Source: http://www.google.com/patents/US20020188488?dq=5,884,271
Timestamp: 2015-08-01 09:00:02
Document Index: 660555251

Matched Legal Cases: ['art 200', 'art 200', 'art 200', 'art 200', 'art 200', 'art 200']

Patent US20020188488 - Methods and systems for simulating business operations - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA method for modeling the operations of a business includes prompting a user to input data relating to at least one of volume, assignments, cash application, adjustments, collections, client services, tolerance, labor, file/mail/imaging, and capacity, determining from the data whether the staff of the...http://www.google.com/patents/US20020188488?utm_source=gb-gplus-sharePatent US20020188488 - Methods and systems for simulating business operationsAdvanced Patent SearchPublication numberUS20020188488 A1Publication typeApplicationApplication numberUS 09/845,397Publication dateDec 12, 2002Filing dateApr 30, 2001Priority dateApr 30, 2001Publication number09845397, 845397, US 2002/0188488 A1, US 2002/188488 A1, US 20020188488 A1, US 20020188488A1, US 2002188488 A1, US 2002188488A1, US-A1-20020188488, US-A1-2002188488, US2002/0188488A1, US2002/188488A1, US20020188488 A1, US20020188488A1, US2002188488 A1, US2002188488A1InventorsBurl HinkleOriginal AssigneeHinkle Burl ShannonExport CitationBiBTeX, EndNote, RefManPatent Citations (5), Referenced by (37), Classifications (6), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetMethods and systems for simulating business operations
DETAILED DESCRIPTION OF THE INVENTION [0020] Exemplary embodiments of systems and methods for modeling the operations of a financial services business are described below in detail. The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independently and separately from other components and processes described herein. Further, each component and process can be used in combination with other components and processes. [0021] The operations modeling system for forecasting business headcount requirements, cost, pricing, and capacity described herein is configured with a plurality of spreadsheets and reports to integrate a forecast of invoice processing, funding, cash application, cash adjustments, and collections involving the financial services business, a calculation of how many full-time employees (FTEs) are required to discharge the tasks relating to the forecast, and a comparison of the forecasted number of FTEs with the number of FTEs presently available. Based on process metrics and data including multiple independent variables, the system provides a forecast for multiple dependent variables including daily, monthly and yearly headcount requirements, process capacities, aspects critical to quality, and costs for multiple, separate process scenarios. The forecasting enables a detailed analysis of the impact of business decisions and allows lead-time to correct process inadequacies before customers are affected. [0022]FIG. 1 is a block diagram of a system 10 in accordance with one embodiment of the present invention. System 10 includes a server sub-system 12, sometimes referred to herein as server 12, and a plurality of user devices 14 connected to server 12. In one embodiment, devices 14 are computers including a web browser, and server 12 is accessible to devices 14 via a network such as an intranet or the Internet. In an alternative embodiment, devices 14 are servers for a network of customer devices. [0023] Devices 14 are interconnected to the network, such as a local area network (LAN) or a wide area network (WAN), through many interfaces including dial-in-connections, cable modems and high-speed ISDN lines. Alternatively, devices 14 are any device capable of interconnecting to a network including a network-based phone or other network-based connectable equipment. Server 12 includes a database server 16 connected to a centralized database 18 containing information. In one embodiment, centralized database 18 is stored on database server 16 and can be accessed by potential users at one of user devices 14 by logging onto server sub-system 12 through one of user devices 14. In an alternative embodiment centralized database 18 is stored remotely from server 12. [0024]FIG. 2 is an expanded version block diagram of an exemplary embodiment of a server architecture of a system 22. Components in system 22 identical to components in system 10 (shown in FIG. 1), are identified in FIG. 2 using the same reference numerals as used in FIG. 1. Server sub-system 12 includes database server 16, an application server 24, a web server 26, a fax server 28, a directory server 30, and a mail server 32. A disk storage unit 34 is coupled to database server 16 and directory server 30. Servers 16, 24, 26, 28, 30, and 32 are coupled in a local area network (LAN) 36. In addition, a system administrator workstation 38, a user workstation 40, and a supervisor workstation 42 are coupled to LAN 36. Alternatively, workstations 38, 40, and 42 are coupled to LAN 36 via an Internet link or are connected through an intranet. [0025] Each workstation 38, 40, and 42 is a personal computer having a web browser. Although the functions performed at the workstations typically are illustrated as being performed at respective workstations 38, 40, and 42, such functions can be performed at one of many personal computers coupled to LAN 36. Workstations 38, 40, and 42 are illustrated as being associated with separate functions only to facilitate an understanding of the different types of functions that can be performed by individuals having access to LAN 36. [0026] In another embodiment, server sub-system 12 is configured to be communicatively coupled to various individuals or employees 44 and to third parties, e.g., users, 46 via an ISP Internet connection 48. The communication in the exemplary embodiment is illustrated as being performed via the Internet, however, any other wide area network (WAN) type communication can be used in other embodiments, i.e., the systems and processes are not limited to being practiced via the Internet. In addition, and rather than a WAN 50, local area network 36 could be used in place of WAN 50. [0027] In the exemplary embodiment, any employee 44 or user 46 having a workstation 52 can access server sub-system 12. One of user devices 14 includes a workstation 54 located at a remote location. Workstations 54 are personal computers having a web browser. Also, workstations 54 are configured to communicate with server sub-system 12. Furthermore, fax server 28 communicates with employees 44 and users 46 located outside the business entity and any of the remotely located user systems, including a user system 56 via a telephone link. Fax server 28 is configured to communicate with other workstations 38, 40, and 42 as well. [0028]FIG. 3 is a flow diagram 70 for a network-based method for simulating the operations of a financial services business. System 10 (shown in FIG. 1) receives 72 information from a user comprising independent variables describing the business. In one embodiment, the user inputs the information into a device (such as device 14 shown in FIG. 1) that transmits the information to a server (such as server 12 shown in FIG. 1). The information is received from the user via a graphical user interface as will be described in greater detail below. [0029] Example independent variables include, but are not limited to, net time worked, productive hours per day, percent of time not spent on primary task, time required per task, average tasks per dollar-based monthly volume (such as $1 million(1 MM)), daily volume, tolerance or critical-to-quality, work days allowable missed tolerance, ratio of assistants to primary task “doers”, and task input related to other part of process (percent errors). [0030] From the independent variables input into the system, server 12 calculates 74 dependent variables relating to the operations of the financial services business. Examples of such dependent variables include daily capacity, number of full time employees (FTEs) (including employees in assignments, cash applications, adjustments, collections, client services and file/mail/imaging), number of new task items, and daily capacity. System 10 then retrieves 76 pre-stored information relating to operations of the financial services business, and in particular to the resources available to carry out the operations. System 10 compares 78 the dependent variables calculated with pre-stored information concerning the resources available to carry out the operations of the financial services business. System 10 decides 80 whether further resources, for example, more client services FTEs, are required to carry out the operations of the financial services business [0031] In one exemplary embodiment, the application as described in flow diagram 70 is developed as an application under Microsoft Excel™. Microsoft Excel is a trademark of Microsoft Corporation, Redmond, Wash. In addition, the application incorporates a revision log for incorporating a listing of all changes and revisions to the spreadsheets by date. In the exemplary embodiment, the application is divided into integrated spreadsheets and reports including, but not limited to, a Base Summary, a Base Model, a Current Capacity, a Capacity Summary, and a Capacity Model. In an alternative embodiment, various customizable charts are provided for graphical analysis. [0032] The embodiments described herein are in relationship with the financial services industry. The system and methods described, however, are adaptable to business environments in which specified work tasks recur and the time necessary to perform them can be determined. The system and methods indicate whether changing workload or staffing levels will create backlogs, the cost of those backlogs, and provides a hiring trigger to avoid creating a backlog. Exceeding the hiring trigger is an indicator that it is desirable to hire additional personnel. [0033] One embodiment of the operations modeling program determines the capacity to carry out the tasks at hand and compares such capacity with the resources available to carry out the tasks. If the calculated capacity substantially exceeds the resources available, the program indicates that additional FTEs need to be hired. The operations modeling program includes capacity calculations to determine a number of employees needed to adequately staff invoice processing, cash applications, collections, cash adjustments, including credit memos and other, client services. Further, the modeling program is configured to determine costs per FTE. [0034] Capacities are calculated from the equation A � B � C � 60   sec  /  min � 60   min  /  hr � ( 1 + D ) E = capacity/day [0035] where, A=a number of FTEs, B=net time worked (hours), C=a productive number of hours per day, D=a percentage of possible overtime, and E=average time to complete tasks (seconds). [0036] If the percent of possible overtime required is zero, then the FTE hiring trigger is not exceeded (i.e., is less than unity). A non-zero percent of possible overtime value indicates that the hiring trigger has been exceeded (i.e., is greater than unity) and additional personnel should be hired. Invoice Processing Capacity (assignments) [0037] For calculation of invoice processing capacity A=number of assignments FTEs, and E=average invoice assignment time in seconds. The assignments FTE hiring trigger is assumed to be less than a number of work days that are allowable with a missed tolerance, and it is further assumed that [0038] (a number of backlogged invoices is greater than allowable assignments backlog invoices per day) OR (a number of backlogged invoices is less than (0.8�allowable assignments backlog invoices day)). [0039] The number of backlogged invoices is defined as [0040] a number of new manual invoices+a number of backlogged invoices−a number of invoices processed, [0041] where the number of new manual invoices is defined as [0042] (daily volume�average manual invoices per 1 MM monthly volume)+(E-commerce business daily volume�E-commerce average manual invoices per 1 MM monthly volume). [0043] In one exemplary embodiment, for invoice processing capacity, a percent net time worked is 90 percent, productive work hours/day is seven, average invoice assignment time is eight seconds, work days allowable missed tolerance is 5, an average number of manual invoices per 1 MM monthly volume is 368 and E-commerce average manual invoices per 1 MM monthly volume is zero. Cash Applications Capacity [0044] For calculation of cash application capacity A=number of cash applications FTEs, and E=average record application time in seconds. The cash application FTE hiring trigger is assumed to be less than work days allowable missed tolerance, and it is further assumed that
[0085] However, if the funding error rate is less than or equal to baseline funding error rate (total fundings), then a number of client services FTEs is defined as a number of client services FTEs−1. [0086] To implement the processes and capacity calculations described above, many variations of particular spreadsheets and reports can be utilized. The following description refers to one set of spreadsheets and reports that can be used to prompt a user to input information pertaining to independent variables for the base model and to display an output of dependent variables from the operations modeling system for the base model. Further, a user is able to determine, by varying input variables, for example, capacity, in the form of a percentage annual increase in volume, to determine when a capacity of the existing staff of FTEs will be exceeded, resulting in a need to add one or more FTEs. [0087] Of course many variations of such spreadsheets are possible. Referring now again specifically to the drawings, FIGS. 4 and 5 are spreadsheets containing a plurality of independent variables, used as inputs to an operations simulation. FIGS. 6 through 11 are reports that display an output of the dependent variables which result from the simulation. [0088]FIG. 4 is a spreadsheet 100 and FIG. 5 is a continuation spreadsheet 102 according to one embodiment of the present invention. The spread sheets are stored within system 10 (shown in FIG. 1) and contain data entry fields for independent variables used in modeling the operations of a financial services business. Examples of independent variables shown in FIG. 4 include those relating to volume, assignments, cash application, adjustments, collections, client services, tolerance, labor, file/mail/imaging, and capacity. [0089] More specifically, volume data input to system 10 include monthly volume, the months of the year (January, February, March, April, May, June, July, August, September, October, November, December), the annual increase in volume for a year, daily volume ratios/monthly volume conversion constant, and the volume for a given day of the month. In an exemplary embodiment, the given days of the month are at least one of a 1st Monday, a 1st Tuesday, a 1st Wednesday, a 1st Thursday, a 1st Friday, a 2nd Monday, a 2nd Tuesday, a 2nd Wednesday, a 2nd Thursday, a 2nd Friday, a 3rd Monday, a 3rd Tuesday, a 3rd Wednesday, a 3rd Thursday, a 3rd Friday, a 4th Monday, a 4th Tuesday, a 4th Wednesday, a 4th Thursday, a 4th Friday, a 5th Monday, and a 5th Tuesday. [0090] Assignments data include a percentage of work/time not on invoice processing, an average number of manual invoices per 1 MM volume, an average hourly wage—assignments, and an average invoice assignment time in seconds. [0091] Cash application data include average hourly wage—cash application, average payment application time, percent cash application work/time not on original applications, cash application error acceleration factor, and average number of manual payments per dollar volume. [0092] Adjustments data include average number of invoices per payment, percent adjustments work actually clearing an item, percent adjustments created by customer accounts receivable item error, percent adjustments from other error, percent adjustments created by client accounts receivable item error, percent accounts receivable items with no error, percent adjustments created by client/customer mix accounts receivable item error, number of credit memos per dollar monthly volume, average cm value, average cm action time, average hourly wage—adjustments, average adjustment action time, and average adjustment item value. [0093] Collections data include average hourly wage—collectors, average hourly wage—collections assistant, ratio of assistants to collectors, maximum number of collectors, average collections call time, percent collections work/time not outbound calls, percent volume becoming overdue, average past due sum per past due customer, percent past due percent becoming bad debt, percent overdue paying without collections activity, number of calls per month required to clear customer, and maximum number of collectors added in one hiring. [0094] Client services data include average hourly wage—client services, baseline funding error rate (percent total fundings), percent client services worktime not funding, client services accuracy coefficient, and number of manual funding requests per dollar monthly volume. [0095] Tolerance data include allowable assignments backlog (invoices/day), allowable cash application backlog (records/day), allowable adjustments backlog (percent monthly volume), and allowable collections percent volume past due. [0096] Labor data include number of productive work-hours per day, percent total available hours not worked, number of workdays allowable missed tolerance, percent overtime possible. [0097] File/mail/imaging data include average hourly wage—file/mail/imaging department and number of file/mail/imaging FTEs required per dollar monthly volume. [0098] Capacity data include maximum allowable assignments FTEs, maximum allowable cash application FTEs, maximum allowable adjustments FTEs, maximum allowable collections FTEs, maximum allowable client services FTEs, and maximum allowable file/mail/imaging FTEs. [0099] Spread sheet 100 further includes a scenario run button 102, which a user selects in order to generate a scenario summary report as displayed in FIGS. 6 through 11. [0100]FIG. 6 is a scenario summary report 104 which includes dependent variable outputs resulting from independent variable inputs into the base model. Dependent variables displayed are calculated from the independent variables introduced as input to system 10 in FIG. 4 and FIG. 5. FIGS. 7 through 11 are continuations of scenario summary report 104. [0101] Scenario summary report 104 includes a column each for year, month, and work day of the month. For each work day there is, a traditional business daily volume, e-commerce business daily volume, total business daily volume, number of assignments FTEs, and assignments FTEs cost. Further, and as displayed in report 104, a maximum number of assignments FTEs is given along with a determination of the number of assignments FTEs over the maximum allowable number of assignments FTEs needed to complete the volume of work in the scenario. Report 104 indicates that in the scenario presented, there is a maximum of five assignments employees. The maximum number may be set based on any number of conditions, for example, budget and office space. FIGS. 7 through 11 continue presenting the analysis of the base model in report form. [0102]FIG. 7 is an exemplary embodiment of a continuation of report 104 displaying various dependent variables in a column format FIG. 7 includes a column for each of an assignments FTEs hiring trigger, a number of new manual invoices, a number of backlog invoices, a daily invoice processing capacity (number of manual invoices), a number of cash application FTEs, a cash application FTEs cost, a cash application FTEs hiring trigger, and a number of new manual payments. Further, and as shown in FIG. 7, a maximum number of cash applications FTEs is given along with a determination of the number of cash applications FTEs over the maximum allowable number of cash applications FTEs needed to complete the volume of work in the scenario. In the scenario presented, there are currently seven cash applications FTEs, and capacity for twenty (20) cash applications FTEs, therefore a number of cash applications FTEs over the maximum allowable is negative thirteen (−13). [0103]FIG. 8 is an exemplary embodiment of a continuation of report 104 displaying various dependent variables in a column format FIG. 8 includes a column for each of a number of backlogged payments, a manual payments application capacity in number of payments, a number of adjustments FTEs, adjustments FTE cost, an adjustments FTEs hiring trigger, a number of credit memos created, a number of other adjustment items created, and a dollar value of adjustment items created. Further, and as shown in FIG. 8, a maximum number of adjustments FTEs is given along with a determination of the number of adjustments FTEs over the maximum allowable number of adjustments FTEs needed to complete the volume of work in the scenario. In the scenario presented, there are currently nine adjustments FTEs, and capacity for fifteen (15) adjustments FTEs, therefore a number of adjustments FTEs over the maximum allowable is negative six (−6). [0104]FIG. 9 is an exemplary embodiment of a continuation of report 104 displaying various dependent variables in a column format FIG. 9 includes a column for each of an amount of credit memos backlog, an other adjustments backlog, a total dollar value the adjustments backlog, a percent volume adjustments backlog, a number of credit memos completed, a number of other adjustments completed, a total dollar value of adjustments completed, a number of collections FTEs, and a number of collections assistants. Further, and as shown in FIG. 9, a maximum number of collections FTEs is given along with a determination of the number of collections FTEs over the maximum allowable number of collections FTEs needed to complete the volume of work in the scenario. In the scenario presented, there are currently fifteen (15) collections FTEs, and capacity for twenty-six (26) collections FTEs, therefore a number of collections FTEs over the maximum allowable is negative eleven (−11). [0105]FIG. 10 is an exemplary embodiment of a continuation of report 104 displaying various dependent variables in a column format FIG. 10 includes a column for each of a total collections FTEs cost, a collections GECIS FTE cost, a collections H.P. FTE cost, a number of customer is becoming past due, a total customers past due, a dollar value of past due accounts, and a percent monthly volume that is past due. [0106]FIG. 11 is an exemplary embodiment of a continuation of report 104 displaying various dependent variables in a column format FIG. 11 includes a column for each of customers “cleared”, a percent volume written off, a number of client services FTEs, a client services FTEs cost, a client services FTEs hiring trigger, a number of manual funding requests, a finding error rate, a number of file/mail/imaging FTEs, a file/mail/imaging FTE cost, and a total FTE cost. Further, and as shown in FIG. 11, a maximum number of client services FTEs is given along with a determination of the number of client services FTEs over the maximum allowable number of client services FTEs needed to complete the volume of work in the scenario. In the scenario presented, there are currently six (6) client services FTEs, and capacity for five (5) collections FTEs, therefore one more client services FTEs is required to complete the work volume than the maximum allowable number of client services FTEs. [0107] Also shown in FIG. 11 a maximum number of file/mail/imaging FTEs is given along with a determination of the number of file/mail/imaging FTEs over the maximum allowable number of file/mail/imaging FTEs needed to complete the volume of work in the scenario. In the scenario presented, there are currently twenty-eight (28) collections FTEs, and capacity for sixty-four (64) file/mail/imaging FTEs, therefore a number of file/mail/imaging FTEs over the maximum allowable is negative thirty-six (−36). [0108]FIG. 12 is an exemplary embodiment of a chart 200, displayed in a graphical format, which is a graph of problems against scenarios intended to solve the problems. For example, one problem identified in the financial collections business is manual invoices. One independent variable which is related to manual invoices is a number of manual invoices per 1 MM monthly volume, which is an assignments independent variable and shown in FIG. 4. One scenario, labeled in FIG. 12 as scenario 4, decreases the number of manual invoices per 1 MM monthly volume by 25 percent. By running such a scenario, a user can determine a ripple effect on business operations and capacity which results in a scenario summary report, similar to report 104, shown in FIGS. 6 through 11. As shown in chart 200, a number of scenarios can be created where one or more independent variables are adjusted, and scenarios are run to determine an affect on business operations and capacity. Although chart 200 includes six scenarios, system 10 is not so limited. [0109] Also as shown in chart 200 a scenario is a response to a business operations problem, separate solutions are employed within a scenario to determine effect on the business related problems. For example, there is shown in chart 200, two separate solutions to a scenario labeled as scenario 6. A first solution 202 adjust independent variables average collections call time, percent accounts receivable items with no error, average invoice assignment time, average cash application time, and percent client services time spent on other than funding. A second solution 204 to scenario six adjusts the independent variables percent time spent on other than outbound calls, average adjustments (credit memos) action time, average adjustments (other) action time, and percent client services time spent on other than funding. [0110]FIGS. 13 and 14 are a spreadsheet 220 where multiple independent variables have been adjusted in order to create a new scenario. Spreadsheet 220 includes the independent variables as in spreadsheet 100 (shown in FIGS. 4 and 5). The independent variables adjusted in spreadsheet 220 are not necessarily the same independent variables described as being adjusted for generating scenarios as described in chart 200 (shown in FIG. 12). [0111]FIGS. 15 through 42 are a scenario summary report 250, which shows results of six scenarios run against a baseline scenario (shown in FIGS. 6 through 11). Report 250 indicates capacity and requirements over a period of years based upon the six scenarios. Although six scenarios are summarized in report 250, system 10 should not be construed as being so limited. [0112] For the baseline scenario and the six scenarios run there is included monthly and annual totals for traditional business volume, E-commerce business volume, total business volume, assignments FTE cost, cash applications FTE cost, adjustments FTE cost, total collections FTE cost, client services FTE cost, a funding error rate, a file/mail/imaging FTE cost, and a total FTE cost. [0113] Further included are monthly and daily averages for a number of assignments FTEs, an assignments FTE hiring trigger, a number of new manual invoices, a number of backlogged invoices, a daily manual invoice processing capacity, a number of cash applications FTEs, a cash applications FTE hiring trigger, a number of new manual payments, a number of backlogged payments, a number of manual payments application capacity, a number of adjustments FTEs, an adjustments FTE hiring trigger, a number of credit memos created, a number of other adjustments items created, an amount for adjustments items created, an amount of credit memos backlog, an amount of other adjustments backlog, a total adjustments backlog, a percent volume adjustments backlog, a number of credit memos completed, a number of other adjustments completed, a total amount of adjustments completed, a number of collections FTEs, a number of collections assistants, a collections FTE hiring trigger, a number of customer becoming past due, a total number of customers past due, a past due amount, a percent of monthly volume past due, a number of customers “cleared”, a percent of volume written-off, a number of client services FTEs, a client services FTE hiring trigger, a number of manual funding requests, and a number of file/mail/imaging FTEs. [0114] Also generated for each scenario are an average FTE cost per invoice manually assigned, an average FTE cost per manual payment, an average FTE cost per manual adjustment, an average FTE cost per past due customer, an average FTE cost per manual funding, and a percentage operations FTE cost per basis point of volume. [0115] The above described systems and methods for simulating business operations allows a user to objectively forecast a business's needs using data and rigor, providing a planning tool that is not based upon past trends and educated guesses. Such a system further allows for accurate and systematic planning for future growth, and thus avoiding consequences of incorrect planning such as bottlenecks, client dissatisfaction and potential client terminations. The system is able to identify problems through data collection and metrics analysis and then identify possible solutions through a determination of capacity, prioritization and benefits. Use of the system and methods described herein further allows user to determine capacity, for example, by increasing daily volume, until the number of employees needed to handle the volume increases beyond employee resources available, allowing a determination of future staffing needs. [0116] While the invention has been described in terms of various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the claims. Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS2151733May 4, 1936Mar 28, 1939American Box Board CoContainerCH283612A * Title not availableFR1392029A * Title not availableFR2166276A1 * Title not availableGB533718A Title not available* Cited by examinerReferenced byCiting PatentFiling datePublication dateApplicantTitleUS7272817Apr 15, 2004Sep 18, 2007Bank Of America CorporationMethod and apparatus for modeling a business process to facilitate evaluation of driving metricsUS7493277 *Aug 21, 2002Feb 17, 2009Mydecide Inc.Business opportunity analytics with dependenceUS7774294Aug 10, 2010Veveo, Inc.Methods and systems for selecting and presenting content based on learned periodicity of user content selectionUS7779011Dec 20, 2005Aug 17, 2010Veveo, Inc.Method and system for dynamically processing ambiguous, reduced text search queries and highlighting results thereofUS7788266Sep 27, 2005Aug 31, 2010Veveo, Inc.Method and system for processing ambiguous, multi-term search queriesUS7792815Sep 7, 2010Veveo, Inc.Methods and systems for selecting and presenting content based on context sensitive user preferencesUS7797185Jul 26, 2006Sep 14, 2010Mydecide Inc.Value driven integrated build-to-buy decision analysis system and methodUS7835998Mar 6, 2007Nov 16, 2010Veveo, Inc.Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second systemUS7840434 *Oct 29, 2002Nov 23, 2010At&T Intellectual Property I, L. P.Methods and systems for assigning multiple tasksUS7885904Feb 8, 2011Veveo, Inc.Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second systemUS7937394Aug 2, 2010May 3, 2011Veveo, Inc.Method and system for dynamically processing ambiguous, reduced text search queries and highlighting results thereofUS7949627Jul 26, 2010May 24, 2011Veveo, Inc.Methods and systems for selecting and presenting content based on learned periodicity of user content selectionUS8019638Aug 21, 2002Sep 13, 2011DecisionStreet, Inc.Dynamic construction of business analyticsUS8037071Apr 14, 2009Oct 11, 2011Veveo, Inc.Methods and systems for dynamically rearranging search results into hierarchically organized concept clustersUS8073848Jun 7, 2010Dec 6, 2011Veveo, Inc.Methods and systems for selecting and presenting content based on user preference information extracted from an aggregate preference signatureUS8112454May 5, 2009Feb 7, 2012Veveo, Inc.Methods and systems for ordering content items according to learned user preferencesUS8155996 *Mar 6, 2008Apr 10, 2012Sprint Communications Company L.P.System and method for customer care complexity modelUS8156113Jul 27, 2010Apr 10, 2012Veveo, Inc.Methods and systems for selecting and presenting content based on dynamically identifying microgenres associated with the contentUS8380726Mar 6, 2007Feb 19, 2013Veveo, Inc.Methods and systems for selecting and presenting content based on a comparison of preference signatures from multiple usersUS8401891 *Jul 15, 2005Mar 19, 2013Bank Of America CorporationEvaluation of a business case based on the cost of poor process opportunitiesUS8429155Jan 25, 2010Apr 23, 2013Veveo, Inc.Methods and systems for selecting and presenting content based on activity level spikes associated with the contentUS8429188Apr 23, 2013Veveo, Inc.Methods and systems for selecting and presenting content based on context sensitive user preferencesUS8433696Aug 27, 2010Apr 30, 2013Veveo, Inc.Method and system for processing ambiguous, multiterm search queriesUS8438160Apr 9, 2012May 7, 2013Veveo, Inc.Methods and systems for selecting and presenting content based on dynamically identifying Microgenres Associated with the contentUS8478794Nov 15, 2011Jul 2, 2013Veveo, Inc.Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collectionsUS8549424May 23, 2008Oct 1, 2013Veveo, Inc.System and method for text disambiguation and context designation in incremental searchUS8583566Feb 25, 2011Nov 12, 2013Veveo, Inc.Methods and systems for selecting and presenting content based on learned periodicity of user content selectionUS8825576Aug 5, 2013Sep 2, 2014Veveo, Inc.Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second systemUS8826179Sep 27, 2013Sep 2, 2014Veveo, Inc.System and method for text disambiguation and context designation in incremental searchUS8943083Nov 15, 2011Jan 27, 2015Veveo, Inc.Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collectionsUS8949231Mar 7, 2013Feb 3, 2015Veveo, Inc.Methods and systems for selecting and presenting content based on activity level spikes associated with the contentUS9075861Nov 15, 2011Jul 7, 2015Veveo, Inc.Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collectionsUS9087109Feb 7, 2014Jul 21, 2015Veveo, Inc.User interface methods and systems for selecting and presenting content based on user relationshipsUS20040093256 *Nov 8, 2002May 13, 2004Luis GarciaAutomated labor overtime projection methodUS20040111313 *Oct 29, 2002Jun 10, 2004Ingman Robert MitchellMethods and systems for assigning multiple tasksUS20060100890 *Jul 15, 2005May 11, 2006Bank Of America CorporationEvaluation of a business case baesd on the cost of poor process opportunitiesUS20130226798 *Jun 13, 2012Aug 29, 2013Bill.Com, Inc.Methods and systems for automating payments utilizing rules and constraints* Cited by examinerClassifications U.S. Classification705/7.12International ClassificationG06Q10/06Cooperative ClassificationG06Q10/0631, G06Q10/06European ClassificationG06Q10/06, G06Q10/0631Legal EventsDateCodeEventDescriptionAug 27, 2001ASAssignmentOwner name: GE CAPITAL COMMERCIAL FINANCE, INC., CONNECTICUTFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:HINKLE, BURL SHANNON;REEL/FRAME:012117/0061Effective date: 20010723RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services