more noise signals in a work environment, thereby improving the efficiency of the user in the work environment.
[0024] Referring to figure 1, an exemplary embodiment according to the present invention is shown. As shown in figure 1, a user is shown working in a work environment 100 at a desk 102. The desk 102 includes a computing device 104, a camera 108 installed at the computing device and a noise cancellation device 120 for cancelling one or more noise signals in the work environment. The noise cancellation device 120 may be in communication with the computing device 104 which may be present outside the computing device. In another embodiment, the noise cancellation device 120 may be integrated inside the computing device 104.
[0025] The work environment 100 may be an office space where a group of people are working together, or it may be a plurality of cubicles in an office space where a group of users are sitting and working. In another embodiment, the work environment 100 may have round or flat tables where the plurality of users work.

Although, only one user is shown in figure 1, there may be a number of users present inside the work environment each having a similar computing device 104, camera 108 and noise cancellation device 120 for cancelling noise in the work environment. The computing device may be but not limited to, computer system, laptop, MacBook, etc. The computing device may also have one or more input and output devices, for example, a keyboard, a mouse, a printer, a display screen.
[0026] Figure 2 depicts block diagram of the computing device 104 and the noise cancellation device 120. The computing device 104 is shown coupled to the noise cancellation device 120.
[0027] The computing device 104 may include a central processing unit (CPU) 202, a memory 204 coupled to the CPU 202, a camera 108, a communication interface 206, one or more input ports 208 and one or more output ports 210.
[0028] The noise cancellation device 120 comprises of a memory 250, a processor 252 coupled to the memory 250, one or more sensors 260, one or more speakers 262, a user interface 264 and a display 268. The display 268 may include knobs, buttons, a touchscreen or other interactive elements to allow the user to enter and receive information. The noise cancellation device 120 may also include one or more communication interfaces 270 to interact with the computing device 104. The noise cancellation device 120 also includes a noise cancellation unit 272.
[0029] The noise cancellation device 120 is configured to determine efficiency of the user in the work environment 100. The efficiency of the user may comprise how efficient a user is in the work environment 100. In one embodiment, the efficiency of the user may be performance of the user when the user is working in the work environment 100 without being distracted e.g. without the presence of the noise in the work environment. The efficiency of the user may be calculated for a predetermined period of time and the calculated efficiency score may be stored in the memory 250. The efficiency score stored in the memory 250 may be used to calculate the change in efficiency of the user in future. In an embodiment, noise cancellation device 120 may calculate a current efficiency score of the user for a

current period of time and may compare it with the historical efficiency score stored in the memory 250 calculated for a predetermined time period in the past. The current session efficiency score and the past session efficiency score of the user may be determined based on a set of parameters. Considering that the user is a software developer, the set of parameters may comprise of lines of codes written by the user in a predetermined period of time, number of errors made by the user while writing the codes in the predetermined period of time, reduction in number of keystrokes by the user in the predetermined time period, usage of scroll keys by the user in the predetermined period of time, movement of eyeballs of the user in the predetermined period of time. Each of the parameter may have a score assigned to it. In an embodiment, the change in efficiency score may be calculated by calculating the change in any one of the above parameters or by calculating the change in a combination of the above parameters. In another embodiment, the current efficiency score and the historical efficiency score may be determined by averaging each of the scores of the parameters.
[0030] In another embodiment, the efficiency of the user may be performance of the user when the user is working in a noisy work environment. In yet another embodiment, the efficiency of the user may be compared with the efficiency of other users present in the work environment.
[0031] The noise cancellation device 120 is configured to determine a current efficiency score of the user and compare it with a past or the historical efficiency score of the user. The current efficiency score of the user is determined for a predetermined period of time.
[0032] The historical efficiency score of the user includes efficiency of the user in a past session defined by a predetermined time period in the past. In one embodiment, the historical efficiency score of the user may be determined when there is no noise source available in the work environment i.e. when there is no noise signals present in the work environment 100. In another embodiment, the historical efficiency score of the user may be determined by calculating average efficiency of the user over

more than one predetermined period of time. For example, in a time period t1, the historical efficiency score of the user may be HE1 and in a time period t2, the historical efficiency score of the user may be HE2. Thus, the average historical efficiency score of the user may be determined by calculating an average of HE1 and HE2.
[0033] In order to determine the efficiency of the user, the processor 252 may determine score of each of the parameters. For example, the number of lines of codes written by the user may be obtained from the computing device 104. The CPU 202 may be linked to the compiler where the user is typing the code to determine the number of lines of codes written by the user in a predetermined time period.
[0034] The detection of number of errors made by the user while writing the code may be determined based on the number of times the user presses backspace/delete key on the keyboard. In one embodiment, the number of times the user presses the backspace key/delete key may be obtained from the camera 108 coupled with the computing device 104. In another embodiment, the number of times the user presses the backspace key/delete key may be obtained by determining the ASCII code of the backspace/delete key.
[0035] The detection of the number of keystrokes by the user may be determined by the camera 108 coupled with the computing device 104. In another embodiment, the detection of the number of keystrokes may be obtained by determining the ASCII code of the keys pressed by the user.
[0036] Similarly, detecting a change in usage of scroll keys by the user may indicate the efficiency of the user. For example, less usage of the scroll keys may be linked to better efficiency and more usage of scroll keys may be lined to lesser efficiency of the user. The usage of scroll keys may be detected by the camera 108 coupled to the computing device 104 of the user in the same manner as the detection in the number of keystrokes. In one embodiment, the number of scroll keys may be determined by the compiler of the computing device.

[0037] Further, for determining the efficiency of the user the movement of eyeballs of the user may be detected. The change in the movement of the eyeballs may be detected by the camera 108 coupled with the computing device 104. The camera 108 may capture the image of the user working at the work desk and may process the captured image to detect average eye-ball movement of the user while he works at his work desk. In another embodiment, the computing may use an iris scanner to detect the movement of the eyeballs. The camera or the iris scanner may be configured to detect movement of users eyeballs on the display of the computing device and off the display of the computing device. This movement of users eyeballs from on and off the display may form the basis of determining the efficiency of the user. For example, in a predetermined time period, ‘T’, it may be determined that the user was looking at the display for a ‘t1’ amount of time and for ‘t2’ amount of time he was looking away from the display. In such a case, the efficiency of the user may be a function of ‘T’, ‘t1’ and ‘t2’.
[0038] The efficiency of the user may be a function of any one of the above parameters or a function of a combination of the above parameters. Each of the above determined parameters are determined by using a camera 108 or a CPU 202 or one or more sensors or using a combination of all.
[0039] To determine the current efficiency score, a user working in the work environment 100 may be monitored over a period of time. For example, each of the parameters of the user may be monitored over a period of time to determine the current efficiency score of the user by the noise cancellation device 120. Once the current efficiency score of the user is determined, the noise cancellation device 120 will compare the current and historical efficiency score obtained from the memory to determine a difference between the current efficiency score and the historical efficiency score. The difference between the current efficiency score and the historical efficiency score indicates a change in the efficiency score of the user, which further indicates change in efficiency of the user in the work environment 100.

[0040] The efficiency score of the user may change due to one or more noise signals in the work environment 100. The one or more noise signals may be from one or more noise sources. The one or more noise sources may include telephone sound, sound of people communicating with each other in the work environment, printer sounds, server sound, sounds or doors opening and closing etc. While the user’s efficiency is being determined for a predetermined period of time, the one or more sensors are operable to detect the one or more noise signals in the work environment 100. These one or more noise signals are termed as a first set of noise signals. The first set of noise signals may include one or more noise signals. In other words, the first set of noise signals may comprise all the noise signals in the work environment 100.
[0041] The first set of noise signals are monitored based on one or more attributes of the first set of noise signals. The one or more attributes may include distance of the noise source from the computing device, and frequency and amplitude of the noise signal produced by the noise source. If a noise source is at a distance greater than a threshold and has a frequency or amplitude that are higher than a threshold frequency or threshold amplitude, then the noise source may qualify to be the first set of noise signal.
[0042] Once the distance from the noise source is determined, the processor 252 may compare the determined distance with a distance threshold stored in the memory 250. If the determined distance is more than the distance threshold, the processor 252 may classify the noise source as the first set of noise source.
[0043] Similarly, once the frequency of the noise signal from the noise source is determined, the processor 252 may compare the determined frequency with a frequency threshold stored in the memory 250. If the determined frequency is more than the frequency threshold, the processor 252 may classify the noise source as the first set of noise source.

[0044] Similarly, once the amplitude of the noise signal from the noise source is determined, the processor 252 may compare the determined amplitude with an amplitude threshold stored in the memory 250. If the determined amplitude is more than the amplitude threshold, the processor 252 may classify the noise source as the first set of noise source.
[0045] Change in efficiency score of the user is determined based on each attribute of the first set of noise signals. Once the change in the efficiency score of the user is detected, the processor 252 is configured to detect if the detected change in the efficiency score of the user is more than a threshold. Once the change in efficiency score of the user is determined to be more than the threshold, the processor 252 identifies a second set of noise signals from the first set of noise signals, the second set of noise signals being the reason for change in the efficiency score of the user.
[0046] Upon identifying the second set of noise signals, the noise cancellation unit generates a noise cancellation signal to attenuate the identified second set of noise signals. The noise cancellation unit may include a speaker which may generate a noise cancellation signal to attenuate the second set of noise signals. For example, figure 3 shows an exemplary embodiment according to the present invention. As shown in figure 3, the noise cancellation device 120 is shown to generate a noise cancellation signal to attenuate the second set of noise signal.
[0047] Figure 4 shows a method for cancelling noise in accordance with the present disclosure. At step 402, the method comprises monitoring a first set of noise signals. The first set of noise signals are the noise signals generated in the work environment 100 from one or more noise sources. The first set of noise signals may include noise signals generated by all the noise sources present in the work environment 100 while the user is working. For example, the first set of noise signals may include telephone or mobile ringtone, noise from a printer, a server, noise of people talking to each other in the neighboring workstations, noise of a fan or an air conditioner, etc. The first set of noise signals may be determined for a predetermined period of time. Such predetermined period of time may be defined by a manager of the user. Each of the

first set of noise signals will have one or more attributes associated with it. The one or more attributes are distance of the noise source from the user, frequency of the noise signal and the amplitude of the noise signal.
[0048] At step 404, the method comprises determining a current efficiency score of the user. The current efficiency score of the user is determined for the pre¬determined period of time. The current efficiency score of the user is determined based on the one or more parameters. The one or more parameters may include lines of codes written by the user, number of errors made by the user while writing the codes, reduction in number of keystrokes by the user in the predetermined time period, usage of scroll keys by the user, and determining movement of eyeballs of the user. Each of the one or more parameters may be assigned a score. An average of scores of all the parameters may be determined to calculate the current session efficiency score of the user.
[0049] At step 406, the current session efficiency score is compared with a historical session efficiency score to determine a change in an efficiency score of the user. The historical session efficiency score of the user is determined for a past session, the past session being defined by a pre-determined time period in the past. The past session may include a session before the current session and may occur at a predetermined period of time before the predetermined time for the current session. The historical session efficiency may be precalculated and stored in the memory of the noise cancellation device. In another embodiment, the historical session efficiency score may be an average of efficiency scores of several historical sessions of the user.
[0050] At step 408, the method comprises identifying a second set of noise signals from the first set of noise signals upon detecting that the change in the efficiency score of the user is greater than a threshold. The threshold may be defined in the memory of the noise cancellation device. Upon detecting that the change in efficiency score of the current session is greater than the threshold, the second set of noise signals are identified. The second set of noise signals are the signals that cause

a change in the efficiency of the user and which need to be attenuated by the noise cancellation device.
[0051] At step 410, the method comprises generating one or more noise cancellation signals, by the noise cancellation unit, to attenuate the identified second set of noise signals.
[0052] Thus, by way of an example, when a user is working in a work environment 100, a first set of noise signals may be generated from one or more noise sources. The one or more noise sources may be, for example, telephone, printer and server and fan. Each of the noise signals will have attribute distance, frequency and amplitude associated with it. While the user is working in the work environment 100, the efficiency of the user may change due to one or more noise signals. The change in the efficiency of the user is determined by comparing the current efficiency score of the user and the historical efficiency score of the user. The current and historical efficiency score of the user are determined for a predetermined period of time.
[0053] For example, during time t1, there may be noise signals from telephone and printer. At this time t1, the efficiency score of the user is calculated and stored in the memory as a historical efficiency score. Now, for example, at time t2, a noise signal from a server is generated. The processor is configured to determine a current efficiency score of the user at time t2 after determining an average of the one or more parameters i.e. number of lines of codes written by the user, number of errors made by the user while writing the codes, reduction in number of keystrokes by the user in the predetermined time period, usage of scroll keys by the user, and movement of eyeballs of the user. The processor then determines a change in efficiency score based on the difference between the current efficiency score and the historical efficiency score of the user. Upon determining that the change in efficiency score of the user is more than a threshold, the processor determines that the change in efficiency score may be due to the noise signal generated by the server.

Upon such determination, the noise cancellation unit generates a noise cancellation signal to attenuate the noise signal generated from the server.
[0054] Referring now to figure 5, a correlation of the first set of noise signals, one or more parameters for determining efficiency of the user and the second set of noise signals is now explained.
[0055] Figure 5 shows first set of noise signals NS1, NS2 and NS3 determined for a user for a predetermined time period. Each of the noise signals have attributes distance (D), frequency (F) and amplitude (A). Each of the attributes of the first set of noise signals are associated with efficiency of the user. The efficiency of the user is determined based on at least one of parameters (P1-P5) i.e. number of lines of codes written by the user (P1), number of errors made by the user while writing the codes (P2), reduction in number of keystrokes by the user (P3), usage of scroll keys by the user (P4), and movement of eyeballs of the user on and off the display (P5). Each of the attribute of the first set of noise signals is associated with each of the parameters of efficiency of the user. For example, if the frequency of the noise signal NS1 from the first set of noise signals (NS1, NS2, NS3) is more than a threshold, the user working in the work environment (100) may be distracted by the noise signal NS1 and his efficiency may reduce beyond a threshold.
[0056] Upon detecting that there is a change in the efficiency score of the user, the processor of the noise cancellation device may identify NS1 as the second set of noise signal which needs to be attenuated by the noise cancellation unit.
[0057] Thus, as shown in figure 5, out of 3 noise signals (NS1, NS2, NS3), only one noise signal (NS1) is identified as the second set of noise signal that needs to be attenuated.
[0058] Considering an exemplary embodiment where distance of the noise source from the user is less than a threshold and frequency and amplitude are more than a threshold. In this case, the noise source may be located close to the user and will generate a first set of noise signals. It may be a case where a supervisor of the user

may be near the user and talking to him. In this case, even if the amplitude of the noise/sound signals generated by the supervisor is more than the threshold, the noise signal may not identify this as a second set of noise signal since it may be an important discussion between the user and his supervisor. In such a case the camera or an iris scanner may determine presence of another set of eyeballs near the users eyeballs and may not constitute this as decrease in his efficiency even if his eyeballs are off the screen. Thus, the efficiency parameters may not change in this case and hence the noise signal generated by the supervisor may not be attenuated.
[0059] Considering another exemplary embodiment where distance of the noise source from the user more than the distance threshold is while the frequency and amplitude of the noise signal is less than the frequency threshold and amplitude threshold respectively. In this scenario, the noise source may be located at a distance far from the user and is generating frequency and amplitude less than the threshold. In this case, since the distance source is far, the user may not get distracted and the efficiency score of the user may not change. Thus, the noise cancellation device will not identify this noise signal to be second set of noise signal.
[0060] Considering another exemplary embodiment where all the attributes of the first set of noise signals i.e. distance, frequency and amplitude of the first set of noise signal are more than a threshold. In this scenario, the user may get distracted and his efficiency score may change beyond a threshold. In such a case, the noise cancellation device may identify such signal to be a second set of noise signal which needs to be attenuated.
[0061] Considering another exemplary embodiment where the attributes- distance and frequency of the first set of noise signals may be more than a threshold while the amplitude of the first set of noise signals may be less than a threshold. In such scenario, a noise source is far away from the user and generating noise signals of frequency more than the threshold while the amplitude of the noise signal is less than the threshold value. In this case, the user may get distracted by the noise signal and the efficiency score of the user may change. In such a case, the noise

cancellation device may identify such signal to be a second set of noise signal which needs to be attenuated.
[0062] The above cases are only for exemplary purpose and there may be several cases possible depending on the value of distance, frequency and amplitude of the first set of noise signals and whether there is a change in efficiency score of the user beyond a threshold.
[0063] One or more embodiments of the present invention will now be explained.
[0064] In one embodiment, the CPU may only monitor the efficiency of the user when the user is sitting and working at the work desk 102. For example, when the camera 108 captures an image of the user and detects the face of the user in the captured image, the CPU may detect that the user is working at the work desk. Similarly, interaction of the user with the computing device may indicate that the user is working on the computing device 104.
[0065] In one embodiment, the noise cancellation device 120 may include a machine learning model. The machine learning model may be trained to determine the efficiency threshold in the above-mentioned parameters. For example, the learning model may receive the determined parameters and may be trained over a period of time to determine the efficiency score of the user.
[0066] In one embodiment, the efficiency of a user may increase over a period of time while the user works in a same work environment. By way of an exemplary embodiment, if a software developer is initially able to write 10 lines of codes in an hour, he may be able to write 30 lines of codes in an hour after 1 year of working in the same work environment. Thus, the efficiency of the user may change, and the noise cancellation device 120 may store the updated efficiency of the user in the memory 252 and may use the updated efficiency score as the historical efficiency score to determine the change in efficiency score of the user.

[0067] In one embodiment, the machine learning model present inside the noise cancellation device 120 may learn the second set of noise signals for a period of time i.e. noise signals because of which the efficiency of the user changes. After learning the second set of noise signals, the noise cancellation device 120 may automatically generate the one or more noise cancellation signals to attenuate the second set of noise signals.
[0068] In one embodiment, the noise cancellation device 120 may send the determined second set of noise signals to the computing device 104. The computing device 104 may send a signal to a supervisor of the user indicating that the user is getting distracted. The signal may be any audio/visual signal. For example, a message may be displayed at a computing device 104 of the supervisor or an alarm may be audible to the supervisor.
[0069] In another embodiment, the user working in the work environment 100 may set preferences using display 268. The preferences may indicate when to operate the noise cancellation device 120. For example, the user may set time of day during which he wants the noise cancellation device to operate.
[0070] In one embodiment, the present disclosure is executed in a computer-readable program product comprising a computer-readable medium. The computer-readable medium may comprise computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks ("DVD"), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.

Advantages of the embodiment of the present disclosure are illustrated herein
a. The present invention helps the user to work efficiently in the work environment by suppressing the noise signals which distract the user.
Referral Numerals:

Reference Number Description
100 Work environment
102 Desk
104 Computing device
108 Webcam
120 Noise cancellation device
202 Central processing unit
204 Memory
206 Communication interface
208 One or more input ports
210 One or more output ports
250 Memory
252 Processor
260 One or more sensors
262 One or more speakers
268 Display
272 Noise cancellation unit
400 Method
402-410 Method steps

We claim:
1. A noise cancellation device (120) in a work environment (100), the device (120) comprising:
a memory (250);
one or more sensors (260) in the work environment (100), configured to monitor a first set
of noise signals having at least one attribute, wherein the first set of noise signals are
generated by one or more noise sources;
a processor (252) operatively coupled to the memory (250) and one or more sensors (260)
and configured to:
determine a current session efficiency score of a user in the work environment, the current
session being defined by a predetermined time period;
compare the current session efficiency score with a historical session efficiency score to
determine a change in the efficiency score of the user, wherein the historical session
efficiency score of the user is calculated for a past session, the past session being defined
by a pre-determined time period in the past, wherein the change in the efficiency score
indicates increase or decrease of an efficiency score of the user in the work environment
(100);
identify a second set of noise signals from the first set of noise signals upon detecting the
change in the efficiency score of the user is greater than a threshold; and
a noise cancellation unit (272) coupled to the processor (252), configured to generate a noise
cancellation signal to attenuate the identified second set of noise signals.
2. The device (120) as claimed in claim 1, wherein the current session efficiency score and the past session efficiency score of the user is determined based on set of parameters comprising lines of codes written by the user, number of errors made by the user while writing the codes, number of keystrokes by the user in the predetermined time period, usage of scroll keys by the user, and movement of eyeballs of the user.
3. The device (120) as claimed in claim 1, wherein the attribute of the first set of noise signals that are monitored are distance, frequency and amplitude.

4. The device as claimed in claim 1, wherein the second set of noise signals are identified by correlating at least one attribute of the first set of noise signal with the change in efficiency score of the user.
5. The device as claimed in claim 1, further comprising:
determining a first set of noise signals whose distance from the user is less than a threshold, said first set of noise signals are not identified as second set of noise signals.
6. A method (400) for attenuating one or more noise signals in a work environment (100), the
method (400) comprising:
monitoring (402), by one or more sensors (260), a first set of noise signals having at least
one attribute, wherein the first set of noise signals are generated from one or more noise
sources;
determining (404), by a processor (252), a current session efficiency score of a user in the
work environment, the current session being defined by a predetermined time period;
comparing (406), by the processor (252), the current session efficiency score with a
historical session efficiency score to determine a change in the efficiency score of the user,
wherein the historical session efficiency score of the user is calculated for a past session,
the past session being defined by a pre-determined time period in the past, wherein the
change in the efficiency score indicates increase or decrease of an efficiency score of the
user in the work environment (100);
identifying (408), by the processor (252), a second set of noise signals from the first set of
noise signals upon detecting the change in the efficiency score of the user is greater than a
threshold; and
generating (410), by a noise cancellation unit (272), a noise cancellation signal to attenuate
the identified second set of noise signals.
7. The method as claimed in claim 6, wherein the current session efficiency score and the past
session efficiency score of the user is determined based on set of parameters comprising
lines of codes written by the user, number of errors made by the user while writing the

codes, reduction in number of keystrokes by the user in the predetermined time period, change in usage of scroll keys by the user, and determining eye-ball movement of the user.
8. The method as claimed in claim 6, wherein the attribute of the first set of noise signals that are monitored are distance, frequency and amplitude.
9. The method as claimed in claim 6, wherein the second set of noise signals are identified by correlating at least one attribute of the first set of noise signal with the change in efficiency score of the user.
10. The method as claimed in claim 6, further comprising determining a first set of noise signals whose distance from the user is less than a threshold, said first set of noise signals are not identified as second set of noise signals.