Abstract:
The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource identification, evaluation, selection, and allocation. However, while the opportunities exist and continue to grow, the realization of those opportunities has fallen behind. A dynamic resource assessment system helps to solve the enormous technical challenges of finding the resources, evaluating the resources, and determining how to allocate the resources to achieve the highest likelihood of successfully completing the task.

Description:
PRIORITY CLAIM 
       [0001]    This application claims priority to Indian non-provisional application serial number 201641019688, filed 10 Sep. 2016 and to Indian provisional application serial number 201641019688, filed 8 Jun. 2016. 
       TECHNICAL FIELD 
       [0002]    This application relates to a technical evaluation of resources to carry out complex tasks. 
       BACKGROUND 
       [0003]    The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource identification, evaluation, selection, and allocation. However, while the opportunities exist and continue to grow, the realization of those opportunities has fallen behind. In part, this is due to the enormous technical challenges of finding the resources, evaluating the resources, and determining how to allocate the resources to achieve the highest likelihood of successfully completing the task. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]      FIG. 1  shows a global network architecture. 
           [0005]      FIG. 2  shows an example implementation of a resource analysis system. 
           [0006]      FIG. 3  shows one example of a multi-dimensional analysis framework that the resource analysis system may implement. 
           [0007]      FIG. 4  shows another example of a multi-dimensional analysis framework that the resource analysis system may implement. 
           [0008]      FIG. 5  shows another example of a multi-dimensional analysis framework that the resource analysis system may implement. 
           [0009]      FIG. 6  shows the resource analysis system in communication with sources of resource data, and with platforms that consume resource analysis services. 
           [0010]      FIG. 7  shows an example potential resource interface. 
           [0011]      FIG. 8  shows an example resource analysis result interface. 
           [0012]      FIG. 9  shows an example resource detail interface. 
           [0013]      FIG. 10  shows additional detail from the resource detail interface. 
           [0014]      FIG. 11  shows an example similar task analysis interface. 
           [0015]      FIG. 12  shows an example ongoing tasks analysis interface. 
           [0016]      FIG. 13  shows an example recent reviews analysis interface. 
           [0017]      FIG. 14  shows an example prior task analysis interface. 
           [0018]      FIG. 15  shows an example summary analysis interface. 
           [0019]      FIG. 16  shows an example resource comparison interface. 
           [0020]      FIG. 17  shows an example of process flow for resource analysis. 
           [0021]      FIG. 18  shows another example of process flow for resource analysis when integrated with a hybrid sourcing platform. 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    Finding, evaluating, and applying the right set of resources to a complex task is a key to successful task execution and completion. The dynamic resource analysis machine (“DRAM”) described below implements a technical multi-dimensional assessment system for resources, performs complex assessments of the resources, and defines and generates improved machine interfaces that deliver the assessments, e.g., for consideration and possible selection of a resource. The DRAM may perform the complex assessments on a wide range of resource types for any type of task. A few examples of resource types include: software programs; trained and untrained machine learning models; artificial intelligence engines; robots; machines; tools; mechanical, chemical, and electrical equipment, database models; machines; individual workers with specific skills; and mechanical, chemical, or electrical components. A few examples of tasks include: deploying cloud infrastructure; building a web site; building an oil rig; performing legal services; creating a new software application; architecting and building an office, factory, or school; or designing, simulating, prototyping, and manufacturing high performance analog or digital circuitry. 
         [0023]    The DRAM may be implemented using any set of dimensions and metrics organized into a multi-dimensional analysis framework that is suitable for the resources in question. For instance, a tailored set of analysis dimensions may be present for chemical or electrical component selection, and those dimensions may be very different form the dimensions in place for assessing workers. As one specific example, while the DRAM may measure timezone match, education compensation goals, or cultural matches for workers, the DRAM may instead measure component tolerance, number of suppliers, cost of materials handling, or other metrics for chemical, electrical, or other resources. For purposes of explanation, the discussion below concerns one possible set of dimensions for worker assessment, but the dimensions and metrics may change in any degree needed to suit the resource and tasks in question. 
         [0024]      FIGS. 1 and 2  provide an example context for the discussion below of the technical solutions in the DRAM. The examples in  FIGS. 1 and 2  show one of many possible different implementation contexts. In that respect, the technical solutions are not limited in their application to the architectures and systems shown in  FIGS. 1 and 2 , but are applicable to many other system implementations, architectures, and connectivity. 
         [0025]      FIG. 1  shows a global network architecture  100 . Connected through the global network architecture  100  are geographically distributed data platforms  102 ,  104 ,  106 , and  108 . The data platforms  102 - 108  provide resource characteristic data on any number or type of available resources. 
         [0026]    Throughout the global network architecture  100  are networks, e.g., the network  110 . The networks provide connectivity between the data platforms  102 - 108  and the DRAM  112 . The networks  110  may include private and public networks defined over any pre-determined and possibly dynamic internet protocol (IP) address ranges. 
         [0027]    The DRAM  112  performs complex technical resource assessments. As an overview, the DRAM  112  may include communication interfaces  114 , assessment engines  116 , and machine interfaces  118 . The communication interfaces  114  connect the DRAM  112  to the networks  110  and the data platforms  102 - 108 , and facilitate data exchange  152 , including exchanging resource characteristic data, and the delivery of machine interfaces (which may include Graphical User Interfaces (GUIs)) for improved interaction with the DRAM  112  regarding the assessments. 
         [0028]      FIG. 2  shows an example implementation  200  of the DRAM  112 . The DRAM  112  includes communication interfaces  202 , system circuitry  204 , input/output (I/O) interfaces  206 , and display circuitry  208  that generates machine interfaces  210  locally or for remote display, e.g., in a web browser running on a local or remote machine. The machine interfaces  210  and the I/O interfaces  206  may include GUIs, touch sensitive displays, voice or facial recognition inputs, buttons, switches, speakers and other user interface elements. Additional examples of the I/O interfaces  206  include microphones, video and still image cameras, headset and microphone input/output jacks, Universal Serial Bus (USB) connectors, memory card slots, and other types of inputs. The I/O interfaces  206  may further include magnetic or optical media interfaces (e.g., a CDROM or DVD drive), serial and parallel bus interfaces, and keyboard and mouse interfaces. 
         [0029]    The communication interfaces  202  may include wireless transmitters and receivers (“transceivers”)  212  and any antennas  214  used by the transmit and receive circuitry of the transceivers  212 . The transceivers  212  and antennas  214  may support WiFi network communications, for instance, under any version of IEEE 802.11, e.g., 802.11n or 802.11ac. The communication interfaces  202  may also include wireline transceivers  216 . The wireline transceivers  216  may provide physical layer interfaces for any of a wide range of communication protocols, such as any type of Ethernet, data over cable service interface specification (DOCSIS), digital subscriber line (DSL), Synchronous Optical Network (SONET), or other protocol. 
         [0030]    The system circuitry  204  may include hardware, software, firmware, or other circuitry in any combination. The system circuitry  204  may be implemented, for example, with one or more systems on a chip (SoC), application specific integrated circuits (ASIC), microprocessors, discrete analog and digital circuits, and other circuitry. The system circuitry  204  is part of the implementation of any desired functionality in the DRAM  112 , including the assessment engines  116 . As just one example, the system circuitry  204  may include one or more instruction processors  218  and memories  220 . The memory  220  stores, for example, control instructions  222  and an operating system  224 . In one implementation, the processor  218  executes the control instructions  222  and the operating system  224  to carry out any desired functionality for the DRAM  112 . The control parameters  226  provide and specify configuration and operating options for the control instructions  222 , operating system  224 , and other functionality of the DRAM  112 . 
         [0031]    The DRAM  112  may include technical data table structures  232  hosted on volume storage devices, e.g., hard disk drives (HDDs) and solid state disk drives (SDDs). The storage devices may define and store database table structures that the control instructions  222  access, e.g., through a database control system, to perform the functionality implemented in the control instructions  222 . 
         [0032]    In the example shown in  FIG. 2 , the databases store resource characteristic data  228 , completed resource assessments  230 , and other data elements supporting the multi-dimensional analysis described below. Any of the databases may be part of a single database structure, and, more generally, may be implemented as data stores logically or physically in many different ways. As one example, the data table structures  232  may be databases tables storing records that the control instructions  222  read, write, delete, and modify in connection with performing the multi-dimensional processing noted below. 
         [0033]    In one implementation, the control instructions  222  include resource analysis instructions  234 . The resource assessment instructions  234  execute resource assessment according to a multi-dimensional assessment framework  236  and according to pre-determined fixed or dynamic framework parameters  238  (e.g., weighting factors). Further, the control instructions  222  include machine interface generation instructions  240  that generate machine interfaces, including GUIs, that achieve improved interaction with the DRAM  112  regarding the assessments. 
         [0034]    The data table structures  232 , resource assessment instructions  234 , multi-dimensional assessment framework  236 , framework parameters  238 , and machine interface generation instructions  240  improve the functioning of the underlying computer hardware itself. That is, these features (among others described below) are specific improvements in way that the underlying system operates. The improvements facilitate more efficient, accurate, and precise execution of complex resource analysis. 
         [0035]      FIGS. 3-5  show example implementations  300 ,  400 ,  500  of the multi-dimensional assessment framework  236 . The multi-dimensional assessment framework  236  may take a very wide range of implementations, including additional, fewer, or different dimensions and dimensional characteristics to evaluate. The example implementation  300 , for instance, includes a resource dimension  302 , a task dimension  304 , a controller dimension  306 , a team dimension  308 , and a goal dimension  310 . 
         [0036]    The resource dimension  302  includes resource specific characteristics  312 . The resource specific characteristics  312  typically provide data on the individual performance of the resource itself. Examples are given below in Table 1. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Resource Dimension 
               
             
          
           
               
                 Dimensional 
                   
               
               
                 Characteristics 
                 Examples 
               
               
                   
               
               
                 Past Rating 
                 numerical rating on prior tasks completed more 
               
               
                   
                 than a threshold amount of time ago 
               
               
                 Recent Rating 
                 numerical rating on prior tasks completed less than 
               
               
                   
                 a threshold amount of time ago 
               
               
                 Hours of Experience 
                 length of time the resource has been working on 
               
               
                   
                 tasks 
               
               
                 Education 
                 specific skills, degrees, training, or other capability 
               
               
                   
                 indicators possessed by the resource 
               
               
                 Work Experience 
                 specific types and characteristics of tasks 
               
               
                   
                 previously performs; specific roles filled by the 
               
               
                   
                 resource 
               
               
                 Billed Assignments 
                 the number of prior tasks for which the resource 
               
               
                   
                 submitted an invoice 
               
               
                 Cost 
                 purchase and maintenance costs, rental rates, 
               
               
                   
                 billing rates 
               
               
                   
               
             
          
         
       
     
         [0037]    The task dimension  304  includes resource-task compatibility characteristics  314 . The resource-task compatibility characteristics  314  typically provide data on how well the resource is suited for the posted task. Examples are given below in Table 2. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Task Dimension 
               
             
          
           
               
                 Dimensional 
                   
               
               
                 Characteristics 
                 Examples 
               
               
                   
               
               
                 Availability 
                 whether or not a resource is available for the 
               
               
                   
                 posted task; the DRAM 112 may derive this metric 
               
               
                   
                 from information about other tasks (e.g., duration, 
               
               
                   
                 start, and termination data) to which the resource is 
               
               
                   
                 assigned 
               
               
                 Skill 
                 data reflecting whether the resource has the ability 
               
               
                 Fitness 
                 to perform a particular task; e.g., whether the skills 
               
               
                   
                 or capabilities possessed by the resource match 
               
               
                   
                 task requirement descriptors; the DRAM 112 may 
               
               
                   
                 take this data from the profile description of the 
               
               
                   
                 resource, may extract the data from descriptions of 
               
               
                   
                 prior tasks completed, or may obtain the data from 
               
               
                   
                 other sources. 
               
               
                   
                 The Skill Fitness score may also take into account 
               
               
                   
                 resource test scores, e.g., if the resource has been 
               
               
                   
                 measured against any given test of skills. 
               
               
                 Similar 
                 The prior experience of a resource demonstrates 
               
               
                 Task 
                 knowledge, skills and ability to do various types of 
               
               
                 Experience 
                 tasks. Also, resources may be better adapted to 
               
               
                   
                 working on tasks similar to prior tasks. As such, the 
               
               
                   
                 DRAM 112 may evaluate resource experience on 
               
               
                   
                 similar tasks when making its evaluation. Further 
               
               
                   
                 details are provided below. 
               
               
                 Profile 
                 This metric provides an assessment of resource 
               
               
                 Overview 
                 competency to perform a task based on a profile 
               
               
                   
                 overview of the resource. The resource profile may 
               
               
                   
                 include a description about the resource, prior tasks 
               
               
                   
                 in academia or industry, and other information. In 
               
               
                   
                 that regard, the resource profile may demonstrate 
               
               
                   
                 work history and how likely the resource is to fit the 
               
               
                   
                 task requirements. The DRAM 112 may implement 
               
               
                   
                 a content matching approach such as that 
               
               
                   
                 described in  Modern Information Retrieval , Volume 
               
               
                   
                 463, ACM press New York, 1999 to find similarity 
               
               
                   
                 between the resource profile and the task 
               
               
                   
                 description. 
               
               
                   
               
             
          
         
       
     
         [0038]    As one example, the DRAM  112  may determine the Skill Fitness metric according to: 
         [0000]    
       
         
           
             
               Skill_Fitness 
                
               
                   
               
                
               
                 ( 
                 
                   
                     S 
                     t 
                   
                   , 
                   
                     S 
                     w 
                   
                 
                 ) 
               
             
             = 
             
               
                 
                   Match 
                    
                   
                       
                   
                    
                   
                     ( 
                     
                       
                         S 
                         t 
                       
                       , 
                       
                         S 
                         w 
                       
                     
                     ) 
                   
                 
                 
                    
                   
                     S 
                     t 
                   
                    
                 
               
               + 
               
                 
                   ∑ 
                   
                     ∀ 
                     
                       S 
                       t 
                     
                   
                 
                  
                 
                     
                 
                  
                 
                   
                     T 
                     s 
                   
                   * 
                   
                     T 
                     p 
                   
                 
               
             
           
         
       
     
         [0039]    where S t  is the set of skills required for a task, S w  is the set of skills possessed by the resource, T s  is the Test score and T p  is the Test percentile. Match (S t , S w ) computes the number of matched skills between S t  and S w  and |S t | is the total number of skills required by the task. 
         [0040]    As one example, the DRAM  112  may determine the Similar Task Experience metric in the following manner. The DRAM represents the task, ‘t’, as a tuple &lt;T t , D t , St, Du t , R t &gt; where T t  is title of the task, D t  is description of the task, S t  is skills required for the task, Du t  is duration of the task, R t  is rating received by the resource on task completion. Let t p &lt;Tt p , Dt p , St p , Du tp &gt; be the posted task and t h  (t h εT h , where T h  is set of tasks completed by the resource in the past)&lt;Tt h , Dt h , St h , Dut h , Rt h &gt; be a past task performed by the resource in past. 
         [0041]    Experience on a similar task does not necessarily ensure better performance by the resource. Therefore, to evaluate similarity between two tasks, the DRAM  112  may use a rating Rt. In that regard, the DRAM  112  may carry out the processing for calculating task similarity described in Algorithm 1. Note that the DRAM  112  evaluates both structured (e.g., explicitly defined task categories) and unstructured information (e.g., free form resource profile text descriptions) about the tasks to identify similar tasks, thus capturing task similarity with more accuracy. 
         [0000]    
       
         
               
             
               
               
             
               
             
               
               
             
           
               
                   
               
               
                 Algorithm 1 
               
               
                 Task Similarity 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Input: 
                 t p  and T h   
               
               
                 Output: 
                 Task similarity score T s   
               
             
          
           
               
                 ∀ t h  ∈ T h , compute the similarity between t h  and t p  by following below 
               
               
                 steps: 
               
             
          
           
               
                 1. 
                 Tokenize the title and description of tasks t p  and t h   
               
               
                 2. 
                 Remove the stop words (most common words) and perform stemming 
               
               
                 3. 
                 Calculate TF-IDF (Term frequency-Inverse document frequency) 
               
               
                   
                 weight for each token 
               
               
                 4. 
                 Compute the cosine similarity between title of t p  and t h ; and 
               
               
                   
                 description of t p  and t h   
               
               
                   
               
               
                   
                  
         cos_sim                   (       T     t   h       ,     T     t   p         )       =           T   →       t   h       ,       T   →       t   p                  T     t   h            ·          T     t   p                    
 
               
               
                   
               
               
                   
                  
         cos_sim                   (       D     t   h       ,     D     t   p         )       =           D   →       t   h       ,       D   →       t   p                  D     t   h            ·          D     t   p                    
 
               
               
                   
               
               
                 5. 
                 Calculate the skill similarity as: 
               
               
                   
               
               
                   
                  
         Skill_similarity                   (       t   h     ,     t   p       )       =       (       S     t   h       ⋂     S     t   p         )       S     t   p             
 
               
               
                   
               
               
                 6. 
                 Compute similarity between Duration of t p  and t h  as: 
               
               
                   
                  Durationl_sim (t h , t p ) = (Du t     h    == Du t     p   ) ? 1 : 0 
               
               
                 7. 
                 Calculate Task similarity score T s  as: 
               
               
                   
                  T s  = (Skill_similarity (t h , t p ) + cos_sim (T t     h   , T t     p   ) + 
               
               
                   
                  cos_sim (T t     h   , T t     p   ) + Duration_sim (t h , t p )) × R t   
               
               
                   
               
             
          
         
       
     
         [0042]    The controller dimension  306  includes resource-task controller compatibility characteristics  316 . The resource-task controller compatibility characteristics  316  typically provide data on how compatible the resource will be with the task controller (e.g., an individual who posted the task). For this compatibility measure, the DRAM  112  may measure parameters such as, e.g., tasks completed by the resource for the task controller and overlapping working hours between the task controller and the resource. In one implementation, the DRAM  112  evaluates the metrics shown in Table 3 to measure this compatibility. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Controller Dimension 
               
             
          
           
               
                 Dimensional 
                   
               
               
                 Characteristics 
                 Examples 
               
               
                   
               
               
                 Cultural Match 
                 The DRAM 112 may define cultural matching of a 
               
               
                   
                 resource to a task controller in terms of values, norms, 
               
               
                   
                 assumptions, interaction with resources, and the like. The 
               
               
                   
                 DRAM 112 may evaluate the cultural match when, e.g., 
               
               
                   
                 resources belong to different geographies and use different 
               
               
                   
                 languages in order to perform a posted task. This metric 
               
               
                   
                 assesses how well a task controller of one country (C tp ) 
               
               
                   
                 collaborates with a resource from another country (C w ). 
               
               
                   
                 This metric may also capture preference of a task 
               
               
                   
                 controller to work with a resource in a particular country. 
               
               
                   
                 The DRAM 112 may evaluate, e.g., Jaccard similarity to 
               
               
                   
                 measure the association between the two, e.g.,: 
               
               
                   
               
               
                   
                  
         Cultural_Match                   (       C   tp     ,     C   w       )       =         C   tp     ⋂     C   w           C   tp     ⋃     C   w             
 
               
               
                   
               
               
                 Task Controller 
                 The DRAM 112 may determine the extent to which the 
               
               
                 Collaboration 
                 resource has collaborated with the task controller (tp) 
               
               
                   
                 from past assignments of the resource (cw). If a resource 
               
               
                   
                 has collaborated well with a task controller in past, the 
               
               
                   
                 task controller may feel more confident selecting the 
               
               
                   
                 resource again. The DRAM 112 may evaluate task 
               
               
                   
                 controller collaboration according to, e.g.: 
               
               
                   
               
               
                   
                  
         Collaboration_Score                   (     tp   ,   cw     )       =         ∑     i   =   1     N                     Rating   i       N         
 
               
               
                   
               
               
                   
                 where N is the total number of tasks for which cw has 
               
               
                   
                 collaborated with tp and Rating i  is the feedback score 
               
               
                   
                 given to cw by tp upon task completion. 
               
               
                 Similar Task 
                 A resource may not have worked directly with the task 
               
               
                 Controller 
                 controller before, but may have worked with similar task 
               
               
                 Experience 
                 controllers. Using this metric, the DRAM 112 identifies 
               
               
                   
                 similarities between task controller of the posted task tpp, 
               
               
                   
                 and the task controllers with which the resource has 
               
               
                   
                 worked in the past TPh. That is, this metric measures 
               
               
                   
                 how well the resource has worked with similar task 
               
               
                   
                 controllers in past. 
               
               
                   
                 The DRAM 112 may define the task controller tp as a 
               
               
                   
                 tuple &lt;feedback score, hiring rate, past hires, total hours 
               
               
                   
                 worked, and total assignments&gt;. The DRAM 112 may use 
               
               
                   
                 a measure such as Euclidean Distance (ED) to evaluate 
               
               
                   
                 the similarity, e.g.: 
               
               
                   
                 TaskPoster_Similarity 
               
               
                   
                 (tp p , tp h ) = max(ED(tp h , tp p )); ∀tp h  ∈ TP h   
               
               
                   
                 The DRAM 112 may also evaluate cosine similarity to 
               
               
                   
                 determine similarity between the task controller of the 
               
               
                   
                 present task, and the other task controllers that the 
               
               
                   
                 resource has worked for: 
               
               
                   
                 Input: c p , C m  // c p  is the information about the task 
               
               
                   
                 controller and C m , is the list of task controllers who 
               
               
                   
                 previously worked with the resource 
               
               
                   
                 Output: Similar controller experience score. 
               
               
                   
                 Steps: 
               
               
                   
                 Step 1: Compute the cosine similarity between c p  and all 
               
               
                   
                 the controllers c m  in C m . The similarity may be computed 
               
               
                   
                 based on feedback score, hiring rate, past hires, total 
               
               
                   
                 hours worked, and total assignments. 
               
               
                   
                 Step 2: Determine the similar controller experience score 
               
               
                   
                 as follows: 
               
               
                   
                 Similarity_client_experience_score = 
               
               
                   
                 max (∀c m  ∈ C m , (cosine_similarity(c p , c m )) 
               
               
                   
                 The above metric measures how well the resource has 
               
               
                   
                 worked with similar task controllers in the past. The 
               
               
                   
                 metric helps the DRAM 112 understand and build 
               
               
                   
                 confidence in the task controller for the resource, e.g., 
               
               
                   
                 when the task controller does not have prior experience 
               
               
                   
                 working with the resource. 
               
               
                 Timezone 
                 Resources from different geographies and time zones 
               
               
                 Match 
                 come together to perform a task. The DRAM 112 may 
               
               
                   
                 measure compatibility of resources from different time 
               
               
                   
                 zones with that of the task controller. For instance, the 
               
               
                   
                 DRAM 112 may measure how many working hours of 
               
               
                   
                 the task controller overlap with the working hours of 
               
               
                   
                 the resource. 
               
               
                   
               
             
          
         
       
     
         [0043]    The team dimension  308  includes resource-team compatibility characteristics  318 . The resource-team compatibility characteristics  318  typically provide data on how compatible the resource will be with a team of other resources selected for the task. In one implementation, the DRAM  112  evaluates the metrics shown in Table 4 to measure this compatibility. 
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                 TABLE 4 
               
             
             
               
                   
               
               
                 Team Dimension 
               
             
          
           
               
                 Dimensional 
                   
               
               
                 Characteristics 
                 Examples 
               
               
                   
               
               
                 Team 
                 The DRAM 112 may measure compatibility with the 
               
               
                 Collaboration 
                 level of collaboration among the task team 
               
               
                   
                 members. High collaboration among team 
               
               
                   
                 members may lead to better performance of the 
               
               
                   
                 resource. The DRAM 112 may evaluate the team 
               
               
                   
                 collaboration score based on the collaboration of 
               
               
                   
                 the resource with team members, if they have 
               
               
                   
                 worked together on any task in past. 
               
               
                   
                 The DRAM 112 may evaluate resource-team 
               
               
                   
                 compatibility according to, e.g.,: 
               
               
                   
               
               
                   
                 
                   
                     
                       
                         
                           Team_Collaboration 
                            
                           
                               
                           
                            
                           
                             ( 
                             
                               w 
                               , 
                               team 
                             
                             ) 
                           
                         
                         = 
                         
                           
                             1 
                             n 
                           
                            
                           
                             
                               ∑ 
                               
                                 i 
                                 = 
                                 1 
                               
                               n 
                             
                              
                             
                                 
                             
                              
                             
                               ( 
                               
                                 
                                   
                                     ∑ 
                                     
                                       j 
                                       = 
                                       1 
                                     
                                     N 
                                   
                                    
                                   
                                       
                                   
                                    
                                   
                                     Rating 
                                     ij 
                                   
                                 
                                 N 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                 
               
               
                   
               
               
                   
                 where n is the total number of resources that part of 
               
               
                   
                 a team, N is the total number of tasks in which 
               
               
                   
                 resource w has collaborated with w i  (w i  ∈ team) and 
               
               
                   
                 Rating ij  is the feedback score received by resource 
               
               
                   
                 w in collaboration with w i  on task tj. 
               
               
                 Timezone Match 
                 In a globally distributed task, resources from 
               
               
                   
                 different time zones work together on 
               
               
                   
                 interdependent tasks. Hence, the DRAM 112 may 
               
               
                   
                 measure the compatibility of the time zone of one 
               
               
                   
                 resource with other team members the resource 
               
               
                   
                 will collaborate with to complete the task. Team 
               
               
                   
                 members having overlapping time zones are likely 
               
               
                   
                 to collaborate better. 
               
               
                   
               
             
          
         
       
     
         [0044]    The goal dimension  310  captures intrinsic and extrinsic forces which drive a resource to accomplish a task. Those forces may vary depending on the type of resource, and may include, as examples, opportunity to learn new skills, domains, career growth, compensation, and other forces. The DRAM  112  determine whether the attributes of a task fulfill a goal or match a motivation of a resource. Table 5 provides some example characteristics the DRAM  112  may analyze in this regard. 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Goal Dimension 
               
             
          
           
               
                 Dimensional 
                   
               
               
                 Characteristics 
                 Examples 
               
               
                   
               
               
                 Important 
                 Resources may have goals to learn what they deem important 
               
               
                 Skill 
                 skills. This metrics captures whether the task gives a resource an 
               
               
                 Opportunity 
                 opportunity to learn those skills. In one implementation, the DRAM 
               
               
                   
                 112 determines whether the task provides an opportunity to learn 
               
               
                   
                 skills that are of high demand in the marketplace and are high 
               
               
                   
                 paying. The DRAM 112 may follow, e.g., Algorithm 2 to identify 
               
               
                   
                 important skills. 
               
               
                 Compensation 
                 The DRAM 112 may evaluate whether the resource may, e.g.: 
               
               
                 Goals 
                 obtain a better monetary payoff for a task, increase in skill level by 
               
               
                   
                 performing the task (e.g., from Beginner to Intermediate, or from 
               
               
                   
                 Intermediate to Expert), or meet other compensation goals. The 
               
               
                   
                 DRAM 112 may evaluate this metric with reference to, e.g., 
               
               
                   
                 expected compensation (e.g., payoff) for each skill required by the 
               
               
                   
                 task, and the expectation expressed by the resource for that skill. 
               
               
                   
                 For instance, the DRAM 112 may define an expected payoff as the 
               
               
                   
                 difference in average pay offered for the skill level required for the 
               
               
                   
                 task and the average compensation expected by the resource for 
               
               
                   
                 those skills at the level possessed by the resource. This differential 
               
               
                   
                 may drive a further determination of monetary goals for the 
               
               
                   
                 resource as shown below: 
               
               
                   
                 ExpectedPayDif f (Skill, l t , l w ) = 
               
               
                   
                 AveragePay(Skill, l t ) − AveragePay (Skill, l w ) 
               
               
                   
                 MontearyMotivation = 
               
               
                   
                 Average (ExpectedPayDiff (Skill, l t , l w )); ∀Skill ∈ S t   
               
               
                   
                 where l t  is the skill level required for the task and l w  is the skill level 
               
               
                   
                 possessed by resource. 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
             
               
               
             
               
             
               
               
             
               
             
               
               
             
           
               
                   
               
               
                 Algorithm 2 
               
               
                 Important Skill Identification 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Input: 
                 All task data T m  from the marketplace, skills required for 
               
               
                   
                 the posted task S t     p    and skillset of the resource S w   
               
               
                 Output: 
                 GoalSkill Opportunity GS 
               
             
          
           
               
                 1. 
                 Rank all the skills listed in tasks Tm based on the percentage of 
               
             
          
           
               
                 tasks requiring them, average pay given for the task requiring those 
               
               
                 skills and percentage of resources possessing those skills. Order skills 
               
               
                 in the descending order along these to identify the goalskills 
               
             
          
           
               
                 2. 
                 Calculate the percentile score for each skill based on their rank. 
               
               
                   
                 Percentile (Skill) = (N s  − R s )/(N s ) 
               
             
          
           
               
                 where N s  is the total number of skills available in a field and R s  is the 
               
               
                 rank of a particular skill. 
               
             
          
           
               
                 3. 
                 Calculate GoalSkill opportunity for resource as: 
               
               
                   
               
               
                   
                 
                   
                     
                       
                         
                           GS 
                            
                           
                             ( 
                             w 
                             ) 
                           
                         
                         = 
                         
                           
                             
                               ∑ 
                               
                                 
                                   
                                     
                                       ∀ 
                                       
                                         Skill 
                                         ∈ 
                                         
                                           S 
                                           
                                             t 
                                              
                                             
                                                 
                                             
                                           
                                         
                                       
                                     
                                     &amp; 
                                   
                                    
                                   
                                       
                                   
                                    
                                   Skill 
                                 
                                 ∉ 
                                 
                                   S 
                                   w 
                                 
                               
                             
                              
                             
                                 
                             
                              
                             
                               Percentile 
                                
                               
                                 ( 
                                 Skill 
                                 ) 
                               
                             
                           
                           
                              
                             
                               
                                 
                                   
                                     Skill 
                                     ∈ 
                                     
                                       S 
                                       t 
                                     
                                   
                                    
                                   
                                       
                                   
                                   &amp; 
                                 
                                  
                                 
                                     
                                 
                                  
                                 Skill 
                               
                               ∉ 
                               
                                 S 
                                 w 
                               
                             
                              
                           
                         
                       
                     
                   
                 
               
               
                   
               
             
          
         
       
     
         [0045]    Analysis using Random Forest provides indicators of importance of the metrics discussed above. Table 6, below, shows one analysis in terms of information gain for ranking importance of each dimension in the multi-dimensional analysis. That is, Table 6 shows the top metrics for making a likely successful resource selection decision, with the metrics are ranked in decreasing order of Information gain. The DRAM  112  may, for instance, give greater weight to these metrics than others when determining an overall evaluation for a potential resource. 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Dimensional Importance 
               
             
          
           
               
                   
                 Metric 
                 Information Gain 
               
               
                   
                   
               
             
          
           
               
                   
                 Similar Task Experience 
                 0.96 
               
               
                   
                 Skill Fitness 
                 0.675 
               
               
                   
                 Cultural Match 
                 0.616 
               
               
                   
                 Billed Assignments 
                 0.612 
               
               
                   
                 Billing Rate 
                 0.602 
               
               
                   
                 Task Controller Collaboration 
                 0.552 
               
               
                   
                   
               
             
          
         
       
     
         [0046]      FIG. 6  shows a system environment  600  in which the DRAM  112  communicates with sources  602 ,  604  of resource data, and with platforms  606  that consume resource analysis services. The sources  602 ,  604  may provide any of the data described above to the DRAM  112  for analysis, e.g., identifications of available resources, resource profiles, reviews, test scores, prior task experience, cost, time zone, and other data. The sources  602  are external to the DRAM  112 , and in this example include crowd sourcing systems Upwork  608  and Applause  610 . The source  604  is internal to the organization hosting the DRAM  112 , and may be an internal talent database or knowledge exchange, as just two examples. In other implementations, the source  604  and the DRAM  112  are present in or implemented by physically separate systems. That is, the source  604  and DRAM  112  may be independent, with, for instance, the source  604  internal to an enterprise, and the enterprise connecting to the DRAM  112  for performing the resource analysis. 
         [0047]    The platforms  606  connect to the DRAM  112 , e.g., via resource assessment machine interfaces  210 , application programming interface (API) calls defined by the DRAM  112 , and the like. The platforms  606  may access the DRAM  112  as a service for resource evaluation, e.g., to find resources for a testing environment  612  that performs testing tasks (e.g., software testing), or for a development environment  614  that performs development tasks (e.g., software development). The DRAM  112  receives task posting data from any source, performs the requested analyses on available resources against the task postings, and returns responses with evaluations to the platforms  606 . In that regard, the DRAM  112  may render and deliver any number of predefined machine interfaces  210  to the platforms  606 , e.g., as GUIs in a web browser. A few examples of the machine interfaces  210  follow. 
         [0048]      FIG. 7  shows an example resource review interface  700 . The resource review interface  700  shows a resource section  704 , a task section  706 , and a customization section  708 . The resource section  704  shows potential resources for a task, e.g., the potential resources  750 ,  752 , whose profile details were retrieved from, e.g., the sources  602 ,  604 . The resource section  704  also provides an overview of specific analysis dimensions  710  applicable to each resource, e.g., the task compatibility dimension, resource characteristic (e.g., personal performance) dimension, task controller compatibility dimension, team compatibility dimension, and goal dimension. The task section  706  provides a description of the task that the task controller has posted. The customization section  708  provides preference inputs for adjusting the processing of the DRAM  112 . 
         [0049]    More specifically, the customization section  708  provides GUI elements that the operator may modify to adjust the DRAM  112  processing. In this example, the customization section  708  includes a score control  712 , which eliminates from the display the resources falling below the score threshold; a task compatibility control  714 , for setting an analysis weight for the task dimension  304 ; a task controller compatibility control  716 , for setting an analysis weight for the controller dimension  306 ; a team compatibility control  718  for setting an analysis weight for the team dimension  308 ; a resource characteristic control  720 , for setting an analysis weight for the resource dimension  302 ; and a goal control  722 , for setting an analysis weight for the goal dimension  310 . The analysis weights may be pre-determined or have default values, and the operator may adjust the analysis weights for any particular task. 
         [0050]    The customization section  708  also includes an analysis section  724 . In the analysis section  724 , a duration control  726  allows the operator to specify task duration. In addition, a budget control  728  allows the operator to specify a task budget. The DRAM  112  evaluates these factors and others when assessing resource availability and cost. 
         [0051]      FIGS. 8-16  show additional examples of the machine interfaces  210  that the DRAM  112  may generate. The machine interfaces  210  facilitate improved interaction with the DRAM  112 , including more efficient understanding and review of each resource and the evaluation of each resource. The machine interfaces  210  may vary widely, and any particular implementation may include additional, fewer, and different interfaces. 
         [0052]      FIG. 8  shows an example resource analysis result interface  800 . The resource analysis result interface  800  displays result rows (e.g., rows  802 ,  804 ). Each result row may include a resource identifier  806 ; overall analysis result  808 , e.g., a numerical score determined responsive to the weights set in the customization section  708 ; and dimensional analysis results  810 . The dimensional analysis results  810  may include any specific results the DRAM  112  has determined along any dimension or dimensional characteristic. Examples in  FIG. 8  include: whether the resource is available, task compatibility, the skill fitment of the resource to the task, the completion percentage of prior tasks taken by the resource, prior rating, cultural similarity, and task controller compatibility. A framework visualization section  812  provides a visualization of the particular multi-dimensional assessment framework  236  that the DRAM  112  is analyzing for the resources. 
         [0053]      FIG. 9  shows an example resource detail interface  900 . The resource detail interface  900  includes a resource details section  902 , and a categorized details section  904 . The resource details section  902 , in this example, includes a profile summary section  906 , a derived metrics section  908 , as well as profile visualizations, e.g., the visualizations  910  and  912 . The resource details section  902  may include profile text, educational highlights, career highlights, or other details. 
         [0054]    The categorized details section  904  may provide a categorized tabbed display of resource profile details that lead to, e.g., derived metrics as well as resource data received from the sources  102 - 106 . That resource data may include profile summary data, displayed in the profile summary section  906 . The derived metrics section  908  may display selected metrics that the DRAM  112  has derived starting from the resource data obtained from the data platforms  102 - 108 . Examples of derived data include the determinations of metrics along the multi-dimensional framework, such as overall score, ranking, task similarity score, task controller similarity score, and the like. The resource detail interface  900  may include any number or type of visualizations  910  and  912  to provide, e.g., a graphical interpretation of resource characteristics. 
         [0055]      FIG. 10  shows additional detail  1000  from the resource detail interface  900 . In this example, the skill section  1002  shows the resource skills that match to the skills required by the task controller in the task that the task controller posted. The skill section  1002  shows those matching skills with a match indicator, in this case highlighting. The skill section  1002  renders skill selectors, such as check boxes  1004 , to allow the operator to make selections of skills. The visualization  910  provides a graphical representation of the tasks taken by the resource under each of the selected skills. 
         [0056]      FIG. 11  shows additional detail from the resource detail interface  900  in the form of an example similar task analysis interface  1100 . The similar task analysis interface  1100  includes a similar task section  1102 . The interface  1100  provides, in the similar task section  1102 , narratives, skills, dates, feedback, comments, and other details concerning tasks that the DRAM  112  has identified as similar to the posted task and performed by the resource under evaluation. 
         [0057]      FIG. 12  shows additional detail from the resource detail interface  900  in the form of an example ongoing tasks analysis interface  1200 . The interface  1200  includes an ongoing task section  1202 . The interface  1200  provides, in the similar task section  1202 , narratives concerning ongoing tasks handled by the resource, including required skills and other ongoing task descriptors. 
         [0058]      FIG. 13  shows additional detail from the resource detail interface  900  in the form of an example recent reviews analysis interface  1300 . The interface  1300  includes a recent reviews section  1302 . The interface  1300  provides, in the recent reviews section  1302 , details concerning evaluations of recent tasks taken by the resource. The evaluations may include details such as task title, review comments, scores or ratings, inception and completion dates, and the like. The interface  1300  may also include visualizations of review data, such as the time history min/max rating visualization  1304 . 
         [0059]      FIG. 14  shows additional detail from the resource detail interface  900  in the form of an example prior task analysis interface  1400 . The interface  1400  includes a past tasks section  1402 . The interface  1400  provides, in the past tasks section  1402 , details concerning tasks previously performed by the resource. The past task details may include, as just a few examples, narratives describing the past task, dates worked, and skills required, learned, or improved. 
         [0060]      FIG. 15  shows additional detail from the resource detail interface  900  in the form of an example summary analysis interface  1500 . The interface  1500  includes a summary section  1502 . The summary section  1502  may provide details on resource characteristics, including how well the resource matches to a particular new task along any dimensions or metrics. For instance, the summary section  1502  may include feedback scores, availability scores, deadline scores, collaboration scores, skill scores, and quality scores. Other types of summary information may be provided, including applicable dates, minimum and maximum scores, averages, and the like. 
         [0061]      FIG. 16  shows an example resource comparison interface  1600 . A resource identification section  1602  identifies each resource being compared. The interface  1600  renders any number or type of displays (e.g., the visualizations  1604 ,  1606 ,  1608 ,  1610 ) that provide a side-by-side comparison of each resource along any specific selected dimension or metric within a dimension. 
         [0062]      FIG. 17  shows an example of process flow  1700  for resource analysis. In this example, the task controller posts a task description to a sourcing platform (e.g., one of the data platforms  102 - 108 ) ( 1 ). The task description includes data characterizing the task, as examples: a text narrative describing the task, required skills, optional skills, skill that will be learned, skill levels required, compensation, start date, end date, location, and team composition characteristics. Resources indicate their availability for tasks, e.g., by transmitting availability notifications to any one or more of the data platforms  102 - 108  ( 2 ). Any source of resource data, including the resource itself, may provide data characterizing the resources to any one or more of the data platforms  102 - 108  ( 3 ). The resource characteristics may include, as examples: skills known, skill levels, skill evaluation scores, experience (e.g., prior task descriptions), resource location, prior task locations, resource goals, availability, education, prior ratings, and cultural characteristics. 
         [0063]    The task controller may set weighting preferences for the DRAM  112  to use in determining assessments ( 4 ). The weighting preferences may include default weights to use unless changed for a particular assessment. That is, the task controller may also set specific weighting preferences for the DRAM  112  to apply for any given assessment. 
         [0064]    The DRAM  112  executes the assessment on the resources with respect to the posted task, and delivers the assessments to the task controller via the machine interfaces  210  ( 5 ). In response, the task controller may review and consider the resource assessments, interacting with the machine interfaces to do so. The task controller may then make resource selections for the task ( 6 ), and transmit the resource selections to the data platforms  102 - 108 . If the resource will take on the task, then the resource may indicate acceptance of the task to the data platforms  102 - 108  ( 7 ). 
         [0065]    The DRAM  112  may execute information retrieval and text mining operations, as examples, to match resources to tasks and determine the assessments. The DRAM  112  may apply these techniques when analyzing, e.g., text narratives of task descriptions and resource descriptions to find matching characteristics.  FIG. 17  shows some examples of processing that the DRAM  112  may perform. For instance, the DRAM  112  may obtain content descriptions such as task descriptions and resource profile narratives ( 1701 ), and tokenize the descriptions ( 1702 ). The DRAM  112  optionally performs stop word removed ( 1704 ), e.g., to eliminate words present on a pre-defined stop word list that have little or no value in matching resource characteristics to task characteristics. The DRAM  112  may also execute term frequency-inverse document frequency (TF-IDF) as part of ascertaining how important a word is within the content descriptions ( 1706 ). To measure similarity, the DRAM  112  employs any desired distance measure between two documents ‘a’ and ‘b’ represented in vector space, such as the Cosine similarity measure ( 1708 ): 
         [0000]    
       
         
           
             
               
                 a 
                 → 
               
               · 
               
                 b 
                 → 
               
             
             = 
             
               
                  
                 
                   a 
                   → 
                 
                  
               
                
               
                  
                 
                   b 
                   → 
                 
                  
               
                
               
                   
               
                
               cos 
                
               
                   
               
                
               θ 
             
           
         
       
       
         
           
             
               cos 
                
               
                   
               
                
               θ 
             
             = 
             
               
                 
                   a 
                   → 
                 
                 · 
                 
                   b 
                   → 
                 
               
               
                 
                    
                   
                     a 
                     → 
                   
                    
                 
                  
                 
                    
                   
                     b 
                     → 
                   
                    
                 
               
             
           
         
       
     
         [0066]      FIG. 18  shows another example of process flow  1800  for resource analysis when integrated with a hybrid sourcing platform.  FIG. 18  extends the example of  FIG. 17 . In  FIG. 18 , an intermediate, hybrid resource data platform  1802  is implemented between the task controller and the external data platforms  102  - 108 . The hybrid resource data platform  1802  may represent, for instance, a private company internal system that initially receives task postings for review and consideration by company personnel. The hybrid resource data platform  1802  may determine when and whether to pass the task postings to the external data platforms  102 - 108 , e.g., when the company desires to extend the resource search outside of the company. In that regard, the hybrid resource data platform  1802  receives resource characteristics from the external data platforms  102 - 108 . 
         [0067]    In the example shown in  FIG. 18 , the DRAM  112  is implemented as part of the hybrid resource data platform  1802 . The hybrid resource data platform  1802  executes the DRAM functionality to determine and report resource assessments to the task controller. Selections of resources (or offers made to resources) for the task flow first to the hybrid resource data platform  1802 , and possibly to the external data platforms  102 - 108 , e.g., when resources external to the company have been selected or offered a task. 
       FURTHER EXAMPLES 
       [0068]    Many variations of the DRAM implementation are possible. The DRAM  112  may include modules for determining similarity between tasks based on the task features such as task type, duration, skills required, and so on. The DRAM  112  may also include modules for computing similarity between task controllers. The similarity computation modules are used for recommending resources for tasks. The DRAM  112  may also include modules for sentiment analysis of the textual feedback given by the task controller for the resources. The sentiment analysis identifies, e.g., whether the task controller is satisfied with the completed task. It takes as input the textual feedback and outputs sentiment details. Some examples of sentiments are: positive, negative, and neutral. Furthermore, the sentiment analysis module may categorize the textual feedback based on any number of pre-defined aspects, such as Skill, Quality, Communication, Collaboration, Deadline and Availability based on the defined rules. Note that the DRAM  112  may determine metrics pertaining to any pre-defined set of dimensions, e.g., as show in  FIGS. 3-5 . 
         [0069]    Combined Assessment Score 
         [0070]    In some implementations, the DRAM  112  determines a combined assessment score for the resource under assessment. In that respect, the DRAM  112  may implement a machine learning module trained to learn the weightings of each metric to arrive at the final assessment score for each resource, as just one example. Expressed another way, the DRAM  112  optionally combines determined metrics to arrive at a final assessment score for each resource. Each metric may be given a weight to arrive at the final score. For example, the equation below combines Availability, Skill Fitness, and Task Similarity metrics to arrive at a final score for a the task dimension  304  according to the dimensional component weights dcw 1 , dcw 2 , dcw 3 , and dcw 4 : 
         [0000]      FinalScore= dcw   1 *Availability+ dcw   2 *SkillFit+ dcw   3 *Experience+ dcw   4 *Profile; 
         [0071]    More generally, the DRAM  112  may assess a resource by combining any selected dimensions using pre-determined dimensional weights to arrive at a final assessment score, across any number of included dimensions, e.g., for the framework  300 : 
         [0000]      ResourceScore= dw   1 *ResourceDimension+ dw   2 *TaskDimension+ dw   3 *ControllerDimension+ dw   4 *Team Dimension+ dw   5 *GoalDimension; 
         [0072]    The DRAM  112  may determine or select weights (including setting default weights) using from the machine learning algorithms or linear or logistic regression techniques. As one example, the DRAM  112  may optimize using the following equation with a specific optimization objective: 
         [0000]    
       
      
       Y=w 
       0 
       +w 
       1 
       f 
       1 
       +f 
       2 
       f 
       2 
       + . . . +w 
       n 
       f 
       n  
      
     
         [0000]    in which w i —represent weights, f i —dimension/metric score, Y—observed value, e.g., selected or not selected for the task. 
         [0073]    The machine learning module in the DRAM  112  may, for instance, learn the importance of each metric from the historical data about tasks and resources. The DRAM  112  may employ an online learning algorithm and the weights may be refined with each new data sample. 
         [0074]    An example machine learning implementation follows: 
         [0075]    Input: Set of tasks and assessment metrics for all the resources who worked/applied on those tasks. 
         [0076]    Output: Weights/importance of each metric that models a successful resource selection behavior. 
         [0077]    Execution 
         [0078]    Step 1: Prepare the training data as follows: 
         [0079]    For each task that has more than one potential resource, create a data point for each resource with the following attributes: 
         [0080]    a) all the evaluation metrics for the resource, task, and task controller 
         [0081]    b) indication of whether the resource was hired for the task. 
         [0082]    Step 2: Apply a machine learning algorithm on the training data. 
         [0083]    Step 3: Returns the weights identified by the algorithm. 
         [0084]    Customizing Assessment Model 
         [0085]    The DRAM  112  also accepts and responds to modifications of the weights supplied by, e.g., task controllers. For instance, a task controller may prefer to select a resource that he has worked with in past and with which he had a good past experience. Hence, the DRAM  112  allows the task controller to override the default weightings to express these preferences. 
         [0086]    What-if Analysis 
         [0087]    The DRAM  112  may perform what-if analyses to help assess resources if, e.g., the pre-defined scores and metrics are not sufficient to make a selection decision. In one implementation, the DRAM  112  predicts the likelihood of task completion and the quality of the completed task if done by a particular resource. The DRAM  112  may execute the analysis on the historical data about tasks and resources, using a machine learning module which trains models for task completion and task quality. The trained model may predict task completion and the quality of the completed task. 
         [0088]    The what-if analyses allow the task controller to vary, e.g., the duration and budget for the task to see anticipated outcomes. The task controller may then use the anticipated outcomes to negotiate (if applicable) with the resources to reach agreement on a set of task characteristics (e.g., duration and budget) that achieve a beneficial outcome. The DRAM  112  responds by assessing and displaying the task completion probability and quality of completed task for all the applicants. The DRAM  112  may estimate and report task completion probability assuming any given resource is assigned to a task. For any or all of the potential resources, the DRAM  112  may determine this probability. The system operator, through an operator GUI, can vary the task duration, budget, skills, and system displays the revised task completion probability for each resource based on a machine learning model trained from the past data. The DRAM  112  may also predicts the quality of a completed task. The system operator, through an operator GUI, can vary the task duration, budget, and skills. The DRAM  112  responds by determining the quality of the completed task for each resource by executing, e.g., a machine learning model trained on the prior data. 
         [0089]    Evaluating New Resources 
         [0090]    Note that the DRAM  112  may obtain and analyze information available from other sources than the data platforms  102 - 108  in its assessments. Examples of additional information include resource public profiles on professional networks, publically available references, and descriptions and feedback on tasks done during their academic training or in an industry. 
         [0091]    Assessment as a Service 
         [0092]    A local or cloud based hosting platform may offer the DRAM  112  as a service subscribed to or paid for by any third party platform. 
         [0093]    Table 7 below provides further examples of metrics the DRAM  112  may assess in any particular implementation, including examples of how the DRAM  112  may derive metrics starting with certain data. 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 Example metrics for assessing resources 
               
             
          
           
               
                 Metric 
                 Description 
                 How to Assess 
               
               
                   
               
               
                 Availability 
                 whether or not the resource 
                 Available working hours/day for a 
               
               
                   
                 is available for the given 
                 resource id determined based on 
               
               
                   
                 duration 
                 number of tasks the resource is doing 
               
               
                   
                   
                 and estimated completion time for each 
               
               
                   
                   
                 task. For example, suppose resource is 
               
               
                   
                   
                 working on two tasks that need to be 
               
               
                   
                   
                 completed in 20 days and estimated to 
               
               
                   
                   
                 take 40 hours. So on an average, the 
               
               
                   
                   
                 resource spends 2 hours on each task. 
               
               
                   
                   
                 Thus the remaining time available is (8 − 
               
               
                   
                   
                 (2 + 2) = 4 hours). 
               
               
                 Skill 
                 Matching score between 
                 % skill matched: Number of skills that 
               
               
                 Fitness 
                 the required skill and 
                 are required by the posted task and also 
               
               
                   
                 resources skills 
                 possessed by the resource/Total 
               
               
                   
                   
                 number of skills required by the task. 
               
               
                 Rating 
                 How the resource has been 
                 Average Rating: Average resource 
               
               
                   
                 rated in past. 
                 rating as given by others in past. 
               
               
                 Goals 
                 What goals the resource 
                 Task-goal match: Percentage skills that 
               
               
                   
                 has specified for itself in 
                 are required by the tasks and also 
               
               
                   
                 information available in the 
                 aspired as ‘must have skills’ by the 
               
               
                   
                 resource profile. 
                 resource/Total number of skills 
               
               
                   
                   
                 required by the task 
               
               
                 Task 
                 How well the resource has 
                 Task completion rate for the task 
               
               
                 Controller 
                 collaborated with the task 
                 controller: 100* Total tasks completed 
               
               
                 Collaboration 
                 controller in the past 
                 by the resource for the task controller/ 
               
               
                   
                   
                 Total tasks completed by the resource 
               
               
                   
                   
                 in past 
               
               
                   
                 Sentiment 
                 Perform sentiment analysis on the 
               
               
                   
                   
                 written comments for the resource to 
               
               
                   
                   
                 score it on aspects such as: ‘motivated’, 
               
               
                   
                   
                 ‘works well with others’, ‘follows well’, 
               
               
                   
                   
                 ‘timeliness’. 
               
               
                   
                 How well he has 
                 Tasks completion rate for similar task 
               
               
                   
                 collaborated with similar 
                 controllers: 100* Total number of tasks 
               
               
                   
                 task controllers in the past 
                 completed by the resource for similar 
               
               
                   
                   
                 task controllers/Total tasks completed 
               
               
                   
                   
                 successfully. Similarity score may be 
               
               
                   
                   
                 defined based on geography, total tasks 
               
               
                   
                   
                 posted, total tasks completed, and other 
               
               
                   
                   
                 factors. 
               
               
                 Cultural/ 
                 Overlapping workhour 
                 Overlapping work hours: number of 
               
               
                 Geographical 
                   
                 overlapping work hours for the resource 
               
               
                   
                   
                 and the task controller. 
               
               
                   
                 Is there a restriction on 
                 Geographical match: Boolean value whether 
               
               
                   
                 geography to choose a 
                 resource geography is matched with task 
               
               
                   
                 resource. 
                 controller specified requirement 
               
               
                 Cost of hiring - 
                 How much the resource is 
                 Cost of similar task: how much resource 
               
               
                 fixed time 
                 likely to charge for the task. 
                 has quoted in past for similar tasks. 
               
               
                 Cost of hiring - 
                 How much resource 
                 Hourly rate: charge rate for the resource 
               
               
                 hourly 
                 charges per hour 
               
               
                 Similar task 
                 How many similar tasks he 
                 Similar task performance: 100* Number 
               
               
                 experience 
                 has performed in the past 
                 of similar tasks performed by the 
               
               
                   
                 and what were the scores 
                 resource in past/Total number of tasks 
               
               
                   
                 for those tasks. 
                 performed by the resource in past. 
               
               
                   
                   
                 Similarity is defined based on some task 
               
               
                   
                   
                 attributes. 
               
               
                 Task 
                 How many tasks resource 
                 Task completion rate: 100* Number of 
               
               
                 completion 
                 has successfully 
                 tasks successfully completed/Total 
               
               
                   
                 completed. 
                 number of tasks undertaken 
               
               
                 Resource 
                 Average score 
                 Averages score: Average of all the 
               
               
                 score 
                   
                 scores given to the resource on a select 
               
               
                   
                   
                 set of (e.g., all) prior tasks. 
               
               
                   
                 Max score 
                 Max score: Maximum score resource 
               
               
                   
                   
                 has obtained so far. 
               
               
                   
                 Min score 
                 Min score: Minimum score the resource 
               
               
                   
                   
                 has obtained so far. 
               
               
                   
                 Recency of max score 
                 Recency of max score: Date when resource 
               
               
                   
                   
                 has scored the maximum score. 
               
               
                   
                 Average recent score. 
                 Average rating of the resource for last 
               
               
                   
                   
                 one year. 
               
               
                 Resume 
                 Educational background 
                 Rating based on educational degrees. 
               
               
                 score 
               
               
                   
                 Diversity of experience 
                 Number of skills resource has 
               
               
                   
                   
                 experience. 
               
               
                   
                 Awards and recognition 
                 Score based on awards and recognition. 
               
               
                 Cost of 
                 This may include cost of 
                 This metric may assess skill gaps, 
               
               
                 onboarding 
                 training, knowledge 
                 domain knowledge, and prior 
               
               
                   
                 transfer, etc. for onboarding 
                 experience of the resource. 
               
               
                   
                 a crowd resource. 
               
               
                 Team 
                 To capture how well the 
                 % tasks worked with team member in 
               
               
                 collaboration 
                 crowd resource fits with 
                 past: percentage of tasks the crowd 
               
               
                   
                 other team member 
                 resource has worked with any of the 
               
               
                   
                   
                 team member in the past. 
               
               
                 Time zone 
                 How well the time zone 
                 Time zone gap: min, max, and average 
               
               
                 compatibility 
                 matches with other team 
                 of time zone difference with rest of the 
               
               
                   
                 resources. 
                 team members. 
               
               
                   
               
             
          
         
       
     
         [0094]    Determining Text Similarity 
         [0095]    In order to find the similarity between text features such as task title, task description, resource profile overview and resource experience details, the DRAM  112  may implement a TF-IDF approach: 
         [0000]      TF-IDF weight=(1+log(term_frequency))*log( N /Document_frequency) 
         [0096]    In the TF-IDF approach, ‘term frequency’ represents the number of times a search term, such as a word in a task title or task description, appears in a document, such as a resource description. In this particular example, the approach uses a log-scaled metric, 1+log(term_frequency), for frequency, although other frequency metrics may be used. Note that the TF-IDF approach includes a term frequency-inverse adjustment, log (N/document_frequency). This adjustment reduces the weight score in log relation to the number of documents in a pre-defined collection of N documents that include the search term. As a result, common terms used in all documents (e.g., “the”, “an”) provide little weight (because log(1) is 0) and are effectively filtered out in the TF-IDF calculation. 
         [0097]    The methods, devices, processing, frameworks, circuitry, and logic described above may be implemented in many different ways and in many different combinations of hardware and software. For example, all or parts of the implementations may be circuitry that includes an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; or as an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or as circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof. The circuitry may include discrete interconnected hardware components or may be combined on a single integrated circuit die, distributed among multiple integrated circuit dies, or implemented in a Multiple Chip Module (MCM) of multiple integrated circuit dies in a common package, as examples. 
         [0098]    Accordingly, the circuitry may store or access instructions for execution, or may implement its functionality in hardware alone. The instructions may be stored in a tangible storage medium that is other than a transitory signal, such as a flash memory, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM); or on a magnetic or optical disc, such as a Compact Disc Read Only Memory (CDROM), Hard Disk Drive (HDD), or other magnetic or optical disk; or in or on another machine-readable medium. A product, such as a computer program product, may include a storage medium and instructions stored in or on the medium, and the instructions when executed by the circuitry in a device may cause the device to implement any of the processing described above or illustrated in the drawings. 
         [0099]    The implementations may be distributed. For instance, the circuitry may include multiple distinct system components, such as multiple processors and memories, and may span multiple distributed processing systems. Parameters, databases, and other data structures may be separately stored and controlled, may be incorporated into a single memory or database, may be logically and physically organized in many different ways, and may be implemented in many different ways. Example implementations include linked lists, program variables, hash tables, arrays, records (e.g., database records), objects, and implicit storage mechanisms. Instructions may form parts (e.g., subroutines or other code sections) of a single program, may form multiple separate programs, may be distributed across multiple memories and processors, and may be implemented in many different ways. Example implementations include stand-alone programs, and as part of a library, such as a shared library like a Dynamic Link Library (DLL). The library, for example, may contain shared data and one or more shared programs that include instructions that perform any of the processing described above or illustrated in the drawings, when executed by the circuitry. 
         [0100]    Various implementations have been specifically described. However, many other implementations are also possible.