Claims:1. A method for recommending a candidate for a project; the method comprising:
receiving, by a skill assessment system; interaction data associated with the candidate from the distributed sensor network;
determining, by the skill assessment system, one or more candidate activities associated with the candidate based on the interaction data;
determining, by the skill assessment system, response to the one or more candidate activities on the distributed sensor network;
receiving, by the skill assessment system, one or more skills necessary for the project;
determining, by the skill assessment system, a skill activity score associated with each of the one or more skills based on a comparison of the one or more candidate activities, response to the one or more candidate activities and the one or more skill necessary for the project;
determining, by the skill assessment system, a cumulative skill score associated with the candidate based on skill activity score associated with each of the one or more skill, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score;
recommending, by the skill assessment system, the candidate for the project based on the cumulative skill score.
2. The method as claimed in Claim 1, wherein one or more candidate activities are queries posted by the candidate, responses made by the candidate, reaction of the responses, challenges undertaken by the candidate, relevance of the responses.

3. The method as claimed in Claim 1, wherein the skill activity score is indicative of the candidate’s activities on each of the one or more skill based on reaction of the distributed sensor network along with candidate’s activities.

4. The method as claimed in Claim 1, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score.

5. A skill assessment system for recommending a candidate for a project; the system comprising:
at least one processor; and
a memory storing instructions executable by the at least one processor, wherein the instructions configure the at least one processor to perform the steps of:
receiving interaction data associated with the candidate from the distributed sensor network;
determining one or more candidate activities associated with the candidate based on the interaction data;
determining response to the one or more candidate activities on the distributed sensor network;
receiving one or more skills necessary for the project;
determining a skill activity score associated with each of the one or more skills based on a comparison of the one or more candidate activities, response to the one or more candidate activities and the one or more skill necessary for the project;
determining a cumulative skill score associated with the candidate based on skill activity score associated with each of the one or more skill, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score;
recommending, the candidate for the project based on the cumulative skill score.
6. The system as claimed in Claim 5, wherein one or more candidate activities are queries posted by the candidate, responses made by the candidate, reaction of the responses, challenges undertaken by the candidate, relevance of the responses, publication of papers.

7. The system as claimed in Claim 5, wherein the skill activity score is indicative of the candidate’s activities on each of the one or more skill based on reaction of the distributed sensor network along with candidate’s activities.

8. The system as claimed in Claim 5, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score.

Dated this 30th Day of May 2019
Signature of Patent Agent:
(Rahul Bagga)
IN/PA-2366
, Description:
DETAILED DESCRIPTION
In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and the scope of the disclosure.
The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device 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 device 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 method.
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.
Figure.1 illustrates environment 100 with a skill assessment system 102 along with a Crowd Mapping Device.
The environment 100 comprises a skill assessment system 102 and crowd mapping device 114. The Crowd Mapping device 114 may be capable of utilizing the sink node’s mobility to facilitate data gathering in a densely distributed sensor network. The distributed sensor network may comprise one or more sensor web; wherein the sensor web is a type of sensor network that is especially well suited for environmental monitoring. The sensor web may be an autonomous, stand-alone, sensing entity – capable of interpreting and reacting to the data gathered. The skill assessment system 102 may include a Processor 104, a Memory 106, an I/O interface 108, a data historian 110 which may be coupled together by bus 112.
The Processor 104 may manage the operation and interaction between Memory 106, an I/O interface 108, a data historian 110. The Processor 104 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. The Processor 104may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other lines of processors, etc. The Processor 104 may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.
The Processor 104 may be disposed of in communication with one or more input/output (I/O) devices via I/O interface 108. The I/O interface 108 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, 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), RF antennas, S-Video, 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), WiMax, or the like), etc.
The Memory 106 may comprise one or more tangible storage media, such as RAM, ROM, flash memory, CD-ROM, floppy disk, hard disk drive(s), solid state memory, DVD, or other memory storage types or devices, including combinations thereof, which are known to those of ordinary skill in the art. The memory 106 may store one or more non-transitory computer-readable instructions of this technology as illustrated and described with reference to the examples herein that may be executed by the one or more processor(s) 104.
The Data Historian 110 may record and retrieve production and process data by time. The data historian 110 may log time-based data associated with one or more users on the Crowd Mapping Device 114. The data historian 110 may store the information in a time series database that can efficiently store data with minimal disk space and fast retrieval.
Figure.2 illustrates Memory 106 which may include Interaction Data Receiver 202, Candidates Activities Module 204, Response Module 206, Skill Receiver Module 208 Skill Score Module 210, Cumulative Module 212 and Recommendation Module 214.
Interaction Data Receiver 202 may be received interaction data associated with a candidate. The interaction data associated with a candidate may be gathered by the crowd mapping device 114. The interaction data associated with a candidate may be the activities of the candidate over the distributed sensor network.
In an embodiment, the interaction data may be discussions of the candidate with other users over the distributed sensor network or publication of the candidate on the distributed sensor network or reviews made by the candidate on other publications or cultural and social behavior of the candidate on the distributed sensor network.
Candidates Activities Module 204 may determine one or more candidate activities associated with the candidate based on the interaction data. The one or more candidate activities may be a derivative of the interaction data; wherein the derivation of the interaction data comprises of normalization, categorization and labeling and sentiment analysis.
In an embodiment, the one or more candidate activities may be defined in terms of Questions derived from discussions engaged by the candidate, or Responses derived from discussions of the candidate, or Critical Points derived from reviews made by the candidate on other publications, or a combination thereof.
The following table illustrates the above-mentioned derivations of Candidate Activities
INTERACTION DATA CANDIDATE ACTIVITIES
discussions engaged by the candidate Questions
discussions engaged by the candidate Responses
Reviews made by the candidate on other publications Critical Points
TABLE-A
Response Module 206 may determine response to the one or more candidate activities on the distributed sensor network based on the interaction data. The Response to the one or more candidate activities may be a reaction, feedback, comment, criticism, observation or a combination thereof made by users on the distributed sensor network other than the Candidate. The Response may be determined based on sentiment analysis of the interaction data.
In an embodiment, the following Table illustrates the various responses to the one or more candidate activities that may be determined based on the interaction data.
CANDIDATE ACTIVITIES RESPONSE BY USER [2…. N]
Questions Positive Remarks
Negative Remarks
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation
Critical Points Longevity

TABLE-B
The Response to the one or more candidate activities is essential to the present invention for the reason that the Response to the one or more candidate activities by users on the distributed sensor network validates the quality of one or more candidate activities.
In an embodiment, a Question enquired by the candidate may solve identical or similar problems of other users on the distributed sensor network. Therefore, the Question would receive a positive remark from other users.
In an embodiment, a response to a question or a solution to a problem provided by the candidate may be appreciated by other users on the distributed sensor network. Therefore, the Response would receive a positive validation from other users.
The quality of the one or one or more candidate activities in turns validates the quality of the one or more skill possessed by the Candidate and such validation is no more subject to a single perspective, but rater changes to a metric to determine the quality of the one or more skill possessed by the Candidate as illustrated in the following steps.
Skill Receiver 208 may receive one or more skills necessary for the project. The project may comprise aspects such as software, hardware, infrastructure, designing. The project is executed as a solution to a targeted problem. To execute the project, one or more skills in relation to aspects such as software, hardware, infrastructure, designing is necessary. The one or more skills may vary depending on the type of the project. The correlation of, the project, the aspects, and the one or more skills required, is well known in the art and therefore is predetermined for the execution of this embodiment of the invention.
Skill Score Module 210 may determine a skill activity score associated with each of the one or more skills based on comparison of the one or more candidate activities, response to the one or more candidate activities and the one or more skill necessary for the project. The skill activity score may be a function of one or more candidate activities and response to the one or more candidate activities by other users on the distributed sensor network and the one or more skill necessary for the project.
In an embodiment, the skill activity score may be determined based on a preassigned score to the response to the one or more candidate activities on the distributed sensor network. Let Positive Remarks incur a score of 100, but a negative remark would incur a score of -200. Acceptance of the Candidate’s responses shall incur a score of 50. Forced Moderation of Candidate’s behavior shall incur a negative score of -300.
In an embodiment, the skill activity score may be determined for User1 by User [2…N]. User 1 is assessed as a candidate. So, the Positive Remarks, Negative Remarks, Acceptance, Forced Moderation and other feedback provided by User [2…N] shall determine the skill activity score associated with skill User1. The following Table illustrates the one or more candidate activities associated with User 1.
CANDIDATE ACTIVITIES of User 1 RESPONSE BY USER [2…. N] Skill Activity Score of User 1
Questions Positive Remarks
Negative Remarks 200
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation 300
Critical Points Longevity
100
TABLE-C
Cumulative Module 212 may determine a cumulative skill score associated with the candidate based on the skill activity score associated with each of the one or more skill, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score. The nonlinear aggregation may be Euclidean aggregate, regression, Nonlinear Regression.
In an embodiment of the present invention, the cumulative skill score of skill activity score associated with each of the one or more skill of USER A, USER B, USERC is determined.
Let User A be the Candidate. Therefore, skill activity score associated with each of the one or more skill of User A, shall be curated from reviews and interaction User B, C with User A
CANDIDATE ACTIVITIES of User A REVIEW BY USER C, B Skill Activity Score of User A
Questions Positive Remarks
Negative Remarks 100
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation 200
Critical Points Longevity 200
TABLE-D
In the present embodiment, the Cumulative Skill Score for USER A may be 162.5.
Recommendation Module 214 may recommend the candidate for the Project based on the cumulative skill score associated with the candidate. The Candidates may be recommended in increasing order of the Cumulative Skill Score.
In an embodiment, USER A has the Cumulative Skill Score is 162.5; USER B has the Cumulative Skill Score is 120 and USER A has the Cumulative Skill Score is 180. The following Table illustrates the above.
Candidate Cumulative Skill Score Recommendation
User A 162.5 Medium Recommendation
User B 120 Low Recommendation
User C 180 Highly Recommended

Figure.3 illustrates a flow chart method for recommending a candidate for a project. The method may involve receiving interaction data associated with the candidate from a distributed sensor network; determining one or more candidate activities associated with the candidate based on the interaction data; receiving one or more skills necessary for the project; determining a skill activity score associated with each of the one or more skills based on a comparison of the one or more candidate activities and the one or more skill necessary for the project; determining a cumulative skill score associated with the candidate based on skill activity score associated with each of the one or more skill, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score; recommending the candidate for the project based on the cumulative skill score.
At step 302, interaction data associated with a candidate may be received by the skill assessment system 102. The interaction data associated with a candidate may be gathered by the crowd mapping device 114. The interaction data associated with a candidate may be the activities of the candidate over the distributed sensor network.
In an embodiment, the interaction data may be discussions of the candidate with other users over the distributed sensor network or publication of the candidate on the distributed sensor network or reviews made by the candidate on other publications or cultural and social behavior of the candidate on the distributed sensor network.
Further at step 304, after receiving the interaction data, determining one or more candidate activities associated with the candidate based on the interaction data. The one or more candidate activities may be a derivative of the interaction data; wherein the derivation of the interaction data comprises of normalization, categorization and labeling and sentiment analysis.
In an embodiment, the one or more candidate activities may be defined in terms of Questions derived from discussions engaged by the candidate, or Responses derived from discussions of the candidate, or Critical Points derived from reviews made by the candidate on other publications, or a combination thereof.
The following table illustrates the above-mentioned derivations of Candidate Activities
INTERACTION DATA CANDIDATE ACTIVITIES
discussions engaged by the candidate Questions
discussions engaged by the candidate Responses
Reviews made by the candidate on other publications Critical Points
TABLE-A
Thereafter, at step 306, response to the one or more candidate activities may be determined on the distributed sensor network based on the interaction data. The Response to the one or more candidate activities may be a reaction, feedback, comment, criticism, observation or a combination thereof made by users on the distributed sensor network other than the Candidate. The Response may be determined based on sentiment analysis of the interaction data.
In an embodiment, the following Table illustrates the various responses to the one or more candidate activities that may be determined based on the interaction data.
CANDIDATE ACTIVITIES RESPONSE BY USER [2…. N]
Questions Positive Remarks
Negative Remarks
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation
Critical Points Longevity

TABLE-B
The Response to the one or more candidate activities is essential to the present invention for the reason that the Response to the one or more candidate activities by users on the distributed sensor network validates the quality of one or more candidate activities.
In an embodiment, a Question enquired by the candidate may solve identical or similar problems of other users on the distributed sensor network. Therefore, the Question would receive a positive remark from other users.
In an embodiment, a response to a question or a solution to a problem provided by the candidate may be appreciated by other users on the distributed sensor network. Therefore, the Response would receive a positive validation from other users.
The quality of the one or one or more candidate activities in turns validates the quality of the one or more skill possessed by the Candidate and such validation is no more subject to a single perspective, but rater changes to a metric to determine the quality of the one or more skill possessed by the Candidate as illustrated in the following steps.
Thereafter, at step 308, one or more skills necessary for the project may be received by the skill assessment system 102. The project may comprise aspects such as software, hardware, infrastructure, designing. The project is executed as a solution to a targeted problem. To execute the project, one or more skills in relation to aspects such as software, hardware, infrastructure, designing is necessary. The one or more skills may vary depending on the type of the project. The correlation of, the project, the aspects and the one or more skills required is well known in the art and therefore is predetermined for the execution of this embodiment of the invention.
At step 310, a skill activity score associated with each of the one or more skills may be determined based on comparison of the one or more candidate activities, response to the one or more candidate activities and the one or more skill necessary for the project. The skill activity score may be a function of one or more candidate activities and response to the one or more candidate activities by other users on the distributed sensor network and the one or more skill necessary for the project.
In an embodiment, the skill activity score may be determined based on a preassigned score to the response to the one or more candidate activities on the distributed sensor network. Let Positive Remarks incur a score of 100, but a negative remark would incur a score of -200. Acceptance of the Candidate’s responses shall incur a score of 50. Forced Moderation of Candidate’s behavior shall incur a negative score of -300.
In an embodiment, the skill activity score may be determined for User1 by User [2…N]. Thereafter, User1 is assessed as a candidate. So, the Positive Remarks, Negative Remarks, Acceptance, Forced Moderation and other feedback provided by User [2…N] shall determine the skill activity score associated with skill User1. The following Table illustrates the one or more candidate activities associated with User 1.
CANDIDATE ACTIVITIES of User 1 RESPONSE BY USER [2…. N] Skill Activity Score of User 1
Questions Positive Remarks
Negative Remarks 200
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation 300
Critical Points Longevity
100
TABLE-C
Further, at step 312, a cumulative skill score associated with the candidate may be determined based on the skill activity score associated with each of the one or more skill, wherein the cumulative skill score is a nonlinear aggregation of the skill activity score. The nonlinear aggregation may be Euclidean aggregate, regression, Nonlinear Regression.
In an embodiment of the present invention, the cumulative skill score of skill activity score associated with each of the one or more skill of USER A, USER B, USERC is determined.
Let User A be the Candidate. Therefore, skill activity score associated with each of the one or more skill of User A, shall be curated from reviews and interaction User B, C with User A
CANDIDATE ACTIVITIES of User A REVIEW BY USER C, B Skill Activity Score of User A
Questions Positive Remarks
Negative Remarks 100
Responses Positive Remarks
Negative Remarks
Acceptance
Forced Moderation 200
Critical Points Longevity
200
TABLE-D
In the present embodiment, the Cumulative Skill Score for USER A may be 162.5.
Lastly, at step 314, the candidate may be recommended for the Project based on the cumulative skill score associated with the candidate. The Candidates may be recommended in increasing order of the Cumulative Skill Score.
In an embodiment, USER A has the Cumulative Skill Score is 162.5; USER B has the Cumulative Skill Score is 120 and USER A has the Cumulative Skill Score is 180. The following Table illustrates the above.
Candidate Cumulative Skill Score Recommendation
User A 162.5 Medium Recommendation
User B 120 Low Recommendation
User C 180 Highly Recommended

Advantages of the embodiment of the present disclosure are illustrated herein.
Embodiments of the present disclosure provide a method for recommending a candidate for a project. This method helps in, accurate and objective determination of a candidate’s skill and removes human error in the selection of a candidate for the project