Patent Abstract:
A call center system in which agents are geographically dispersed based on agent skill-set, agent location and caller location that results in a call being delivered to the best available agent. The call center system is comprised of a call center application module coupled to a database module with a communications network being used to couple incoming calls from customers, as well as various call center agents, to the system. The database contains a ranking of available agents, based on a dataset including information regarding skill-set, previous interaction with the customer, proximity to the customer, language capability, current availability, and so forth. The system then chooses the best available agent to service a customer call based on the agent rankings. In the case where the customer has a preference for proximity of the agent to the customer, the system adjusts the agent rankings according to the agent&#39;s distance from the customer prior to making a selection.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present patent application is a continuation of U.S. patent application Ser. No. 11/421,841, filed on Jun. 2, 2006 entitled “SYSTEM FOR GEOGRAPHIC AGENT ROUTING” and is related to U.S. patent application Ser. No. 11/421,846, filed on Jun. 2, 2006, now issued patent number 7,961,866 entitled “A METHOD AND COMPUTER READABLE MEDIUM FOR GEOGRAPHIC AGENT ROUTING” and is related to U.S. patent application Ser. No. 13/115,297, filed on May 25, 2011 entitled “METHOD AND COMPUTER READABLE MEDIUM FOR GEOGRAPHIC AGENT ROUTING”, each of which are incorporated in its entirety by reference herein. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to the general field of routing agents from call centers and in particular to a system for optimally routing such agents. 
     The present invention is a system for routing a call or other communication to a best available individual, such as a call center agent, customer service representative, and the like, who has a certain relationship with a caller, such as, for example, a physical proximity between the individual and the caller. 
     Currently, many businesses utilize call centers, each with multiple agents, to provide customer service. Typically, businesses employ multiple physical call centers to enable around-the-clock call handling and to utilize cheaper labor markets. Current call center applications enable call routing by a number of methods including time-of-day (TOD), agent availability, caller location and agent skill-set. In some cases, these methods can be combined to form a routing plan. There are limitations to this approach however, including the need to group agents at certain physical locations and the strict prioritization of one routing method over another. These limitations may result in a customer who is not very comfortable with his agent due to accent, lack of local knowledge, etc. 
     Therefore, what is needed to overcome the aforementioned limitations, is a call center system in which agents are geographically dispersed and a call routing method, based on agent skill-set, agent location and caller location, that results in call delivery to the best available agent, while allowing a certain preference towards agents who are geographically closer to the caller. 
     SUMMARY OF THE INVENTION 
     The present invention, accordingly, provides a call center system in which agents are geographically dispersed and a call routing method, based on agent skill-set, agent location and caller location, that results in call delivery to the best available agent. 
     In a preferred embodiment of the invention, the call center system is comprised of a call center application module coupled to a database module. A communications network is used to couple incoming calls from customers, as well as various call center agents, to the system. The communications network will accommodate both static (fixed location) and dynamic (wireless) type communications. The database contains a ranking of available agents based on a dataset including information regarding skill-set, previous interaction with the customer, proximity to the customer, language capability, current availability, and so forth. The system chooses the best available agent to service a customer call based on the ranking of all agents. In the case where the customer has a preference for proximity of the agent to the customer, the system adjusts the agent rankings according to their distance from the customer prior to making a selection. 
     In operation, the call center application system specifies a proximity preference factor (PPF) from 0%-100%. If the PPF is 0% then the customer does not care about the distance between the customer (caller) and the agent, then the system selects an agent solely on the initial agent ranking. However, if the customer specifies a PPF &gt;0 with an agent range preference (ARP), then a distance adjustment is made, as follows: First, an agent ranking range (ARR) is calculated by subtracting the lowest agent ranking from the highest agent ranking. Then a distance adjustment is made for each agent within the ARP according to the formula:
 
ARR*PPF*(ARPmax+ClosestDistanceInARR−AgentDistance)/ARPmax,
 
and the final ranking is determined by subtracting the distance adjustment from the initial ranking for each agent. The agent with the lowest ranking is then assigned to service the call.
 
     The present invention provides a fast, automated selection of the best available agent to service an incoming request based on the customer&#39;s preferences. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above listed and further advantages of the invention may be better understood by referring to the following description in conjunction with the accompanying drawings, in which: 
         FIG. 1  depicts a call center system in accordance with a preferred embodiment of the present invention; 
         FIG. 2  depicts a flowchart for agent selection in accordance with a first embodiment of the present invention; 
         FIG. 3  depicts a first configuration in accordance with a first embodiment of the present invention; 
         FIG. 4  depicts a second configuration in accordance with a first embodiment of the present invention; 
         FIG. 5  depicts a third configuration in accordance with a first embodiment of the present invention; 
         FIG. 6  depicts a fourth configuration in accordance with a first embodiment of the present invention; and 
         FIG. 7  depicts a flowchart for agent selection in accordance with a preferred embodiment of the present invention; 
         FIG. 8  depicts a first exemplary configuration in accordance with a preferred embodiment of the present invention; 
         FIG. 9  depicts a second exemplary configuration in accordance with a preferred embodiment of the present invention; 
         FIG. 10  depicts a third exemplary configuration in accordance with a preferred embodiment of the present invention; and 
         FIG. 11  depicts a fourth exemplary configuration in accordance with a preferred embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the description that follows, like elements are marked throughout the specification and drawings with the same reference numerals, respectively. The drawing figures are not necessarily to scale and certain elements may be shown in generalized or schematic form in the interest of clarity and conciseness. Certain routine steps, in flow charts, normally included in the operation of the present invention have been omitted in the interest of conciseness. However, the steps which include methodology in accordance with the present invention are indicated in the charts. As is conventional, the letters Y and N designate “yes” and “no”, respectively. 
     Referring now to  FIG. 1 , the overall system  10  of the present invention includes a call center system  12 , which is comprised of a call center application module  14  and a database module  16  containing customer data, agent data, and the like. It should be noted that the functionality performed by the modules  14 , 16  can be performed by one of these modules or by another module (not shown) that may be a part of the call center system  12  or communicate with the system  12 . These modules  14 , 16  may include software, hardware, firmware, and/or a combination of software, hardware, and/or firmware. 
     A customer  18 , who may desire to purchase a product or a service, for example, communicates (for example, calls, emails, FAX, etc.) with a call center agent  20 - 24  via the call center system  12  over a communications network  26 . The network  26  may be a Public Switched Telephone Network (PSTN), an Internet Protocol Network, a wired network, a wireless network, or any combination of these networks. The call center system  12  of the present invention, uses the procedure of flowchart  28 , described herein, along with computer readable media to determine the best available agent from a set of geographically dispersed agents  20 - 24  for servicing a customer call, and routes the call accordingly to a particular agent. For purposes of this example only, the call is depicted as being routed to agent  24 . 
     Referring now to  FIG. 2 , a first agent selection procedure of the present invention is depicted. The method begins  30  by calculating  32  an initial ranking for each agent based on skill-set and/or other attributes, which include race, sex, etc. 
     The method proceeds by retrieving  34  two pieces of data associated with the caller. The first is a Proximity Preference Factor (PPF). This allows the caller to weight the importance of proximity in agent selection. A PPF of 100% turns even the worst agent into the best agent if they happen to be the closest. A PPF of 50% turns the worst agent who happens to be the closest to an agent better than 50% of the available agents. A PPF of 0% effectively disables distance factors in agent selection. 
     The second piece of retrieved data is an Agent Range Preference (ARP). This allows a customer to specify a distance range in which proximity is going to be given consideration. The ARP consists of a minimum and a maximum distance value. The minimum can be used to filter out agents who might be calling themselves. The maximum can be used to stop giving preference to agents outside a particular range. For example, an agent 2500 miles away is probably no more preferable to an agent 2600 miles away. 
     Once the data has been retrieved, a decision point  36  is reached. If the PPF=0, then agent distance is not a factor for this caller. Given this, the method proceeds to agent selection  52  based on the initial agent ranking. 
     However, if the PPF &gt;0, then distance is a factor for this caller. Given this, a distance between the caller and each agent is calculated  38 . This can be done with simple calculations that take advantage of static (address, NPA-NXX, zip code, etc.) and/or dynamic (cell site, GPS coordinates, etc.) data associated with the caller and the available agents. 
     Once complete, the method determines  40  if there is at least one agent whose distance falls within the ARP. If not, then the distance of the available agents is still not a factor, so the method proceeds to agent selection  52 . 
     If there is at least one agent that falls within the ARP, then the method proceeds to calculate an ARP Delta  42 . The ARP delta is the difference between the maximum and the minimum ARP distance values as shown by the formula below:
 
ARP Delta=ARP Maximum−ARP Minimum  (1)
 
     The ARP Delta is then used to calculate  44  an Adjusted Distance Scale (ADS), which is determined by subtracting the closest agent distance (CAD) within the ARP range from the ARP Delta as shown by the formula below:
 
ADS=ARP Delta−Closest Agent Distance  (2)
 
     The method continues by calculating  46  an Agent Ranking Range (ARR), which is determined by subtracting the lowest agent ranking from the highest agent ranking or setting the value of equal to 1 if the result of the subtraction is zero, as derived by the following formula:
 
ARR=Maximum(1,High Agent Ranking−Lowest Agent Ranking)  (3)
 
     Once the ARP Delta, ADS and ARR have been calculated ( 42 - 46 ), a ranking adjustment is calculated  48  for each agent whose distance falls within the ARP. The adjustment is calculated using the formula:
 
Adjustment=ARR*PPF*(ARPDelta−AgentDistance)/ADS  (4)
 
     This formula uses the Agent Ranking Range (ARR) and the callers Proximity Preference Factor (PPF) to scale the adjustment. The closest agent will receive the largest adjustment. The furthest agent will receive the smallest adjustment. 
     Once the adjustments have been calculated, the method proceeds to calculate  50  the final ranking of all the agents. This calculation is performed by subtracting any adjustment from the initial ranking determined previously  32 . 
     With the final rankings calculated, the selection ends  54  by selecting  52  the lowest ranking and therefore, the best agent. 
     In order to understand the benefits of the method, several applications of the invention in various caller/agent configurations will now be described. Referring now to  FIG. 3 , in a first exemplary configuration, the initial ranking  62  of the various agents  70 - 78  listed in the agent column  60  is determined as listed. Once determined, the PPF  80  and the ARP  82  submitted by the call center application are retrieved. In this case the PPF is 50% and the ARP is 100 to 500. Since the callers PPF (50%) is greater than zero, the distance  64  between the caller and each agent  70 - 78  is calculated. 
     Three agents,  72 - 76  are within the ARP range, so their rankings must be adjusted. To do this, the ARP Delta  86  is calculated first. As shown by the formula 1, the ARP delta is calculated by subtracting the agent range minimum from the agent range maximum. In this case, given that the maximum is 500 and the minimum is 100 ( FIG. 3-82 ), the ARP Delta  86  is 400. The method then proceeds to calculate the ADS  88 , which is calculated by formula 2. In this case, the closest agent within the range is Tom  87  at 120 miles, so the ADS  88 =400−120=280. 
     The procedure then proceeds using formula 4 to calculate the distance adjustment  66  for each agent  72 - 76  within the ARP  82  range. Note that agents  70 ,  78  outside the ARP range receive a 0 adjustment. The adjustment values  66  are then calculated according to formula 4. Tom  72  receives the biggest adjustment, 12, as he is closest to the caller. The figure is arrived at by the following calculation of equation 4:24*0.50*(400−120)/280=12. The ARR is 24 and 0.50 is the callers&#39; 50% PPF. The remaining within-range agent adjustments are calculated similarly, with Joe  74  receiving an adjustment of 7.3 and Mary  76  receiving an adjustment of 6.4. 
     The final rankings  68  for the agents are then calculated by subtracting the adjustment value  66  from the initial ranking  62 . The result in this exemplary configuration is that Mary  76  has the lowest final ranking, 11.6, and therefore is chosen as the best agent. Note that Mary is not the closest agent within the agent range preference, but the adjustment to her already low initial ranking of 18 moved her ahead of Jim, the agent with the best initial ranking. 
     Referring now to  FIG. 4 , in a second exemplary configuration, the caller&#39;s PPF  110  is now set to 100, indicating that proximity is of utmost importance to the caller. In this case, since the PPF=100, instead of 50, the PPF factor in formula 4 equals 1, instead of 0.5. Here, the ARP  112  is 100-500 so that the ARP delta  116  is 400, minimum distance within the ARP  117  is 120, the ARR  114  is 24, and the ADS  118  is 280. Again, Jim  100  and Frank  108  are outside the ARP  112  range and receive zero adjustments  96 . Adjustments  96  of 24 for Tom  102 , 14.6 for Joe  104 , and −4.3 for Mary  106  are calculated. The resulting final rankings  98  depict that Joe has the lowest ranking and is therefore chosen as the best agent. Although Mary has a better initial ranking than Joe, Joe is closer than Mary and that is more important to the caller in this exemplary configuration. 
     Referring now to  FIG. 5 , in a third exemplary configuration, the caller&#39;s PPF  140  is again set to 50% and the ARP  142  is 100-400. However, the ranking  122  for the Agents  120  shows Jim  130  at 20.3, Tom  132  at 19.5, Joe  134  at 20.1, Mary  136  at 19.9, and Frank  138  at 20.4. Since the distances  124  for Jim  130  and Frank  138  are 20 and 450, respectively, these are outside the ARP  142  range of &gt;100 and &lt;400, so the adjustment for each of these two agents is set at zero. Furthermore, the ARP delta  146  is 300, the minimum distance within ARP  147  is 120, and ADS  148  is 180. In this case then, using formula 3 (ARR=Maximum (1, High Agent Ranking−Lowest Agent Ranking), the ARR  144  is calculated to be 1 as a result of the agent rankings  122  being tightly packed. This results in an adjustment  126  of 0.50 for Tom  132 , 0.19 for Joe  134 , and 0.14 for Mary  136 . The resulting final rankings  128  depict that Tom has the lowest ranking and is therefore chosen as the best agent. In this example, Tom had both the best initial ranking and the best adjusted ranking. 
     Referring now to  FIG. 6 , a fourth exemplary configuration is shown, which has the same initial ranking  122  and distance  124  for the agents  130 - 138  as for the first exemplary configuration discussed in  FIG. 3 . However, here the caller&#39;s PPF  170  is now set to 90%, indicating that proximity is of fairly high importance to the caller, the ARP  172  is 0-600, the ARR  174  is 24, the ARP delta  176  is 600, the minimum distance in ARP  177  is 20, and the ADS  178  is 580. Although this example is much like the first exemplary configuration, now all five agents  160 - 168  are within the ARP  172  range and therefore need to be adjusted. In this case, since the PPF=90%, instead of 50%, the PPF factor in formula 4 equals 0.9, instead of 0.5. This results in adjustments  156  of 21.6 for Jim  160 , 17.9 for Tom  162 , 13.8 for Joe  164 , 13.0 for Mary  166 , and 3.7 for Frank  168 . The resulting final rankings  158  depict that Jim  160  with and a final ranking  158  of −6.6 has the lowest ranking after adjustment and is therefore chosen as the best agent. 
       FIG. 7  shows a simplified preferred embodiment of the agent selection procedure of the present invention. The method uses the procedure illustrated in flowchart  230 . Here, the method begins  232  by calculating  234  an initial ranking for each agent based on skill-set and/or other attributes, which include race, sex, etc. 
     The method proceeds by retrieving  236  two pieces of data associated with the caller. The first is a Proximity Preference Factor (PPF). This allows the caller to weight the importance of proximity in agent selection. A PPF of 100% turns even the worst agent into the best agent if they happen to be the closest. A PPF of 50% turns the worst agent who happens to be the closest to an agent better than 50% of the available agents. A PPF of 0% effectively disables distance factors in agent selection. 
     The second piece of retrieved data is an Agent Range Preference (ARP). This allows a customer to specify a distance range in which proximity is going to be given consideration. The ARP consists of a minimum and a maximum distance value. The minimum can be used to filter out agents who might be calling themselves. The maximum can be used to stop giving preference to agents outside a particular range. For example, an agent 2500 miles away is probably no more preferable to an agent 2600 miles away. 
     Once the data has been retrieved, a decision point  238  is reached. If the PPF=0, then agent distance is not a factor for this caller. Given this, the method proceeds to agent selection  248  based on the initial agent ranking. 
     However, if the PPF &gt;0, then distance is a factor for this caller. Given this, a distance between the caller and each agent is calculated  240 . This can be done with simple calculations that take advantage of static (address, NPA-NXX, zip code, etc.) and/or dynamic (cell site, GPS coordinates, etc.) data associated with the caller and the available agents. 
     Once complete, the method determines  242  if there is at least one agent whose distance falls within the ARP. If not, then the distance of the available agents is still not a factor, so the method proceeds to agent selection  248 . 
     However, if at least one agent falls within the ARP, then the method proceeds to calculate 243 an Agent Ranking Range using the formula;
 
ARR=Highest Agent Ranking−Lowest Agent Ranking  (5)
 
     Next, this Agent Ranking Range is used to calculate a distance adjustment  244  for each agent within the ARP, using the formula:
 
Adjustment=ARR*PPF*(ARPmax+ClosestDistanceInARR−AgentDistance)/ARPmax,  (6).
 
     This formula uses the Agent Ranking Range (ARR) and the callers Proximity Preference Factor (PPF) to scale the adjustment. The closest agent will receive the largest adjustment. The furthest agent will receive the smallest adjustment. 
     Once the adjustments have been calculated, the method proceeds to calculate 246 the final ranking of all the agents. This calculation is performed by subtracting each adjustment from the initial ranking determined previously  234 . 
     With the final rankings calculated, the selection ends  250  by selecting  248  the lowest ranking and therefore, the best agent. 
     Again, in order to understand the benefits of the method for this preferred embodiment of the invention, several applications of the invention in various caller/agent configurations will now be described. Referring now to  FIG. 8 , in a first exemplary configuration, the initial ranking  252  of the various agents  260 - 268  listed in the agent column  250  is determined as listed. Once determined, the PPF  270  and the ARP  272  submitted by the call center application are retrieved. In this case the PPF is 50% and the ARP is 100 to 500. Since the callers PPF (50%) is greater than zero, the distance  254  between the caller and each agent  260 - 268  is calculated. Finally, the ARR  274  is calculated as the Highest Ranked Agent−Lowest Ranked Agent. In this case, since Tom  262  is the highest ranked agent with a ranking of 39 and Jim  260  is the lowest ranked agent with a ranking of 15, the ARR=39−15=24 (5). 
     Since three agents,  262 - 266  are within the ARP range, their rankings must be adjusted using formula (6), as follows to calculate the distance adjustment  256  for each agent  262 - 266  within the ARP  272  range. Note that agents  260 ,  268  outside the ARP range receive a 0 adjustment. The adjustment values  256  are then calculated according to formula (6). Tom  262  receives the biggest adjustment, 12, as he is closest to the caller. This figure is arrived at by the following calculation of formula (6): Adjustment=24*0.50*(500+120−120)/500=12. The ARR  274  is 24 and the PPF  270  is 0.50 or 50%. The remaining within-range agent adjustments are calculated similarly, with Joe  264  receiving an adjustment of 9.4 and Mary  266  receiving an adjustment of 8.9. 
     The final rankings  258  for the agents are then calculated by subtracting the adjustment  256  values from the initial ranking  252  values. The resulting final rankings 27.0 for Tom  262 , 10.6 for Joe  264 , and 9.1 for Mary  266  depict that Mary  266  has the lowest final ranking of 9.1 and is therefore chosen as the best agent. 
     Referring now to  FIG. 9 , in a second exemplary configuration, the caller&#39;s PPF  300  is now set to 100%, indicating that proximity is of utmost importance to the caller. In this case, since the PPF=100%, instead of 50%, the PPF factor in formula (6) equals 1.0, instead of 0.5. Here, the ARP  302  is 100-500 and the ARR  304  is 24. Again, Jim  290  and Frank  298  are outside the ARP  310  range and receive zero adjustments  286 . Adjustments  286  of 24 for Tom  292 , 18.7 for Joe  294 , and 8.2 for Mary  296  are calculated using equation (6). The resulting final rankings  288  of 15.0 for Tom  292 , 1.3 for Joe  294 , and 9.8 for Mary  296  depict that Joe  294  has the lowest final ranking of 1.3 and is therefore chosen as the best agent. Although Mary  296  has a better initial ranking than Joe  294 , Joe is closer than Mary and that is more important to the caller in this exemplary configuration. 
     Referring now to  FIG. 10 , in a third exemplary configuration, the caller&#39;s PPF  330  is again set to 50% and the ARP  332  is 100-400. However, the initial ranking  312  for the Agents  310  shows Jim  320  at 20.3, Tom  322  at 19.5, Joe  324  at 20.1, Mary  326  at 19.9, and Frank  328  at 20.4. The ARR  334  is 0.9 determined as the difference between the highest and lowest ranking of 20.4 and 19.5. Since the distances  314  for Jim  320  and Frank  328  are 20 and 450, respectively, and are outside the ARP  332  range of 100 and 400, the adjustment for each of these two agents is set to zero. Equation (6) is then used to calculate the adjustments for the remaining three agents, which results in an adjustment  316  of 0.45 for Tom  322 , 0.33 for Joe  324 , and 0.31 for Mary  326 . The resulting final rankings  318  of 19.05 for Tom  322 , 19.8 for Joe  324 , and 19.6 for Mary  326  depict that Tom  322  has the lowest ranking and is therefore chosen as the best agent. In this example, Tom had both the best initial ranking and the best adjusted ranking. 
     Referring now to  FIG. 11 , a fourth exemplary configuration is shown, which has the same initial ranking  342  and distance  344  for the agents  350 - 358  as for the first exemplary configuration discussed in  FIG. 8 . However, here the caller&#39;s PPF  360  is now set to 90%, indicating that proximity is of fairly high importance to the caller, the ARP  362  is 0-600, and the ARR  364  is 24. Although this example is much like the first exemplary configuration of  FIG. 9 , now all five agents  350 - 358  are within the ARP  362  range and therefore need to be adjusted. In this case, since the PPF=90%, instead of 50%, the PPF factor in formula (6) is set to 0.9. This results in adjustments  346  of 21.6 for Jim  350 , 18.0 for Tom  352 , 14.0 for Joe  354 , 13.3 for Mary  356 , and 4.3 for Frank  358 . The resulting final rankings  348  of −6.60 for Jim  350 , 21.0 for Tom  352 , 6.0 for Joe  354 , 4.7 for Mary  356 , and 15.7 for Frank  358  depict that Jim  350  with and a final ranking  348  of −6.6 has the lowest ranking after adjustment and is therefore chosen as the best agent. 
     Although embodiments of a system for geographic agent routing have been described in detail herein, it will be appreciated that the present invention may provide applicable inventive concepts that can be embodied in a wide variety of specific contexts. For example, while the preferred embodiment of the invention has principally referenced a system for optimally routing agents it should be understood that the system may also be utilized for alternative applications, such as selecting particular computers, security systems, imaging systems, and the like. Also, a lesser or greater number of modules or components may be utilized to make the selection of the best available agent. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention, and do not delimit the scope of the invention. Those skilled in the art will recognize that various substitutions and modifications and a lesser or greater number of modules or components may be utilized in the invention without departing from the scope and spirit of the appended claims.

Technology Classification (CPC): 7