FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION (See section 10, rule 13)
1. Title of the Invention:
“A SYSTEM AND METHOD FOR COHESIVE TEAM SELECTION BASED ON THEIR INTERACTION”
2. APPLICANT (S) -
(a) Name : Zensar Technologies Limited
(b) Nationality India
(c) Address Plot#4 Zensar Knowledge Park, MIDC,
Kharadi, Off Nagar Road, Pune, Maharashtra - 411014, India
The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD

[0001] The present disclosure relates to data processing. More particularly, but
not exclusively, the present disclosure relates to a method and a system for selecting a team of entities for handling a task.
BACKGROUND
[0002] In various organizations, a group of entities forms a team to work on a
project or task. The entities may be a human or a robot. The formation of the team involves bringing right set of entities together so that the task is accomplished in an effective manner. Often, it is observed that either the entities lack technical knowledge for handling the project/task or have poor collaboration with each other. These conditions affect the project quality and timeline. In today’s competitive environment, it becomes utmost important for the organizations to not only deliver the project/task in timely manner, but also with a great quality. Delivering project/task with time and quality requires not only technical knowledge, but also perfect collaboration amongst the entities in the team.
[0003] Different entities in the team have different mindsets, behaviors, likes,
and dislikes. It may happen that the team of three or four entities possess a very good technical knowledge however not able to collaborate with each other. In such a scenario, it becomes a challenge to form a team of entities which are technically sound and can also collaborate with each other. Specially, in the organization having offices at different locations world-wide, it becomes a technical challenge to observe the behavior/sentiments between two or more entities sitting at different locations, who might have to work in a team. Since in today’s environment, in which, talking between devices/machines are increasing day by day, it becomes another challenge to efficiently and accurately observe the communication between the devices/machines.
[0004] Therefore, there exists a need in the art to provide a technique which
overcomes the above-mentioned problems by forming a team from a group of entities who can work together to accomplish a task in an effective manner.
SUMMARY

[0005] 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.
[0006] In one non-limiting embodiment of the present disclosure, a method of
selecting a team for assigning a task is disclosed. The method comprises a step of receiving a request for performing a task, the task being associated with a technical domain. The method further comprises a step of identifying a set of entities, from a plurality of entities, for handling the task. The set of entities are related to the technical domain associated with the task. The method further comprises a step of analyzing at least one of verbal communication parameters and written communication parameters of each entity with other entities of the set the entities by using at least one of a natural language processing (NLP) technique and a text analytics technique. The method further comprises a step of determining a collaboration level of each entity with other entities of the set the entities based on the analyzing. The collaboration level indicates at least one of a positive collaboration, a negative collaboration, and a neutral collaboration. The method further comprises a step of forming a team of two or more entities of the set of entities based on two or more collaboration levels, associated with the two or more entities, results in an optimal collaboration level. The task being assigned to the team of the two or more entities.
[0007] In another non-limiting embodiment of the present disclosure, a system
of selecting a team for assigning a task is disclosed. The system comprises a memory, receiving unit, identifying unit, analyzing unit, determining unit, and team forming unit in communication with each other. The receiving unit is configured to receive a request for performing a task. The task is associated with a technical domain. The identifying unit is configured to identify a set of entities, from a plurality of entities, for handling the task. The set of entities are related to the technical domain associated with the task. The analyzing unit is configured to analyze verbal communication parameters and written communication parameters of each entity with other entities of the set the entities by using at least one of a natural language processing (NLP) technique and a

text analytics technique. Further, the determining unit is configured to determine a collaboration level of each entity with other entities of the set the entities based on the analyzing. The collaboration level indicates at least one of a positive collaboration, a negative collaboration, and a neutral collaboration. Furthermore, the team forming unit is configured to form a team of two or more entities of the set of entities based on two or more collaboration levels, associated with the two or more entities, results in an optimal collaboration level. The task is assigned to the team of the two or more entities.
[0008] 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.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0009] The features, nature, and advantages of the present disclosure will
become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout. 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:
[0010] Fig. 1 shows an exemplary environment illustrating a system for team
formation and task assignment, in accordance with an embodiment of the present disclosure;
[0011] Fig. 2 shows a flow chart illustrating an exemplary method of selecting
a team for assigning a task, in accordance with another embodiment of the present disclosure;
[0012] Fig. 3(a) shows a block diagram illustrating a system for selecting a
team for assigning a task, in accordance with another embodiment of the present disclosure;

[0013] Fig. 4 illustrates a block diagram illustrating an exemplary computer
system for implementing embodiments consistent with the present disclosure;
[0014] It should be appreciated by those skilled in the art that any block
diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams 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
[0015] 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.
[0016] 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.
[0017] Fig. 1 shows an exemplary environment 100 illustrating a system for
team formation and task assignment, in accordance with an embodiment of the present disclosure.

[0018] In one embodiment of the present disclosure, the environment 100
comprises a system 101 and plurality of entities from various technical domain such as Tech-A, Tech-B, and Tech-C. The system 101 may receive a request for task, which is associated with a specific technical domain (e.g. Tech-B). The system 101 may then identify a set of entities (entities 1, 2, 3, and 4) from the plurality of entities, who are related to the technical domain (Tech-B) associated with the task.
[0019] The system 101 may then analyze verbal communication parameters
and written communication parameters between the set of entities using natural language processing (NLP) technique and text analytics techniques. The NLP technique and the text analytics techniques are used to extract information from the verbal communication parameters and written communication parameters.
[0020] The system 101 may then determine a collaboration level of each entity
with other entities of the set the entities based on the analysis of the verbal communication parameters and written communication parameters. As shown in fig. 1, entities 1, 2, and 3 have a collaboration level with each other (indicated by bold lines), whereas entity 4 has no collaboration with entities 1, 2, and 3 (indicated by dotted lines).
[0021] The system 101 may then select a team (entities 1, 2, and 3) from the
set of entities (1, 2, 3, and 4) based on two or more collaboration levels results an optimal collaboration level. The optimal collaboration level may be a best or maximum possible collaboration level with identified set of entities. The system 101 may then assign a task to the selected team.
[0022] The selection or the formation of the team based on the collaboration
level of the entities facilitates accomplishing the task in an effective manner. The system 101 may determine an optimal collaboration level of the team and generate an alert if the optimal collaboration level of the selected team is a negative collaboration. The alert may be used for monitoring the entities while the task is being performed.

[0023] Fig. 2 shows a flow chart illustrating an exemplary method 200 of
selecting a team for assigning a task, in accordance with another embodiment of the present disclosure.
[0024] At block 201, a request for performing a task is received. The task may
be associated with a specific technical domain. The task may comprise a project or group of projects. The technical domain may comprise at least one of coding, computing, configuration, customer support, debugging, design, development, hardware, implementation, Information Technology, ICT (Information and Communications Technology), infrastructure languages, maintenance, Network Architecture, Network Security, Networking, New Technologies, Operating Systems, Programming, restoration, security, servers, software, solution delivery, storage, structures, systems analysis, testing, tools, etc. The technical domain is not limited to domains mentioned above. Any other technical domain in an enterprise is well within the scope of the present disclosure.
[0025] At block 203, a set of entities, from a plurality of entities, for handling
the task is identified. The set of entities may be related to the technical domain associated with the task. The set of entities may be identified based on plurality of parameters comprising an availability of an entity for the task, a past experience of an entity, an entity time-zone, and a deadline for finishing the task.
[0026] In another embodiment of the present disclosure, the set of entities may
also be identified based on the technical capability score. The technical capability score may be calculated using the following equation:
technical capability score = (y1*bugs-filed-per-line-of-code + y2*number-of-edits-
made-to-the-code-by-hitting-delete-key+ y3*rate-of-
change-in-bugs-filed+ y4*number-of-functions-in-the-
code + y5*number-of-corrections-made-per-line-after-
static-code-analysis + y6*number-of-compiler-errors-
per-line-of-code+ y7*number-of-warnings-per-line-of-
code+ y8*number-of-words-of-comments-per-line-of-
code + y9* speedo-of-writing-code(words-per-second) +

y10*number-of-lines-of-code-in-each-method+
y11*number-of-recursion-methods-used+ y12*number-
of-unique-libraries-used + y13*number-of-library-
functions-used + y14*number-of-checkins-per-month+
y15* number-of-programming-langueges-in-which-
person-has-coded-more-than-1000-lines-of-his-code +
y16*number-of-databases-connected-in-code,
where y1, y2, y3, y4, y5, y6, y7, y8, y9, y10, y11, y12, y13, y14, y15, and y16 are the weights assigned to a respective parameter. The weights may be selected based on a preference of the industry or the administration or the criticality of the task. In one non-limiting embodiment, the weights assigned to respective parameter may be based on the technical domain of the task. The weight may be any fraction or decimal value between 0 and 1.
[0027] At block 205, at least one of verbal communication parameters and
written communication parameters of each entity with other entities of the set the entities are captured by one or more sensors. The sensor may comprise a camera sensor, audio sensor, microphone, etc., According to an embodiment, the captured verbal communication parameters and written communication parameters may be stored in memory. The verbal communication parameters may comprise at least one of call duration, call frequency, and sentiment of verbal/voice communication, whereas the written communication parameters may comprise at least one of sentiment of emails and text messages exchanged, length of characters in the emails and text messages, and number of informal chats. The verbal communication parameters and the written communication parameters of each entity with other entities of the set the entities are then analyzed using at least one of a natural language processing (NLP) technique and a text analytics technique.
[0028] The NLP technique and the text analytics techniques may perform
sentiment analysis of one entity with another entities using voice data and/or text data. The voice or text communication data may be processed to extract the sentences and phrases. The sentences/phrases are further split into words and meaning of each word

in context of the sentences/phrases are analyzed for determining sentiments of the verbal/written communication.
[0029] In an embodiment of the present disclosure, the system may perform the
sentiment analysis by using a Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. The VADER tool may be specifically attuned to sentiments expressed in text messages and emails exchanged between the entities. The VADER tool may use a combination of a sentiment lexicon comprising list of lexical features (e.g., words) which are generally labelled according to their semantic orientation as either positive or negative. The VADER tool may easily detect sentiment from emojis and slangs in the sentences of the text messages and email and classify them as formal and informal.
[0030] At block 207, a collaboration level of each entity with other entities of
the set of entities may be determined based on the analysis as described in the above block 205. The collaboration level determined indicates at least one of a positive collaboration, a negative collaboration, and a neutral collaboration. In another embodiment of the present disclosure, the positive collaboration may be detected when the length of characters in the emails and text messages between two entities is beyond a length range, the number of informal chats between two entities is beyond a chat range, the sentiment of emails and text messages exchanged between two entities is informal, the call duration between two entities is beyond a call duration range, the call frequency between two entities is beyond a call frequency range, and the sentiment of voice/verbal communication between two entities is informal. It may be noticed that the positive collaboration is determined when the parameters values goes “beyond” their respective ranges.
[0031] Similarly, the neutral collaboration may be detected when the length of
characters in the emails and text messages is within the length range, the number of informal chats is within the chat range, the sentiment of emails and text messages exchanged is informal and formal, the call duration is within the call duration range, the call frequency is within the call frequency range, and the sentiment of voice/verbal communication is both informal and formal. It may be noticed that the neutral

collaboration is determined when the parameters values are “within” their respective ranges.
[0032] Similarly, the negative collaboration may be detected when the length
of characters in the emails and text messages is below the call duration range, the number of informal chats is below the chat range, the sentiment of emails and text messages exchanged is formal, the call duration is below the call duration range, the call frequency is below the call frequency range, and the sentiment of voice/verbal communication is formal. It may be noticed that the negative collaboration is determined when the parameters values are “below” their respective ranges. It may be understood to a person skilled in art that there may be other parameters and their permutations and combination, which may be used for determining the collaboration level between the two entities.
[0033] In an exemplary embodiment of the present disclosure, a length range
of the email and text messages may be 500-1000 characters, a chat range for informal chat may be 10-20 chats/week, a call duration range may be 15-30 minutes/call, a call frequency range may be 5-10 calls/week. For example, if an average length of characters in the emails and text messages between entity two entities is 2000 characters, an average number of informal chats between two entities is 25 chats/week, an average call duration range between two entities is 35 minutes, a call frequency is 15 calls/week, sentiment of verbal/voice communication is informal, and the sentiment of emails and text messages exchanged is informal, then the “positive collaboration” is detected.
[0034] For example, if an average length of characters in the emails and text
messages between entity two entities is 750 characters, an average number of informal chats between two entities is 15 chats/week, an average call duration range between two entities is 20 minutes, a call frequency is 8 calls/week, sentiment of verbal/voice communication is both formal and informal, and the sentiment of emails and text messages exchanged is both formal and informal, then the “neutral collaboration” is detected.

[0035] For example, if an average length of characters in the emails and text
messages between entity two entities is 250 characters, an average number of informal chats between two entities is 5 chats/week, an average call duration range between two entities is 10 minutes, a call frequency is 3 calls/week, sentiment of verbal/voice communication is formal, and the sentiment of emails and text messages exchanged is formal, then the “negative collaboration" is detected. However, it may be understood to the person skilled in art that the length range of the email and text messages, chat range, call duration range, and call frequency range are not limited to values discussed above and may be altered as per the requirement of the task.
[0036] In another embodiment of the present disclosure, the collaboration level
of each entity with other entities of the set the entities is determined based on a collaboration score. The collaboration score may be calculated using the following equation:
collaboration score = x1* number-of-emails+ x2*number-of_phonecalls+ x3*number-
of-messages+ x4*total_words_used_in_conversation+
x5*proportions-of-casual-words-used+ x6*proportion-of-
formal-words-used+ x7*positive-sentiment-word-
proportion+ x8*negative-sentiment-sentiment-word-
proportion+ x9*average-length-of-emails+x10*average-
length-of-calls+x11*average-length-of-messages
+x12*difference-in-time-zone(in hours)+ x13*distance-
between-places-of-seating-of-the-two-associates +
x14*success-rate-of-the-projects-on-which-both-have-
worked-together,
where x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, and x14 are the weights assigned to a respective parameter. The weights may be selected based on a preference of the industry or the administration. In one non-limiting embodiment, the weights assigned to respective parameter may be based on the criticality of the task. The weight may be any fraction or decimal value between 0 and 1.

[0037] At block 209, after determining the collaboration level, a team of two
or more entities of the set of entities may be formed based on two or more collaboration levels, associated with the two or more entities, results in an optimal collaboration level. The optimal collaboration level may be a maximum cumulative collaboration level possible with identified set of entities. The optimal collaboration level may be at least one of a positive collaboration, a negative collaboration, and a neutral collaboration. The task is assigned to the team of the two or more entities. The formation of team based on the collaboration level of the entities in the team ensures accomplishing the task in an effective and timely manner.
[0038] In one embodiment of the present disclosure, if the optimal
collaboration level is negative collaboration, an alert may be generated. The alert may help the task administrator or project manager to know that the team has a negative collaboration and may require more monitoring than the scenarios in which the collaboration level is positive and neutral. In another embodiment of the present disclosure, the alert may also be used for training the entities with the soft skills and improve the collaboration level. The steps of method 200 may be performed in an order different from the order described above. The above method 200 may be understood by an exemplary embodiment mentioned below.
[0039] Fig. 3 shows a block diagram illustrating a system for selecting a team
for assigning a task, in accordance with another embodiment of the present disclosure.
[0040] In an embodiment of the present disclosure, a system 300 for selecting
a team for assigning a task is disclosed. The system 300 may comprise a memory 301, a receiving unit 303, identifying unit 305, an analyzing unit 307, a determining unit 309, a team forming unit 311, and an alert generator 313 in communication with each other. According to one embodiment, the units 303-313 may be dedicated hardware units capable of performing various operations of the system 301. However, according to other embodiments, the units 303-313 may a processor or an application-specific integrated circuit (ASIC) or any circuitry capable of executing instructions stored in the memory 301 of the system 301.

[0041] In an embodiment of the present disclosure, the system 300 addresses
the technical challenge of how efficiently and accurately observe the communication between the devices/machines associated with entities so that a team of entities could be formed, which are not only technically sound but have high collaboration amongst them. In first step, the receiving unit 303 may receive a request for performing a task. The task may be associated with a specific technical domain. The task may comprise a project or group of projects. The technical domain may comprise any of the technical domain as discussed above. For example, the task may be to develop a search engine using some technology.
[0042] Once the task is received and its technology and requirements are
understood, the next task is to identify for a team of entities and assign a task to them. However, to identify the entities, various parameters have to be checked, for example availability of an entity for the task, a past experience of an entity, an entity time-zone, and a deadline for finishing the task. There may be number of entities available in the organization who may satisfy the above criteria. However, not everyone can be considered just because they are available, and their time zones matches with requirement. Hence, along with the above parameters, the other important parameter is technical capability of the entities. For this, the identifying unit 305 may identify a set of entities, from a plurality of entities, for handling the task based on the abovementioned entity related parameters and their technical capability score. The technical capability for each entity may be determined as explained in above paragraphs of the specification. Hence, the set of entities are those entities which are not only available but are also technically sound to handle the task to be assigned.
[0043] However, the next challenge is to determine how the set of entities will
behave or collaborate with other when the task is assigned to them. It may happen that few entities, amongst the set of entities, may have high technical qualification and experience but they may dislike each other. This raises a serious concern because determining human nature with each other located in remote environment becomes a challenge. Now a days, with the advancement of technology, people to people interaction is becoming less compared to device to device interaction. That is, most of our communication now happens over phones, emails, social media, text messaging

applications and the like. Hence, another technical challenge is to efficiently observe the devices which are talking to each other over the communication network for determining behavior of the entities.
[0044] To address this challenge, the system 300 may enable one or more
sensors to capture at least one of verbal communication parameters and written communication parameters of each entity with other entities of the set the entities. The one or more sensors may comprise, but not limited to, a camera sensor, audio sensor, microphone, etc. The written communication parameters may also be determined by accessing the databases of the entities. The verbal communication parameters may comprise at least one of call duration, call frequency, and sentiment of verbal/voice communication. The written communication parameters may comprise at least one of sentiment of emails/text messages exchanged, length of characters in the emails/text messages, number of informal chats. In another embodiment of the present disclosure, the system 300 may comprise one or more sensors.
[0045] Once the sensor inputs are received, in next step, the analyzing unit 307
analyzes the verbal communication parameters and the written communication parameters of each entity with other entities of the set the entities by using at least one of a natural language processing (NLP) technique and a text analytics technique.
[0046] For example, the analyzing unit 307 may use the NLP and text analytics
technique to analyze the voice and/or text data and perform sentiment analysis. The voice or text communication data may be processed to extract the keywords in the communication data. Then, the NLP technique may perform sentiment analysis to determine whether the context of voice or text communication data is formal or informal. As discussed in the above paragraphs of the specification, such sentiment analysis may also be performed using a Valence Aware Dictionary and Sentiment Reasoner (VADER) technique. The purpose of sentiment analysis is to determine the behavior of the entities with each other, as some entities may not like each other, while some other entities may have a good personal bonding or at least neutral bonding. In case of the disliking scenario, it becomes are issue because their personal issues or non-bonding nature may affect the delivery of the task. Hence, this analysis helps the system

300 to understand bonding amongst the entities and filter out those entities which may have poor bonding. The bonding level or the collaboration level is explained in detail in subsequent paragraphs of the specification.
[0047] In next step, the determining unit 309 may determine a collaboration
level of each entity with other entities of the set the entities based on the above analysis of the text and voice data. The collaboration level may be determined as a positive collaboration, a negative collaboration, and a neutral collaboration. For example, the positive collaboration may be determined or detected for each entity with other entities when the length of characters in the emails and text messages is beyond a length range, the number of informal chats is beyond a chat range, the sentiment of emails and text messages exchanged is informal, the call duration is beyond a call duration range, the call frequency is beyond a call frequency range, and the sentiment of voice/verbal communication is informal.
[0048] Similarly, the neutral collaboration may be determined or detected for
each entity with other entities when the length of characters in the emails and text messages is within the length range, the number of informal chats is within the chat range, the sentiment of emails and text messages exchanged is informal and formal, the call duration is within the call duration range, the call frequency is within the call frequency range, and the sentiment of voice/verbal communication is both informal and formal.
[0049] Similarly, the negative collaboration may be determined or detected for
each entity with other entities when the length of characters in the emails and text messages is below the call duration range, the number of informal chats is below the chat range, the sentiment of emails and text messages exchanged is formal, the call duration is below the call duration range, the call frequency is below the call frequency range, and the sentiment of voice/verbal communication is formal. In one non-limiting embodiment, the collaboration level may be determined using different ranges and equations as explained in the above paragraphs of the specification.
[0050] Once the collaboration level is determined, the team forming unit 311
forms a team of two or more entities from the set of entities based on two or more

collaboration levels, associated with the two or more entities, results in an optimal collaboration level. The optimal collaboration level may be a maximum cumulative collaboration level possible with identified set of entities. The optimal collaboration level may be at least one of a positive collaboration, a negative collaboration, and a neutral collaboration. The task is assigned to the team of the two or more entities. The formation of team based on the collaboration level of the entities in the team ensures accomplishing the task in an effective and timely manner.
[0051] In one embodiment of the present disclosure, the determining unit 309
may determine an optimal collaboration level of the team and if the optimal collaboration level of the team is a negative collaboration, the alert generator 313 may generate an alert. The alert may be used by the administration to monitor the task. In another embodiment of the present disclosure, the alert may be used for training the entities with the soft skills and improve the collaboration level.
[0052] Fig. 4 illustrates a block diagram of an exemplary computer system 400
for implementing embodiments consistent with the present invention.
[0053] In an embodiment, the computer system 400 can be the system 101
which is used for selecting a team for assigning a task. According to an embodiment, the computer system 500 may receive appliance data 410 which may include, for example, request for performing a task and verbal communication parameters and written communication parameters from the one or sensor 305 or the databases. The computer system 400 may comprise a central processing unit (“CPU” or “processor”) 402. The processor 402 may comprise at least one data processor for executing program components for executing user- or system-generated business processes. The processor 402 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
[0054] The processor 402 may be disposed in communication with one or more
input/output (I/O) devices (411 and 412) via I/O interface 401. The I/O interface 401 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared,

PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface 401, the computer system 400 may communicate with one or more I/O devices (411 and 412).
[0055] In some embodiments, the processor 402 may be disposed in
communication with a communication network 409 via a network interface 403. The network interface 403 may communicate with the communication network 409. The network interface 403 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 409 can be implemented as one of the different types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 409 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 409 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
[0056] In some embodiments, the processor 402 may be disposed in
communication with a memory 405 (e.g., RAM 413, ROM 414, etc. as shown in Fig. 4) via a storage interface 404. The storage interface 404 may connect to memory 405 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive,

Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
[0057] The memory 405 may store a collection of program or database
components, including, without limitation, user/application data 406, an operating system 407, web browser 408 etc. In some embodiments, the computer system 400 may store user/application data 406, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
[0058] The operating system 607 may facilitate resource management and
operation of the computer system 600. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like. I/O interface 501 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, I/O interface may provide computer interaction interface elements on a display system operatively connected to the computer system 500, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.
[0059] In some embodiments, the computer system 400 may implement a web
browser 508 stored program component. The web browser 408 may be a hypertext viewing application, such as Microsoft™ Internet Explorer, Google™ Chrome, Mozilla™ Firefox, Apple™ Safari™, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS) secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming Interfaces (APIs), etc. In

some embodiments, the computer system 400 may implement a mail server stored program component. The mail server 416 may be an Internet mail server such as Microsoft Exchange, or the like. The mail server 416 may utilize facilities such as Active Server Pages (ASP), ActiveX, American National Standards Institute (ANSI) C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 400 may implement a mail client 415 stored program component. The mail client 415 may be a mail viewing application, such as Apple™ Mail, Microsoft™ Entourage, Microsoft™ Outlook, Mozilla™ Thunderbird, etc.
[0060] The user interface 301 may include at least one of a key input means,
such as a keyboard or keypad, a touch input means, such as a touch sensor or touchpad, a sound source input means, a camera, or various sensors, and the user interface may include a gesture input means. Further, the user interface may include all types of input means that are currently in development or are to be developed in the future. The user interface may receive information from the user through the touch panel of the display and transfer the inputted information to the processing system, processor, AI module.
[0061] The processing system 303 may comprise one or more processors,
memory, and communication interface. The memory may be software maintained and/or organized in loadable code segments, modules, applications, programs, etc., which may be referred to herein as software modules. Each of the software modules may include instructions and data that, when installed or loaded on a processor and executed by the processor, contribute to a run-time image that controls the operation of the processors. When executed, certain instructions may cause the processor to perform functions in accordance with certain methods, algorithms and processes described herein.
[0062] 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. 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.
[0063] 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.
[0064] 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.
Advantages of the embodiment of the present disclosure are illustrated herein.

[0065] In an embodiment, the present disclosure provides a method of selecting
a team of two or more entities for assigning a task facilitates effective delivery of project/task in a timely manner and with best quality.
[0066] In an embodiment, the present disclosure provides a method of
understanding the collaboration level of the team and improving thereof.
Reference Numbers:

Reference Number Description
100 ENVIRONMENT
101 SYSTEM
300 SYSTEM
301 MEMORY
303 RECEIVING UNIT
305 IDENTIFYING UNIT
307 ANALYZING UNIT
Reference Number Description
309 DETERMINING UNIT
311 TEAM FORMING UNIT
313 ALERT GENERATOR
400 COMPUTER SYSTEM
401 I/O INTERFACE
402 PROCESSOR
403 NETWORK INTERFACE
404 STORAGE INTERFACE
405 MEMORY
406 USER/APPLICATION
407 OPERATING SYSTEM

408 WEB BROWSER
409 COMMUNICATION NETWORK
410 TASK REQUEST
411 INPUT DEVICES
412 OUTPUT DEVICES
413 RAM
414 ROM
415 MAIL CLIENT
416 MAIL SERVER

We Claim:
1. A method (200) of selecting a team for assigning a task, the method comprising:
receiving (201), by a receiving unit, a request for performing a task, wherein
the task is associated with a technical domain;
identifying (203), an identifying unit, a set of entities, from a plurality of entities, for handling the task, wherein the set of entities are related to the technical domain associated with the task;
analyzing (205), by an analyzing unit, at least one of verbal communication parameters and written communication parameters of each entity with other entities of the set the entities by using at least one of a natural language processing (NLP) technique and a text analytics technique;
determining (207), by a determining unit, a collaboration level of each entity with other entities of the set the entities based on the analyzing, wherein the collaboration level indicates one of a positive collaboration, a negative collaboration and a neutral collaboration; and
forming (209), by a team forming unit, a team of two or more entities of the set of entities based on two or more collaboration levels, associated with the two or more entities, results in an optimal collaboration level.
2. The method (200) as claimed in claim 1, further comprising identifying the set of entities based on at least one of an entity availability, entity’s experience, technical capability score, and entity time-zone.
3. The method (200) as claimed in claim 1, wherein the verbal communication parameters comprise at least one of call duration, call frequency, and sentiment of verbal/voice communication and wherein the written communication parameters comprise at least one of sentiment of emails/text messages exchanged, length of characters in the emails/text messages, number of informal chats.
4. The method (200) as claimed in claim 3, wherein determining (207) a collaboration level of each entity comprises detecting, for each entity with other entities, at least one of:

the positive collaboration when the length of characters in the emails/text messages is beyond a length range, the number of informal chats is beyond a chat range, the sentiment of emails/text messages exchanged is informal, the call duration is beyond a call duration range, the call frequency is beyond a call frequency range, and the sentiment of voice/verbal communication is informal;
the neutral collaboration when the length of characters in the emails/text messages is within the length range, the number of informal chats is within the chat range, the sentiment of emails/text messages exchanged is informal and formal, the call duration is within the call duration range, the call frequency is within the call frequency range, and the sentiment of voice/verbal communication is both informal and formal; and
the negative collaboration when the length of characters in the emails/text messages is below the call duration range, the number of informal chats is below the chat range, the sentiment of emails/text messages exchanged is formal, the call duration is below the call duration range, the call frequency is below the call frequency range, and the sentiment of voice/verbal communication is formal.
5. The method (200) as claimed in claim 1, further comprising
generating an alert when the optimal collaboration level of the team is a negative collaboration.
6. A system (300) for selecting a team for assigning a task, the system comprising:
a memory (301);
a receiving unit (303) in communication with the memory (301) and configured to receive a request for performing a task, wherein the task is associated with a technical domain;
an identifying unit (305) in communication with the memory (301) and the receiving unit (303), and configured to identify a set of entities, from a plurality of entities, for handling the task, wherein the set of entities are related to the technical domain associated with the task; and
an analyzing unit (307) in communication with a plurality of sensors and identifying unit (305), and configured to analyze at least one of the verbal

communication parameters and the written communication parameters of each entity with other entities of the set the entities by using at least one of a natural language processing (NLP) technique and a text analytics technique;
a determining unit (309) in communication with the analyzing unit (307) and configured to determine a collaboration level of each entity with other entities of the set the entities based on the analysis, wherein the collaboration level indicates at least one of a positive collaboration, a negative collaboration and a neutral collaboration; and
a team forming unit (311) in communication with the determining unit (309) and configured to form a team of two or more entities of the set of entities based on two or more collaboration levels, associated with the two or more entities, results in an optimal collaboration level, wherein the task is assigned to the team of the two or more entities.
7. The system (300) as claimed in claim 6, wherein to identify the set of entities, the identifying unit (305) is configured to identify the set of entities based on at least one of an entity availability, entity’s experience, technical capability score, and entity time-zone.
8. The system (300) as claimed in claim 6, wherein the verbal communication parameters comprise at least one of call duration, call frequency, and sentiment of verbal/voice communication and wherein the written communication parameters comprise at least one of sentiment of emails/text messages exchanged, length of characters in the emails/text messages, number of informal chats.
9. The system (300) as claimed in claim 8, wherein to determine a collaboration level of each entity with other entities of the set the entities, the determining unit (309) is configured to detect, for each entity with other entities, at least one of:
the positive collaboration when the length of characters in the emails/text messages is beyond a length range, the number of informal chats is beyond a chat range, the sentiment of emails/text messages exchanged is informal, the call

duration is beyond a call duration range, the call frequency is beyond a call frequency range, and the sentiment of voice/verbal communication is informal;
the neutral collaboration when the length of characters in the emails/text messages is within the length range, the number of informal chats is within the chat range, the sentiment of emails/text messages exchanged is informal and formal, the call duration is within the call duration range, the call frequency is within the call frequency range, and the sentiment of voice/verbal communication is both informal and formal; and
the negative collaboration when the length of characters in the emails/text messages is below the call duration range, the number of informal chats is below the chat range, the sentiment of emails/text messages exchanged is formal, the call duration is below the call duration range, the call frequency is below the call frequency range, and the sentiment of voice/verbal communication is formal.
10. The system (300) as claimed in claim 6, further comprising an alert generator
(313) in communication with the determining unit (309) and the team forming unit (311) and configured to:
generate an alert when the optimal collaboration level of the team is a negative collaboration.