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  Knowledge based performance management systemUSPTO Application #: 20080027769Title: Knowledge based performance management system Abstract: An automated system (100) and method for knowledge based performance management for an organization. After extracting data from existing narrowly focused systems, mission measures and organization levels are defined for one or more organizations. The elements, factors and risks that contribute to mission measure performance by organization level and organization are systematically defined and stored in a ContextBase using up to six context layers. ContextBase information is extracted for specified combinations of context layers, organization levels and organizations as required to produce complete context frames. The complete context frames are then used by a series of applications for reviewing, analyzing, forecasting, planning and optimizing organization performance. (end of abstract) Agent: Asset Trust, Inc. - Bothell, WA, US Inventor: Jeff Scott Eder USPTO Applicaton #: 20080027769 - Class: 705 7 (USPTO) The Patent Description & Claims data below is from USPTO Patent Application 20080027769. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS REFERENCE TO RELATED APPLICATIONS AND PATENTS [0001]The subject matter of this application is related to the subject matter of: application Ser. Nos. 09/295,337 filed Apr. 21, 1999 (now abandoned), 09/421,553 filed Oct. 20, 1999 (now abandoned), 09/775,561 filed Feb. 5, 2001 (now abandoned), application Ser. No. 09/678,109 filed Oct. 4, 2000, application Ser. No. 09/938,555 filed Aug. 27, 2001 (now abandoned), application Ser. No. 09/994,720 filed Nov. 28, 2001, application Ser. No. 09/994,739 filed Nov. 28, 2001, application Ser. No. 10/046,316 filed Jan. 16, 2002, application Ser. No. 10/012,375 filed Dec. 12, 2001, application Ser. No. 10/025,794 filed Dec. 26, 2001, application Ser. No. 10/036,522 filed Jan. 7, 2002, application Ser. No. 10/124,240 filed Apr. 18, 2002, application Ser. No. 10/166,758 filed Jun. 12, 2002, U.S. Pat. No. 5,615,109 "Method of and System for Generating Feasible, Profit Maximizing Requisition Sets, U.S. Pat. No. 6,321,205 "Method of and System for Modeling and Analyzing Business Improvement Programs" and U.S. Pat. No. 6,393,406 "Method and System for Business Valuation" by Jeff S. Eder, the disclosure of which is incorporated herein by reference. BACKGROUND OF THE INVENTION [0002]This invention relates to a computer based method of and system for knowledge based performance management for any organization with one or more quantifiable mission measures. [0003]Leaders in business, industry and government have collectively invested billions of dollars in a wide variety of software systems over the last twenty years. This enormous investment was initially focused on automating core processes such as order management, payroll, procurement and production. In the last several years a wide variety of analytical applications have been developed to supplement the capabilities of the increasingly complex systems and suites that manage core processes. As a result, the size and cost of the information technology infrastructure in the average organization has increased dramatically over the last twenty years. Information technology (IT) is now the second largest cost--after personnel--in many corporations and the average Fortune 500 firm now uses over thirty separate software systems to manage performance. In the global marketplace there are over seventy different types of enterprise software systems being offered to manage a narrow slice of organization performance (hereinafter, will be referred to as narrow systems). [0004]The good news is that many of these separate, narrow systems provide useful information and have become integral parts of the organizations that they support. The bad news is that these independent, narrow systems utilize a wide variety of languages, platforms and technologies. This complexity has made it challenging and expensive to integrate these systems. As a result, the number of systems that are being integrated is limited. The limited integration of disparate, independent systems has several negative impacts on the effectiveness of the overall IT infrastructure including: [0005]the ability to flexibly respond to constantly evolving business requirements is restricted; [0006]the ability to partner with other companies to improve quality and efficiency is limited; [0007]the ability to analyze and manage business performance is constrained by the functional "silos" defined by the different systems; and [0008]the ability to transition to an Internet-centric operating mode is compromised. [0009]Many feel that these limitations are already responsible for the poor financial returns a number of organizations have received from their IT investments. A recent article in an industry trade magazine echoed this sentiment when noting that "most businesses are home to scores of information systems that remain uselessly disconnected from one another. Until those systems are integrated, technology investments won't live up to expectations." The sheer complexity of managing and maintaining this large number of systems is another factor contributing to poor financial returns for many companies that have installed these systems. Another factor contributing to the poor financial returns is the fact that the explosive growth in the size and complexity of their information technology systems has surpassed the ability of many companies to organize and apply the data generated by these same systems. The caption from a recent cartoon in the New Yorker summarized the situation of many "we have lots of information technology . . . we just don't have any information." [0010]Unfortunately, the New Yorker cartoon reflects the reality in most organizations that have mountains of digital content and data that cannot be located, accessed or used. These mountains of data and content contain nuggets of knowledge that have high creation costs and high value. To date there has been no economical way to catalog such information or provide effective ways to search, identify, retrieve and use the valuable knowledge and business data available on the world wide web and hidden in the hard drives, databases, datamarts and data-warehouses of corporations and government agencies around the globe. Examples of the enormous waste caused by not being able to identify and retrieve the right information at the right time are legion. The most visible example is obviously the Sep. 11.sup.th disaster. Many feel that if only the "FBI knew what the FBI knew" about flight training in Phoenix and a suspected terrorist in Minnesota, then the entire disaster could have been avoided with the consequent savings of human life, suffering and property damage. The FBI is not the only organization having a problem identifying and applying all the information contained in its systems. For example, Hewlett Packard recognizes that its knowledge about markets, products and customers is its biggest source of competitive advantage. But because the firm is highly decentralized, its knowledge is dispersed across business units that have little perceived need to share with one another. Many large hospitals and HMO's also suffer from the same sort of diffusion of expertise and knowledge throughout the organization. All too often, a crisis is solved before the expert can be located. A method for organizing and applying the knowledge that is already available would clearly help this alleviate this problem. Many feel that business process integration will be a cure all for many of these ills. While business process integration is of some help, it is not a panacea because it only replaces the vertical "functional silos" with horizontal, "process silos". [0011]Improving our ability to develop and apply knowledge will do more than prevent disasters and ease crises it will also help improve performance and create value. Value will be created in several ways. First, the limitations on IT infrastructure effectiveness described previously will be reduced or eliminated. Second, support for the new collaborative approach to projects and work that increasingly pervades the modern economy will be increased if knowledge can be more readily shared. The head of research from a large pharmaceutical company recently noted that "the creation of value is coming increasingly from the collaboration of groups . . . the point is no longer to manage the silos, but to bring together around a problem the right group of people with the right knowledge." The increasing amount of partnerships that are being formed between different companies is another force that will increase the value impact of providing a more systematic, method for developing, storing and sharing knowledge. [0012]From the preceding discussion, it is clear that in an era of data overload and increasing collaboration, we need a new approach to get the right information to the right person and/or to the right system at the right time. As discussed later, once a system is in place to get the right information to the right person and/or system at the right time new systems to process the information will also be needed. Fortunately, these new systems will also reduce the complexity associated with using information technology systems to manage organization performance by an order of magnitude. [0013]A critical first step in defining a new approach to solving the problem of "getting the right knowledge to the right place" is to clearly define the terms: data, information, context and knowledge. Data is anything that is recorded. This includes records saved in a digital format and data stored using other means. A subset of the digital data is structured data such as transaction data and data stored in a database for automated retrieval. Data that is not structured is unstructured data. Unstructured data includes data stored in a digital format and data stored in some other format (i.e. paper, microfilm, etc.). Information is data plus context of unknown completeness. Knowledge is data plus complete context. Complete context is defined as: all the information relevant to the decision being made using the data at a specific time. If a decision maker has data and the complete context, then providing additional data or information that is available at the time the decision is being made will not change the decision that was made. If additional data or information changes the decision, then the decision maker had "partial context". [0014]We will use an example to illustrate the difference between data, partial context, complete context and knowledge. The example is shown in Table 1 and Table 2. TABLE-US-00001 TABLE 1 Data: We received a check for $6,000 from Acme Tool today. Partial Context: Acme Tool owed our division $36,000 and promised to pay the entire balance due last week. We are due to ship them another 100 widgets next Tuesday, since we have only 50 in the warehouse we need to start production by Friday if we are going to meet the promised date. Decision based on data + partial context: Stop production and have customer service put a credit hold flag on their account, then have someone call them to find out what their problem is. TABLE-US-00002 TABLE 2 Data: We received a check for $6,000 from Acme Tool today. Complete context: Acme Tool owed our division $36,000 and promised to pay the entire balance due last week. We are due to ship them another 100 widgets next Tuesday, since we have only 50 in the warehouse we need to start production by Friday if we are going to meet the promised date. Acme is a key supplier for Project X in the international division. The international division owes Acme over $75,000. They expected to pay Acme last week but they are late in paying because they have had some problems with their new e.r.p. system. Netting it all out, our organization actually owes Acme $45,000. We have also learned that our biggest competitor has been trying to get Acme to support their efforts to develop a product like Project X. Decision based on knowledge (data + complete context): See if there is anything you can do to expedite the widget shipment. Call Acme, thank them for the payment and see if they are OK with us deducting the money they owe us from the money the materials division owes them. If Acme OKs it, then call the international division and ask them to do the paperwork to transfer the money to us so we can close this out. The example in Tables 1 and 2 illustrates that there is a clear difference between having data with partial context and having knowledge. Data with partial context leads to one decision while data with complete context creates knowledge and leads to another completely different decision. The example also suggests another reason (in addition to not being able to find anything) that so many firms are not realizing the return they expect from their investments in narrow performance management systems. Virtually every information technology system being sold today processes and analyzes data within the narrow silo defined by the portion of the enterprise it supports. As a result, these systems can not provide the complete context required to turn data into knowledge. Recently announced products for federated data analysis do not fully address this problem because they are not capable of developing and/or processing all the types of information required to produce a complete context analysis. [0015]Another limitation of all known performance management systems is their complete reliance on structured historical data. The problem with this is that not all data are stored and that most of the data that is stored is stored in an unstructured format that is difficult to process. The most common estimate is that 80% of the data that is stored digitally is stored in an unstructured format. A number of products are being developed to help structure unstructured digital data. The system of the present invention is capable of accepting input from these systems. The system of the present invention also has the ability to structure and process unstructured: text data, video data, geo-coded data and web data on its own. This leaves the problem of data that has not been stored in any system as an area needing further development. While much of the data that has not been stored may not be useful for performance management, the data that resides with subject-matter experts is potentially very valuable. In fact, as the world moves into an increasingly uncertain environment with a growing number of non-traditional threats and increasingly volatile weather patterns, the need to rely on information from subject-matter experts is expected to increase dramatically. [0016]A method for systematically incorporating data from subject-matter experts into knowledge based systems is clearly needed. However, to be successful, this method needs to overcome a few potential problems. While subject-matter experts have a great deal of knowledge about a particular field, it is more likely than not that: [0017]1. they do not have any expertise in knowledge representation, and [0018]2. they do not have any expertise in probability theory.As a result, the subject-matter experts may have difficulty communicating their expertise in a manner that can be readily processed by a data fusion analysis. While overcoming both problems is important, solving the second problem is particularly important because subject-matter experts involvement is most likely to be critical in developing assessments for the increasing number of situations that have little or no precedent, very limited data and a consequent high degree of uncertainty. [0019]In light of the preceding discussion, it is clear that it would be desirable to develop methods and systems that could define the complete context required for effectively managing performance. The system should support individuals working alone, individuals working in teams, teams working independently, teams working together, organizations working alone and organizations that are collaborating with other organizations. Ideally, this system would be capable of reducing IT infrastructure complexity while sifting through all the available data and incorporating newly created data as required to define the full context for performance related analysis and decision making. In short, the new methods and systems should help organizations improve their performance by developing, storing, retrieving and applying complete context knowledge in an automated fashion. SUMMARY OF THE INVENTION [0020]It is a general object of the present invention to provide a novel, useful system that develops, analyzes, stores and applies complete context information for use in improving the performance of any organization combination, organization or subset of an organization with a quantifiable mission. For simplicity, we will refer to the collection of different subsets of an organization that can be supported by the system for knowledge based performance management as organization levels. This new system overcomes the limitations and drawbacks of the prior art that were described previously. [0021]Processing in the Knowledge Based Performance Management System is completed in three steps: The first step in the novel method for knowledge based performance management involves using data provided by existing narrow systems and the nine key terms described previously to define mission measures for each organization level. As part of this processing data from the world wide web. unstructured data, geo-coded data, and video data are processed and made available for analysis. The automated indexation, extraction, aggregation and analysis of data from the existing, narrow computer-based systems significantly increases the scale and scope of the analyses that can be completed by users. This innovation also promises to significantly extend the life of the narrow systems that would otherwise become obsolete. The system of the present invention is capable of processing data from the "narrow" systems listed in Table 3. TABLE-US-00003 TABLE 3 1. Accounting systems; 2. Alliance management systems; 3. Asset management systems; 4. Brand management systems; 5. Budgeting/financial planning systems; 6. Business intelligence systems; 7. Call management systems; 8. Cash management systems; 9. Channel management systems; 10. Commodity risk management systems; 11. Content management systems; 12. Contract management systems; 13. Credit-risk management system 14. Customer relationship management systems; 15. Data integration systems; 16. Demand chain systems; 17. Decision support systems; 18. Document management systems; 19. Email management systems; 20. Employee relationship management systems; 21. Energy risk management systems; 22. Executive dashboard systems; 23. Expense report processing systems; 24. Fleet management systems; 25. Fraud management systems; 26. Freight management systems; 27. Human capital management systems; 28. Human resource management systems; 29. Incentive management systems; 30. Innovation management systems; 31. Insurance management systems; 32. Intellectual property management systems; 33. Intelligent storage systems 34. Interest rate risk management systems; 35. Investor relationship management systems; 36. Knowledge management systems; 37. Learning management systems; 38. Location management systems; 39. Maintenance management systems; 40. Material requirement planning systems; 41. Metrics creation system 42. Online analytical processing systems; 43. Ontology management systems; 44. Partner relationship management systems; 45. Payroll systems; 46. Performance management systems; (for IT assets) 47. Price optimization systems; 48. Private exchanges 49. Process management systems; 50. Product life-cycle management systems; 51. Project management systems; 52. Project portfolio management systems; 53. Revenue management systems; 54. Risk management information system 55. Risk simulation systems; 56. Sales force automation systems; 57. Scorecard systems; 58. Sensor grid systems; 59. Service management systems; 60. Six-sigma quality management systems; 61. Strategic planning systems; 62. Supply chain systems; 63. Supplier relationship management systems; 64. Support chain systems; 65. Taxonomy development systems; 66. Technology chain systems; 67. Unstructured data management systems; 68. Visitor (web site) relationship management systems; 69. Weather risk management systems; 70. Workforce management systems; and 71. Yield management systems [0022]The quantitative mission measures that are initially created using the extracted narrow system data from each organization can take any form (please note: a new organization could use the Knowledge Based Performance Management System to generate the information required to create mission measures without the use of narrow system data). For many of the lower organization levels (combinations being the highest level and an element being the lowest organization level) the mission measures are simple statistics like percentage achieving a certain score, average time to completion and the ratio of successful applicants versus failures. At higher levels more complicated mission measures are generally used. For example, Table 5 shows a three part mission measure for a medical organization mission--patient health, patient longevity and financial break even. As discussed in the cross-referenced patent application Ser. Nos. 10/071,164 filed Feb. 7, 2002; 10/124,240 filed Apr. 18, 2002 and 10/124,327 filed Apr. 18, 2002, commercial businesses that are publicly traded generally require five risk adjusted measures per enterprise--a current operation measure, a real option measure, an investment measure, a derivatives measure and a market sentiment measure. The system of the present invention will support the use of each of the five measures described in the cross referenced patent applications in an automated fashion. Also, as described in the cross-referenced patent application Ser. Nos. (10/124,240 filed Apr. 18, 2002 and 10/124,327 filed Apr. 18, 2002) the total risk associated with these five measures equals the risk associated with equity in the organization. The Knowledge Based Performance Management System will also support the automated definition of other mission measures including: each of the different types of event risks alone or in combination, each of the different types of factor risks alone or in combination, cash flow return on investment, accounting profit, and economic profit. [0023]The system of the present invention provides several other important advances over the systems described in these cross-referenced applications, including: [0024]1. the same performance management system can be used to manage performance for all organization levels; [0025]2. the user is free to specify more than five mission measures for every organization level; [0026]3. the user can assign a weighting to each of the different mission measures which is different than the risk adjusted value measure; and [0027]4. the user is free to specify mission measures that are different than the ones described in the prior cross-referenced patent applications. Continue reading... Full patent description for Knowledge based performance management system Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Knowledge based performance management system patent application. 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