WO2000043935A2 - Systeme, procede et articles manufactures pour decision consultative informatisee et services de surveillance faisant appel a des systemes de decision avec protection de licence et de modele - Google Patents

Systeme, procede et articles manufactures pour decision consultative informatisee et services de surveillance faisant appel a des systemes de decision avec protection de licence et de modele Download PDF

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Publication number
WO2000043935A2
WO2000043935A2 PCT/US2000/000335 US0000335W WO0043935A2 WO 2000043935 A2 WO2000043935 A2 WO 2000043935A2 US 0000335 W US0000335 W US 0000335W WO 0043935 A2 WO0043935 A2 WO 0043935A2
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Prior art keywords
decision
decision support
support system
aggregate
model
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PCT/US2000/000335
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English (en)
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WO2000043935A8 (fr
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Mehdi Aftahi
Pierre Boudreault
Perry Drobetsky
Donald J. Lobley
Edward S. Robins
Salim Tharani
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Technologyevaluation.Com (Tec)
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Priority to AU24935/00A priority Critical patent/AU2493500A/en
Publication of WO2000043935A2 publication Critical patent/WO2000043935A2/fr
Publication of WO2000043935A8 publication Critical patent/WO2000043935A8/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to automated and assisted decision making.
  • Automated assisted decision making is a relatively new field made possible by the advent and large-scale use of powerful general purpose and special purpose processors, and advances in the fields of decision science, game and utility theories, and graphical user interface technologies.
  • Automated and assisted decision making may require initial input information from human experts and automated data collection systems in order to assist less knowledgeable or time restricted human operators or systems to make decisions.
  • Such methods require detailed information gathered by knowledgeable experts in a particular field and formulated by said experts into a detailed knowledge base, where said knowledge base may be represented by a model consisting of criteria, and alternative strategies or items for selection, and criteria weights, evaluation information in the form of numeric values and textual and multimedia meta-data, and other such attributes as related to the criteria, the relationships amongst said attributes, and alternatives and alternative specific attributes and relationship of the said alternative attributes with the criteria attributes.
  • Summarized and specifically tailored information, including attributes and attribute relationship may be required by those less knowledgeable in the field but who may need to make a complex decision relying on said information, where said information can be very large.
  • Assistance by means of human intervention and/or automated machine and automated processes can be applied to best tailor the complex information required for a specific decision and make the decision manageable and well informed.
  • the detailed information underlying the specifically tailored data may need to be aggregated, hidden, removed, or appropriately filtered to create a summarized decision model for said decision makers in order to achieve a rapid, cost-saving, informed decision.
  • said data is generated by human experts and manually reduced, or said information is arbitrarily reduced without a specific process and is subject to human error.
  • said detail may also relate to a decision maker's conditional acceptance of any given solution, thereby providing means for negotiation in reducing cost of the selection, and means to assess the benefit and challenges represented by the selected alternative.
  • Said means because of the overwhelming complexity of a typical problem, does not often provide adequate detail for detailed negotiation between parties. This can be important to the eventual implementation project resulting from a decision, an important final part of an advisory service.
  • the present invention relates to a novel decision support process that determines decision making procedures through a series of appropriate graphical and verbal prompts, and provides adequate detailed meta-data without necessarily revealing other attributes which may be judged inapplicable, confidential, proprietary or simply overwhelming in volume and complexity to a decision-maker.
  • a system could in of itself create objects to enable a means to evaluate multiple variables within a multicriteria decision problem, and cause from the results of the method an action or provide reasons for a selection of at least one alternative amongst at least two alternatives, or issue a signal thereto to a human operator or device for decision and control action.
  • Such systems are known as automated advisory and control systems, and constitute a decision support system and process means for customizing a process and aggregating, disaggregating and analyzing data which may result in conditional decision making. In a control process, this can provide for conditional systems control.
  • Decision support systems are well known, and many methods for their use are known.
  • information is entered which may consist of a plurality of criteria arranged in a hierarchical tree to demonstrate relationships amongst the criteria, and one attribute related to each criterion to determine the importance of the criterion, commonly called a weight.
  • At least two alternatives which are to be compared and rank ordered are scored (sometimes referred to as rated) usually against the lowest level leaf criteria in the hierarchy.
  • the ratings and weights are aggregated in a mathematical formula to give a final score to rank the alternatives.
  • meta-data attributes (which may include text and links to objects over a computer network) with comparative qualitative information are added to assist a human decision maker in qualifying a decision, or selecting an alternative that may not be the first rank by the final score and may be selected when meta-data is provided that mitigates the score and causes preference for another alternative.
  • hierarchical decision models are evaluated on the basis of a single mathematical formula, requiring either the decision maker to understand the formula so presented, or taking in blind faith that the formula directly represents the aspirations of the decision maker, and sum-totals the decision. Often this is not the case and rule-based or other means are required to better represent the evaluation of alternatives.
  • rule based expert systems such as that disclosed by Rhonda L. Alexander et. al. in US patent 5182793 "Computer-aided decision making with a symbolic spreadsheet” have been developed.
  • rule-based systems are classically complex, often requiring many rules that can confuse and do not address the clarity of the decision process. Obviousness of the process is masked by the complex number of rules, and rigidity of the process where such rules are essentially "Yes/No", precluding gray areas of understanding or negotiable points.
  • Decision making software such as Expert Choice [manufactured by Expert Choice Inc.,5001 Baum Blvd., Suite 650, Pittsburg, PA, USA, 15213] and ERGO [manufactured by Arlington Software Corporation, 740 Saint Maurice Street, Suite 410, Montreal, Quebec] also provide the means to evaluate alternatives to arrive at a decision, including only text information as meta-data.
  • no means is provided by which the decision maker is assisted through the decision process based on prior knowledge of other decision makers, and prior knowledge can also provide qualification information for selecting alternatives other than the top ranked alternatives.
  • the volume of such information can be very large, and summarized information is a painstaking process of peeling down the data, as F. N. Burt demonstrates in US patent 4829426.
  • a decision support system for analyzing client satisfaction with vendor choices is disclosed by Richard H. Case et. al. of Gartner in US patent 5734890.
  • the patent discloses a means to aggregate ratings and weights from a number of decision makers using a plurality of criteria with the intent on enhancing client satisfaction through a systematic and thorough methodology for collecting information and data related to weights and criteria.
  • Such methods are based on spreadsheet techniques, and the volume of data such methods can handle is limited by practical considerations.
  • Gartner Group subsidiary Decision Drivers Incorporated [56 Top Gallant Road, P.O.
  • a full analysis can be limited by the size of models as judged from the number of criteria and alternatives the software can accommodate, for example models with 16,000 criteria and a dozen alternatives or more are likely, and may often be composed of component models which in themselves can be viewed as complete decision models for specific applications.
  • the simplification process causes the "invisibility" of all the underlying data as it is removed, and manual summarizing processes are easily subject to human error where employed, nor can the models be easily tailored to specific clients - indeed, it can become physically and economically impractical to do so with the manual process.
  • the "data invisibility” can further make the process appear as a 'black box' process, and leaves many questions unanswered.
  • Prior art has therefore given cause to doubt in the efficacy of the process, resulting in limited applications and low market penetration of currently available systems. Automating this process would therefore provide a great advantage, and enable automated decision tools to be more widely accepted, as well as proving the efficacy of the process. Automating such decision tools gives a major advantage to such a player in the new - industry called the Automated Advisory Services Industry.
  • a more sophisticated methodology is required to translate differences that are not only linear summed differences in alternatives against a simple linear weighted sum decision strategy, but against a decision strategy obtained from a cluster of appropriate questions, and represented by a set of easily interpreted charts and numbers by one familiar with the art. Said decision strategy may be performed by those less knowledgeable in the art, and yet who may be the major decision makers.
  • Providing assistance on an as-needed-just-in-time (AN JIT) basis can be provided with through networked technologies. Providing said AN JIT services would give significant advantage to a company providing services in the automated advisory services industry, particularly if said services are integrated with an integrated approach to the decision support system and the generated model.
  • 'unbiased' data implies the reconciliation of differing views and experiences from at least two sources of information, set in the context of the current decision, and this can represent a problem to said expert in terms of retention of past experience, maintenance of information concerning the field, and frequent reconciliation of differing and contradictory information. Without an appropriate tool, said expert cannot claim to be unbiased. Often said experts are linked with one or two preferred vendors, and have deeper knowledge of said preferred vendors than other vendors. Thus it can be said that most experts in the field cannot provide an unbiased view of all available information, and have no tool to back up such a claim.
  • aspects of the detailed analysis may be proprietary or confidential or not relevant in a specific decision, or necessary in order to compute aggregate values, and consequently a means to tailor decision models 'on-the-fly' may present significant advantage.
  • Tools such as Expert Choice have limited capabilities in this respect - indeed, it is not even possible for some attributes - particularly in regard to hiding/revealing specific data and.
  • ERGO has several tools which provide flexibility to the model building process such as Append and Save sub-trees, but none for automatically creating aggregate and hidden nodes, with meta-data information embedded in the aggregate fields for example.
  • knowledge based aggregating processes and knowledge aggregating systems would add a significant competitive advantage, providing clients an unbiased and systematic process for making decisions in highly complex environments. Currently, no such system is known to exist.
  • no decision support tool provides a systematic means to aggregate, accumulate, and store the methods and procedures by which decision makers reach decisions, including the practical experience and true costs of, for example, a missing vendor feature, and the constraints employed to assist in price negotiations as another example, or methods by which specific issues are resolved with particular vendors and clients.
  • a decision maker the means to utilize this knowledge in a structured fashion, would result in a significant advantage for any decision maker. It would also apply to any process making decisions where consequences can measurably result, and where said processes can benefit by prior knowledge of procedures used for making similar decisions.
  • Said process is also of value to vendors seeking to improve performance, and provide direction for future product development in competitive markets since it would provide the ability of knowing how clients select their products and competitive products.
  • the information for the said knowledge base may be garnered from non-proprietary and proprietary sources, from clients, public literature, vendors, and other sources general by at least one expert in the field.
  • Said knowledge base may include attributes specific to at least one alternative, and may provide at least one significant summary information a client of an advisory service may find relevant.
  • Such information as the tradeoff a vendor may have made, knowingly or unknowingly, between feature capability and complexity of the product, compared to other vendors, may be of significant value in understanding the challenges that may be faced by the client, and may be of interest to a vendor who may find at a glance the summarized data and position of the vendor product with respect to the industry.
  • Other attributes may be related to costs and business risk, vendor market positioning and future vendor development goals, and so forth.
  • some products may be composed of a cluster of products, and hence a hierarchy of products, possibly designated in terms of an alternative hierarchy, may provide a breakdown of a vendor's offerings in component form. To date, no such decision aid capable of examining hierarchical alternative structures exists.
  • such data and meta-data may consist of engineering information related to risk in a control process such as that in a nuclear reactor where many thousands of nodes represent a hierarchical control structure, the accumulated risk as components of a system deviate from a norm (constraint) being so estimated by the decision support system, and possibly assisted by prior knowledge from similar situations in other systems.
  • Data and meta-data for and aggregated node in the , hierarchy can be continuously updated, the advantage thereby being that operators are not inundated by information in a large complex system, and only such data is revealed by a process of node disaggregation when for example a local constraint is exceeded or a particular pattern of information occurs below the node.
  • Data and meta-data for a knowledge base may be garnered from experience, statistical reliability data, and so forth. It is also obvious that it would be advantageous to minimize the intervention of a human expert, who may have specific biases from limited knowledge and other interests, through a systematic process of model customization and use of the accumulated systematic knowledge in said knowledge database.
  • aggregation of numeric data would mitigate problems related to rapid and accurate dynamic feedback to develop what-if scenarios 'on-the-fly' for large and complex systems.
  • Said dynamic analysis uses values and parameters of the model - such as weights and ratings - which are changed 'on-the-fly' to evaluate the effects on a particular decision or evaluation of at least one alternative, usually compared to at least one other alternative. Because computation times can require at least several seconds or even minutes for such large models, such dynamic analysis is not feasible for interactive use, and is difficult to perform even with the highest speed desk-top systems once model sizes and number of data values, plus updating of chart information on a graphical display for example, are included. If the information is aggregated, the computing problem would significantly be mitigated.
  • a customized set-by-step guideline for making a decision in the context of a particular client would provide the said client and advisory service with a clear procedure and process.
  • a means to determine a step-by-step procedure, and execute it, incorporating in at least one of the means to mitigate said problems described herein, would be highly advantageous to an automated advisory service.
  • a novel and comprehensive decision support system and process for knowledge accumulation, knowledge aggregation and disaggregation, and process for its controlled use with knowledge based systems in automated decision processing is disclosed herein.
  • the decision support system provides a means to aggregate data and meta-data, determine decision procedures and related costs and risks, and disaggregation techniques. Means to prevent unauthorized use of model and model data is further described.
  • a method and process is described for extending these means and processes to automated large decision and control systems, and process means to provide a customized step-by-step customized and packaged procedure.
  • a computer- implemented decision support machine for comparing two alternatives, comprising: memory means for storing at least one decision data structure with each said decision structure having a plurality of decision criteria organized in parent category nodes with child (descendant) nodes; input means to assign to each said decision node a weight value to represent the importance of each said node to the decision, the plurality of weights and nodes comprising a predetermined two-dimensional benchmark pattern; input means for inputting a first plurality of scores for a first competing alternative to the decision factors of said decision data structure, and input means for inputting a second plurality of scores for a second competing alternative to the decision factors of said decision data structure; input means for entering meta-data for at least one node, wherein said meta-data consists of text information and can further include means for multimedia links to local objects and can also contain links to remote objects and experts located on a computer network or telecommunications network; input means for entering at least one raw score which can be ordinal or cardinal data; input means
  • input means to determine at least one constraint for at least one decision factor; processing means to determine comparison between at least one alternative score attribute value and at least one constraint; input means to determine at least one constraint from at least one score of at least one alternative; processing means to select at least one 'worst constraint' condition and at lesat one 'best constraint' condition where at least one worst constraint and at least one best constraint may be computed from input data and from a selected set of alternative data; storage means to store at least one decision object with at least one constraint attribute as a decision scenario object with constraints; processing means to compare the score of at least one decision factor for at least one alternative against at least one constraint; processing means to determine an aggregate of all values of comparison to provide at least one final value representing the overall difference value for at least two alternatives when compared to the said at least one constraint; output means to output at least one output signal corresponding to at least one aggregate value differences to provide a conditional comparison value or worth of said competing alternatives.
  • input means to input at least one code, said code determining license
  • the aggregation procedures are executed by at least one Analyst who is knowledgeable in the field of the model and its applications.
  • the at least one Analyst is a trained expert the model and determines the aggregate model from a larger model.
  • the aggregate model outcome determination method represents as closely as possible the required means by the at least one client. Determination of said decision outcome by means of a multiattribute value such as the linear sum of weights and scores is one preferred embodiment.
  • a second preferred embodiment is the use of pattern match algorithms such as Arlington Software Corporation's Percent Match pattern matching algorithm. Supporting meta-data for a decision outcome, in one preferred embodiment, is customized to meet client requirements.
  • said at least one aggregation parameter are obtained by machine readable executable code on the general purpose processor on which the model aggregation procedure is executed.
  • said aggregation parameters enable aggregate values to closely estimate exact values without requiring the larger data set to obtain the exact values, said estimation being necessary in the event of changes of weights, scores and other values during investigation of various scenarios using said aggregated model by said at least one client.
  • the Analyst provides the data of the customized Analyst model prior to aggregation of the said at least one determined aggregate node, and uses machine readable executable code on at least one processor such as a neural net, thereby generating said at least one aggregation parameter. Said at least one aggregation parameter is then stored with the said aggregate model as at least one attribute of the said at least one aggregate node.
  • the Analyst determines that the aggregation parameters are not required, and does not generate said aggregation parameters.
  • the aggregate data and meta-data is selectively output to a printer or disk file.
  • said aggregate model is encapsulated in an electronic message to generate a request for proposal (RFP) to elicit responses from the 'best set' of alternative vendors.
  • RFP request for proposal
  • said vendors are replaced by alternative strategies, actions, project alternatives, and personnel, and other such choices as would occur to, and be evaluated by, the at least one Analyst.
  • the aggregate data and meta-data is selectively output to a printer.
  • said selected data and meta-data is saved as a disk file such as a spreadsheet or as a word processor document as is commonly known, and other known disk file formats as is available at the time.
  • Said selected data and meta-data in one embodiment is transmitted over a computer network.
  • the determined format contains a summarized report on a limited number of preferred vendors most likely to satisfy the requirements of a client.
  • Said preferred vendors can, in another preferred embodiment, be selected by automatic process means, said selection being performed by said decision support system.
  • said preferred vendors are manually selected from the list in the full Analyst model by the said Analyst prior to storing the aggregate model.
  • a report on said selected vendor can in one embodiment include exception reporting and summarized vendor strengths and weaknesses, and other data and meta-data that is deemed relevant by one knowledgeable in the art.
  • the at least one Analyst determines at least one attribute to assign to at least one alternative, said attribute having at least one value summarizing at least one aspect of the alternative.
  • At least two attributes can be arranged in a hierarchical tree indicating hereditary relationships amongst the said attributes.
  • said hierarchical arrangement determines sub-alternatives. Said sub-alternatives are scored as independent alternatives, and said scores are used to determine the outcome score of the parent alternative.
  • said attributes represent a hierarchical- relationship of attributes for a single alternative. In either case, said at least one Analyst indicates by input means said means by which at least two attributes are to be aggregated and retained in the aggregate model.
  • said at least one Analyst causes said at least one attribute for at least one alternative to be provided in the form of a chart.
  • said chart is given in the form of meta-data, thereby providing a client with the summarized values and meta-data information applicable to the specific requirements of the client.
  • the said limits on access and distribution at a client site for a provided model may include, but not exclusively, timed availability, availability of different features of the model and the decision support system, model storage location limitations, limitation of access by specific at least one individual, and number of licensees with access to the model in a file server environment, and limitation on the number of scenarios a client is allowed to generate.
  • the first reading from storage of said model by a decision support system supplied to a client causes said decision support system to prompt for a second licensing key code to initiate time and feature availability.
  • An authorizing site may then be requested to provide the second key code, and may be provided on determining said client is authorized.
  • the rights and privileges of access and distribution to said aggregate mode are accorded for the said provided aggregated model.
  • means to protect from the unauthorized determination of the internal data structure of said model is provided by means of encryption and decryption methods incorporated in said decision support system.
  • the decision support system is provided to the client. In one embodiment of said client provided decision support system, at least one feature is removed.
  • the said removed feature is the ability to determine aggregate nodes.
  • said decision support system model access code limitation input/output features are removed.
  • said client provided decision support system includes means to determine access rights and limitations to at least one said customized model.
  • the aggregate model is provided with a code to prevent further aggregation by said decision support system.
  • the said provided model license may be transferred by a process of license transfer from one processor to another, thereby maintaining the number of licenses to a specified number.
  • said license may require renewal from time-to-time.
  • a validation of the expiration status of the said provided model is determined by the client provided decision support system. If the expiration status indicates the said provided model has exceeded its limited period of use, processor implemented code in the said decision support system may initiate a request for license renewal, said process in one embodiment being by automated means over a computer network such as the Internet.
  • Another embodiment of said process is an appropriate indication to the licensee of the expiration status of the said provided model.
  • such expiration warnings may appear prior to complete expiration as a warning to the licensee, thereby providing the licensee the opportunity to ensure continued use of the model.
  • the at least one conditional difference value to compare at least two alternatives is determined by relating at least one cost attribute to at least one criterion node score difference value, said cost usually being expressed, but not exclusively, in terms of a monetary unit.
  • said conditional value difference may be in terms of a project success risk value, and in another embodiment said success risk value is expressed in terms of a success probability.
  • a computer- implemented decision support system for comparing two alternatives, comprising: memory means for storing at least one decision model data structure having a plurality of decision factors organized in parent category nodes with child (descendant) nodes, input means to assign at least one weight to at least one decision factor input means to input at least one alternative; input means to input at least one plurality of scores, said at least one score being assigned to at least one decision criterion for at least one alternative processing means to determine at least one multiattribute score for at least one alternative from said at least one plurality of scores for said alternative; input means to input meta-data into said model; input means to determine at least one chart comprising at least one data value in said model; input means to determine at least one report template; input means to assign at least one meta-data attribute to said chart; input means to input meta-data to at least one meta-data attribute field, where said meta-data includes at least one instruction on using said chart, thereby causing said chart to be determined as a decision object;
  • said assignment of meta-data instructions and associated meta-data for charts, decision objects, visual decision dictionaries and decision procedures is performed by an Analyst who is an Expert in the decision area to which the model is applied.
  • said definition and assignment is performed when aggregating the complete model, and in one embodiment is saved in the aggregate model.
  • the Analyst edits the aggregate model to add, modify and delete decision objects, visual decision dictionaries and decision procedures.
  • said at least one decision object, visual decision dictionary and decision procedure, report template, customized report is transmitted by the decision support system over a computer network, and are sent from one decision support system operating on one processor on the computer network, and read by a second processor on said same computer network executing a second decision support system.
  • a decision procedure, visual decision dictionary and decision object are identical.
  • said decision procedure contains a plurality of visual decision dictionaries with meta-data guidance to guide in the use combined use of said plurality of visual decision dictionaries, wherein each said visual decision dictionary consists of a plurality of decision objects, said visual decision dictionary containing meta-data instructions concerning use of said plurality of decision objects in combination.
  • Said meta-data guidance can be considered in this embodiment as a set of scripts.
  • a computer- implemented decision support system for comparing two alternatives comprising: storage means for an aggregate model data structure; output means to output meta-data guidance in selecting said at least one decision procedure; output means to output meta-data guidance in selecting said at least one visual decision dictionary; output means to output meta-data guidance in selecting said at least one decision object; input means to select at least one decision procedure; input means to select at least one visual decision dictionary; input means to select at least one decision object; input means to input meta-data in said at least one decision procedure; input means to input meta-data in said at least one decision object, at least one visual dictionary, at least one decision procedure; storage means to store at least one decision object; input means to select at least one decision object; and output means to store said aggregate model.
  • an Analyst who is an Expert in the field of the model determines at least one decision object, at least one visual decision dictionary and at least one decision procedure for the decision making process based on prior knowledge acquired by facilitation with said client and from experience of the Expert.
  • Said customized aggregate model then incorporates client requirements and said client constraints.
  • said client further customizes the aggregate model as determined here.
  • Said at least one Analyst in one embodiment assists in said customization process.
  • aggregate score and value differences represent as closely as possible the aspirations of the decision-maker. Aggregation by linear sums of weights and ratings is one preferred embodiment.
  • a second preferred embodiment is the use of pattern match algorithms such as Arlington Software Corporation's Percent Match and Composite Index pattern matching algorithms.
  • said at least two decision procedures are bound together to form a complete guide to at least one decision process. Said guide may be determined by at least one knowledgeable in the art of making said decision.
  • Input means may be provided by said decision support system to build said guide
  • said guide may include at least one of, but not exclusively, report template, request for proposal template, decision procedure, visual decision dictionary, decision procedure library containing at least two decision procedures, bid letter template, and may include meta-data and support meta-data indicating the use of each decision procedure constituting said guide.
  • said guide consists of machine readable code executed on a special purpose or general purpose processor.
  • said guide resides on a computer network and is accessed by means of at least one output signal from a device such as an intelligent paper clip, said clip being provided with an appropriate code that can be read by an infra-red or radio frequency device, and said code containing information on location of the said guide on the computer network.
  • a computer- implemented decision support system for comparing two alternatives, comprising: input means to select at least one aggregate model; output means to store all attribute numeric data set of said aggregate model in as a structured data set; output means to determine a copy of said all attribute data set; input means to select at least one chart; input means to select at least one object on said chart, said at least one chart object representing at least one numeric attribute data value; input means where input causes said selected at least one chart object to change size, position, shape; processing means to determine said at least one attribute value resulting from change of size, position, shape of said at least one chart object; processing means to determine at least one attribute value at predetermined at least one interval, said at least one interval being based on size, shape, position of said at least one chart object; output means to output at least one signal representing said at least one changed position, shape and size of the at least one chart object; output means to transmit at least one signal to at least one other chart in the current chart set, causing said chart to determine at least one
  • the client uses the method to devise and compare different decision scenarios.
  • the attribute data and meta-data are assigned to report templates to determine at least one report, said at least one report in one embodiment being output on a device such as a printer.
  • said at least one report is stored in a disk file, and said disk file sent as an encapsulated item in an electronic message over a computer network.
  • said at least one scenario is stored as a distinct model in a data structure, where said model represents one scenario.
  • Said data structure in another embodiment enables selective access to at least one attribute datum, thereby providing means to develop further scenarios consisting of attribute data from at least two sets of attribute data.
  • said further scenario consists of one set of decision factor weights, and one set of alternative scores from at least one other scenario.
  • an automated advisory service process comprising: means to store data in a knowledge base, said knowledge base being a structured data set determined for a specific type of decision and consisting of at least one decision object; stored data structure consisting of at least one non-aggregate model, said model consisting of a plurality of decision factors arrange in a hierarchical relationship, a plurality of alternatives, a plurality of alternative attributes, a plurality of reconciled scores where reconciled scores are determined from scores from at least two sources, for each of said plurality of alternatives, and a plurality of meta-data indicating at least said contents of said model; means to determine industry standard attribute values from said at least one non- agregate model; means to determine the performance of individual alternatives; means to determine client requirements; means to provide client with at least one decision procedure; means to provide client with at least one decision process; means to determine at least one best alternative to meet said client requirements; means to customize said at least one non-aggregate model; means to provide alternative overviews to assist client in
  • said at least one Analyst uses said decision support system to aggregate and customize an aggregate model for said client.
  • Said means to provide services in the preferred embodiment uses at all stages said decision support system.
  • a separate knowledge base is used to store decision objects, constraint data, meta-data and other data as may occur to one knowledgeable in the field, said data being determined from prior experience of said at least one alternative.
  • said alternatives are vendors.
  • characterization of said vendors and said characterization of Client provides means to compare future clients against vendors, and can be used to determine expected success attributes and attributes wherein challenges are likely to be faced by said client on the selection of at least one specific vendor.
  • a decision support system for use with a controlled process or system, comprising: storage means for a decision data structure having a plurality of decision criteria organized in parent category nodes with child (descendant) nodes; means to assign each said decision node a weight value representing its importance to the decision, the plurality of weights and nodes comprising a predetermined two-dimensional benchmark pattern; means to assign a first plurality of scores for a first competing alternative to the decision factors of said decision data structure, and means to assign a second plurality of scores for a second competing alternative to the decision factors of said decision data structure and; means to assign reference meta-data for each node; means to assign a method to translate at least one score into a utility value representing the worth of the said at least one score toward said decision for at least one alternative; processing means to compute at least one utility value from at least one score value for at least one of said alternatives; means to determine at least one node as an aggregate node; means to determine at least one node as an hidden aggregate no
  • At least one Analyst or persons knowledgeable in the field determines at least one decision procedure and at least one constraint for at least one decision object. Said at least one procedure and at least one constraint for the control process is based on prior knowledge which is acquired from facilitation with a client, and is additionally determined from experience with same or similar automated processes.
  • an automated system provides as output for at least one human operator at least one decision procedure wherein attribute values and metadata represent the aggregate information and the decision making precepts and status of the automated system.
  • at least one exception datum is output when an attribute value exceeds said at least one constraint.
  • said exception output is to a graphical display device.
  • said output signal is directed to an automated process or system.
  • said at least one exception is output to a printer, and can further be output to a disk file, and further, said disk file can be encapsulated in an electronic mail message, said message being transmitted over a computer network.
  • Figure 1 shows a block diagram of computing hardware embodying a preferred embodiment of the present invention
  • Figure 2 shows a pictorial representation of an embodiment of the computing hardware of Figure 1 indicating a hardware automated decision and control process
  • Figure 3 shows a pictorial representation of a second embodiment of the computing hardware of Figure 1, indicating computer network client/server capability and input/output devices
  • Figure 4 indicates the aggregation and tailoring procedure of reducing a large model to a client specific aggregate model with aggregate and hidden nodes
  • Figure 5 shows the procedure used to construct decision objects with meta-data and numeric data
  • Figure 6 shows the procedure to compute conditional values such as costs from constraint information specific to client and to the alternatives
  • Figure 7 shows an example of the different fields and visual cues for the aggregate status of criteria and preview of meta-data aggregated text fields
  • Figure 8 illustrates means to preview aggregate text fields, and graphic images that may be saved as objects in the meta-data
  • Figure 9 shows means to assign aggregate status for the node
  • Figure 10 shows the means to customise the aggregation process by selecting the different fields to aggregate, what is to be aggregated, and where the aggregate information is to go. It also indicates how the aggregated evaluation is to be represented and evaluated;
  • Figure 11 shows the way in which different chart objects may be dynamically changed in order to produce dynamic evaluations and scenarios;
  • Figure 12 shows a decision procedure in which rating values are adjusted, and how a chart value may be locked, refreshed and in order to control the comparison;
  • Figure 13 shows the report generation stages of the procedures to generate RFP's for example, and final reports
  • Figure 14 shows the licensing and license management process at a client site, and at an authorisation site
  • Figure 15 shows the license transfer process to transfer a license from one processor to another.
  • Figure 16 shows one part of a visual decision dictionary / decision procedure composed of decision objects and means by which criteria attributes may be used in chart data;
  • Figure 17 shows a graph of weighted average value against the absolute sum difference function, indicating the approximation method results from the optimised selection of a fitting function
  • Figure 18 shows a visual decision dictionary in which chart objects are divided between data modification charts and impact charts, and where meta-data technology is used to provide assistance and guidance to the client;
  • Figure 19 shows an alternative hierarchy and associated attributes showing how attributes are inherited for a composite alternative;
  • Figure 20 shows means to assign attributes and attribute values and properties such as equations relating to other attribute values and attributes of factors in the model tree;
  • Figure 21 shows the attribute summary table with attribute values for the at least one alternative, and means to filter alternatives by said attributes;
  • Figure 22 shows an overview of a scripted process for assisting a client through a procurement selection process, and means by which continuous improvement and updating of model information is obtained;
  • Figure 23 shows the part 1 of the procedure for initial client needs specification and procedure for tailoring model and decision procedure scripts;
  • Figure 24 shows part 2 of the procedure for further detailed analysis of vendors with assistance from at least one Analyst
  • Figure 25 shows parts 3 and 4 of the procedure wherein vendor selection is finalised, bid letters are provided to vendors, and proposal-counter-proposal procedures may be used.
  • Part 4 indicates means for post- vendor selection and follow-up on execution of project in order to provide not only added revenue, but means to update information in the area of the decision model;
  • Figure 26 shows means a decision process guide by which a decision support system may provide a guide through one or more steps in a decision process
  • Figure 27 shows a decision procedure governing an automated process in which decision objects may be employed to reveal or hide data regarding the condition of a system.
  • FIG. 1 shows a block diagram of a decision support system 20 in accordance with a preferred embodiment of the present invention.
  • System 20 includes processor means 24, input means 28, output means 32 and storage means 36.
  • processor means 24 is an embedded control processor, such as a Motorola 68HC16 and associated circuitry 40; storage means 36 comprises a ROM memory 44; input means 28 comprises a series of sensors 48; and output means 32 comprises a known controller 52 for a process which produces appropriate output signals 56.
  • controller 52 can, for example, be a rocket vehicle launch controller and sensors 48 may comprise launch status sensors, type of rocket (manned, unmanned) containing meta-data and data input signals, service request counters, timers, etc. and radio receivers for remote site data input. This specific implementation of the embodiment of Figure 2 is discussed in more detail below.
  • system 20 may be implemented from appropriate discrete components.
  • input means 28 can comprise one or more appropriate sensors 48, for example position, load, demand, velocity, pressure and/or temperature sensors, and database information for each of a plurality of reactors
  • output means 32 will comprise one or more means to generate control signal outputs, for example variable voltage signals to control dc motor speeds, solenoid valve or brake actuation signals, etc.
  • the signals from sensors 48 can be translated into an appropriate format for processing by processor and associated circuitry 40 by, for example, an analog to digital converter, a protocol converter (in the event that the sensor signals are provided over a network), or other suitable conversion means as would occur to those of skill in the art.
  • output means 32 may provide at least one input to a second decision support system wherein the input may control data and meta-data presentation to human operators.
  • process is intended to encompass commercial, industrial and other processes and to include, without limitation: mechanical and/or electromechanical operations; chemical process control; HVAC (heating ventilation and air conditioning) systems; robotic systems; aerospace flight control; biological injection systems; medical monitoring, control and/or alarm systems; power station and electrical power delivery and supply systems; vehicle systems and transportation control or any system where a pattern of inputs is analysed and a decision process is invoked to deliver a pattern of actions and outputs to an apparatus or system through mechanical, electrical or other means.
  • HVAC heating ventilation and air conditioning
  • processor means 24 is a general purpose processor and related circuitry 60, such as an Intel Pentium family processor; storage means 36 comprises a mass storage device 64, such as a Winchester-style disk drive, a removable media storage device 68, such as a 3.5 inch high density disk drive, and RAM and/or ROM memory 72 which is operably connected to processor 60; input means 28 comprises a keyboard 76 and/or pointing device such as a mouse 80; and output means 32 comprises a video display terminal 84, such as a SVGA graphic display, and/or a printer 88, such as a HP LaserJet IV.
  • Intel Pentium family processor storage means 36 comprises a mass storage device 64, such as a Winchester-style disk drive, a removable media storage device 68, such as a 3.5 inch high density disk drive, and RAM and/or ROM memory 72 which is operably connected to processor 60; input means 28 comprises a keyboard 76 and/or pointing device such as a mouse 80; and output means 32 comprises a video display terminal 84,
  • input means 28 and/or output means 32 may comprise a communications link 90, either in addition to or in place of the above-mentioned components, and communications link 90 may be in the form of a local or wide area network, a radio link, etc. which is also operably connected to processor 60.
  • system 20 can also embody a graphical user interface, provided by an operating system such as Microsoft Windows 95 operating system, executing on processor 60.
  • multimedia input means comprising camera 91, sound recording microphone 92, and output means of graphic images displayed on at least one graphical display terminal 84 and sound from at least one speaker 93 connected to said general purpose processor 60, may be used to record and may be used to play images and sounds accordingly.
  • input and output means may be provided by transmission signals 94, 97.
  • Said transmission may consist of electromagnetic emissions such as visible light, radio frequency and infra-red, and may consist of sound waves.
  • Said transmission may be passive from said monitor 84 and may be converted and stored into meta-data as provided by an instrument such as a C-PenTM 98.
  • said input and output may be realised using a video camera 91 , or special purpose input/output device such as a transceiver 95 and device 96.
  • said device may be an intelligent paper clip providing direction to at least one data source on a storage device.
  • Said device 96 may also provide data to at least one data file located on a storage device.
  • said data and meta-data input and output means may be used over a computer network using said network connection interface 90 for providing for at least one output signal and at least one input signal with at least one other node on said computer network.
  • system 20 receives various inputs through input means 28, processor 60 acting on these inputs according to instructions stored in storage means 36 and providing one or more outputs, via output means 32, which outputs recommend and/or implement a desired selection between alternatives and/or the operating state of the process or apparatus under consideration.
  • decision factor is used herein to refer to a factor or criterion used as an element to construct a hierarchically related tree.
  • Said tree contains a root decision factor that is the base of the tree from which all other decision factors are descendant decision factors.
  • a parent decision factor has at least one descendant decision factor below it.
  • a leaf decision factor has no descendant decision factor.
  • a decision factor with a parent is called a child of he parent decision factor.
  • Decision factors which directly share the same parent are called herein sibling decision factors.
  • Said decision factor tree elements may also be referred to as nodes and as criteria, said meaning and all respective definitions within this paragraph remaining the same as decision factor.
  • aggregate node is used herein to determine a designated decision factor that has at least one descendant decision factor and where at least one attribute is determined from at least one directly descendant (child) node attribute to be aggregated in said aggregate node attribute. Said descendant decision factors and all said attributes of said descendant child factors are removed when an aggregate model is stored or the aggregate node is caused to aggregate. Said determined attributes from descendant decision factors are retained in the selected at least one parent aggregate node at least one aggregate attribute.
  • An aggregate parameter as used herein is a parameter used to determine at least one attribute or multiattribute value to a determined adequate degree of accuracy when aggregated data is removed during an aggregation process.
  • Said at least one aggregation parameter provides means of representing lost attribute data, and may be regarded as providing means for a compression algorithm representing said missing data.
  • said at least one aggregate parameter may be assigned to an individual aggregate node following aggregation and removal of descendant nodes of said aggregate node.
  • said at least one aggregation parameter may be related to at least one alternative-specific attribute.
  • Said aggregation parameter is useful in dynamic data changing where said aggregation parameters provide means to represent missing node attributes such as ratings and weights of said missing descendant nodes.
  • said aggregation parameter may determine a mathematical formula, set of rules relating attribute values, and at least one table of values.
  • said aggregation parameters may represent a set of coefficients to be used in a default mathematical equation determined as most likely to accurately represent missing attribute data in an aggregate model.
  • a hidden aggregate node is a node that is present and may be displayed on an output device such a graphical display, and may provide visibility for selected data and meta-data from at least one descendant node, where said at least one descendant node is hidden (i.e. is not exposed and is invisible) on any display device such as a graphical display. Attempting to expand the tree below the hidden aggregate node cannot expose said descendant hidden nodes. The descendant nodes are physically present in the aggregate model.
  • the hidden node may appear like an aggregate node and have meta-data fields containing aggregate data.
  • a hidden descendant node may be used for purposes of accurate numeric computations and may be revealed to selectively show data and meta-data ⁇ when at least one specific event occurs to expose the node.
  • Said event may be the result of at least one manually inserted key, and may be by means of at least one manually input value through a graphical user interface, and may be from at least one input signal over a network.
  • Said hidden descendant node may contain proprietary data and may further contain secret data where it is desired to have said data available for purposes of determining a value. By this means said proprietary and secret information is prevented from disclosure, while use is made of said data in value aggregation.
  • a trimmed node is a parent node from which all descendant decision factors have been removed.
  • a typical trimmed node becomes a leaf node on the tree of the output aggregate model whose aggregate rating for at least one alternative is determined from the normalised weights and ratings of descendant decision factors that are removed on trimming.
  • said aggregate rating is the normalised weighted average determined from descendant node ratings and normalised weights. No other information may be presented or retained in a trimmed node.
  • a rating or score as used herein is a value assigned to a decision factor to represent the worth, impact, direct value, or relative amount of the factor for at least one alternative toward the decision.
  • Said value may be a cardinal or an ordinal value, or a verbal expression identified with at least one numeric value.
  • a rating is a value applied to leaf decision factor, and a score is the value determined from combining weight and rating value of at least one decision factor.
  • a rating value may be translated into a utility value by means of a utility function or rating method.
  • a 'standard' score or rating may be an industry standard score or rating obtained from published industry standard target and requirement data. In another embodiment, said standard may be determined by the decision support system from data among all alternatives rated in the model.
  • a sub-set of alternatives may be selected to provide a 'decision-specific standard'. If the alternatives are vendors, said sub-set standard values are referred to as vendor-specific standards. In one embodiment, said standard may be an average value. In another embodiment, said standard may be determined as for example a statistical mean or mode.
  • utility method and rating method as used herein define a computational means of transforming at least one raw input value for at least one criterion to at least one utility value, thereby providing a sense of worth of the at least one raw input value toward the goal of the current model on the basis of a common scale of utility. Said raw rating is applied for at least one alternative for which said criterion is applicable.
  • the said raw rating value and utility value may further be translated into a verbal statement to indicate in more familiar terms the worthiness of the assigned value toward the model goal.
  • the term alternative represents a physical item, or may mean a choice between at least two actions to perform. Said definition is intended to include the case wherein no physical alternative or action is selected, and may represent for example at least one state of a system against which a performance measurement is made, and said selection of an alternative represents a means to measure a system's performance for said alternative.
  • said alternative may represent a vendor product, and said vendor product may be considered composed of a set of at least two vendors component products, said at least two vendor component products being thereby evaluated individually and said vendor component products may then be combined accordingly to provide a composite solution, and said composite solution may incorporate at least one other vendor product or component product.
  • said alternative may be at least one system state, said at least one system state may be composed of a set of at least two subsystem states. The performance of a set of inputs may then be rated for each said system state to determine by numeric means the validity of said system state and said at least one subsystem state.
  • the term decision factor attribute is used herein to designate a value or meta-data field that is directly related to at least one decision factor.
  • An alternative-specific attribute is an attribute that is directly related to at least one alternative.
  • said alternative specific attribute may be determined from a rating or score value of at least one decision factor in the hierarchical model tree.
  • said rating and weight attribute of a decision factor for at least one alternative may be determined from at least one alternative-specific attribute.
  • a constraint is a value assigned to node to which at least one node attribute for at least one alternative is compared, the outcome of which generates a numeric output.
  • said output may be in the form of meta-data which in of itself may be useful and may provide information to assist in determining the suitability of an alternative.
  • said output may not of itself eliminate an alternative in a selection process, and is therefore a weak constraint in this sense.
  • said output may become a strong constraint wherein in of itself said output may eliminate an alternative from the decision process.
  • a typical strong constraint may be a mandatory requirement.
  • Meta-data refers to non-numeric information that is required to qualify rating, weight or other numeric data, and may provide other non-quantitative information useful in determining the suitability of an alternative in the context of a decision, and may contain instructive information on the usefulness of the model, and may further contain explanatory text, sound, graphic image and animated and moving images concerning a decision factor and attributes such as weight for the decision factor. Meta-data may also be applied to result information, alternative-specific attributes, charts and decision procedures, import/export procedures of data, and report templates and report generation.
  • Meta-data may contain embedded links to other documents consisting of text and multimedia reference objects such as sound images and animation and movie files that may be located locally and at locations on a computer network, and may contain direct links to expert advisors over a telecommunications network which may be by means of electronic mail and may include direct interactive voice and image connections such as those provided by products like Microsoft NetMeetingTM and WhiteboardTM [ESR1] and may use direct voice and images through at least one microphone 92 and at least one local camera 91, and at least one speaker 93 connected to at least one general purpose processor 60.
  • text and multimedia reference objects such as sound images and animation and movie files that may be located locally and at locations on a computer network
  • direct links to expert advisors over a telecommunications network which may be by means of electronic mail and may include direct interactive voice and image connections such as those provided by products like Microsoft NetMeetingTM and WhiteboardTM [ESR1] and may use direct voice and images through at least one microphone 92 and at least one local camera 91, and at least one speaker 93 connected
  • An Analyst as used herein may be at least one expert knowledgeable in the art of building decision models, and may be knowledgeable in the areas of application of the model.
  • a client is defined herein as one who may use an aggregate decision model in order to reach a decision, and may take input from the aggregate decision model to reach a decision independent, of or with assistance from one knowledgeable in making decisions in the field to which the aggregate model applies.
  • Said client may be a decision-maker, and may be knowledgeable in the application area of the model.
  • the term "to tailor” means the act of reducing and customizing the data set of a model through a process including node aggregation, elimination of criteria and alternatives and said attributes of said criteria and alternatives, causing to store the reduced model as a customised aggregate model.
  • One preferred embodiment of tailoring is by intervention of one knowledgeable in the art of tailoring a model for specific applications.
  • Another preferred embodiment is the process of reduction and aggregation by means of at least one signal representing an automated message which may cause at least one node to aggregate selected descendant data in the said model, and may cause at least one node to hide descendant children and their attributes, and at least one second signal that may cause at least one node to disaggregate, and may cause at least one hidden node to be made visible.
  • Said at least one signal may also cause at least one attribute of at least one criterion to change, said attribute being, but not limited to, the weight of the criterion, score for an alternative, the indicated relevance of the criterion in the model, and the presence or absence, inclusion or exclusion of said criterion in a decision and control process.
  • a decision model is comprised of a plurality of decision factors organized in a hierarchical fashion, and containing information including values and other decision factor related data therein, and a plurality of alternatives with associated data including rating values assigned to decision factors, weights and meta-data, model goal information and other related meta-data as deemed necessary to assist in making a decision by one knowledgeable in the art.
  • a chart is defined herein as a means of representing numeric data and meta-data in the form of a table or a graph or combination of both thereof.
  • a chart contains numeric and text data of at least two attributes, said attributes consisting of at least one numeric value and may contain at least one meta-data datum attribute contained in the model.
  • Said chart may further comprise of a table of rating and score values expressed as verbal or numeric data.
  • a chart object as used herein may be a bar or point or other symbol or shape representing at least one data value.
  • the term 'dragging' refers to the action of focusing on at least one chart object presented on a graphic display with an input pointing device such as a mouse, selecting said at least one chart object and causing said at least one chart object to change in size, position, shape by moving said pointer.
  • an input pointing device such as a mouse
  • such dragging may cause on a time delayed basis re-determination of the at least one value represented by the at least one new size, position, shape of said at least chart one object.
  • “Simultaneous dragging” refers to the act of selecting at least two chart objects such as bars or points representing at least one data value in the chart, and changing each independently prior to re-determination of at least one value represented by the said at least one change in position, size, shape of said simultaneously dragged objects. Such simultaneous dragging selections and changes may be performed by providing sufficient time to select a subsequent object prior to re-determination of said at least one value.
  • said chart objects may be pre-selected as a group and caused to change by dragging simultaneously. Multiple points on a chart may therefore be changed in one dragging operation.
  • a decision object may consist of at least one meta-data field containing at least one instruction on the use of said decision object, and may include at least one chart and at least one meta-data instruction on the use of said chart.
  • said decision object provides output means to instruct a client in the method of proceeding through a step in the decision process using said representation provided by a chart or at least one numeric value or meta-data value.
  • the said decision object may include meta-data identifying purpose of said at least one chart, and may include at least one instruction on the use of said at least one chart.
  • a visual decision dictionary consists of at least one decision object and at least one associated meta-data field wherein is entered at least one instruction pertaining to the use of the said visual decision dictionary.
  • a decision procedure consists of at least one visual decision dictionary and at at least one associated meta-data field wherein is entered at least one instruction on the use of said decision procedure.
  • a decision process consists of at least one decision procedure and at least one metadata field wherein is entered at least one instruction pertaining to the use of said decision process.
  • a decision procedure, visual decision dictionary and decision object are identical.
  • said decision procedure contains a plurality of visual decision dictionaries with meta-data guidance to guide in the use combined use of said plurality of visual decision dictionaries, wherein each said visual decision dictionary consists of a plurality of decision objects, said visual decision dictionary containing at least one meta-data instruction concerning use of said plurality of decision objects in combination.
  • Said meta-data guidance can be considered in this embodiment as a set of scripts.
  • a decision process consists of at least two steps taken in sequence, the said steps being determined by at least one Analyst. In another embodiment of said process, said steps may be determined by at least one other who may be knowledgeable in the art of making the said decision.
  • a decision process guide is a guide wherein said decision steps are provided in an outline format, said each step being provided with means to execute said step and complete said step as best as may be possible, as may be determined by at least one expert in the step procedure. Said guide may provide guidance for the use of means to execute and proceed through said step.
  • the terms to store or output in reference to a data structure consists of the action of processor means in writing said model consisting of a determined data structure from temporary storage medium such as random access memory attached to a computer processor to a medium regarded as permanent storage such as a CD-ROM, Winchester style hard disk, programmable read only memory (PROM), removable flexible diskette, magnetic tape and any other medium deemed by one knowledgeable in the art as a permanent storage for said data structure.
  • temporary storage medium such as random access memory attached to a computer processor
  • a medium regarded as permanent storage such as a CD-ROM, Winchester style hard disk, programmable read only memory (PROM), removable flexible diskette, magnetic tape and any other medium deemed by one knowledgeable in the art as a permanent storage for said data structure.
  • PROM programmable read only memory
  • Said storage process in one preferred embodiment consists of means to encrypt said data structure on output by means of an encryption process such as PGP (a public domain encryption/decryption procedure) or any such decryption technique as may occur to one knowledgeable in the art
  • PGP public domain encryption/decryption procedure
  • the decision support system may use PGP decryption techniques to decrypt said model structure.
  • An Authorising site is an authorized entity able to service license key code requests from client sites, and that has access to information to validate the eligibility of a client to a license.
  • said entity may provide service through a phone-in help desk system.
  • the entity may be an automated system resident on a computer network such as the Internet.
  • said service may be on an internal corporate network.
  • Said automated system may be able to review client license requests automatically, and transmit at least one message with a response in regard to said license request, where said response may be to provide said license key code, reject said license key code request, and send a request for additional and repeat information.
  • a Client is an entity serviced by an automated advisory service and may make use of an aggregate model.
  • the entity may be a corporation, a government organisation, or at least one individual.
  • the entity may be a machine consisting of machine readable code executed on at least one processor, said processor having storage means such as random access memory and read only memory, and at least one permanent storage device such as a computer readable disk.
  • a licensee as used herein refers to a Client who has applied for and been granted a valid license by at least one authorising site.
  • a decision support system is a processor implemented code that, in accordance with Figure 1, the processor 20 reads the code from a storage device such as a Winchester type disk 64 or from a removable storage medium such as a 3.5" disk or CD ROM 68.
  • An Analyst using said code causes said processor to read data representing at least one decision model from at least one storage device.
  • the decision model may have been designed, assembled and populated with data and meta-data by at least one Analyst familiar with creating such models in the field to which said model applies .In one embodiment, said populating may be done using said same decision support system.
  • At least one Analyst knowledgeable in the art of tailoring said models, and knowing the purpose to be accomplished by current said model proceeds through the process 101 indicated in Figure 4.
  • the Analyst selects a node using a pointing device such as the mouse 80 in Figure 3 or by keyboard entry 76 using arrow keys and a selection key on the keyboard, and indicates at least one criterion node at which aggregation is to take place such as Manufacturing 250 in Figure 7.
  • the Analyst may select functions from a menu and sub-menus 350 and 351 of Figure 9 actions to designate said selected node as an aggregate node or as a hidden node, or may trim the current node, its status so indicated by a mark or symbol 250 beside the designated node.
  • icons designating said aggregation functions may be present in for example, at least one Microsoft Windows standard toolbar for the model tailoring window.
  • Descendant nodes whereof the data is to be aggregated may be indicated by other symbols 251.
  • the Analyst may select at least one other node such as 'Supply chain management' 253 wherein at least one descendant node and said descendant node's data and meta-data may be hidden but retained in the aggregated model.
  • the Analyst may further select for each aggregate node specific descendant node data to be aggregated as indicated in figure 10 401 and apply further conditions to further filter descendant node attributes to be in or out of the aggregation procedure 404 and 405.
  • Such conditions may include the descendant factors with the best and worst scores for at least one alternative, the descendant factors which most deviate from the average score; determining the level below the aggregate node at which descendant factors are represented in the aggregate node 408.
  • the meta-data from the lowest descendant nodes may not be aggregated included in said aggregation node.
  • only descendant node attributes directly descendant (the level below) from the aggregate node are aggregated as a default condition, all descendant data from the lower nodes being aggregated transparently (i.e. aggregated and removed on aggregation without choice of preservation of specific parameters for calculation and choice of aggregating meta-data information other than the, on one preferred embodiment, the weighted average and other parameters as required time-to-time by the decision support system). Aggregation may be selected to go to lower levels 408.
  • the Analyst may select the aggregate node field 403 into which meta-data and summary numeric data are placed for client review.
  • the Analyst may select any of the available data and meta-data fields 401 for aggregation into at least one of the aggregate node fields selected in 403, and include specific items such as multimedia files and direct ('hot') links to experts via E-mail or dial-up line 410, and may include a specific tutorial related to the node.
  • a restriction of selecting only one aggregate field for each sub-factor meta-data field may be imposed as a preferred embodiment.
  • the Analyst may select how the scores and weights may be represented in the aggregate field by selecting specific rating and weight interpretation methods 405, 406.
  • a score of '90' may be translated into the verbal score of 'Excellent', and a score of '30' may indicate a 'Poor' score.
  • a weight which is over 5% may be regarded as 'very important', while a weight of 0.2% may be considered 'Unimportant'; the most important factor may be identified by the verbal statement 'Most Important', and the least by 'Least Important', and so on.
  • Said rating and weight interpretation methods may be created and edited as part of the customisation process 407.
  • the verbal scores and weights may be combined into sentences such as indicated in 256. Numeric values may also be included 409 to show at least one score and at least one weight, and at least one verbal statement to indicate the sense of the value 255.
  • a sentence in a selected aggregated meta-data field 403 may then read
  • Score information may be written, for example, a typical output may read: 'Manufacturing scored Average for alternative Softwares Inc. [53.8], Good for Barndon Plus Corp. [68.2] and Poor for Pertall Office Suite [42.4] and carries a weight of 5.75% toward the decision.
  • For further source information contact Harry.Biggs@arlingsoft.com or at URL www.manuf.com/ ⁇ Biggs or call 999-123-4567'
  • a Request for Proposal (RFP) 804 may be generated by the Analyst for the client following aggregation. Said RFP may be generated from the aggregate model.
  • Question and Instruction meta-data fields such as 'Score Question' 300 in the descendant factors and aggregation node meta-data fields may assist in generating questionnaires.
  • Said reports may be generated by the decision support system as a meta-data output, and may include charts generated by the client. Reports may be generated and sent to an output device such as a printer 88, or encapsulated in an electronic message and sent by electronic mail over a communications network using a network interface 90.
  • At least one minimum requirement may be established from at least one constraint.
  • at least one exception report may be generated for at least one alternative in the aggregated data and said at least one exception report submitted to the at least one client.
  • Such information may be selected 40 land assigned to the selected aggregate meta-data fields 403, and may be generated automatically by the decision support system for automatic presentation to the Analyst. The Analyst may then generate exception reports and may submit said at least one report to the at least one client.
  • the Analyst may recognise an alternative may not have at least one feature, said at least one feature being represented by at least one decision factor, and these at least one feature may be compensated by at least one other related decision factor, and it may be desirable to avoid reducing the value of the alternative for a particular category of criteria containing the at least one decision factor representing the at least one missing feature.
  • the Analyst may then mark the at least one criterion as 'Not Applicable', causing weight of said at least one criterion representing said at least one absent feature to be distributed to sibling and related criteria in the model parent category that compensate for the absent feature, thereby mitigating the undesirable result of reducing the value of the category containing said at least one missing feature for said alternative.
  • the Analyst may store the aggregate model to a storage medium such as a Winchester drive 64 or removable medium such as a CD-ROM or removable disk 68.
  • the model may also be exported to a spreadsheet or other formatted document 72, and may be encapsulated and transmitted in an electronic mail message.
  • said nodes designated as aggregate nodes in figure 4 104 may be caused to aggregate immediately 121, either before parameters are determined from 106, or after said parameters are determined from 107.
  • storing the aggregated model 110 causes said selected descendant aggregated nodes to aggregate data according to the selection choices made by the Analyst prior to assigning a unique model identification code 109, and storing the aggregate model 122.
  • the Analyst may select to cause the aggregation process to occur on designating the currently selected node as an aggregate node 103, using default aggregation settings.
  • the Analyst may be unable to store the aggregated model under the same name as the originally opened model 110, and may be forced to save the aggregate model at a different location.
  • Said model may be given a different unique code 109, and said unique code may provide indication of the parent model from which the model was tailored.
  • Aggregation parameters may be required for estimating at least one multiattribute value used to assess the rank and value of at least one alternative.
  • Said aggregation parameters may be used in certain decision procedures and may be used to generate different scenarios that may include changes to at least one weight and at least one score, yet such weight and score and other data may be removed through the aggregation process.
  • Said parameters provide means to approximate values enabling calculation of at least one required multiattribute value.
  • a multiattribute value may be any value incorporating a plurality of other values and used to determine the rank ordering of a plurality of alternatives.
  • said multiattribute values may include standard deviation, Weighted Average, Matching Index, Weighted Average Composite Index, and Percent Match as calculated in Arlington Software's product ERGO.
  • other common multiattribute values may be calculated as required from time-to-time, and may include cost and risk values that may be related to at least one criterion attribute and at least one alternative attribute.
  • the executing machine readable code causes said processor to process said tailored model generated by the Analyst, but not yet aggregated, and causes said process to determine and store said aggregate parameters 107 for at least one alternative.
  • said general purpose processor may not be adequate, and it may be determined that at least one other processor executing machine readable code to read the Analyst tailored and not yet aggregated model data, thereby causing said other processor to generate at least one aggregation parameter 114. Said at least one aggregation parameter may then be stored with the aggregated model 108.
  • the Analyst may determine that the aggregation parameters may not need to be processed, and thereby saves the aggregate model without determining said aggregation parameters 110.
  • the said at least one Analyst may determine relevant additional alternative-specific attributes (as in figure 19 and 20) of the Alternatives 1300, 1400 that are separate from decision factor attributes.
  • Said alternative-specific attributes may be inherited in a hierarchical manner as indicated by 1302 and 1303 in figure 19.
  • Attributes may be assigned a name 1401 and may be assigned meta-data information 1402.
  • Other data may be given and may include at least one value 1403 selected from a list of at least two values 1404.
  • An indication that the attribute may contain numeric data may be given 1405.
  • said numeric value may be related through an equation 1406 to other alternative attributes 1410 and may be related to at least one factor attribute, said at least one factor being selected from the model tree 1412.
  • At least one factor attribute 1413 may be selected for inclusion in the equation 1409.
  • Various mathematical functions may be selected 1411 for inclusion in the equation 1409.
  • Said at least one factor and at least one factor attribute and said alternative and at least one alternative-specific attribute may be identified internally in the model data structure through an identification code.
  • Machine readable code of the decision support system may then be executed to read and interpret said identification codes and mathematical functions, thereby causing said processor to determine the value outcome of the alternative attribute.
  • said value list 1404 may contain text and meta-data.
  • the default value may be selected from the list of values, and assigned to the attribute of that alternative if no other value is selected for the attribute of the alternative.
  • the at least one assigned value and at least one calculated value may be assigned to all alternatives 1407.
  • Said alternative attribute may be processed according to selected means 1414. Said values may be displayed as summary information for the alternative 1501, and text and meta-data information about a highlighted alternative attribute may be indicated in the same window 1502. Further, said at least one alternative attribute may then be used to sort and filter at least two alternatives according to mathematical or textual relationships 1504 and rules 1505, said rules combining said at least two alternative attributes into a common means to sort and filter at least said two alternatives.
  • a decision support system is provided to the client wherein said decision support system may have at least one feature rendered ineffective.
  • said feature is removed from said decision support system.
  • Said decision support system is referred to as a client decision support system.
  • said means to determine key codes and sub-keys may be removed.
  • means to determine aggregate state of at least one decision factor may be rendered ineffective or may be removed.
  • Other feature limitations may be determined according to required product delivery functionality and may be determined by said at least one Analyst prior to delivery to the client.
  • Said client decision support system may further be enabled to input only one determined at least one aggregate customised model
  • said client decision support system may be determined as providing a limited set of features that have been determined as a common requirement by most clients, enabling a standard client decision support system to be provided.
  • the Analyst may determine prior to final storing and providing the aggregate model to the client, determine a security code key 120 for said model.
  • Said security code key may include sub-keys to determine additional restrictions on distribution and availability of said aggregate model, said conditions and limitations being determined by said client decision support system on input of said aggregate model.
  • said security code key and sub-keys may determine the number of licensees, and may cause limits on the number of simultaneous readable copies that can be distributed at the client site over a computer network.
  • said key codes and sub-keys may limit the number of concurrent accesses to a single model, the model being located on a single storage device.
  • an additional sub-key may also be provided to limit the number of storage devices on which the model may reside at a client site, thereby allowing multiple concurrent copies to exist.
  • said code keys and sub-keys may enable only named users to input said aggregate model. Said key and sub-keys restrictions may be activated following delivery of said model, and by an additional process initiated between the client decision support system at the client site 900 and an authorising site 920, following initial reading of said model by the client decision support system. Once the authorisation codes have been input, the model may be used according to the conditions in the license as determined by the code key and sub-keys.
  • Processes of model customization may be by means of at least one facilitation process with at least one decision-maker 111.
  • specifications 112 from at least one expert in the field may be provided to at least one Analyst, said specifications providing the basis for the said at least one Analyst to tailor the decision model.
  • One such embodiment of an expert may be a consultant in the service of the client.
  • the Analyst or one Expert in the field of a specific aggregate model may execute the machine readable code and select at least one aggregate model.
  • the Analyst or Expert may then select at least one chart from a set of charts 150 ( Figure 5) or determine at least one chart therein.
  • an appropriate question may be formulated to ascertain whether a client may require the chart as an attribute of the chart 155, and explanatory and instructional text, meta-data and links may be entered and edited 156 to correspond to the said selected chart, thereby customising the meta-data for the client, and creating a decision object for said client.
  • Multimedia embedded documents and animated figures may be included in the meta-data makeup 165 to provide additional information and clarification of the use of the chart.
  • the meta-data may be used to assist said Analyst and Expert and client so that said Analyst/Expert/client may effectively utilize the chart to assist in the decision.
  • Said meta-data can also serve in training sales staff and future experts and analysts, and provide means to effectively communicate the decision process to clients.
  • Each decision object resulting from said chart and meta-data instructions thereby renders at least one complete decision aspect as relevant as possible toward the decision for the said client.
  • a single decision object is insufficient in dealing with all aspects of a decision.
  • a visual decision dictionary may then be used where said visual decision dictionary consists of at least one decision object used respond to one aspect of the decision 158, and may have its own meta-data associated with it 159.
  • a visual decision dictionary may incorporate at least two charts in combination.
  • An example of a visual decision dictionary is the sensitivity analysis 1200 whereby the sensitivity of a particular alternative to its ratings and weights may demand the simultaneous presence of three charts: one representing alternative ratings such as Option Rating Bars 1210 in Figure 18, one representing the model criteria weights such as the benchmark chart 1211, and one representing the final evaluation of the alternatives such as the score breakdown 1212. Preferred embodiments of said charts are further illustrated in Figure 16 as charts 501, 502, and 507 respectively.
  • the sensitivity visual decision dictionary may be used to observe the effect of changing the weight and rating individually or together, to observe the outcome in terms of rank.
  • FIG 11 An embodiment of said process is indicated in figure 11 whereby said at least one rating value may be changed by dragging 606, and at least one weight may be changed by dragging 603, and said effect noted in the score breakdown graph 609, representing a summary of the value outcome on said alternatives resulting from said changes of ratings and/or weights.
  • Meta-data for the visual decision dictionary may be used for example to explain the implications of changing the rating of certain alternatives (for example, if the alternative is a vendor, in six months the vendor may have added new features and improved current features to its product, and hence the rating improvement impact may be assessed), and weight change implications of certain criteria may be explained from expert opinion, thereby making sensitivity analysis meaningful and realistic.
  • Another such visual decision dictionary may be to observe the change in cost-effectiveness and rank of an alternative as rating and cost information are changed.
  • a combination of visual decision dictionaries may be provided to determine a decision procedure 167 wherein different aspects of the decision - in this case sensitivity outcome and cost - may be examined, and the combination provided with meta-data instructions and guidance in use of said combination.
  • the decision objects illustrated by 1210-1212 may be replaced by visual decision dictionary titles, and associated meta-data fields may then refer to the decision procedure and included visual decision dictionaries.
  • Decision procedures may further be stored in a structured knowledge database 162 for later retrieval 161, 164 and incorporated into other customised decision models where they be edited and customised for the another client. Additional constraint data 163 may also be included and may be incorporated as separate object data sets 163, or may be bound with each decision object 165 in the knowledge base.
  • the visual decision dictionary may be stored with the model 160.
  • each dictionary may be accompanied by at least one equation 157 determined by the Analyst or one skilled in the art.
  • Said at least one equation may use the at least one said numeric datum available in the accompanying chart and at least one result of the at least one said equation used to assist in determining at least one negotiation point for at least one alternative.
  • Said at least one equation may use at least one constraint 165 applicable to the at least one chart composing the decision object.
  • the visual decision dictionary may be selected from a list of visual decision dictionaries in the knowledge base 161. Said selection may be assisted by at least one question requiring at least one response by one skilled in the art in order to select at least one visual decision dictionary from the knowledge database.
  • Said visual decision dictionary knowledge base may also contain at least one constraint datum 164 for at least one chart contained in a visual decision dictionary. The at least one constraint datum may be determined from proprietary or confidential data received through interview processes with clients and vendors, and from publicly available material.
  • the decision support system may provide the Analyst means to create a flow-through process for the client, said flow-through process being indicated by a set of instructions.
  • an Analyst or one knowledgeable in the art may have provided code keys and sub-keys as part of a license condition, said keys and sub- keys being stored with the aggregate model.
  • said client decision support system On inputting the aggregate model, said client decision support system is caused to determine the license validation state of said model. If the client decision support system determines that the license requires a second key to continue inputting said model 901, said client decision support system issues a request for a second key, said request may be output to a graphical display.
  • said request may be transmitted by electronic mail, or may prompt for personal communication between requesting site 900 and authorizing site 920.
  • the authorizing site Upon receipt of the request for authorization 921, the authorizing site verifies the request is eligible for license 922. If the requesting site is eligible, the authorizing site issues an appropriate at least one license code 925. Alternatively, it may provide a message indicating the request for license code is refused 924.
  • Said information may be transmitted verbally or by an automated response system such as electronic mail 926, or automated signal over a computer network to said client decision support system.
  • receipt of an electronic message containing said at least one license code causes machine readable code at the authorizing site to transmit message to the said machine readable code currently executing on said processor at the client site, the said at least one license code may then be entered by automatic process through machine readable code at the client site.
  • a licensee may enter the license code manually.
  • said client decision support system verifies said at least one second key is a valid key, prior to continuing input.
  • Said second validation key is then stored 906 where in one embodiment said stored license may be with said model.
  • said validation key is stored in a location known to the client decision support system. In either case said model is licensed for use.
  • the model is not read and the said model file is closed 907. If the license key code is valid, the model file data is read by the client decision support system 908.
  • the license period is verified 904 for the said model, and if said license period is exceeded, said machine readable code may prompt the licensee to update the license 903, whereupon the process of authorization 921 through 926, and validation 905 would again take place.
  • the status of the license may, in another embodiment, be indicated prior to license expiration, thereby warning the licensee of the time left before expiration of said model usage.
  • said license verification process may determine that the license is violated during an attempt to input said model by a client decision support system. In this case, input is denied for the at least one client decision support system.
  • specific features of the model may be made available for limited time periods.
  • At least one sub-key may be issued to activate at least one the feature of the model.
  • hidden aggregated data may be made available for a limited period, or the ability to use at least one report template and at least one specific visual decision dictionary.
  • At least one sub-key may be issued to de-activate said features, and may be transmitted automatically over a computer network.
  • Control of distribution sensitive information can be provided through this methodology by at least one central administrative center with means to provide said service. Such control may be provided as a service in the automated advisory services and decision and control products.
  • machine readable code may be executed to transfer the model license as in figure 15 from the current processor on which the license resides 1001, to another processor 1007. The process may involve the following steps:
  • the model may then be copied from the current processor, and the license code for the model marked invalid 1005.
  • the model file may be deleted from the current processor 1006.
  • only the license information may be transferred, the act of transferring said license to the intermediate medium then being followed by invalidation of the model on the first processor 1005.
  • the license may be transferred directly to the second machine over a computer network 1003, and said model may remain resident on a network server with a shared storage device 1020.
  • the limiting on the number of licenses issued to a client is maintained by the transfer-out-transfer-in process by adding a restriction.
  • only the authorized administrator can initially provide licenses up to the limit set by the license agreement.
  • a set number of license keys are provided and given one at a time by the administrator up to the limit allowed by the license. From then on, only this number of licenses can be transferred through the transfer in-transfer out process 1000.
  • the processor identification number is read automatically on first installation of the model by the client decision support system executed on the specific processor authorized by the administrator, and may not be read by another client decision support system executing on a processor with an identification number not corresponding to the authorized processor identification number.
  • the said client decision support system consisting of machine readable code is executed at a client site, causing said code to read at least one aggregate model.
  • the licensee may change at least one weight, and may change at least one rating, and may change at least one meta-data field.
  • the change of at least one datum may cause said at least one processor to determine a new outcome, and output said data to a display device such as graphical display monitor. In so doing, aggregate values are utilised in the process.
  • a licensee may be provided with at least one Decision Procedure in the form of at least one decision object contained in at least one Visual Decision Dictionary (VDD) that may be contained in a decision procedure 181 by the said at least one Analyst with said aggregate model 182.
  • VDD Visual Decision Dictionary
  • the licensee may select at least one decision object in the decision procedure and display the at least one decision object on a graphical display. Said client may then proceed to analyse the information contained in the model with said guidance of the meta-data, and may add additional information such as constraint data and meta-data comments and embedded data links.
  • the licensee may select at least one visual decision dictionary by pressing, for example, a tool button 600 in the window provided. Data from the visual decision dictionary may then be included in at least one report template, and the licensee may then select at least one alternative amongst the at least two alternatives for further examination and report generation.
  • separate at least one constraint information may be determined to characterise the requirements of a client.
  • Said constraint information may be entered manually by means of a keyboard 76, thereby modifying or adding at least one constraint data item in the said provided model (183 and 185).
  • constraint information may come from a client database, data warehouse or other source available to the client 184. Constraint data from all sources may then be combined 187.
  • Said constraint information may be, for example, the requirement that a product feature includes the ability to read files of a specific format. Said feature may be present to a limited extent - e.g.
  • the file read supports only a version of the file format that is lower than that of the version used at the client site, thereby reducing the attractiveness of said item, and may cause a need for in-house customisation, and said customisation may cause increased cost, and said cost resulting in a negotiation requirement with the vendor (the alternative).
  • the decision model by the Analyst in step 157 that may relate the rating value of the alternative to added cost can be customised by the Analyst for the client, and evaluated and noted as a negotiation point in steps 182 through 190.
  • at least one decision object may be used to provide information of total cost of ownership. As indicated previously, if a value does not meet a specific constraint condition, cost of ownership may increase.
  • chart 504 represents a quadrant analysis graph with weight of at least one decision factor plotted on the y-axis, and rating value of said at least one decision factor on the x-axis.
  • Decision factor values on the left of the cross-hairs 506 in figure 16 are important decision factors judged as poor performers, where poor performance indicates a need for additional customisation costs of a vendor's selection.
  • the related costs of the poor performance may be aggregated into a net cost, and added to the total cost of the alternative. Such costs may then be used for negotiation purposes.
  • the formulae to determine said costs may be embedded with the attributes of the decision factor as part of the model data structure.
  • Said formulae and associated values may be inherited by charts, or assigned and used in estimating costs and other values in a chart, as executed in step 157. Said assignment may be done by the Analyst, and may later be modified by the client using the client decision support system. Cost data may be determined from other clients, and vendor projects, and may be determined from similar experience at the client site.
  • the licensee may determine the need to operate at least one decision object in a dynamic mode to explore various scenarios of attribute values.
  • the data for said at least decision object is transferred to temporary storage.
  • Said temporary storage may be a computer storage device such as a disk data structure, or it may be in the form of random access memory. In either case, the data is maintained separate and apart from that in the said provided model data.
  • at least one data point in at least one decision object chart representing at least one variable in the decision may be changed by use of a pointing device and dragging at least one chart object to a new position on the chart, or entering new values in a table.
  • Figure 11 demonstrates at least one example of said dynamic mode.
  • rating values for a plurality of alternatives and decision factors are represented by the chart bar objects 606, and said chart bars may be adjusted 610, and the effect observed as a change in position indicating a change in the rating, and said change may be transferred to another chart such as the quadrant chart where a new value is determined by a change in position 608.
  • the weight and rating of at least one decision factor may be simultaneously changed, and the change reflected in the length of the y- value bars 607 and 608. Changing both weights and ratings of a decision factor can be accomplished by moving points in the quadrant chart 608, thereby simultaneously changing the ratings of at least one alternative, and the weight of at least one decision factor.
  • Cost/Weighted Average chart 605 may be changed as in the Cost/Weighted Average chart 605 by means of moving the data point chart objects.
  • the calculation update may be performed 'on-the-fly'.
  • the updates may be selectively applied to each chart. Said changes may then result in at least one what-if scenario to be established.
  • Each new scenario may then be saved using a toolbar tool 602, and retrieved using a second toolbar button 601.
  • menu items may be added in for example the File menu item, as is standard practice in Microsoft Windows operating system applications.
  • said scenarios may be stored in the same model structure, and presented by means of tabs, and special charts wherein said scenarios may be displayed at the same time, and may be compared.
  • Said comparison process may follow a scripted procedure as determined by the Analyst or one knowledgeable in the art, and may provide grounds for negotiation input by, for example, increasing the rating of one feature such as Product Functionality to indicate at least one improvement of the vendor product feature set. Said required improvement may then lead to specific analysis of product features that are lacking, as in the absence of support for a particular operating system. Said vendor may then be required to reduce the price, or provide at least limited support for the operating system, the details of which may be provided by the at least one Analyst using the Analyst's more detailed model and information content. Said information content may for example indicate the cost to the vendor in upgrading said product to provide the support, and may include the anticipated cost to the client due to its absence.
  • said point is available for negotiation, and negotiations may thereby be assisted by the at least one Analyst.
  • the value outcome of the said changes in ratings and weights may be detailed in table format, as indicated in 611.
  • values may be entered into the cells and may then change said values as though the points had been moved on the chart, and may cause said chart objects to change accordingly.
  • a proprietary algorithm developed by Arlington Software Corporation for its product ERGO a special data aggregation technique is required.
  • One component of the equation relies on the generation of summed absolute differences between the expected (desired) contribution of a criterion to the decision, and the actual contribution the item provided for each alternative score, for each alternative.
  • the aggregate node may contain a linear sum of prior values, but in the case of the Composite Index, the final aggregation value cannot be known if any weight or rating is changed in the system. This is because the contribution of an aggregated criterion is based on the original weighted average of the original aggregated model supplied by the Analyst, not the new weighted average value arising from changes in the data. In order to understand this, we consider the determination of the Composite index.
  • the weighted average is a linear transform and it is easily aggregated.
  • the Composite Index is different. For aggregation of the descendant data of node L from descendant nodes J for an alternative T we may write the composite index in the form
  • w are the global weights normalized to node J
  • S JT is the rating for node J and alternative T
  • V r is the weighted average for alternative T which is the sum of the product of each weight and rating for each descendant leaf node and wherein the leaf node weights are normalized and add to 100% at the root node, that is, for ⁇ ⁇ leaf criteria the weights rfor alternative T are:
  • Equation 1 can then be rewritten in the form of the following parameters 1 - e T + e X + h Lr + h x
  • V ⁇ ⁇ w JT S JT 4 i
  • VJcL JT I v ⁇ T w min;LT mln [ k w mmiinn;; 7 Tr;Y/.
  • equation 9 it is clear that the values outside the summation are all known. If b ⁇ on the right of the equation is retained as a parameter, then it is a simple matter to determine h ' LT . Similarly, the two other parameters in equations 6 and 7 can be preserved as aggregate node parameters and transform as simply as in equation 9. However, equation 8 presents a problem due to the absolute value calculation in the summation. It can be shown that an approximation to calculate e'z,r is given by
  • x is a variable (a weighted score contribution in our current embodiment, but it may also include a weight or a cost distribution for example) over the range [x mm , x max J, then find a function g[f(x)J, such that for a given value of x, the value z(x) is given by
  • variable x is the weighted average V ⁇ of an alternative T.
  • range of V ⁇ is [0,100].
  • the function of concern is the weighted absolute sum difference of contributions, that is
  • the function g n [f(V ⁇ )] is generated by evaluating f(V T ) at discrete intervals of V ⁇ . over the range of Vj. This provides a spectral graph 1100 as indicated in figure 17.
  • the expression for g n [f(V ⁇ )] in calculating z(V ) 1102 may be
  • said at least one rating value of the at least one descendant node must be considered modified.
  • said rating changes may be provided with a rule base to determine which of said descendant decision ratings may be changed for said alternative.
  • an aggregation rule would cause an increase in Computer Aided Design 306 rating prior to Engineering Change Management 305 rating, until said Computer Aided Design 306 rating reaches the maximum rating for said nodes, when the rating of Engineering Change
  • the process of rating change is subject to an optimization process wherein said rules may be applied and the value of equation 14 determined for determined values of the rating. Thence a function of the illustrative form in figure 17 is generated and a series of parameters for each new rating value determined .
  • a function of the illustrative form in figure 17 is generated and a series of parameters for each new rating value determined .
  • several sets of a hail coefficients may be generated, each set representing a specific rating, weight and weighted average scenarios for each alternative.
  • said at least one rating may determine at least one alternative attribute.
  • determination of the alternative attribute value may further be represented as a function of the score value of the aggregate node. In this procedure the rating values and weights no longer appear in g n L ⁇ (V ⁇ )], and the equation is purely dependent on the value of JN.
  • the at least one set of coefficients a treat 1103 are stored for at least one alternative, and generally are specific for each alternative, and assigned to said aggregate node. It is normally the experience that the number of coefficients will be much fewer than storing the number of raw scores and weights, and hence the data compression is achieved. Further it is anticipated that calculation times may be greatly reduced, and further may significantly reduce demands on system resources, thereby further assisting in reducing the calculation time. Further, said compression may mean that smaller machines than otherwise may be able to perform calculations with sufficient accuracy without the requirement of the full data set.
  • the set of coefficients for each alternative may be stored as attributes for each aggregation node.
  • the said coefficients may be generated by the Analyst prior to data aggregation and saving the model in at least one of the steps 106, 107, 114.
  • the requirement for generation of at least one aggregation parameter may be determined by the Analyst
  • Parameters to enable determination of the Weighted Average Composite Index (as in equations 5-8), standard deviation, and Percent Match may be calculated, but cannot be used in dynamic mode, the aggregate node is treated as if it were a leaf node in dynamic mode. 3.
  • said transformations 5-8 may be modified and approximations sought by means of pre-changing the complete Analyst model prior to aggregation.
  • Said resulting output of values is modeled by means of statistical distributions. However, said method is dependent on knowing which decision factors and which decision factor attributes are likely to be changed.
  • a table of values may be stored with the aggregate model as a result of said pre-aggregated generated scenarios.
  • at least one statistical distribution may be determined such as a Weibull distribution for the ratings and weight distributions, score distributions and final outcomes. This method is complex and not well understood because of the variety of possible distributions, and is not the preferred embodiment.
  • aggregate parameters may further be assessed.
  • Total cost of ownership of a vendor product may depend significantly on the development direction the vendor is taking for its products.
  • aggregate parameters for alternative attributes may need to be determined, as by means of one of the three embodiments herein disclosed.
  • Client X seeks to purchase an Enterprise Resource Planning (ERP) suite of products.
  • ERP Enterprise Resource Planning
  • ERP enterprise resource planning
  • Said ERP must support interfaces to a wide range of corporate databases, and be linked to the databases through customized interfaces.
  • Said ERP must also be web enabled, and be able to provide reports for web display, for the sales force, for administrative personnel, and for reports for senior management.
  • the ERP must also be capable in providing up-to-the-minute inventory information, maintain lists of clients and integrate sales inquiries, ordering systems, supply requests, purchase orders etc.
  • Help desk information is to be tied to product types, help desk personnel performance measures, and performance of sales personnel.
  • AAS automated advisory service
  • the at least two vendors are independently assessed for their financial stability and likely future by at least one Analyst familiar with the field.
  • Said at least one Analyst may have constructed the said multicriteria model for an ERP procurement decision process using the decision support system herein disclosed, said model containing all possible features of the said at least two vendors' ranges of product.
  • Said model may have accumulated several thousand said criteria representing different aspects of the decision and may further contain privileged information concerning the vendor, and may include product features represented by criteria in the model. Ratings for said criteria and said vendor products may have been obtained from former client implementations, independent evaluations of vendor products, and other sources that may be found and included.
  • the said at least one Analyst may generate questionnaires and may provide said questionnaires to Client X that may be distributed by means of electronic mail, and by any other means as may be suitable at the time, including but not exclusively web based forms and automated response and analysis processes.
  • An example of an automated response may be the establishment of a website document with controlled access, and response data processed and set into a database from which the information may then extracted and may be processed through machine readable code, and may cause said processor to tailor said ERP model.
  • the at least one Analyst may then assess the responses and may accordingly use the information gathered to assist in tailoring the said model.
  • the Analyst may make use of interactive computer network group processes to interactively determine said decision model, thereby characterizing Client X.
  • the preferred interactive network product is NetMeetingTM manufactured and developed by Microsoft Corporation, Seattle, Washington.
  • the Analyst may then interact with at least one member of Client X over the computer network, and may provide the use of the model to at least one Client X member through the interactive group process, thereby jointly editing the said model, and may determine the criteria required for Client X, and may remove those criteria not required by Client X, and may further tailor the said model with other information and determine decision procedures such as sensitivity analysis, as required by Client X, and may be available from the said model and at least one knowledge base.
  • the said at least one Analyst may visit the client site and through a process of questioning and elicitation from at least one decision-maker of Client X and further characterize Client X.
  • Client X is duly characterized, and the at least one Analyst may then determine similar past clients who may have similar characterization profiles.
  • a measure of said closeness of Client X to another client may be determined by means of estimating the Matching Index value (this value is determined as part of the composite index in equation 1) as determined from the common criteria count in the two aggregate models, and weights of said common decision factors as determined by Client X and that used by said other client of said advisory services.
  • Other measures such as a linear regression fit may be applied, said means being determined by one knowledgeable in the art. Said characterization then assists the Analysis in determining the decision objects and procedures that may be required by Client X and have been stored in the automated advisory services knowledge base.
  • the at least one Analyst may have used the decision support engine to build and populate the model, and may at least one part of said model by executing machine readable code to translate the format of the stored model data structure stored in another structure data format into the format of the said model and may display said model for tailoring purposes. Said at least one Analyst may then proceed from the information provided by Client X, to tailor the model.
  • Mandatory data may be tailored, and at least one mandatory component of the model may be disabled, and may be removed from the model, and others may remain enabled.
  • the said at least one Analyst may then designate at least one node for aggregation. For example, it may be agreed by the at least one Analyst and at least one member of Client X that the criterion "Product Functionality" may be aggregated at the second level above the root, as the detail below the node is not required at this time.
  • the said at least one Analyst may determine that the information below the said criterion "Product Functionality" is important, but it is desirable to remove them for simplification requirements, and therefore aggregation parameters will be calculated for the node.
  • the item "Corporate Service and Support” is determined asunimportant in terms of the information content below the node, possibly because preferred vendors may be closely the same in this parameter, thence the said at least one Analyst may determine that there is no requirement to generate aggregation parameters for said node, and consequently no aggregation parameters are generated, and the node is simply trimmed, retaining only the normalized weighted average from all descendant nodes as representing the score for the said node for each alternative in the model.
  • the evaluation for the ability of the vendor to actually deliver, under the factor "Corporate Viability” has score distributions for at least some alternatives that are not amenable to current methods to calculate aggregation parameters of sufficient accuracy in the calculations. This may be indicated by prior comparisons of exact calculations and current methods as may exist at the time in calculating the aggregation parameters.
  • the node "Corporate Viability” is determined by the at least one Analyst to be marked 'Hidden', preserving said data for calculation purposes, while simplifying the data presentation to Client X members, and retaining confidential any proprietary information therein.
  • Decision factor attributes such as weights may be determined from prior information as disclosed earlier. Some criteria weights may be reduced to zero as these criteria are determined by the said at least one Analyst to not be relevant to the needs of Client X. Similarly, costs related to the presence and the absence of a feature may have been obtained from prior experience of similar Clients of particular Vendors. The costs of ownership of particular solutions from each Vendor suite of ERP products may further have been garnered from other installation experiences. Said data may be present in the Analyst model, and may be aggregated as factor attributes and Alternative attributes. On the basis of required attributes and aggregated information, as well as the agreed at least one decision procedure to be used for the decision, said at least one decision procedure is applied and the at least one Analyst may then generate an initial ranking of the alternatives.
  • the at least one Analyst may determine a short list of vendors who best meet the Client requirements. This may include in one embodiment selecting those Vendors that fail the least set number of mandatory requirements, as well as those that may have the highest overall scores, according to the chosen decision method that may include, amongst others, lowest cost, best score, and best benefit per unit cost or best cost per unit benefit. Said at least one Analyst may then add instructions and other meta-data to the aggregated model, and this may include adding, removing and editing decision procedures, factor attributes and alternative attributes. Once said short list of preferred vendors is determined and decision procedures appropriately tailored, the aggregate model may be saved, and a report generated for Client X.
  • Said report may be customized from a template and include aggregated information, and may be distributed to at least one member of Client X. Said at least one aggregate model may further be provided to Client X.
  • a report may be provided to Client X, where said report may disclose aggregated model criteria and may show criteria weights, and may include preferred alternatives and determined alternative attributes, and may further include meta-data.
  • Said report may be generated for management level presentation to Client X.
  • Client X may require the generation of a Request for Proposal (RFP) to be sent to the preferred vendors.
  • RTP Request for Proposal
  • the Analyst may select at least one report template within the decision support system to automatically generate the said RFP,
  • An RFP is generated by machine readable code that may be a component of the client decision support system, causing said processor to process said model andselect data from said model, and organize and format said selected data and output said data in the form of an RFP report.
  • the output is in the form of a printed report executed on a printer.
  • said report is saved in a known application format such as Microsoft MS Word, and may be stored on a storage medium such as a CD-ROM, and may be sent by electronic messaging system to at least one member of Client X.
  • said report may contain at least one decision object.
  • the Analyst may assign and store keys and sub- keys in the aggregate model in order to limit the distribution and time of availability of the model, as may be required according to at least one license agreement with at least one member of Client X.
  • the analyst may then provide at least one member of Client X with at least one aggregate model and at least one client decision support system.
  • Client X upon receipt of the aggregate model, and having obtained the necessary authorization keys from an authorized site, may then proceed to further analyze the vendors using the aggregate model and dynamic decision procedures to generate at least one scenario.
  • Said AAS may assist Client X to generate a short list of vendors for which RFP's may be generated from at least one report template and to whom said RFP's may be sent.
  • the said responses when said RFP responses are returned, the said responses may be supplied to Client X and may be provided to the said AAS.
  • Data may also be provided automatically over a computer network into which vendors have access, said computer network thereby providing means to automatically update data contained in the automated advisory services knowledge database.
  • the AAS benefits by updating its information base and content.
  • the AAS may then provide a subscription update service to its clients, including Client X.
  • weight and criteria attribute information may be obtained by the AAS and added to the knowledge database for future reference. Additional decision procedures may also be obtained and added to the decision knowledge base, said procedures obtained from the Client that may be by means of agreement and may be garnered from Client X's staff.
  • the AAS may provide further decision procedures and data obtained from other sources, thereby updating the client aggregate model, said process being made automatic by machine readable code that causes a general purpose processor to modify existing model attributes and content in the said tailored Client X aggregate model.
  • assistance in bid evaluation by said AAS may be provided.
  • Said assistance may be by presence of at least one Analyst at the client site.
  • Selection of said vendor may follow proposal and counterproposal steps, and scripted decision procedures may be made available to Client X to provide assistance to Client X in selecting at least one vendor.
  • Said decision procedures may require determination of scores, cost information, and challenges of said at least one selected vendor, and determination of business risk based on prior performance of the at least one selected vendor.
  • the ASS may provide additional services to provide a detailed summary report generated from a detailed report template.
  • Said report may include graphic, numeric and meta-data in a format suitable for presentation for senior management summary as well as detailed information for project management requirements that may include cost and project success risk estimations.
  • said assistance may be delivered by remote conferencing as earlier disclosed using customized group networking products such as NetMeetingTM.
  • the said at least one Analyst may assist Client X by using scripted decision support procedures, said script may have been determined for Client X prior to delivery of the said aggregate model and said aggregate report, and may be incorporated in the knowledge base of said AAS. Additional information and decision procedures, including negotiation approaches may be garnered from Client X against at least one vendor by the advisory service, enabling the storage of said methods for future reference in the knowledge base and within decision procedures. Said information may then provide the at least one Analyst with means to determine other scripts for other clients.
  • said issues raised by Client X may provide vendors with information to determine reasons for lost and won sales, and thereby the impact of Client X views of the at least one preferred vendor's weaknesses and strengths may then allow at least one vendor to plan future development and market strategy.
  • said script may provide to Client X prior analysis and procedures to enable negotiations with said at least one preferred vendor.
  • the lack of a feature such as the non-support for a particular E-mail system may result in an incurred cost in procurement and adaptation to the new E-mail system.
  • Said cost may be assigned to the vendor as a cost attribute dependent on the rating value for the support of said E-mail system provided by the vendor. The sum of such costs may then be assessed and may constitute at least a component of a report on least one preferred vendor.
  • a means to deduct such costs from the ERP implementation assists in reduction of final ERP cost implementation and better planning for said insured costs.
  • the ERP system is assessed at $3,500,000, the average cost from prior experience at other similar ERP installations due to the lack of support for the E-mail system is estimated at $200,000.
  • negotiation may allow reduction of the ERP system cost to $3,300,000, or assist the vendor in determining that the cost of implementing support for the E-mail system would ultimately pay-back since a number of significant future sales may be affected by said feature absence.
  • said vendor and Client X may determine that only partial support is required, hence the rating value may increase and indicate a cost to the Client is minimal if vendor supplies said partial support, and vendor and Client X may then share the risk.
  • Statistics of failure of sales as a result of missing feature, or profit margin detriment, maintained in said ERP model may provide vendors with adequate information to determine future directions of development.
  • said advisory service may provide means to select outsourcing consultants through at least one other aggregated model, and may assist in selection of said outsourcing agency.
  • said means to assist Client X in selecting project personnel may be offered to Client X as a service.
  • said advisory service may provide key personnel to assist in decision support purposes, which may include but not exclusively procurement of required items, project management decisions and project, personnel and vendor performance analysis.
  • each said assistance aspect may comprise of additional models, and a set of said models may then be described as comprising a model library, said library thereby consisting of at least two tailored models covering at least two aspects of a project process. Said library may then be treated as a single model in terms of licensing, and thence the licensing procedures applicable to models may then be applied to said model library.
  • said advisory services may follow a detailed scripted process as indicated in figures 22, 23,24, 25, and 26 wherein the service may be divided into four parts, the first 1601 may be to determine the requirements of Client X.
  • the market overview may be provided to Client X 1701-1703, wherein a broad view of the ERP process may be provided, and said major vendors 1704, 1705 and criteria 1706 are presented in broad terms to Client X. Said process thereby educates Client X as to the major issues and relative market placement of the vendors with respect to Client X's requirements.
  • the advisory service analyst may obtain sufficient detailed information of Client X's broad requirements to begin the tailoring process through a needs definition process 1707, 1708 including mandatory requirements 1709 and other criteria 1710.
  • Requirements definition templates that provide means to further identify the needs of the client may be generated 1707 and distributed to at least one Client X member.
  • Responses to said document distribution may provide sufficient information to eliminate at least one vendor from the list of vendors through compliance analysis 1716-1718, 1715.
  • a tailored model may then provide a selection overview 1719 and first management review documents 1720, 1721.
  • Client X may receive a client decision support system and at least one aggregate model under a license agreement.
  • Part II of the process may consist of further detailed examination of the reduced number of vendors 1602, and may consist of the steps indicated in figure 24 1800.
  • metadata and Analyst assistance is provided to Client X to evaluate vendors and further customize said aggregate model that may have been provided to at least one member of Client X, and interactive means process over a computer network may be employed as indicated herein to facilitate said detailed evaluation.
  • Templates and decision processes 1804-1806 using visual decision dictionaries and decision procedures 1807-1811 may assist in further elimination of at least one vendor, and may provide a short list of vendors for which a further report and supporting documents may be provided to Client X.
  • Client X at least one member may also generate said supporting documents and reports from customized template reports, and may be assisted by at least one Analyst 1813-1815.
  • decision justification may include the strengths and challenges in the selection of said at least one preferred vendor.
  • Various scenarios may be explored, said scenarios being performed by use of visual decision dictionaries and decision procedures 1812-1818, and may be assisted by the Analyst.
  • Decision justification documents may then be prepared for senior management and senior decision makers, 1819-1821.
  • the final selected at least one vendor may be provided a Letter of Bid that may be generated from templates within the decision support system using selected data from the tailored aggregate model, and may be prepared by the at least one Analyst who may assist at least one member of Client X .
  • Negotiation procedures with final at least one vendor may then proceed 1901-1903, utilizing said procedures and information contained in the said tailored decision model, and may further be assisted from meta-data procedures known by the at least one Analyst, and may be contained within the knowledge base of said main ERP decision model 1904-1906.
  • the Analyst may assist in the final selection through facilitation procedures between the at least one vendor and Client X, or through remote conferencing and multimedia interaction as disclosed herein, and as may be available at the time 1907, 1908, and occur to one knowledgeable in the art.
  • Final management reports may be generated from templates concerning justification of the decision, and may contain details of decision issues, negotiation points and results and such items as may be required by senior management 1909-1911.
  • said analyst may assist project planning by identifying the detailed challenges that need to be overcome, and identifying deficiencies and strengths within vendor features that may or may not be compatible with Client X systems and processes.
  • said Analyst further obtains information and data for model improvement.
  • follow-up procedures may also be performed to assist Client X in measuring the performance of said vendor, and thereby this process may provide added information on cost of implementation, vendor performance and project risk at the detailed level, thence giving the opportunity for using said information 1605 in refining the knowledge base and Analyst model 1606 of the automated advisory service.
  • Said refinement may be determined by one knowledgeable in the art and may include industry standards 1607where said industry standards may include industry averages and industry means, industry-accepted levels and legal requirements imposed on the industry. Said refinement may further include vendor performance standard values for at least one vendor and other vendor specific information 1608, and client characteristics 1608. Vendor information may be of great interest to prospective clients of the vendors as well as other competing vendors, and conversely, client characterization may be important for vendors. Thence advisory services may be realized through the process of systematic knowledge acquisition and utilization in assisting clients and vendors based on actual case studies and scenarios.
  • Such scenarios may thereby be characterized by the aggregate model containing at least one decision factor and at least one decision procedure and at least one decision procedure attribute that was implemented, and said characterization may provide means of comparison between prospective clients of vendors, and of vendor performance in said client scenarios, and among all clients of the automated advisory service.
  • means may be provided to the said at least one Analyst to customize all scripts into a single decision process herein called a Decision Process Guide, as may be indicated in figure 26.
  • At least one of the steps as may be determined by the Analyst may be provided 1951, and selected decision procedures as may be determined by the Analyst included 1953, with customized text and other meta-data 1954 that may assist in linking each step and sub-step in the process, and provide direct access to at least one Analyst or other information related to the decision process.
  • Said combination of indicated steps, decision procedures, and link data and meta-data may be combined to comprise a customized decision process guide for the Client.
  • Client X for example, may require at least three steps, and thereby be presented on a graphical display an image as indicated in figure 26.
  • Said image presents the major steps 1958, 1963 that may be selected in one preferred embodiment by means of buttons pressed using a pointing device such as a computer mouse.
  • said vendors and vendor products may be presented in a tree 1965, and details of at least one selected vendor displayed elsewhere as in, for example, frames 1962, 1969.
  • Said views may have been determined by the Analyst prior to delivery of customized decision process.
  • Link information in one embodiment may be displayed 1966, and contact means added for additional sites information that may include vendor web locations 1967 and expert contacts 1968. NetMeetingTM interactive procedures may be invoked as may be desired and arranged with the Analyst 1960.
  • said Decision Process Guide may be an independent computer readable executable code and may be supplied to the client, the execution of which may then read at least one selected aggregate model at the client site.
  • said tree may consist of the aggregate model decision factors, as may be determined by one knowledgeable in the art, and as may be relevant to the data displayed in the at least one chart in frame 1962.
  • the Analyst may determine certain visibility features for the said frames, causing meta-data to appear in for example the chart frame 1962, and vice- versa.
  • the analyst may, in another preferred embodiment, add or remove frames from said decision process guide, as may be determined for each process sub-step of the Decision Process.
  • said Decision Process Guide may be integrated in with the decision Support system, and may only be licensed to read selected licensed aggregate models, as may be determined by the Analyst, and said licensing procedures as indicated in 900 may then be applied to the Decision Process Guide with the additional restriction of reading only specific aggregate models.
  • Thence input means may be provided to the said at least one Analyst to disable said aggregation ability of the decision support system.
  • Said means may be by inputting a code into the aggregate model, causing said decision support system to prevent further aggregation of said aggregate model.
  • Said disabling may be preferred since the aggregation process may require external means to compute said aggregation parameters, and said means may not be available to said client, thereby compromising the accuracy of the aggregate model further since aggregation of an aggregated node into a higher node may cause loss of said parameters.
  • said Decision Process Guide may consist of at least one stored aggregate model and client decision support system, and may be provided as executable machine readable code, wherein said code upon execution on a processor causes said processor to read only the at least one stored aggregate model provided within said executable code's data structure.
  • said means to estimate costs of implementation project may be determined from combined factor and alternative attributes within the aggregate model.
  • said likelihood of success of a project may be determined from at least one measure of the degree of success accredited to at least one vendor known from prior experience of clients that have used and may still be using said vendor.
  • Said at least one measure of the degree of success may be estimated in terms of numeric values representing the difference between targeted deliverables - representing a target alternative - in terms of functionality and solution quality, and estimates of actual functionality and quality of said deliverables. Said difference may be interpreted as a vendor success risk.
  • Said example illustrates an advisory service process in a procurement procedure for an ERP system. Said process can be applied to any procurement process, and can further include any decision process open to a systematic decision making process. Said systematic decision making processes can include, and may not necessarily limited to, hiring, performance measurement, systems analysis, project performance measurement, medical decision making, and strategic planning.
  • said tailored model may be stored on at least one storage medium such as a Winchester hard disk drive 64 or CD-ROM 68, and may be read by at least one special purpose processor 2004 in figure 27, said processor thereby caused to read said model by machine readable code executing on said special purpose processor 2004.
  • Said processor may receive input to from sensors 2001, 2002. For example, in a nuclear power plant such sensors may read temperature of coolant. In another embodiment, said sensors may measure coolant fluid flow. Said sensors may thereby be any sensor generating a calibrated signal as may be determined by one knowledgeable in the art, and said signal converted to a rating value of significance to the decision model, where said signal may be represented by a criterion within the decision model.
  • weights in regard to a critical situation developing may be determined.
  • sensor 1 (2001) near the reactor may provide temperature readings where it is known that such readings indicate a likely reactor failure.
  • Sensor 2 (2002) is further away, and its readings toward a reactor failure may not be so significant.
  • Sensor 1 is therefore given for a condition (alternative) called FAILURE CONDITION A a weight significantly higher than that of sensor 2 - i.e the local weight of sensor 1 may be much larger that of sensor 2.
  • the sensors, grouped under the criterion "Temperature Sensors" in the model 2009 may be provided ratings according to the temperature signal. Said ratings and weights may then be aggregated into the hidden node “Temperature Sensors", 2011 which represents the aggregated signal utility value from special processor 2004.
  • the aggregated node attributes may be determined by a machine readable code executed on special purpose processor 2004, said aggregated attributes may then be transmitted to the higher level general processor 2007, where said aggregated attribute may be displayed to an operator on a graphic display device 2008, where only node 2011 may be seen.
  • Said device and attribute data may be aggregated information indicating for example the status of the processor determined worst temperature reading by sensors under the hidden node "Temperature Sensors" 2011.
  • a signal may be generated by said general purpose processor, and said signal causes machine readable code to execute on a special purpose or general purpose computer 2007, causing said hidden node to expand and expose the underlying data of node 2011, said underlying data may then be displayed to at least one operator, and may cause the generation of at least one signal to indicate that at least one threshold may have been exceeded.
  • said weights 2012 and 2013 may be changed to cause a new perspective to be examined.
  • said temperature sensors in respect to calibration signals may be measured against a target set of values, and the importance of said calibration may change.
  • sensor 1 may be calibrated against sensor 2, since sensor 2 is amenable to human access, and hence the importance of calibration of sensor 2 may be four times that of sensor 1, thence the local weights of the two may be sensor 1 80%, and sensor 2 20%, in confidence of the calibration performance of the system.
  • the action of selecting CALIBRATION PERFORMANCE STATUS as the alternative causes said processor to select and tailor said model of system for calibration performance status of the system for alternative CALIBRATION PERFORMANCE STATUS, including thereby "Temperature Sensors" criteria, but may not include other criteria such as mandatory items with discrete values such as valve closed/open signals.
  • said selection of alternative CALIBRATION PERFORMANCE STATUS may cause machine readable code to execute on a general purpose or special purpose processor, causing said processor to disaggregate hidden nodes 2010 and may cause additional signals to cause automatic measurement of calibration of said sensors.
  • said signals may cause the enabling of at least one decision procedures, said at least one decision procedure being present according to the aggregated/disaggregated state of the aggregated model.
  • Said procedures may for example provide instructions concerning the current state, and may indicate consequences of action by means of dynamic graphs and generation of what-if scenarios.
  • processor 2007 may not have within its model the nodes known to 2004. Thence at least one output signal 2006 carries information pertaining to said unknown nodes to 2007, causing said processor 2007 to create said nodes 2010 with attributes that may be contained in said at least one signal 2006. Said newly created nodes may then be displayed on the graphical display 2008, and may include at least one attribute of said created nodes.
  • said attributes may include weights related to a decision or analysis condition, and may include the temperature readouts of said sensors 2001 and 2002.
  • nodes 2001 and 2002 may be processors providing 2004 with aggregate information of sensors which are descendant nodes to said nodes 2001 and 2002, and so forth.
  • Said data for weights that may represent FAILURE CONDITION A may be generated from prior data determined from similar systems.
  • said data may be generated by means of simulation procedures run on a general purpose or special purpose processor.
  • said prior data may be processed by machine readable code to determine at least one utility function and at least one rating method for ratings, and weight of importance toward, for example, the goal of CALIBRATION PERFORMANCE STATUS of the system.

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Abstract

L'invention concerne un système d'aide à la décision dans lequel des structures de données (modèles élémentaires) consistant en un agencement hiérarchique de critères et contenant des métadonnées de texte et des valeurs numériques, peuvent être réduites pour former un sous ensemble de critères dans lesquels le sous ensemble de critères (le modèle agrégé) représente un échantillon sans signification du contenu d'informations du critère descendant, adapté aux besoins d'un client spécifique. On décrit un processus dans lequel un expert es adaptation d'une grande structure hiérarchique personnalise ledit modèle élémentaire afin de créer le modèle agrégé. En outre, cet expert peut fournir un ensemble de cartes et d'explications appelées objets de décision qui peuvent être regroupées dans des dictionnaires de décisions visuelles (VDD). Ces VDD peuvent être ensuite regroupés en procédures de décision. Ces procédures de décision peuvent ensuite être utilisées pour guider un client dans une analyse approfondie systématique des données du modèle personnalisé afin d'arriver à une décision souvent négociée fondée sur de bonnes informations..
PCT/US2000/000335 1999-01-08 2000-01-07 Systeme, procede et articles manufactures pour decision consultative informatisee et services de surveillance faisant appel a des systemes de decision avec protection de licence et de modele WO2000043935A2 (fr)

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US8095392B1 (en) 2005-01-20 2012-01-10 Owen Daniel L System, method and computer program product for facilitating informed decisions relating to family risk
US8463625B1 (en) 2005-01-20 2013-06-11 Daniel L. Owen System, method and computer program product for facilitating informed decisions relating to the fair sharing of the costs of insurance between a group and a third party
US10796319B2 (en) 2015-04-07 2020-10-06 International Business Machines Corporation Rating aggregation and propagation mechanism for hierarchical services and products
CN113485983A (zh) * 2021-06-07 2021-10-08 上海众言网络科技有限公司 问卷数据处理方法及装置

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8095392B1 (en) 2005-01-20 2012-01-10 Owen Daniel L System, method and computer program product for facilitating informed decisions relating to family risk
US8165903B1 (en) 2005-01-20 2012-04-24 Owen Daniel L System, method and computer program product for facilitating informed decisions relating to family risk
US8463625B1 (en) 2005-01-20 2013-06-11 Daniel L. Owen System, method and computer program product for facilitating informed decisions relating to the fair sharing of the costs of insurance between a group and a third party
US10796319B2 (en) 2015-04-07 2020-10-06 International Business Machines Corporation Rating aggregation and propagation mechanism for hierarchical services and products
US10846710B2 (en) 2015-04-07 2020-11-24 International Business Machines Corporation Rating aggregation and propagation mechanism for hierarchical services and products
CN113485983A (zh) * 2021-06-07 2021-10-08 上海众言网络科技有限公司 问卷数据处理方法及装置
CN113485983B (zh) * 2021-06-07 2023-12-26 上海众言网络科技有限公司 问卷数据处理方法及装置

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