FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
AND
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. Title of the Invention:
“METHOD AND SYSTEM FOR MANAGING DATA FOR
EFFECTIVE COLLABORATION BETWEEN SERVICE
PROVIDER AND CLIENT”
2. APPLICANT (S) -
(a) Name : Zensar Technologies Limited
(b) Nationality : Indian
(c) Address : Plot#4 Zensar Knowledge Park, MIDC, Khardi,
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
The present invention relates to a field of data analysis, and more particularly to managing data for effective collaboration between a service provider and a client.
BACKGROUND OF INVENTION
Currently, automating evaluation of customer’s or client’s demand and helping in forecasting the demand for the resources to provide an input for skills required for the upcoming projects is a major challenge which every organization may have encountered at some stage. There exist no systems which analyse inputs such as winning probability, ability of salesperson (resource) to convert the opportunity into a winning deal and customer commitments.
Because of the unavailability of such systems, cumbersome tasks including, but not limited to, identifying optimum number of resources, skills development needed to fulfil the requirements need to be done manually resulting into error prone output. Thus, there exists a need of a system and method to determine a winning probability of the project, develop its own method to compute number of resources required to fulfil the impending demands and indicates skillsets needed for fulfilling the demand.
Conventionally, there exist two major issues while dealing with work orders/workflow or collecting data from the existing system, Firstly, the data sharing/synchronization between multiple data nodes (devices storing databases) which are remotely located across the globe is an overhead. Also, handling such huge amount data stored in various data nodes to bring synchronization between various entities/resources involved in the entire process flow (work order) is also an overhead, and practically not possible to achieve data synchronization. Without sharing the data with the constituent systems (data nodes, enterprise systems etc.), it would be very much difficult to monitor the process and data flow/change in real time. Also, if in a workflow if any of the system goes down or

undergoes maintenance/updates activities it will directly impact all the systems that are dependent on the application(s) via API or other delta integration. There is, therefore, a need for a system and method of managing the data vastly distributed across various data nodes.
The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
SUMMARY OF THE INVENTION
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.
In one embodiment of the present disclosure, a method of managing data for effective collaboration between a service provider and a client is disclosed. The method comprises fetching entity data and client data from a plurality of data nodes arranged in a decentralized manner such that the entity data and the client data are capable of being fetched from anyone of a data node of the plurality of data nodes. Each data node indicates a separate data storing device. The client data comprises a plurality of assignment information provided against a plurality of assignment parameters associated with the client. The entity data comprises a plurality of entity information provided against a plurality of entity parameters associated with the entity. The method further comprises correlating the plurality of entity information with the plurality of assignment information to generate a set of entity group such that each entity group comprises one or more entities having maximum probability of efficiently handling at least one assignment of the plurality of assignments.

In one embodiment of the present disclosure, a system for managing data for effective collaboration between a service provider and a client is disclosed. The system comprises a fetching unit configured to fetch entity data and client data from a plurality of data nodes arranged in a decentralized manner such that the entity data and the client data are capable of being fetched from anyone of a data node of the plurality of data nodes. Each data node indicates a separate data storing device. Further, the client data comprises a plurality of assignment information provided against a plurality of assignment parameters associated with the client. Furthermore, the entity data comprises a plurality of entity information provided against a plurality of entity parameters associated with the entity. The system further comprises a generating unit configured to generate a set of entity group such that each entity group comprises one or more entities having maximum probability of efficiently handling at least one assignment of the plurality of assignments by correlating the plurality of entity information with the plurality of assignment information.
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 DESCITPION OF DRAWINGS
The embodiments of the disclosure itself, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings in which:
Figure 1 shows an exemplary environment 100 for managing data for effective collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure;

Figure 2 shows a block diagram 200 illustrating a system for managing data for effective collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure;
Figures 3A-3C illustrate various exemplary tables which include data for collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure; and
Figures 4A-4B shows a method 400 of managing data for effective collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure.
The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.
The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

Disclosed herein is a system and method for managing data for effective collaboration between a service provider and a client. Currently, there exists no system that can help in automating the process of evaluating the demand and various parameters and generate an output that can help in forecasting the demand for the resources (also referred to as entities in the present disclosure) and provide an input for skills required for the upcoming projects/assignments. Since the data is distributed across multiple data nodes, it becomes a challenge to synchronize such huge amount of data to arrive at a meaningful information.
The present disclosure provides an AI powered automated system backed by blockchain technology that will monitor and drive entire process of obtaining demand information from the subsystems along with supportive parameters such as win probabilities, historic performance of the sales person (entity), client’s current and past relationships, client’s current market position and commitment to initiate the project being evaluated etc. to generate demand forecast. The output will consist of demand forecasting for the resources required and skillets expected. The system will take into account various internal projects/assignments running, number of resources/entities and their skill-sets, probabilities and possibilities of developing the skill-sets and required skill-sets input for the cross-training of resources from pool, the ones in other projects as well as newly hired.
Further, the issues delated to dependency on multiple data nodes for data retrieval is also addressed by present disclosure. That is, the present disclosure implements a blockchain function such that each data node act as node in a blockchain topology, which will save data in blockchain ledger whenever there is a transaction or data insertion/updation. The blockchain function(s) (developed smart contracts in the network) will get invoked and will store the relevant data in the system [data model that are defined for smart contracts functions] on real time basis. Also, if any of the data node goes down still the end product will work as the data input(s) will be given from Blockchain Ledger and AI module can still work or process its forecast. The detailed working of the present disclosure has been explained in the upcoming paragraphs.

Figure 1 shows an exemplary environment 100 for managing data for effective collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure. It must be understood to a person skilled in art that the system disclosed in the present disclosure may also be implemented in various environments, other than as shown in Fig. 1. As shown in Fig, 1, entity data (ED1-ED3) 209 and client data (CD1-CD3) 207 are stored in plurality of data nodes. The entity data 209 includes information pertaining entities associated with the service provider, whereas the client data 207 includes information pertaining to various assignments/projects received from the client side. The environment 100 also includes entity groups G1-G3 which is finally created after processing the entity data and the client data such that each entity group comprises one or more entities grouped for handling the assignments.
The detailed explanation of the exemplary environment 100 is explained in conjunction with Figure 2 that shows a block diagram of a system 200 for managing the data for effective collaboration between the service provider and the client, in accordance with an embodiment of the present disclosure. Also, the detailed explanation of the exemplary environment is also explained using an exemplary scenario shown in Figures 3A-3C. Although the present disclosure is explained considering that the system 100 is implemented on a server, it may be understood that the system 100 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment. According to an aspect of the present disclosure, the system 100 may be referred to as an artificial intelligence (AI) powered automated system backed by blockchain technology that will monitor and drive the entire process of the present disclosure.
In one implementation, the system 100 may comprise a processor 201, an I/O interface 203, a memory 205 and the units 211. The memory 205 may be communicatively coupled to the processor 201 and the units 211. The entity data (ED1-ED3) 209 and the client data (CD1-CD3) 207 may be fetched from the plurality of data nodes and stored in the memory 205. In some embodiments, the plurality of data nodes may be fully or partially associated

with the service provider. The significance and use of each of the stored quantities is explained in the upcoming paragraphs of the specification. The processor 201 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 201 is configured to fetch and execute computer-readable instructions stored in the memory 205. The I/O interface 203 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 203 may enable the system 100 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 203 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 203 may include one or more ports for connecting many devices to one another or to another server.
In one implementation, the units 211 may comprise a fetching unit 213, a generating unit 215, a scoring unit 217, and a selecting unit 219. According to embodiments of present disclosure, these units 213-221 may comprise hardware components like processor, microprocessor, microcontrollers, application-specific integrated circuit for performing various operations of the system 100. In one embodiment, the units 213-219 may be dedicated hardware units capable of executing one or more instructions stored in the memory 205 for performing various operations of the system 101. In another embodiment, the units 213-219 may be software modules stored in the memory 205 which may be executed by the processor 201 for performing the operations of the system 100. It must be understood to a person skilled in art that the processor 201 may perform all the functions of the units 213-219 according to various embodiments of the present disclosure.
Now referring back to Figure 1, the environment 100 shows a system 102 may be configured to receive inputs in form of entity data (ED1-ED3) and client data (CD1-CD3) from a plurality of data nodes. In other words, the fetching unit 213 may be configured to fetch entity data 209 and client data 207 from the plurality of data nodes. The data nodes

are the data storing devices arranged in a decentralized manner such that the entity data 209 and the client data 207 are capable of being fetched from anyone of a data node of the plurality of data nodes. In a non-limiting embodiment, the entity data 209 and the client data 207 may be fetched during a particular duration or a period, for example a day, week, month and year or any predefined period. The data nodes may correspond to different types blockchain nodes residing in the blockchain topology such that some data nodes may store data related to entities (entity data 209) and other data nodes may store data related to client (client data 207)... The entity data 209 comprises a plurality of entity information provided against a plurality of entity parameters associated with the entity. According to an embodiment , the plurality of entity parameters comprises at least one of skills, experience, designation, awards, historic performance, win probabilities, and bandwidth. Whereas, the plurality of entity information comprises the actual information provided against each entity parameters like skill information (what skills the entity or resource possess), experience information (how much years of experience the entities have), designation information (what designation does the entity holds in the organization), awards information (number and types of awards and recognitions received by the entities), historic performance information (how the entity has performed in the past while working on different assignments of clients), win probabilities information (what is the possibility of the entity to successfully execute the assignments), and bandwidth availability information (whether the entity is currently available for taking up the assignment or when the entity will be available).
] Similarly, the client data 207 comprises plurality of assignment information provided against a plurality of assignment parameters associated with the client. According to an embodiment, the plurality of assignment parameters may comprise, but not limited to, domain, timeline, and team size. Whereas, the plurality of assignment information may comprise information against each assignment parameter such as domain information (in which domain the assignment belongs to – computer, networking), timeline information (what is the expected time period in which the client wants the assignment to be delivered), language information (what are the languages/skills will be required for handling the assignment), experience information (how much experience will be required by the entity

who will be handling the assignment), and team size information (how much entities will be required for handling the assignment). It may be worth noted that the plurality of entity information and the plurality of assignment information is not limited to above-mentioned information and may include other information as per requirements of the client. For illustrative purposes, entity data 209 having the plurality of entity parameters and the corresponding plurality of entity information is shown in Fig. 3A. Similarly, for illustrative purposes, the client data 207 having the plurality of assignment parameters and plurality of assignment information is shown in Fig. 3B.
As stated earlier, the above discussed entity data 209 and the client data 207 are received from various data nodes. These data nodes may be associated with servicer provider or organization. However, these data nodes may also be connected with external systems such as Sales Force Dot Com (SFDC). The SDFC is a system or subsystem where Sales Executive or Associates (for example, entity 1 – as shown in Fig. 3A) enters various parameters based on his/her engagements with client(s) (for example client X – as shown in Fig. 3B). The various parameters from the SDFC system will be taken into account and will have relevant blockchain configuration to store the data into the blockchain ledger or data node. The various parameters for the inputs from SDFC are defined below:
• SuccessRateofTheProposal: This parameter defines about the success rate of the proposal that the organization is dealing with.
• Influence: This parameter defines the level of influence that the sales executive (i.e. entity) is having with a particular client.
• Probability: Defined by the sales executive for winning the project.
• NegotiationRate: negotiation rate of the entity.
Further, the data nodes may also be connected with other systems such as customer past relationship record system (CPR) from which the entity data 209 and the client data 207 may be received. The CPR record system drives the equations that the organization or the service provider is having with the client(s) or customer(s) they are engaged with. All the parameters identified will be stored to blockchain. The parameter may be defined as:

• PastWinSuccessRate: This parameter is for getting the win percentage of the projects from the client/customers for which service provider/organization has participated via request for proposal RFPs and other engagements.
• ValidShare: Client Holding share in the organization.
• Competitors: Number of competitors bidding or participating for the projects/assignments.
• BusinessCriticality: This may be provided in different categories such as Low (which may be related to proof of concept (PoC) or Demoable Projects), Moderate [which may be related to Pilot or Internal Apps], and High (which may be related to complete product that may directly impact customer’s reputation).
Furthermore, the data nodes may also be connected with other systems such as client relationship and engagement system which includes parameters such as:
• InfluenceOfMgmt: This parameter is derived from CSAT Score i.e. Customer SATisfaction Score. If the CSAT score is less than5, then the customer satisfaction may be considered as “Low”. If the CSAT score is in a range of 5.1 to 7.5, then customer satisfaction may be considered as “Medium”. Whereas, if the CSAT score is more than 7.5, then the customer satisfaction may be considered as “Strong”.
• TotalVendors: Total number of vendors/service providers participating in the Bidding for winning the project/assignment.
• TotalVendorsParticipating: Total number of vendors/service providers participated.
Now, once the entity data 209 and the client data 207 are received, in next step, the generating unit 215 may be configured to generate a set of entity group by correlating the plurality of entity information with the plurality of assignment information such that the entity group may comprise one or more entities having maximum probability of efficiently handling at least one assignment of the plurality of assignments. The subsequent paragraphs will now explain referring to the tables shown in Figs. 3A-3C the generation of the set of entity group.

Starting from the entity data table (Fig. 3A), it can be observed that the entity data 209, fetched from the plurality of data nodes, comprises information pertaining to the five entities Entity 1-Entity 5. Corresponding to each entity, the entity information is also provided against the entity parameters (skills, experience, designation, win probability, awards, historic parameters, and bandwidth). Also, weightages are preassigned (on a scale of 1-10 such that 1 being the lowest and 10 being the highest) to each entity parameter to define the importance level for them. For example, among other entity parameters, skills has the highest weightage i.e. “10”, whereas the experience has the lowest weightage i.e. “6”. However, it may be understood that the above defined weightages is merely an example, and therefore the weightages may be assigned in different permutation and combination as per the service provider requirement and client.
Apart from the preassigned weightages, entity information scores are also assigned for entity against their entity information which the entity possess. For ease of understanding, the entity information score may be assigned on a scale of 1-5 such that 1 being the lowest and 5 being the highest. For the entity information provided against the entity parameters “Skills”, “Experience”, “Designation”, “Win probability”, “Awards”, “Historic parameters”, and “Bandwidth”, the below tables show how the entity information scores are assigned based on the scale of 1-5.
Skills
No. of languages known Scale (1-5)
1 Programming Language 1
2 Programming Languages 2
3 Programming languages 3
4 Programming languages 4 More than 4 PL 5
Experience
No. of years of Experience Scale (1-5)
0-2 2

2-5 3
5-10 4
Above 10 5
Designation

Designation Scale (1-5)
Software Engineer 1
Sr. Software Engineer 2
Manager 3
Lead 4
Above Lead 5
Win Probability

Win Probability Scale (1-5)
Above 70% 2
70%-80% 3
80%-90% 4
Above 90% 5
Awards

No. of Awards Scale (1-5)
1-2 1
3-4 3
5-7 4
Above 7 5
Historic Performance

Performance category Scale (1-5)
Average 1
Good 3
Outstanding 5

Bandwidth

Availability Scale (1-5)
Not Available 1
Partially Available 3
Fully available 5
Now referring to the entity data table (Fig. 3A), the entity information scores are generated for each entity. For example, for Entity 1, the entity information score may be generated using the above tables as:
Entity Information Score – Entity 1

Entitie s Skill s Experienc e Designatio n Win
Probabilit
y Award s Historic
Performan
ce Bandwidt h
Entity 1 Java, VB 3 Software Engineer Client X-75% 2 Average Fully Available
Score s 2 3 1 2 1 1 5
Considering the above scale and technique, the entity information score is generated against each entity. Now once the entity information score is generated, in next step, the scoring unit 217 generates a cumulative entity score for each entity using the preassigned weightages of the entity parameters and the entity information scores. For this, the scoring unit 217 may first multiply the preassigned weightages with the entity information scores, and then averages the values obtained after the multiplication as shown in below table:
Cumulative Score – Entity 1

Entities Skill Experien Designati Win Awar Historic Bandwid
s ce on Probabili ty ds Performan ce th
Entity 1 Java, 3 Software Client X- 2 Average Fully
VB Engineer 75% Availabl e
Scores 2 3 1 2 1 1 5
10*2 6*3= 8*1= 9*2= 7*1= 9*1= 8*5=
= 20 18 8 18 7 9 40
Cummulat 20+18+8+18+7+9 +40/7= 85.71
ive Score
Using the above technique, the cumulative score is calculated for remaining entities Entity 2-5 which quantifies the overall strength of the entities.
In next table i.e. client data table (Fig. 3B), it can be observed that the client data 207, fetched from the plurality of data nodes, comprises information pertaining to the three client i.e. Client X, Client Y and Client Z. For example, the assignment which belongs to Client X has the requirement of domain as “Computer”, languages as “C, C++, Mainframe, Angular.JS”, experience as “up to 9 years” and team size as “3”. The selecting unit 219 considering the above determined cumulative scores and the client requirement, selects one or more entities to form an entity group for handling the assignment of Client X. In this case, as an example, it can be observed that the Entity 2, 3 and 4 are the most suitable ones for handling the assignment of client X. It may be observed that Entity 5 is suitable for handling the assignment of Client X, however it is not selected due to the unavailability. It may be understood that the selection of the entities as described above is merely an example and shall not be considered to limit the scope of the present disclosure.

However, it may so happen that cumulative score of an entity might be very low indicating that the entity is now skilled enough to take up the assignment. To tackle this situation, the system 100 may predefine an entity score threshold 208 (stored in the memory 205) which indicates a benchmark score expected from an entity to be attained before being a part of an entity group. As an example, in the current situation as described above, the entity score threshold 208 may be “70”. Hence, this indicates thatif the cumulative entity score for an entity is below the expected mark i.e. “70”, then the entity may not be a part of the entity group. In such situation, the system 100 will generate one or more recommendations based on the comparison between the cumulative entity score and entity score threshold. According to an embodiment, the one or more recommendations may comprise, for example, one or more new skill-sets to be attained by the entity and hiring new entities having the one or more new skill-sets. It may be understood to a skilled person that the above mentioned recommendations are merely an example, and the present disclosure may provide various other recommendations.
Consider a scenario, in which, an assignment is in a pipeline (potential case for winning the project) and there is a requirement to develop the user interface (UI) in Vue.JS Framework. Now out of 5 Entities, there is no one having the required skill sets. Since the Vue.JS falls under Single Page Application Technology. The system 100 can now refer to the skillsets of entities 4 and 5, and it can be found that both the entities are having a bit experience on Single Page Application development using Angular.JS and React.JS. With that, the system 100 can identify the skill gaps and may ask or recommend for getting entities 4 and 5 trained for Vue.JS framework.
Consider a scenario, in which, as assignment is in a pipeline (potential case for winning the project) and there requirement for 5 UI developers (with Angular 5 framework & experience of 5+ years). In this case since there are only 2 developers (entity 4 and entity 5) that are available for the role and there is no one who is familiar with SPA technology, in such case the system 100 may recommends for external hiring of 3 UI developers who are 5+ years experienced working in Angular Framework.

Considering another scenario, where in Phase I – entity 3, and entity 5 are identified as best fit resources and are ranked 1 and 2 on the list, as both have completed “ABC Professional Certification”, whereas entity 4 is ranked 3, because in the project requirement which is supposed to be get started after 6 weeks, has a mandatory requirement that entities should have completed “ABC Professional Certification” to get boarded to the project/assignment and 3 such resources are required. Since 6 weeks of time is still there, now let us consider that if entity 4 will complete the certification before 6 weeks, then the entity 4 will become best fit resource (Rank 1) in that case, and the entity group is formed based on the entities 3, 4 and 5 by the system 100.
It may be worth noted that the above-mentioned examples should not be taken into limiting sense and may include n number of permutations and combinations to achieve the desired goal of the invention within the scope of the disclosure. This way, the present disclosure provides a technical advantage of efficiently handling such huge amount of data (client data and entity data) stored across various data nodes and arrive at the meaningful information i.e. forming the entity group. Also, another technical advantage achieved is that, it not only saves human time and effort of the service provider in recognizing the best fit of entities, but also minimizes the resource overhead (bandwidth, computing time, processing time) which would have been otherwise required for processing the huge amount of data. Also, since the data nodes are arranged in the decentralized manner, the present disclosure also efficiently synchronizes the information stored at various data nodes.
Figure 4 depicts a method 400 of managing data for effective collaboration between a service provider and a client, in accordance with an embodiment of the present disclosure.
As illustrated in Figure 4, the method 400 includes one or more blocks illustrating a method of managing data for effective collaboration between a service provider and a client. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions may include routines, programs, objects,

components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.
The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described.
At block 401, the method 400 may comprise step of fetching entity data and client data from a plurality of data nodes arranged in a decentralized manner such that the entity data and the client data are capable of being fetched from anyone of a data node of the plurality of data nodes. Each data node indicates a separatee data storing device. The client data may comprise a plurality of assignment information provided against a plurality of assignment associated with the client. The entity data may comprise a plurality of entity information provided against a plurality of entity parameters associated with the entity. In some embodiments, the plurality of entity parameters may comprise, but not limited to, at least one of skills, experience, designation, awards, historic performance, win probabilities, and bandwidth. Further, the plurality of entity information may comprise, but not limited to, at least one of skill information, experience information, designation information, awards information, historic performance information, win probabilities information, and bandwidth availability information. In some embodiments, the plurality of assignment parameters comprises at least one of domain, timeline, team size. Further, the plurality of assignment information comprises domain information, timeline information, team size information.
At block 403, the method 400 may comprise step of correlating the plurality of entity information with the plurality of assignment information to generate a set of entity group such that each entity group comprises one or more entities having maximum probability of efficiently handling at least one assignment of a plurality of assignments.

At block 403a, the method 400 may comprise step of generating a plurality of cumulative entity scores for a plurality of entities using a plurality of preassigned weightages of the plurality of entity parameters and a plurality of entity information scores.
At block 403b, the method 400 may comprise step of selecting one or more entities for each entity group such that the selected one or more entities have the highest cumulative entity scores relative to remaining entities.
In some embodiments, the method may further comprise adaptively updating the set of entity group in the data nodes based on the cumulative entity score against the plurality of assignment information of the client data.
Advantages of the embodiments of the present disclosure are illustrated herein:
In an embodiment, the present disclosure provides a method for efficiently managing data for effective collaboration between a service provider and a client.
In an embodiment, the present disclosure will minimize the data loss as data are being stored to the blockchain ledger or data nodes on real time basis.
In an embodiment, the present disclosure deals with less noise and errors since the data stored on the blockchain immutable.
In an embodiment, the present disclosure provides a system for efficiently managing data for effective collaboration between a service provider and a client since the data is stored in blockchain ledger the system will work even if any one of the data nodes is inactive.
The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.
The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.
The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.
When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
Reference Numerals:

Reference Numeral Description
100 Exemplary environment of a system for managing data for effective collaboration between a service provider and a client.
200 Block diagram of the system
201 Processor
203 I/O Interface
205 Memory
207 Client data
209 Entity data
211 Units
213 Fetching Unit
215 Generating Unit
217 Scoring Unit
219 Selecting Unit
Figs. 3A-3C Exemplary tables showing different types of data
Figs. 4A-4B Method flowchart
402-404 Method steps
403A-403B Method steps

WE CLAIM:
1. A method of managing data for effective collaboration between a service provider and a client, the
method comprising:
fetching, by a fetching unit (213), entity data (209) and client data (207) from a plurality of data nodes arranged in a decentralized manner such that the entity data (209) and the client data (207) are capable of being fetched from anyone of a data node of the plurality of data nodes, wherein:
each data node indicates a separate data storing device,
the client data (207) comprises a plurality of assignment information provided against a plurality of assignment parameters associated with the client; and
the entity data (209) comprises a plurality of entity information provided against a plurality of entity parameters associated with the entity;
correlating, by a generating unit (215), the plurality of entity information with the plurality of assignment information to generate a set of entity group such that each entity group comprises one or more entities having maximum probability of efficiently handling at least one assignment of a plurality of assignments.
2. The method as claimed in claim 1, wherein the plurality of entity parameters comprises at least one of skills, experience, designation, awards, historic performance, win probabilities, and bandwidth, and wherein the plurality of entity information comprises at least one of skill information, experience information, designation information, awards information, historic performance information, win probabilities information, and bandwidth availability information.
3. The method as claimed in claim 1, wherein the plurality of assignment parameters comprises at least one of domain, timeline, team size, and wherein the plurality of assignment information comprises domain information, timeline information, experience information, and team size information.
4. The method as claimed in claim 1, wherein the set of entity group is generated by:
generating, by a scoring unit (217), a plurality of cumulative entity scores for a plurality of entities using a plurality of preassigned weightages of the plurality of entity parameters and a plurality of entity information scores of the plurality of entity information;
selecting, by a selecting unit (219), one or more entities for each entity group such that the selected one or more entities have the highest cumulative entity scores relative to remaining entities.
5. The method as claimed in claim 1, further comprising:

comparing each of the plurality of cumulative entity scores with an entity score threshold wherein the entity score threshold (208) indicates a benchmark score expected from an entity to be attained before being a part of an entity group;
generating one or more recommendations based on the comparing, wherein the one or more recommendations comprises at least one of:
one or more new skill-sets to be attained by the entity, and hiring new entities having the one or more new skill-sets.
6. A system (100) for managing data for effective collaboration between a service provider and a
client, the system comprising:
a fetching unit (213) configured to fetch entity data (209) and client data (207) from a plurality of data nodes arranged in a decentralized manner such that the entity data (209) and the client data (207) are capable of being fetched from anyone of a data node of the plurality of data nodes, wherein:
each data node indicates a separate data storing device,
the client data (207) comprises a plurality of assignment information provided against a plurality of assignment parameters associated with the client; and
the entity data (209) comprises a plurality of entity information provided against a plurality of entity parameters associated with the entity;
a generating unit (215) configured to generate a set of entity group such that each entity group comprises one or more entities having maximum probability of efficiently handling at least one assignment of a plurality of assignments by correlating the plurality of entity information with the plurality of assignment information.
7. The system (100) as claimed in claim 6, wherein the plurality of entity parameters comprises at least one of skills, experience, designation, awards, historic performance, win probabilities, and bandwidth, and wherein the plurality of entity information comprises at least one of skill information, experience information, designation information, awards information, historic performance information, win probabilities information, and bandwidth availability information.
8. The system (100) as claimed in claim 6, wherein plurality of assignment parameters comprises at least one of domain, timeline, team size, and wherein the plurality of assignment information comprises domain information, timeline information, team size information.
9. The system (100) as claimed in claim 6, further comprising:
a scoring unit (217) configured to generate a plurality of cumulative entity scores for a plurality of entities using a plurality of preassigned weightages of the plurality of entity parameters and a plurality of entity information scores of the plurality of entity information;

a selecting unit (219) configured to select one or more entities for each entity group such that the selected one or more entities have the highest cumulative entity scores relative to remaining entities.
10. The system (100) as claimed in claim 6, is further configured to:
compare each of the plurality of cumulative entity scores with an entity score threshold (208), wherein the entity score threshold (208) indicates a benchmark score expected from an entity to be attained before being a part of an entity group; and
generate one or more recommendations based on the comparing, wherein the one or more recommendations comprises at least one of:
one or more new skill-sets to be attained by the entity, and hiring new entities having the one or more new skill-sets.