FIELD OF THE INVENTION:
[001] The present disclosure relates to a technique for tagging data.
BACKGROUND OF THE INVENTION:
[002] In today's digital era, digital multimedia content is used for various applications. Thus, the volume of the digital multimedia content is increasing day by day. The users utilize the available digital multimedia content over various platforms or create new digital multimedia content as per the requirement. The digital multimedia content is generally tagged/labeled so that the content may be searched or retrieved whenever required. Hence, accurate tagging/labeling of the digital media content is very important for efficient searching and retrieval of the digital multimedia content from various platforms.
[003] Therefore, there exists a need in the art for a technique that provides efficient and accurate tagging of the digital multimedia content to enable more efficient search and retrieval of the digital multimedia content form various platforms.
SUMMARY OF THE INVENTION:
[004] 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.
[005] In one non-limiting embodiment of the present disclosure, the present application discloses a method for tagging data. The method comprises receiving data and at least one parameter defining criteria for tagging the data. The method further comprises determining a current difficulty level in tagging the data based on the at least one parameter. Furthermore, the

method comprises selecting at least one tagger for tagging the data from a group of taggers defined in a database based on the current difficulty level and the at least one parameter.
[006] In another non-limiting embodiment of the present disclosure, the present application discloses that the at least one parameter is selected from a group comprising data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data.
[007] In another non-limiting embodiment of the present disclosure, the present application discloses that for each tagger in the group of taggers, the database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data, and wherein the at least one accuracy level in tagging the data is dependent on a time of day.
[008] In another non-limiting embodiment of the present disclosure, the present application discloses that selecting the at least one tagger for tagging the data from the group of taggers defined in the database based on the current difficulty level and the at least one parameter, comprises: mapping the data type for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data; selecting a subset of taggers from the filtered plurality of taggers based on the current difficulty level; and selecting at least one tagger from the subset of taggers based on the accuracy level for tagging the received data.
[009] In another non-limiting embodiment of the present disclosure, the present application discloses a system for tagging data. The system comprises a receiving unit which is configured to receive data and receive at least one parameter defining criteria for tagging the data. The system further comprises at least one processing unit coupled with the receiving unit. The at least one processing unit is configured to: determine a current difficulty level in tagging the data based on the at least one parameter, and select at least

one tagger for tagging the data from a group of taggers defined in a database based on the current difficulty level and the at least one parameter.
[010] In another non-limiting embodiment of the present disclosure, the present application discloses that the at least one parameter is selected from a group comprising data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data.
[Oil] In another non-limiting embodiment of the present disclosure, the present application discloses that for each tagger in the group of taggers, the database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data, and wherein the at least one accuracy level in tagging the data is dependent on a time of day..
[012] In another non-limiting embodiment of the present disclosure, the present application discloses that the at least processing unit is configured to select the at least one tagger for tagging the data from the group of taggers defined in the database based on the current difficulty level and the at least one parameter, by: mapping the data type for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data; selecting a subset of taggers from the filtered plurality of taggers based on the current difficulty level; and selecting at least one tagger from the subset of taggers based on the accuracy level for tagging the received data..
OBJECTS OF THE INVENTION:
[013] The main object of the present invention is to tag the data accurately and efficiently.
[014] Further object of the present invention is to tag the data with a time limit without compromising accuracy of tagging.

BRIEF DESCRIPTION OF DRAWINGS:
[015] 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:
[016] Fig. 1 illustrates an environment facilitating the present invention according to an embodiment of the present disclosure.
[017] Figure 2 illustrates a block diagram of a system for tagging data according to an embodiment of the present disclosure.
[018] Figure 3 discloses a flowchart of a method for tagging data according to an embodiment of present disclosure.
[019] 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 OF DRAWINGS:
[020] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any embodiment or implementation of the present

subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
[021] 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 alternative falling within the scope of the disclosure.
[022] 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.
[023] 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.
[024] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[025] The present disclosure relates to a system that efficiently and accurately tags data. The data may be any of image, video, animation, audio, multimedia content, text, etc., but not limited thereto. For tagging data, the system utilizes signals provided by brain computer interface (BCI). The BCI recognizes the intent of the tagger through brain signals, decodes neural activity, and translates it into output commands that accomplish the tagger's goal. The BCI signals are utilized to determine the accuracy level of data tagging by a data tagger. The tagger is a user which performs the task of tagging. Every tagger has his/her corresponding level of accuracy for tagging the data and the accuracy of the tagger may vary through the time of day. Thus, the system considers all the factors which might affect the tagging of the data, to select a tagger to perform the tagging of the data. In this manner, the data may be tagged more efficiently and accurately within the provided timeline.
[026] Referring to figure 1, a network 100 is disclosed which may include various elements such as a system 102, plurality of data sources 104a... 104n, and a server 106. The various elements such as the system 102, the plurality of data sources 104a... 104n, and the server 106 may communicate with each other through web presence 108. The network 100 may include one of the Internet, a local area network, a wide area network, an intranet, a peer-to-peer network, and/or other similar technologies for connecting various elements. The system 102 may receive the data for tagging form plurality of data sources 104 which may have different criteria for tagging.
[027] FIG. 2 shows a detailed block diagram 200 illustrating the system 102 in accordance with an embodiment of the present disclosure. The system 102 may comprise various elements such as an input/output (I/O) interface 202, at least one processing unit 204, a receiving unit 206, a BCI unit 208, and a memory 210. The various elements such as the input/output (I/O) interface 202, the at least one processing unit 204, the receiving unit 206, the BCI unit 208, and the memory 210 may communicate with each other through wired or wireless connection. The I/O interface 202 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, input device, output device, and the like. The I/O interface 202 may allow the system 102 to interact with the users directly or through other devices.

[028] Further, the memory 210 is communicatively coupled to the at least one processing unit 204 and the receiving unit 206. In an embodiment, the memory 210 may be a computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[029] In an embodiment, the system 102 may receive data to be tagged via the receiving unit 206. The system 102 may also receive at least one parameter which may define criteria for tagging the data via the receiving unit 206. The at least one parameter may be any of following, but not limited to, data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data. The data type defines the type of received data such image type, animation type, video type, or text type, but not limited thereto.
[030] Further, the at least one processing unit of the system 102 may determine a current difficulty level in tagging the data based on the at least one parameter. The system 102 may store the received data, the parameters and the determined difficulty level in tagging the data in the memory 210 of the system 102. Further, the at least one processing unit 204 of the system 102 may select at least one tagger for tagging the data from a group of taggers defined in a database stored in the memory 210, based on the current difficulty level and the at least one parameter. In accordance with a non-limiting exemplary embodiment of the present disclosure, the database may be stored in the server 106.
[031] The database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data for each of the tagger of the plurality of taggers , as shown in the Table 1:

Difficulty level

Timing

Data
type

Volume of data
VI

Time limit (Hours)
Tl
T2

Accuracy
>85
>70 & <85
>85

Tagger ID
Tagger 1 Tagger 4
Tagger 3 Tagger 2
Tagger 1 Tagger 3 Tagger 4

Dl

>70 & <85
>85

Tagger 2
Tagger 1

Low

Morning

V2
VI

Tl
T2
Tl
T2

>70 & <85
>85
>70 & <85
>85
>70 & <85
>85

Tagger 3 Tagger 2 Tagger 4
Tagger 1 Tagger 3
Tagger 2 Tagger 4
Tagger 2 Tagger 5
Tagger 1 Tagger 3 Tagger 4
Tagger 2 Tagger 3 Tagger 4 Tagger 5

D2

V2

Tl
T2

>70 & <85
>85
>70 & <85
>85
>70 & <85

Tagger 1
Tagger 2
Tagger 1 Tagger 3 Tagger 4 Tagger 5
Tagger 2 Tagger 3 Tagger 5
Tagger 1 Tagger 4

>85

Tagger 1

Evening

Dl

VI

Tl
T2

>70 & <85
>85
>70 & <85

Tagger 3 Tagger 2 Tagger 4
Tagger 1 Tagger 3
Tagger 2 Tagger 4

V2

Tl

>85
>70 & <85

Tagger 1

Tagger 3

Tagger 2

Tagger 4

T2 >85 Tagger 1

>70 & <85 Tagger 2

Tagger 3

Tagger 4

D2 VI Tl >85 Tagger 2

Tagger 1

>70 & <85 Tagger 3

Tagger 4

Tagger 5

T2 >85 Tagger 2

Tagger 1

Tagger 4

>70 & <85 Tagger 3

Tagger 5

V2 Tl >85 Tagger 2

>70 & <85 Tagger 1

Tagger 3

Tagger 4

Tagger 5

T2 >85 Tagger 2

Tagger 1

Tagger 3

>70 & <85 Tagger 5

Tagger 4
High Morning Dl VI Tl >85 Tagger 2

Tagger 4

>70 & <85 Tagger 3

Tagger 1

T2 >85 Tagger 2

Tagger 3

Tagger 4

>70 & <85 Tagger 1

V2 Tl >85 Tagger 2

>70 & <85 Tagger 1

Tagger 3

Tagger 4

T2 >85 Tagger 2

Tagger 3

>70 & <85 Tagger 1

Tagger 4

D2 VI Tl >85 Tagger 2

>70 & <85 Tagger 1

Tagger 3

Tagger 4

T2 >85 Tagger 2

Tagger 1

>70 & <85 Tagger 3

V2

Tl
T2

>85
>70 & <85
>85
>70 & <85

Tagger 4 Tagger 2 Tagger 1 Tagger 3
Tagger 4
Tagger 2
Tagger 1
Tagger 3
Tagger 4

>85

Tagger 1

Tl

>70 & <85

Tagger 3
Tagger 2

VI

T2

>85
>70 & <85

Tagger 4
Tagger 1
Tagger 2
Tagger 3
Tagger 4

Dl

>85

V2

Tl

>70 & <85

Tagger 1
Tagger 3
Tagger 2
Tagger 4

>85

Tagger 4

Evening

T2

>70 & <85

Tagger 1
Tagger 2
Tagger 4

VI

Tl
T2

>85
>70 & <85
>85

Tagger 2
Tagger 4
Tagger 1
Tagger 3
Tagger 2
Tagger 1
Tagger 4

D2

>70 & <85
>85

Tagger 3
Tagger 2

V2

Tl
T2

>70 & <85
>85
>70 & <85

Tagger 1
Tagger 3
Tagger 4
Tagger 2
Tagger 3
Tagger 1
Tagger 4

[032] As shown in above table, the difficulty level may be low or high, but not limited thereto.
In accordance with a non-limiting exemplary embodiment of the present disclosure, the difficulty level may also be defined as low, medium and high, or level 1, level 2, level

3... etc. For each difficulty level, the mapping table defines a relationship between the various parameters such as data type, volume of data, accuracy level in tagging data, time limit, and tagger ID. The accuracy level in tagging the data is dependent on a time of day and may vary according to the time of day. Although the mapping table defines only morning and evening timing, but the timing may be any of morning, evening, afternoon, night, etc. In an embodiment, for creating the database, the accuracy level of the taggers is determined using their BCI signals. In accordance with a non-limiting exemplary embodiment of the present disclosure, the system 102 may also capture and process the facial expression to determine the accuracy level more accurately and efficiently
[033] In an embodiment, the accuracy of data tagging by the taggers may be affected by various parameters defined in the table such data type, timing, volume of data, time limit for competing the tagging. The mapping table clearly shows how the accuracy of the tagger varies based on the defined parameters. For example, in case of low difficulty level of tagging, if the VI volume of the data Dl is tagged in the time limit Tl in the morning time, the accuracy level of more than 85% is provided by taggers Tagger 1 and Tagger 4, whereas if the time limit is T2, the Tagger 1, Tagger 3, and Tagger 4 may provide more than 85% accuracy level in tagging the same type of data Dl.
[034] In an embodiment, the system 102 may generate the mapping table as shown in table 1 for the plurality of taggers and store in the database in memory. The system 102 may regularly update the stored mapping table at predefined time interval.
[03 5] Upon receiving the data and the at least one parameter defining criteria for tagging the data, the at least one processing unit 204 of the system 102 may determine the level of difficulty in tagging the received data. The level of difficulty may be determined based on the volume of data received, type of received data, time limit to tag the data and desired level of accuracy of the tagging. The at least one processing unit 204 may determine the level of difficulty based on pre-defined rules. The at least one processing unit 204 of the system 102 may select at least one tagger for tagging the received data from the group of taggers defined in the database based on the current difficulty level and the at least one parameter.

[036] The at least one processing unit 204 may select the at least one tagger for tagging the data from the group of taggers defined in the database by mapping the data type for the received data with the predefined data type stored in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data. Further, the at least one processing unit 204 may select a subset of taggers from the filtered plurality of taggers based on the current difficulty level. Furthermore, the processing unit 204 may select at least one tagger from the subset of taggers based on the required accuracy level for tagging the received data.
[037] In this manner, by mapping one or more received parameters in the database, the system 102 may determine one or more taggers to whom the data may be assigned for tagging the data more accurately and efficiently.
[038] For explaining the embodiments defined in paragraphs [0029]- [0040], let us consider that the database in the memory 210 defines the relationship between the difficulty level low and high, data types Dl and D2, volume of data VI and V2, time limit Tl and T2 and different accuracy level for plurality of taggers Taggerl, Tagger2, Tagger3, and Tagger4 as shown in table 1 above. Let us consider, the system 102 receives data of type D2 of volume V2 for tagging within T2 time limit and with accuracy greater than 85%. The system 102 may determine the level of difficulty of the tagging the data is low based on received parameters of data.
[039] Let us consider that the tagging is performed as per the received parameters in the morning timing. The system 102 may map the data type D2 for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data. The system 102 may filter out the plurality of taggers Tagger 1, Tagger 2 Tagger 3, Tagger 4, and Tagger 5. The system 102 may select a subset of taggers from the filtered plurality of taggers based on the current difficulty level which is determined as high. The subset of tagger may comprise Tagger 1, Tagger 2, Tagger 3, and Tagger 4. Further, the system 102 may select at least one tagger from the subset of taggers based on the target or required accuracy level i.e. more than 85% for tagging the received data which

is 85%. The at least one tagger may be any of Tagger 1 and Tagger 2. In this manner the system 102 may determine the appropriate tagger for tagging the data as per the criteria for tagging the data defined by the received parameters.
[040] Fig. 3 shows a flowchart of an exemplary method 300 for tagging data in accordance with another embodiment of the present disclosure. At block 302, the method may describe receiving data via the receiving unit 206 of the system 102. The data may be any image, animation, audio, video, text, etc., but not limited thereto. At block 304, at least one parameter defining criteria for tagging the data may be received by the receiving unit 206. The at least one parameter is selected from a group comprising data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data.
[041] At block 306, a current difficulty level in tagging the data is determined based on the at least one parameter by the at least one processing unit 204 of the system 102. The at least one processing unit 204 may determine the current difficulty level according to predefined rules stored in the memory 210. At step 308, at least one tagger for tagging the data is selected from a group of taggers defined in a database based on the current difficulty level and the at least one parameter. For each tagger in the group of taggers, the database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data, the at least one accuracy level in tagging the data is dependent on a time of day.
[042] The at least one tagger for tagging the data is selected from the group of taggers defined in the database based on the current difficulty level and the at least one parameter by mapping the data type for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data. Further, a subset of taggers is selected from the filtered plurality of taggers based on the current difficulty level. Lastly, at least one tagger is selected from the subset of taggers based on the accuracy level for tagging the received data. In this manner, the system 102 may determine the

appropriate tagger for accurately and efficiently tagging the data as per the criteria for tagging the data defined by the received parameters.
[043] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.
[044] Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[045] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term "computer- readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[046] Suitable processors include, by way of example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a

controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), and/or a state machine.

We Claim:
1. A method of tagging data comprising:
receiving data;
receiving at least one parameter defining criteria for tagging the data;
determining a current difficulty level in tagging the data based on the at least one parameter; and
selecting at least one tagger for tagging the data from a group of taggers defined in a database based on the current difficulty level and the at least one parameter.
2. The method as claimed in claim 1, wherein the at least one parameter is selected from a group comprising data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data.
3. The method as claimed in claim 1, wherein for each tagger in the group of taggers, the database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data; and
wherein the at least one accuracy level in tagging the data is dependent on a time of day.
4. The method as claimed in claim 3, wherein selecting the at least one tagger for
tagging the data from the group of taggers defined in the database based on the current
difficulty level and the at least one parameter comprises:
mapping the data type for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data;
selecting a subset of taggers from the filtered plurality of taggers based on the current difficulty level; and
selecting at least one tagger from the subset of taggers based on the accuracy level for tagging the received data.

5. A system of tagging data comprising:
a receiving unit configured to:
receive data, and
receive at least one parameter defining criteria for tagging the data; and
at least one processing unit coupled with the receiving unit, wherein the at least one processing unit is configured to:
determine a current difficulty level in tagging the data based on the at least one parameter, and
select at least one tagger for tagging the data from a group of taggers defined in a database based on the current difficulty level and the at least one parameter.
6. The system as claimed in claim 5, wherein the at least one parameter is selected from a group comprising data type for the received data, volume of the received data to be tagged, an accuracy level in tagging the received data and a time limit for tagging the received data.
7. The system as claimed in claim 5, wherein for each tagger in the group of taggers, the database defines a pre-stored mapping table defining a relationship between at least one difficulty level and one or more of data type, volume of data to be tagged, at least one accuracy level in tagging data, a time limit for tagging data; and
wherein the at least one accuracy level in tagging the data is dependent on a time of day.
8. The system as claimed in claim 7, wherein the at least processing unit is configured
to select the at least one tagger for tagging the data from the group of taggers defined in
the database based on the current difficulty level and the at least one parameter, by:
mapping the data type for the received data in the database to filter out a plurality of taggers from the group of taggers corresponding to the data type for the received data;
selecting a subset of taggers from the filtered plurality of taggers based on the current difficulty level; and
selecting at least one tagger from the subset of taggers based on the accuracy level for tagging the received data.