US20020049704A1 - Method and system for dynamic data-mining and on-line communication of customized information - Google Patents

Method and system for dynamic data-mining and on-line communication of customized information Download PDF

Info

Publication number
US20020049704A1
US20020049704A1 US09845012 US84501201A US2002049704A1 US 20020049704 A1 US20020049704 A1 US 20020049704A1 US 09845012 US09845012 US 09845012 US 84501201 A US84501201 A US 84501201A US 2002049704 A1 US2002049704 A1 US 2002049704A1
Authority
US
Grant status
Application
Patent type
Prior art keywords
search
data
user
mining
interest
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US09845012
Inventor
Ingrid Vanderveldt
Christopher Black
Original Assignee
DRYKEN TECHNOLOGIES Inc A CORP OF DE
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • G06F17/30864Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation

Abstract

A method and system for dynamically searching databases in response to a query is provided by the present invention. More specifically, a system and method for dynamic data-mining and on-line communication of customized information. This method includes the steps of first creating a search-specific profile. This search-specific profile is then inputted into a data-mining search engine. The data-mining search engine will mine the search-specific profile to determine topic of interests. These topics of interest are outputted to at least one search tool. These search tools match the topics of interest to at least one destination data site wherein the destination data sites are evaluated to determine if relevant information is present in the destination data site. Relevant information is filtered and presented to the user making the inquiry.

Description

    RELATED APPLICATIONS
  • This application claims benefit of U.S. Provisional Application No. 60/095,308 filed on Aug. 4, 1998. Additionally this application incorporates by reference the prior U.S. Provisional Application No. 60/095,308 filed on Aug. 4, 1998 entitled “Method and System for Dynamic Data-mining and On-line Communication of Customized Information” to Ingrid Vanderveldt and U.S. patent application Ser. No. 09/282,392 filed on Mar. 31, 1999 entitled “An Improved Method and System for Training an Artificial Neural Network” to Christopher L. Black.[0001]
  • TECHNICAL FIELD OF THE INVENTION
  • This invention relates generally to the use of a dynamic search engine and, more particularly, to a dynamic search engine applied to the Internet that allows for customized queries and relevant responses. [0002]
  • BACKGROUND OF THE INVENTION
  • Current Internet search tools often provide irrelevant data sites or web sites. Often, current search tools provide a score of relevance according to text frequency within a given data site or web page. For example, “termites” and “Tasmania” and “not apples”: [0003]
  • If a web page has several instances of the word “termites” (600 for example), the web page would receive a high relevance score. [0004]
  • A web page with 600 “termites” and one “Tasmania” would receive a slightly higher score. [0005]
  • A web page with the above plus “apples” would then receive a slightly lesser score. [0006]
  • Therefore, a score of relevance according to a data site or web page is often based on text or word frequency. Therefore current search tools often provide a list of irrelevant web pages. Furthermore, there is the opportunity for abuse in and associated with the method of the available search tools. Current search tools often provide links that are stale (old data that is no longer at the address of the data site). Existing search tools utilize indices that are compiled in the background continuously. However, with respect to an individual query, a historical result is received. Therefore, the search process involves a large amount of filtering by the individual user. [0007]
  • Therefore, there is a need to more efficiently utilize search tools to overcome irrelevant results. At present, it is desirable to have an efficient method for performing a search which would take into account demographic as well as historical user information to filter irrelevant data from the results from existing search tools. [0008]
  • Furthermore, it is desirable to have a search engine which will evaluate and filter stale data responses from an existing search tool response. [0009]
  • SUMMARY OF THE INVENTION
  • In accordance with the present invention, a method and system for searching databases in response to a query is provided that substantially eliminates or reduces disadvantages and problems associated with previous methods and systems for searching databases. [0010]
  • More specifically, the present invention provides a system and method for dynamic data-mining and on-line communication of customized information. This method includes the steps of first creating a search-specific profile. This search-specific profile is then inputted into a data-mining search engine. The data-mining search engine will mine the search-specific profile to determine at least one topic of interest. The at least one topic of interest may comprise a specific and/or related topics to interest. The at least one topic of interest is outputted to at least one search tool. These search tools match the at least one topic of interest to at least one destination data site. The destination data sites (web page) are evaluated to determine if relevant information is present in the destination data site. If relevant information is present at the destination data site, this data site may be presented to a user. [0011]
  • One broad aspect of the present invention includes the coupling of a data-mining search engine to at least one search tool. This data-mining search engine can review and evaluate data sites. Current search tools available may create a massive index of potential data sites. The data-mining engine of the present invention evaluates whether data accumulated by current search tools are relevant to a user and filters out nonrelevant information. [0012]
  • The present invention provides an advantage by providing a search engine algorithm that provides fresh (as opposed to stale) links to more highly relevant web pages (data sites) than provided by the current search engines. [0013]
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following description taken in conjunction with the accompanying drawings in which like reference numerals indicate like features and wherein: [0014]
  • FIG. 1 shows a diagram of the present embodiment of the invention; [0015]
  • FIG. 2 illustrates an example of operating the present invention; [0016]
  • FIG. 3 explains the related patent applications to the present invention; [0017]
  • FIG. 4 depicts the use of a training scheme according to the teachings of BLACK; and [0018]
  • FIG. 5 details a flow chart illustrating the method of the present invention. [0019]
  • DETAILED DESCRIPTION OF THE INVENTION
  • Preferred embodiments of the present invention are illustrated in the FIGUREs, like numerals being used to refer to like and corresponding parts of the various drawings. [0020]
  • In accordance with the present invention, a method and system for dynamically searching databases in response to a query is provided that substantially eliminates or reduces disadvantages and problems associated with previous methods and systems for searching databases. [0021]
  • More specifically, the present invention provides a system and method for dynamic data-mining and on-line communication of customized information. This method includes the steps of first creating a search-specific profile. This search-specific profile is then inputted into a data-mining search engine. The data-mining search engine will mine the search-specific profile to determine at least one topic of interest. The at least one topic of interest may comprise a specific and/or related topics to interest. The topic of interest is outputted to at least one search tool. These search tools match the topic of interest to at least one destination data site. The destination data sites are evaluated to determine if relevant information is present in the destination data site. If relevant information is present, this data site is assigned a relevance score and presented to user requesting the query. [0022]
  • One broad aspect of the present invention includes the coupling of a data-mining search engine to at least one search tool. This data-mining search engine reviews and evaluates available data and data sites. Current search tools available may create a massive index of potential data sites. The data-mining engine of the present invention evaluates whether the available data accumulated by current search tools are relevant to a user and filters out all non-relevant information, creating a more effective and efficient search engine. [0023]
  • In one embodiment, the present invention includes a web site containing several data-mining tools. These tools fall into two separate categories: a dynamic approach to generating a list of links that are well correlated to a user provided search string using a novel search strategy (e.g., incorporating simple text matching, text associations, synonym and near text matching—to handle misspellings, profile information, a recursive definition of document importance/relevance—important/relevant documents link to other important/relevant—and weighting of the previous factors based upon Al), and stand-alone models (e.g., neural networks and NSET models, as well as others known to those skilled in the art), which would provide useful predictions or estimations (such as described in the U.S. patent application Ser. No. 09/282,392 entitled “An Improved Method and System for Training An Artificial Neural Network” filed Mar. 31, 1999 to Christopher L. Black, hereafter BLACK. [0024]
  • The stand alone models would be created with implementer or user interaction, and could be ever increase in number, as desired and as data was discovered/licensed/acquired. Eventually, the web site would contain a portal to hundreds of thousands of interesting and useful models. [0025]
  • Neither the search engine nor the models would necessarily be limited to medical information and topics. However, the present invention primarily focuses on healthcare-related applications. The system and method of the present invention need not be limited to such health care database. [0026]
  • The present invention provides a method for data-mining that provides use of many different Al models derived for many different applications from many different datasets. The present invention provides the benefit of a neural network training algorithm, genetic algorithms expert and fuzzy logic systems, decision trees, and other methods known to those skilled in the art applied to any available data. [0027]
  • Secondly, the present invention allows the compact storage, retrieval, and use of relationships and patterns present in many datasets, each made up of very many patterns of examples, each made of several different measurements or values, each requiring several bytes when stored conventionally or explicitly (as in a relational database or a flat file). Single datasets consisting of multiple gigabytes and terabytes of data are routinely being generated, with exabyte datasets looming on the horizon. With the use of multiple modeling techniques (different approaches are appropriate to different applications), models encapsulating and summarizing useful information contained within hundreds or even thousands of these datasets could stored on a single consumer level personal computer hard drive. [0028]
  • FIG. 1 illustrates one physical implementation of the present invention. The number of servers, interconnections, software modules, and the like would largely be determined by scalability concerns. The web site [0029] 12 would consist of a graphical user interface (GUI) to present dynamically generated indexes and forms that allow the user 10 to provide a search profile and submit their search requests or feed inputs into a selected Al model. The web site 12 could reside upon a single or on a standard farm of web server machines. Search engine requests 15 would be provided to a single or a farm of search machines 16, which would either query a static public or proprietary databases 18/indices of links either pre-created (and continually updated) or licensed from, for example, Yahoo and other link search engines. This static list (formed from data sites 18) would provide a starting point for a dynamic (live) search. Both search machines/machine farms 16 would require extremely high speed access to the Internet or other like data networks.
  • Data-mining is the process of discovering useful patterns and relationships within data. This is typically accomplished by training and then applying a neural network, or inducing and then applying a decision tree, or applying a genetic algorithm, etc. Once the training aspect of many of the techniques is performed, the result is the data-mining tool (e.g., a trained neural network—into which someone who knows nothing about Al can simply input values and receive results). [0030]
  • Data-mining “tools” are discrete and specific. Certain models are appropriate for certain tasks. When explanation of a particular result is important (as in credit approval/rejections), and the available data supports the generation/formulation of rules, an expert or fuzzy logic system might be appropriate. When optimization of a particular quantity is important, a genetic algorithm or another evolutionary algorithm might be more useful. When prediction/estimation is important, the neural network training algorithm might be used. [0031]
  • The Dynamic Search Engine [0032] 100 can extract/provide useful information from publicly and freely available databases 18. However, the present invention can do the same with proprietary databases 18.
  • One embodiment of the present invention incorporates an enhanced version of simple text matching (allowing reduced weight for synonym and possible misspelling matches) at the first level. Associations with profile information provides a second metric of relevance (e.g., certain words and word combinations are found to correlate with interest for people providing certain combinations of search profile factors). The final metric is whether other articles possessing high (normalized) relevance (using all 3 levels—a recursive definition) link to the page in question. If so, then the relevance as established by this metric is high. [0033]
  • The spidering/crawling/roboting starts from the static index found in response to the initial query [0034] 15 of databases 18. Data sites included in the index are scanned and assigned relevance using the 3 facts above. Data Sites with high levels of relevance are scanned deeper (a links are followed, as well as the links revealed on those subsequent pages) than non-relevant pages. After a maximum number of links have been followed, or the total relevance of pages indexed exceeds a threshold, the search stops and results 20 are returned to user 10, organized by a weighted conglomeration of the 3 factors (generated by a neural network trained upon the user profile and previous searches and relevance results).
  • For the pre-created models, the present invention also has a page indexing the available canned models that the user could simply choose from. Alternatively, based upon text entered at the dynamic search engine GUI [0035] 12, the dynamic search engine could suggest appropriate models, where appropriate (e.g., if user enters blue book, the present invention could return at the top of a list of links, a link to a used car value estimator neural network).
  • FIG. 2 illustrates one embodiment of the present invention wherein the search tools comprise a privately licensed search tool [0036] 22 accessing privately held databases 24 and publicly available database 18 accessed by search tools provided by YAHOO, EXCISE, LYCOS and other search tools known to those skilled in the art.
  • FIG. 3 provides an overall description of three processes which occur within FIGS. 1 and 2. Process [0037] 30 illustrates the dynamic search engine application which performs the function of mining search profile data as provided from user 10 via GUI 12. Mining or cross referencing the search profile data against subject information includes the dynamic search capabilities of evaluating data sites 18. Process 32 in FIG. 1 illustrates the interaction between a user 10, the dynamic search engine and an available search tool 16, which accesses individual web sites 18. Search tool 16 for each individual may be customized to the protocols associated with each search engine. Process 34 illustrates the process between a user 10, a dynamic search engine of the present invention and a proprietary search engine when the search tool 16 is a proprietary search engine accessing proprietary databases.
  • The improvements to previously existing artificial neural network training methods and systems mentioned in the various embodiments of this invention can occur in conjunction with one another (sometimes even to address the same problem). FIG. 4 demonstrates one way in which the various embodiments of an improved method for training an artificial neural network (ANN) can be implemented and scheduled. FIG. 4 does not demonstrate how representative dataset selection is accomplished, but instead starts at train net block [0038] 101 with representative training dataset already selected.
  • The training dataset at block [0039] 101 can consist initially of one kind of pattern that is randomly selected, depending on whether or not clustering is used. Where clustering takes place, it takes place prior to any other data selection. Assuming, as an example, that clustering has been employed to select twenty training patterns, ANN can then be randomly initialized, all the parameters can be randomly initialized around zero, and ANN can take those 20 data patterns and for each one calculate the gradient and multiply the gradient by the initial value of the learning rate. The adaptive learning rate is user-definable, but is usually initially set around unity (1). For each of the representative data patterns initially selected, the training algorithm of this invention calculates the incremental weight step, and after it has been presented all twenty of the data patterns, it will take the sum of all those weight steps. All of the above occurs at train net block 101.
  • From train net block [0040] 101, the training algorithm of this invention goes to step 102 and determines whether the training algorithm is stuck. Being stuck means that the training algorithm took too large a step and the prediction error increased. Once the training algorithm determines that it is stuck at block 104 it decreases the adaptive learning rate by multiplying it by a user-specified value. A typical value is 0.8, which decreases the learning rate by 20%.
  • If the training algorithm reaches block [0041] 102 and determines there has been a decrease in the prediction error (i.e., it is not stuck), the training algorithm proceeds to block 108 and increases the learning rate. The training algorithm returns to block 101 from block 108 to continue training the ANN with a now increased adaptive learning rate.
  • The training algorithm proceeds to block [0042] 106 after decreasing the adaptive learning rate in block 104 and determines whether it has become “really stuck.” “Really stuck” means that the adaptive learning rate decreased to some absurdly small value on the order of 106. Such a reduction in the adaptive learning rate can come about as a result of the training algorithm landing in a local minimum in the error surface. The adaptive learning rate will normally attempt to wiggle through whatever fine details are on the error surface to come to a smaller error point However, in the natural concavity or flat spot of a local minimum there is no such finer detail that the training algorithm can wiggle down to. In such a case the adaptive learning rate decreases to an absurdly low number.
  • If at block [0043] 106, if the training algorithm determines that it is really stuck (i.e., that the learning rate has iteratively decreased to an absurdly small value), it proceeds to block 110 and resets the adaptive learning rate to its default initial value. In the event that the training algorithm is not really stuck at block 106, it returns to block 101, recalculates the weight steps, and continues training with newly-modified weights. The training algorithm continues through the flow diagram, as discussed above and below.
  • Once the adaptive learning rate is reset at block [0044] 110, the training algorithm proceeds to block 112, where it determines whether the minimum in which it is currently stuck is the same minimum in which it has been stuck in the past (if it has been stuck before). This is because as the training algorithm is learning it will sometimes get out of a local minimum and wind up in the same minima at a future time. If it finds itself stuck in the same minimum, the training algorithm checks, at block 114, whether it has achieved a maximum on the gaussian distribution from which a random value is chosen to perturb the weights (i.e., whether the maximum jog strength has been achieved). The “maximum jog strength” is the maximum value from the gaussian distribution. If the maximum jog strength has been achieved, at block 116 the training algorithm resets the jogging strength.
  • The jogging strength is reset at block [0045] 116 because the problem is not so much that the training algorithm has found itself in a local minimum, but that the ANN is not complicated enough. The training algorithm moves to block 118 and determines whether it has, prior to this point, trimmed any weights. “Trimming weights” means to set those weights to zero and take them out of the training algorithm. The procedure for trimming of weights will be described more fully with respect to FIG. 13 below.
  • If at step [0046] 118 the training algorithm determines that weights have previously been trimmed (i.e., that the weights have been previously randomly affected but the training algorithm still wound up in the same minimum because the network was not complex enough to get any more accuracy out of the mapping), the training algorithm moves to step 120 and untrims 5% of the weights. This means that weights that were previously trimmed are allowed to resume at their previous value, and from this point on they will take part in the training algorithm. The training algorithm returns to step 101 and continues to train as before.
  • By untrimming 5% of the weights, the training algorithm returns a little more complexity back to the model in hopes of decreasing the prediction error. If prediction error does not decrease, the training algorithm will once again reach a local minimum and the training algorithm will determine once again at block [0047] 112 whether it is stuck in the same minimum as before. Note, however, that at block 110 the adaptive learning rate is reset before addressing the complexity issue of untrimming previously trimmed weights, so it takes some iterations through blocks 101, 102, 104, 106 and 110 before getting back to the process of untrimming any more weights. In the event the training algorithm does wind up in the same minimum, the maximum jog strength will not have been reached, since it was previously reset at block 116 in a prior iteration. Instead, the training algorithm will proceed to block 136. At block 136 the weights are jogged, and at block 140 the jogging strength is slightly increased according to a gaussian distribution. Following block 140, the training algorithm proceeds to train net block 101 and continues training.
  • If in the course of training the training algorithm again reaches the same minimum, the procedure above is repeated. In the event the jog strength once again reaches the maximum level at block [0048] 114, the training algorithm resets the jogging strength as previously discussed. If the training algorithm reaches block 118 after several rounds of untrimming weights that there are no longer any trimmed weights, the training algorithm proceeds along the “no” path to block 122.
  • At block [0049] 122, the training algorithm determines if this is the first time it has maxed out the jog strength on this size ANN. The training algorithm keeps a counter of how many times the jog strength has maxed out with an ANN of a given size. If this is the fist time the jog strength has maxed out for the current ANN size, the training algorithm proceeds along the “yes” path to block 124 and completely re-initializes the ANN. All of the weights are re-initialized and the ANN is restarted from scratch. The training algorithm proceeds to block 101 and commences training the net anew. The ANN, however, remains whatever size it was in terms of number of hidden layers and number of nodes when training resumes at train net block 101 with the newly re-initialized weights.
  • At block [0050] 122, if the answer is “no,” the training algorithm proceeds along the “no” path to block 126. At block 126 the training algorithm has already maxed out the jog strength more than once for the current size ANN. Block 126 tests to see how many new nodes have been added for the current state of the representative training dataset. The training algorithm determines if the number of new nodes added for this size ANN is greater than or equal to five times the number of hidden layers in the ANN. If the number of new nodes added is not equal to or in excess of 5 times the number of hidden layers in the ANN, the training algorithm proceeds along the “no” path to block 128. At block 128, a new node is added according to the procedures discussed above and the training algorithm proceeds to train net block 101 to continue training the artificial neural network with the addition of the new node. The training algorithm of this invention will then proceed as discussed above.
  • If the number of new nodes added exceeds five times the number of hidden layers, the training algorithm proceeds along the “yes” path from block [0051] 126 to block 130. At block 130, the training algorithm determines whether a new layer has previously been added to the ANN. If the training algorithm has not previously added a new layer (since the last time it added a training data pattern), it proceeds along the “no” path to block 132 and adds a new layer to the artificial neural network. The training algorithm then proceeds to block 101 and continues to train the net with the newly added layer. If a new layer has been added since the last training pattern was added, the training algorithm proceeds along the “yes” path to block 134.
  • If a new layer has previously been added, it means that the training algorithm has previously added a number of nodes, has jogged the weights a number of times, and has added a layer because of the new training data pattern that has been added in the previous iteration. The training algorithm decides by going to block [0052] 134 that the training data pattern added recently is an out-lier and does not fit in with the other patterns that the neural network recognizes. In such a case, at block 134 the training algorithm removes that training data pattern from the representative training dataset and also removes it from the larger pool of data records from which the training algorithm is automatically selecting the training dataset. The training algorithm once again proceeds to train net block 101 and continues to train the network without the deleted data pattern.
  • Returning to block [0053] 112, if the training algorithm decides that it has not fallen into the same minimum, it proceeds along the “no” path to block 138. At block 138, the training algorithm resets the jogging strength to give only a small random perturbation to the weights and parameters in an attempt to extricate itself from a new local minimum. If the training algorithm reaches a new local minima, we want the training algorithm to start over again. It is desirable to reset the jogging strength because to give a small random perturbation to the weights and parameters. The intent is to start off with a small perturbation and see if it is sufficient to extricate the training algorithm from the new local minimum.
  • After resetting the jogging strength in block [0054] 138, the training algorithm proceeds to block 136 and jogs the weights. The training algorithm proceeds to block 140, increases the jogging strength, and proceeds to block 101 and trains the net with the newly increased jogging strength.
  • FIG. 4 thus gives us an overview in operation of the various embodiments of the training algorithm of BLACK. [0055]
  • FIG. 5 provides a flow chart of the present invention illustrating one method of dynamic data-mining. [0056]
  • At step [0057] 202, user 10 arrives at a GUI 12 and logs on. Once logged in, the system queries the user for their specific search profile.
  • Once the user has entered the data, the specific profile is output to data-mining search engine [0058] 12 at step 204.
  • In step [0059] 206, the dynamic search engine 100, data mines the specific profile to determine what other related topics of interest would be relevant and of greatest to the user 10.
  • The information is categorized so that it can be transferred to both existing and future search engines. [0060]
  • These related topics of interest are fed back to user [0061] 10. In step 208 user 10 then determines the topic outputs the specific and related topics to be researched. The dynamic search engine then connects existing public and proprietary search tools 16.
  • At step [0062] 210, the information is transferred, over the Internet, or other like communication pathway, to other sites and/or licensed search tools (Yahoo, Lycos or others known to those skilled in the art) to find matching the search query 15.
  • At step [0063] 212, information is gathered from the search destination site(s) pertaining to the request.
  • At step [0064] 214, information is sent, from the search engine (Yahoo, etc.) to the dynamic search engine. Relevant information is gathered from the destination databases.
  • The information is sent back to the data-mining search engine [0065] 14 at which point the information is cross-referenced to the user's profile. Depending on the profile, the presentation will rate, weigh and organize each search to present the most relevant and related topics of interest.
  • The information will be presented back to the user in a way such as: [0066]
  • The most relevant topics/areas of interest: #1-10 [0067]
  • The most related topics/area of Interest: #1-10 [0068]
  • This information will include subjects such as areas of interest that have shown to have a strong correlation and/or relationship to the specific topic of interest. [0069]
  • Once the user has received the information, they will be asked if they would like to see more information. Each time the user requests additional information, it will be presented in subsequent to the most recent, most relevant, information previously presented. [0070]
  • Over time, the profile information database will continue to grow and become more intelligent. Therefore, each subsequent searches will become more intelligent and relevant to the previous user. This data will continue to collect in a profile database located within Dynamic search engine [0071] 14. Over time, one can monitor the searches, and rate each search a success or failure (or some degree of one or the other), to then optimize with Artificial Neural Nets and Genetic algorithms, or other empirical techniques used in conducting the search.
  • The Dynamic search engine becomes an intelligent agent that specifically pulls back better (and more recent—also implying more thorough) results than the static search engines that require more user information. Results are specifically searched for with user needs expressed prior to the search. Resulting in explicitly tailored searches to a user request. [0072]
  • One embodiment of the present invention provides for a multi-component tool, with six main interacting components—Web servers, Highspeed Internet Connections, Web pages, Health-related Databases, Database Query and Responses Scripts/Code, and the Dynamic Internet Search Scripts/Code. [0073]
  • The web servers are the computer equipment, operating systems, and communications software that will contain and execute the web pages, (GUI) [0074] 2 and Dynamic search engine 14. The equipment may also contain the databases, provide highspeed Internet connections, and perform the database 18 and Internet searches. This equipment may be configured from off-the-shelf workstations, peripherals, and software. Initially, only one system must be configured. However, as use grows a search response-time per user can be estimated (and a scalability strategy developed). This will enable projection of the number of servers necessary per user. Estimates may be arrived from data provided by similar web service companies.
  • The communication pathways, Highspeed Internet connections, consist of T1s, T3s, or other connections known to those skilled in the art. Those connections provide wide-bandwidth communication to and from the entire Internet, and any associated equipment which is not considered a part of the web server. As with the web servers, the amount of necessary bandwidth will be a function of number of concurrent users. [0075]
  • Web pages (GUI) [0076] 12 present search prompts and results via the Internet to user 10 and define the interface to the system of the present invention to the user.
  • The web pages define the format of the query pages and search result pages. The query pages must have multiple forms/options to allow flexibility in searching (which databases to query, simple/Boolean forms, whether to search the Internet, how deep/long to search the Internet, etc.). The search result pages will take multiple forms, depending on the specified request, but will include relevance scores, titles and links, and summaries, much as resulting from internet search engine requests. For internet search results, links would lead to web pages. For other database results, the links would lead to graphical/textual reports for each “hit.”[0077]
  • The present invention may utilize databases containing licensed and public domain. This component includes only bare-data and “pre-processing” thereof. Data-mining (e.g., a hypothetical diagnostic tool “what illness you probably have” based upon a neural network trained from a symptom/illness database) and analysis are considered part of the following component and its development. [0078]
  • The database query scripts direct the simple searching and querying of the databases, accesses custom data-mining solutions developed for some of the databases, and allows visualization for exploration of the databases. These scripts are also responsible for returning the results of searches in the HTML format design. [0079]
  • Each data-mining tool to be implemented may be custom developed for the appropriate database. Such tools will continue to be added, as appropriate data becomes available to the present invention, even after deployment of the system. [0080]
  • These scripts, based upon the text-based query, and possibly a demographic and historical search profile, perform a “blind” an “dynamic” search of world wide web pages, returning those deemed most “relevant.” This search is blind, in that prior to the search, no index (such as those compiled and used by existing search engines) has been generated. This search will be dynamic, in that contrary to the manner in which other search engines return their results (based upon a pre-compiled though continuously updated index) the web is searched anew with each request. [0081]
  • Based upon the top N (adjustable by the user) results returned by the static search, the dynamic search would assign a relevance to each page. The dynamic search would then proceed to “spider” to each of the links contained in each page, according to a function of the relevance. The search would spider several levels beyond extremely relevant pages, and none beyond irrelevant pages. As listed below, initially the relevance function would consist of simple text matching and counting of keyword occurrences (as do the other search engines). [0082]
  • Based upon a historical profile of search successes and failures as well as demographic/personal data, technologies from artificial intelligence and other fields will optimize the relevance rating function. The more the tool is used (especially by a particular user) the better it will function at obtaining the desired information earlier in a search. The user will not have to be a computer or information scientist. The user will just be aware that with the same input the user might give a static search engine, the present invention finds more relevant, more recent and more thorough results than any other search engines. [0083]
  • A method and system for dynamically searching databases in response to a query is provided by the present invention. More specifically, a system and method for dynamic data-mining and on-line communication of customized information. This method includes the steps of first creating a search-specific profile. This search-specific profile is then inputted into a data-mining search engine. The data-mining search engine will mine the search-specific profile to determine topic of interests. These topics of interest are outputted to at least one search tool. These search tools match the topics of interest to at least one destination data site wherein the destination data sites are evaluated to determine if relevant information is present in the destination data site. Relevant information is filtered and presented to the user making the inquiry. [0084]
  • The present invention provides an advantage by providing a search engine algorithm that provides fresh (as opposed to stale) links to more highly relevant web pages (data sites) than provided by the current search engines. [0085]
  • Although the present invention has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as described by the appended claims. [0086]

Claims (3)

    What is claimed is:
  1. 1. A method of dynamically searching databases in response to a query, comprising the steps of:
    profiling a user to create a user-specific profile;
    inputting said user-specific profile to a data-mining search engine;
    mining said user-specific profile to determine at least one topic of interest;
    outputting said at least one topic of interest to at least one search tool;
    using said at least one search tool to match said at least one topic of interest to at least one destination data site;
    evaluating said at least one destination data site for relevant information; and
    presenting said relevant information to said user.
  2. 2. The method of claim 1, wherein said at least one topic of interest further comprises specific and related topics of interest.
  3. 3. A dynamic search engine comprising:
    a server system;
    a software program executed on said server system wherein said software program is operable to provide a graphical user interface to a user in which a search query may be received;
    a data-mining engine operable to receive said search query;
    at least one search tool coupled to said data-mining engine operable to execute said search query and receive a response; and
    a filtering system to evaluate said response and pass relevant response data from said response to said user.
US09845012 1998-08-04 2001-04-27 Method and system for dynamic data-mining and on-line communication of customized information Abandoned US20020049704A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US9530898 true 1998-08-04 1998-08-04
US09366590 US6266668B1 (en) 1998-08-04 1999-08-04 System and method for dynamic data-mining and on-line communication of customized information
US09845012 US20020049704A1 (en) 1998-08-04 2001-04-27 Method and system for dynamic data-mining and on-line communication of customized information

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US09845012 US20020049704A1 (en) 1998-08-04 2001-04-27 Method and system for dynamic data-mining and on-line communication of customized information

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US09366590 Continuation US6266668B1 (en) 1998-08-04 1999-08-04 System and method for dynamic data-mining and on-line communication of customized information

Publications (1)

Publication Number Publication Date
US20020049704A1 true true US20020049704A1 (en) 2002-04-25

Family

ID=26790067

Family Applications (2)

Application Number Title Priority Date Filing Date
US09366590 Active US6266668B1 (en) 1998-08-04 1999-08-04 System and method for dynamic data-mining and on-line communication of customized information
US09845012 Abandoned US20020049704A1 (en) 1998-08-04 2001-04-27 Method and system for dynamic data-mining and on-line communication of customized information

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US09366590 Active US6266668B1 (en) 1998-08-04 1999-08-04 System and method for dynamic data-mining and on-line communication of customized information

Country Status (1)

Country Link
US (2) US6266668B1 (en)

Cited By (66)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020194151A1 (en) * 2001-06-15 2002-12-19 Fenton Nicholas W. Dynamic graphical index of website content
WO2002103954A2 (en) * 2001-06-15 2002-12-27 Biowulf Technologies, Llc Data mining platform for bioinformatics and other knowledge discovery
US20040054662A1 (en) * 2002-09-16 2004-03-18 International Business Machines Corporation Automated research engine
US20040215651A1 (en) * 2001-06-22 2004-10-28 Markowitz Victor M. Platform for management and mining of genomic data
US20050038893A1 (en) * 2003-08-11 2005-02-17 Paul Graham Determining the relevance of offers
US20050080783A1 (en) * 2000-01-05 2005-04-14 Apple Computer, Inc. One Infinite Loop Universal interface for retrieval of information in a computer system
US20050108207A1 (en) * 2003-11-17 2005-05-19 International Business Machines Corporation Personnel search enhancement for collaborative computing
US20060013487A1 (en) * 2004-07-09 2006-01-19 Longe Michael R Disambiguating ambiguous characters
US20060112079A1 (en) * 2004-11-23 2006-05-25 International Business Machines Corporation System and method for generating personalized web pages
US20060156293A1 (en) * 2004-12-27 2006-07-13 Stephan Hetzer Quantity offsetting service
US20060230097A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Process model monitoring method and system
US20060229753A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Probabilistic modeling system for product design
US20060229769A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Control system and method
US20060229854A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Computer system architecture for probabilistic modeling
US20060229852A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Zeta statistic process method and system
US20060241911A1 (en) * 2005-04-20 2006-10-26 Leong Kian F Systems and methods for aggregating telephony and internet data
US20070061144A1 (en) * 2005-08-30 2007-03-15 Caterpillar Inc. Batch statistics process model method and system
US20070094048A1 (en) * 2005-10-25 2007-04-26 Caterpillar Inc. Expert knowledge combination process based medical risk stratifying method and system
US20070118487A1 (en) * 2005-11-18 2007-05-24 Caterpillar Inc. Product cost modeling method and system
US20070198491A1 (en) * 2006-02-10 2007-08-23 Hon Hai Precision Industry Co., Ltd. System and method for searching and filtering web pages
US20070203864A1 (en) * 2006-01-31 2007-08-30 Caterpillar Inc. Process model error correction method and system
US20070203810A1 (en) * 2006-02-13 2007-08-30 Caterpillar Inc. Supply chain modeling method and system
US20070260585A1 (en) * 2006-05-02 2007-11-08 Microsoft Corporation Efficiently filtering using a web site
US20070288648A1 (en) * 2002-11-18 2007-12-13 Lara Mehanna Host-based intelligent results related to a character stream
US20080021681A1 (en) * 2005-04-08 2008-01-24 Caterpillar Inc. Process modeling and optimization method and system
US20080097939A1 (en) * 1998-05-01 2008-04-24 Isabelle Guyon Data mining platform for bioinformatics and other knowledge discovery
US20080097938A1 (en) * 1998-05-01 2008-04-24 Isabelle Guyon Data mining platform for bioinformatics and other knowledge discovery
US20080154811A1 (en) * 2006-12-21 2008-06-26 Caterpillar Inc. Method and system for verifying virtual sensors
US20080154459A1 (en) * 2006-12-21 2008-06-26 Caterpillar Inc. Method and system for intelligent maintenance
US20080183449A1 (en) * 2007-01-31 2008-07-31 Caterpillar Inc. Machine parameter tuning method and system
US20080312756A1 (en) * 2007-06-15 2008-12-18 Caterpillar Inc. Virtual sensor system and method
US20090024367A1 (en) * 2007-07-17 2009-01-22 Caterpillar Inc. Probabilistic modeling system for product design
US20090037153A1 (en) * 2007-07-30 2009-02-05 Caterpillar Inc. Product design optimization method and system
US7499842B2 (en) 2005-11-18 2009-03-03 Caterpillar Inc. Process model based virtual sensor and method
US20090063087A1 (en) * 2007-08-31 2009-03-05 Caterpillar Inc. Virtual sensor based control system and method
US20090112334A1 (en) * 2007-10-31 2009-04-30 Grichnik Anthony J Fixed-point virtual sensor control system and method
US20090119275A1 (en) * 2007-10-29 2009-05-07 International Business Machines Corporation Method of monitoring electronic media
US20090119065A1 (en) * 2007-11-02 2009-05-07 Caterpillar Inc. Virtual sensor network (VSN) system and method
US20090132216A1 (en) * 2005-04-08 2009-05-21 Caterpillar Inc. Asymmetric random scatter process for probabilistic modeling system for product design
US7580767B2 (en) 2004-07-10 2009-08-25 Kla-Tencor Corporation Methods of and apparatuses for maintenance, diagnosis, and optimization of processes
US20090216748A1 (en) * 2007-09-20 2009-08-27 Hal Kravcik Internet data mining method and system
US20090300052A1 (en) * 2008-05-30 2009-12-03 Caterpillar Inc. System and method for improving data coverage in modeling systems
US20090293457A1 (en) * 2008-05-30 2009-12-03 Grichnik Anthony J System and method for controlling NOx reactant supply
US20100050025A1 (en) * 2008-08-20 2010-02-25 Caterpillar Inc. Virtual sensor network (VSN) based control system and method
US20100077049A1 (en) * 2002-11-18 2010-03-25 Aol Llc Reconfiguring an Electronic Message to Effect an Enhanced Notification
US20100250202A1 (en) * 2005-04-08 2010-09-30 Grichnik Anthony J Symmetric random scatter process for probabilistic modeling system for product design
US20110131168A1 (en) * 2008-04-10 2011-06-02 Ntt Docomo, Inc. Recommendation information evaluation apparatus and recommendation information evaluation method
US7962504B1 (en) 2005-05-26 2011-06-14 Aol Inc. Sourcing terms into a search engine
US8036764B2 (en) 2007-11-02 2011-10-11 Caterpillar Inc. Virtual sensor network (VSN) system and method
US20110270788A1 (en) * 2010-04-30 2011-11-03 Moore Douglas A Neural Network For Clustering Input Data Based On A Gaussian Mixture Model
US8156193B1 (en) 2002-11-18 2012-04-10 Aol Inc. Enhanced buddy list using mobile device identifiers
CN102929952A (en) * 2012-10-08 2013-02-13 北京奇虎科技有限公司 Web page image display device and method
US8478506B2 (en) 2006-09-29 2013-07-02 Caterpillar Inc. Virtual sensor based engine control system and method
US8577972B1 (en) 2003-09-05 2013-11-05 Facebook, Inc. Methods and systems for capturing and managing instant messages
US8701014B1 (en) 2002-11-18 2014-04-15 Facebook, Inc. Account linking
US8793004B2 (en) 2011-06-15 2014-07-29 Caterpillar Inc. Virtual sensor system and method for generating output parameters
US8874672B2 (en) 2003-03-26 2014-10-28 Facebook, Inc. Identifying and using identities deemed to be known to a user
US8965964B1 (en) 2002-11-18 2015-02-24 Facebook, Inc. Managing forwarded electronic messages
US9203879B2 (en) 2000-03-17 2015-12-01 Facebook, Inc. Offline alerts mechanism
US9203647B2 (en) 2002-11-18 2015-12-01 Facebook, Inc. Dynamic online and geographic location of a user
US9246975B2 (en) 2000-03-17 2016-01-26 Facebook, Inc. State change alerts mechanism
US9313046B2 (en) 2012-09-15 2016-04-12 Facebook, Inc. Presenting dynamic location of a user
CN105488054A (en) * 2014-09-17 2016-04-13 阿里巴巴集团控股有限公司 Method and device for browsing image
US9647872B2 (en) 2002-11-18 2017-05-09 Facebook, Inc. Dynamic identification of other users to an online user
US9667585B2 (en) 2002-11-18 2017-05-30 Facebook, Inc. Central people lists accessible by multiple applications
US9846885B1 (en) * 2014-04-30 2017-12-19 Intuit Inc. Method and system for comparing commercial entities based on purchase patterns

Families Citing this family (111)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7364068B1 (en) 1998-03-11 2008-04-29 West Corporation Methods and apparatus for intelligent selection of goods and services offered to conferees
US8315909B1 (en) 1998-03-11 2012-11-20 West Corporation Methods and apparatus for intelligent selection of goods and services in point-of-sale commerce
US6055513A (en) 1998-03-11 2000-04-25 Telebuyer, Llc Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce
US7729945B1 (en) 1998-03-11 2010-06-01 West Corporation Systems and methods that use geographic data to intelligently select goods and services to offer in telephonic and electronic commerce
US7437313B1 (en) 1998-03-11 2008-10-14 West Direct, Llc Methods, computer-readable media, and apparatus for offering users a plurality of scenarios under which to conduct at least one primary transaction
US6567814B1 (en) * 1998-08-26 2003-05-20 Thinkanalytics Ltd Method and apparatus for knowledge discovery in databases
US6842782B1 (en) * 1998-12-08 2005-01-11 Yodlee.Com, Inc. Method and apparatus for tracking functional states of a web-site and reporting results to web developers
US6532459B1 (en) * 1998-12-15 2003-03-11 Berson Research Corp. System for finding, identifying, tracking, and correcting personal information in diverse databases
US6792412B1 (en) * 1999-02-02 2004-09-14 Alan Sullivan Neural network system and method for controlling information output based on user feedback
JP2000276470A (en) * 1999-03-23 2000-10-06 Seiko Epson Corp Method and device for information retrieval performance evaluation and recording medium for recording information retrieval performance evaluation processing program
KR100328670B1 (en) * 1999-07-21 2002-03-04 정만원 System For Recommending Items With Multiple Analyzing Components
US7346605B1 (en) * 1999-07-22 2008-03-18 Markmonitor, Inc. Method and system for searching and monitoring internet trademark usage
US6792576B1 (en) * 1999-07-26 2004-09-14 Xerox Corporation System and method of automatic wrapper grammar generation
JP2001184369A (en) * 1999-12-24 2001-07-06 Yoshiko Kido Information distribution system using electronic computer and information distribution transacting method using the same
US6606659B1 (en) * 2000-01-28 2003-08-12 Websense, Inc. System and method for controlling access to internet sites
CA2399641A1 (en) * 2000-02-10 2001-08-16 Involve Techology, Inc. System for creating and maintaining a database of information utilizing user opinions
US20020002554A1 (en) 2000-03-09 2002-01-03 Herdman Rachelle B. Systems and methods for distributing personalized information over a communications system
US6957218B1 (en) * 2000-04-06 2005-10-18 Medical Central Online Method and system for creating a website for a healthcare provider
US7222120B1 (en) * 2000-04-12 2007-05-22 Making Everlasting Memories, L.L.C. Methods of providing a registry service and a registry service
US7228327B2 (en) * 2000-05-08 2007-06-05 Hoshiko Llc Method and apparatus for delivering content via information retrieval devices
US6728695B1 (en) * 2000-05-26 2004-04-27 Burning Glass Technologies, Llc Method and apparatus for making predictions about entities represented in documents
US20040073617A1 (en) 2000-06-19 2004-04-15 Milliken Walter Clark Hash-based systems and methods for detecting and preventing transmission of unwanted e-mail
WO2002010984A2 (en) * 2000-07-21 2002-02-07 Triplehop Technologies, Inc. System and method for obtaining user preferences and providing user recommendations for unseen physical and information goods and services
US6604098B1 (en) * 2000-07-24 2003-08-05 Viagold Direct Network Limited Method and system in a computer network for searching and linking web sites
DE60017727D1 (en) * 2000-08-18 2005-03-03 Exalead Paris Search tool and process to search using categories and keywords
US7590556B1 (en) * 2000-08-24 2009-09-15 International Apparel Group, Llc System and method for providing lifestyle specific information services, and products over a global computer network such as the internet
US7099304B2 (en) 2000-09-05 2006-08-29 Flexiworld Technologies, Inc. Apparatus, methods and systems for anonymous communication
CA2319871A1 (en) * 2000-09-15 2002-03-15 James D. Chesko Internet privacy system
US6907465B1 (en) * 2000-09-22 2005-06-14 Daniel E. Tsai Electronic commerce using personal preferences
US20020128871A1 (en) * 2000-12-07 2002-09-12 Dan Adamson Method, apparatus, and system for aggregating, targeting, and synchronizing health information delivery
US7363308B2 (en) * 2000-12-28 2008-04-22 Fair Isaac Corporation System and method for obtaining keyword descriptions of records from a large database
US6636864B1 (en) * 2000-12-30 2003-10-21 Bellsouth Intellectual Property Corporation Methods and systems for automatically creating a data feed file for use with desktop applications
US8156051B1 (en) * 2001-01-09 2012-04-10 Northwest Software, Inc. Employment recruiting system
US20020123996A1 (en) * 2001-02-06 2002-09-05 O'brien Christopher Data mining system, method and apparatus for industrial applications
US6643646B2 (en) * 2001-03-01 2003-11-04 Hitachi, Ltd. Analysis of massive data accumulations using patient rule induction method and on-line analytical processing
US7146335B2 (en) * 2001-03-15 2006-12-05 E*Trade Group, Inc., A Corp. Of California Online trading system having ally-initiated trading
US6714929B1 (en) * 2001-04-13 2004-03-30 Auguri Corporation Weighted preference data search system and method
US6636860B2 (en) * 2001-04-26 2003-10-21 International Business Machines Corporation Method and system for data mining automation in domain-specific analytic applications
US7739162B1 (en) 2001-05-04 2010-06-15 West Corporation System, method, and business method for setting micropayment transaction to a pre-paid instrument
US6856992B2 (en) * 2001-05-15 2005-02-15 Metatomix, Inc. Methods and apparatus for real-time business visibility using persistent schema-less data storage
US8572059B2 (en) * 2001-05-15 2013-10-29 Colin P. Britton Surveillance, monitoring and real-time events platform
US7058637B2 (en) * 2001-05-15 2006-06-06 Metatomix, Inc. Methods and apparatus for enterprise application integration
US7890517B2 (en) 2001-05-15 2011-02-15 Metatomix, Inc. Appliance for enterprise information integration and enterprise resource interoperability platform and methods
US6912533B1 (en) * 2001-07-31 2005-06-28 Oracle International Corporation Data mining agents for efficient hardware utilization
US20030028353A1 (en) * 2001-08-06 2003-02-06 Brian Gventer Production pattern-recognition artificial neural net (ANN) with event-response expert system (ES)--yieldshieldTM
US7836057B1 (en) 2001-09-24 2010-11-16 Auguri Corporation Weighted preference inference system and method
US7194464B2 (en) 2001-12-07 2007-03-20 Websense, Inc. System and method for adapting an internet filter
US20030135493A1 (en) * 2002-01-15 2003-07-17 Jeffrey Phelan Method and apparatus for consuming information based on a geographic location profile of a user
US6714893B2 (en) * 2002-02-15 2004-03-30 International Business Machines Corporation Enhanced concern indicator failure prediction system
US7949648B2 (en) * 2002-02-26 2011-05-24 Soren Alain Mortensen Compiling and accessing subject-specific information from a computer network
WO2003077142A1 (en) * 2002-03-04 2003-09-18 Medstory.Com Method, apparatus, and system for data modeling and processing
US8635690B2 (en) 2004-11-05 2014-01-21 Mcafee, Inc. Reputation based message processing
US20060015942A1 (en) 2002-03-08 2006-01-19 Ciphertrust, Inc. Systems and methods for classification of messaging entities
US8132250B2 (en) * 2002-03-08 2012-03-06 Mcafee, Inc. Message profiling systems and methods
US8578480B2 (en) 2002-03-08 2013-11-05 Mcafee, Inc. Systems and methods for identifying potentially malicious messages
US8561167B2 (en) 2002-03-08 2013-10-15 Mcafee, Inc. Web reputation scoring
US20030182284A1 (en) * 2002-03-25 2003-09-25 Lucian Russell Dynamic data mining process
US7346534B1 (en) 2002-05-22 2008-03-18 Brunswick Corporation Method for facilitating supplier-customer collaboration using the internet
US7069256B1 (en) * 2002-05-23 2006-06-27 Oracle International Corporation Neural network module for data mining
US7813951B2 (en) * 2002-06-04 2010-10-12 Sap Ag Managing customer loss using a graphical user interface
US7813952B2 (en) 2002-06-04 2010-10-12 Sap Ag Managing customer loss using customer groups
US20040039593A1 (en) * 2002-06-04 2004-02-26 Ramine Eskandari Managing customer loss using customer value
US20040015483A1 (en) * 2002-07-16 2004-01-22 Hogan Ronald W. Document tracking system and method
US7035841B2 (en) * 2002-07-18 2006-04-25 Xerox Corporation Method for automatic wrapper repair
US8306908B1 (en) 2002-12-31 2012-11-06 West Corporation Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce
US8712857B1 (en) 2003-03-31 2014-04-29 Tuxis Technologies Llc Methods and apparatus for intelligent selection of goods and services in mobile commerce
US20040215656A1 (en) * 2003-04-25 2004-10-28 Marcus Dill Automated data mining runs
US20040220903A1 (en) * 2003-04-30 2004-11-04 Emarkmonitor Inc. Method and system to correlate trademark data to internet domain name data
US20050010392A1 (en) * 2003-07-10 2005-01-13 International Business Machines Corporation Traditional Chinese / simplified Chinese character translator
US20050010391A1 (en) * 2003-07-10 2005-01-13 International Business Machines Corporation Chinese character / Pin Yin / English translator
US20050027547A1 (en) * 2003-07-31 2005-02-03 International Business Machines Corporation Chinese / Pin Yin / english dictionary
US8137105B2 (en) 2003-07-31 2012-03-20 International Business Machines Corporation Chinese/English vocabulary learning tool
US8160914B1 (en) * 2003-10-31 2012-04-17 Versata Development Group, Inc. Identifying quality user sessions and determining product demand with high resolution capabilities
US7231399B1 (en) * 2003-11-14 2007-06-12 Google Inc. Ranking documents based on large data sets
US7548968B1 (en) 2003-12-10 2009-06-16 Markmonitor Inc. Policing internet domains
US7761447B2 (en) * 2004-04-08 2010-07-20 Microsoft Corporation Systems and methods that rank search results
US7386485B1 (en) 2004-06-25 2008-06-10 West Corporation Method and system for providing offers in real time to prospective customers
US7178720B1 (en) 2004-09-30 2007-02-20 West Corporation Methods, computer-readable media, and computer program product for intelligent selection of items encoded onto portable machine-playable entertainment media
US8620717B1 (en) 2004-11-04 2013-12-31 Auguri Corporation Analytical tool
US7516062B2 (en) * 2005-04-19 2009-04-07 International Business Machines Corporation Language converter with enhanced search capability
US20060259356A1 (en) * 2005-05-12 2006-11-16 Microsoft Corporation Adpost: a centralized advertisement platform
US7734632B2 (en) * 2005-10-28 2010-06-08 Disney Enterprises, Inc. System and method for targeted ad delivery
WO2007106826A3 (en) 2006-03-13 2008-02-21 Markmonitor Inc Domain name ownership validation
US8024235B2 (en) * 2006-06-21 2011-09-20 Microsoft Corporation Automatic search functionality within business applications
US8615800B2 (en) 2006-07-10 2013-12-24 Websense, Inc. System and method for analyzing web content
US8020206B2 (en) 2006-07-10 2011-09-13 Websense, Inc. System and method of analyzing web content
US7624118B2 (en) * 2006-07-26 2009-11-24 Microsoft Corporation Data processing over very large databases
WO2008044242A3 (en) * 2006-07-28 2009-01-22 Persistent Systems Private Ltd Gene expression analysis using genotype-pheontype based programming
US7685199B2 (en) * 2006-07-31 2010-03-23 Microsoft Corporation Presenting information related to topics extracted from event classes
US7577718B2 (en) * 2006-07-31 2009-08-18 Microsoft Corporation Adaptive dissemination of personalized and contextually relevant information
US7849079B2 (en) * 2006-07-31 2010-12-07 Microsoft Corporation Temporal ranking of search results
US9654495B2 (en) 2006-12-01 2017-05-16 Websense, Llc System and method of analyzing web addresses
US8214497B2 (en) 2007-01-24 2012-07-03 Mcafee, Inc. Multi-dimensional reputation scoring
US8763114B2 (en) 2007-01-24 2014-06-24 Mcafee, Inc. Detecting image spam
US7779156B2 (en) * 2007-01-24 2010-08-17 Mcafee, Inc. Reputation based load balancing
US20080222132A1 (en) * 2007-03-07 2008-09-11 Jiangyi Pan Personalized shopping recommendation based on search units
US7698422B2 (en) 2007-09-10 2010-04-13 Specific Media, Inc. System and method of determining user demographic profiles of anonymous users
US20090119276A1 (en) * 2007-11-01 2009-05-07 Antoine Sorel Neron Method and Internet-based Search Engine System for Storing, Sorting, and Displaying Search Results
US8185930B2 (en) 2007-11-06 2012-05-22 Mcafee, Inc. Adjusting filter or classification control settings
US8589503B2 (en) 2008-04-04 2013-11-19 Mcafee, Inc. Prioritizing network traffic
US20090319484A1 (en) * 2008-06-23 2009-12-24 Nadav Golbandi Using Web Feed Information in Information Retrieval
EP2318955A1 (en) 2008-06-30 2011-05-11 Websense, Inc. System and method for dynamic and real-time categorization of webpages
US8041710B2 (en) * 2008-11-13 2011-10-18 Microsoft Corporation Automatic diagnosis of search relevance failures
US20100217616A1 (en) 2009-02-25 2010-08-26 HCD Software, LLC Methods, apparatus and computer program products for targeted and customized marketing of prospective customers
US8621638B2 (en) 2010-05-14 2013-12-31 Mcafee, Inc. Systems and methods for classification of messaging entities
US20120246139A1 (en) * 2010-10-21 2012-09-27 Bindu Rama Rao System and method for resume, yearbook and report generation based on webcrawling and specialized data collection
US8170971B1 (en) 2011-09-28 2012-05-01 Ava, Inc. Systems and methods for providing recommendations based on collaborative and/or content-based nodal interrelationships
US8732101B1 (en) 2013-03-15 2014-05-20 Nara Logics, Inc. Apparatus and method for providing harmonized recommendations based on an integrated user profile
US20160048781A1 (en) * 2014-08-13 2016-02-18 Bank Of America Corporation Cross Dataset Keyword Rating System
US10019522B2 (en) 2014-09-12 2018-07-10 Microsoft Technology Licensing Llc Customized site search deep links on a SERP
US9836452B2 (en) 2014-12-30 2017-12-05 Microsoft Technology Licensing, Llc Discriminating ambiguous expressions to enhance user experience

Family Cites Families (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5408655A (en) * 1989-02-27 1995-04-18 Apple Computer, Inc. User interface system and method for traversing a database
US5446891A (en) * 1992-02-26 1995-08-29 International Business Machines Corporation System for adjusting hypertext links with weighed user goals and activities
DE69432503D1 (en) * 1993-10-08 2003-05-22 Ibm Information archiving system with object-dependent functionality
WO1995012173A3 (en) 1993-10-28 1995-05-18 Teltech Resource Network Corp Database search summary with user determined characteristics
US5623652A (en) * 1994-07-25 1997-04-22 Apple Computer, Inc. Method and apparatus for searching for information in a network and for controlling the display of searchable information on display devices in the network
US5694594A (en) * 1994-11-14 1997-12-02 Chang; Daniel System for linking hypermedia data objects in accordance with associations of source and destination data objects and similarity threshold without using keywords or link-difining terms
DE69531599T2 (en) 1994-12-20 2004-06-24 Sun Microsystems, Inc., Mountain View Method and apparatus for locating and obtaining personalized information
WO1996023265A1 (en) * 1995-01-23 1996-08-01 British Telecommunications Public Limited Company Methods and/or systems for accessing information
US5842200A (en) * 1995-03-31 1998-11-24 International Business Machines Corporation System and method for parallel mining of association rules in databases
US5649186A (en) * 1995-08-07 1997-07-15 Silicon Graphics Incorporated System and method for a computer-based dynamic information clipping service
JP3072708B2 (en) * 1995-11-01 2000-08-07 インターナショナル・ビジネス・マシーンズ・コーポレ−ション Database search method and apparatus
US5787424A (en) 1995-11-30 1998-07-28 Electronic Data Systems Corporation Process and system for recursive document retrieval
US5778367A (en) * 1995-12-14 1998-07-07 Network Engineering Software, Inc. Automated on-line information service and directory, particularly for the world wide web
US5913215A (en) * 1996-04-09 1999-06-15 Seymour I. Rubinstein Browse by prompted keyword phrases with an improved method for obtaining an initial document set
US5765028A (en) 1996-05-07 1998-06-09 Ncr Corporation Method and apparatus for providing neural intelligence to a mail query agent in an online analytical processing system
US5890149A (en) * 1996-06-20 1999-03-30 Wisdomware, Inc. Organization training, coaching and indexing system
US5933827A (en) 1996-09-25 1999-08-03 International Business Machines Corporation System for identifying new web pages of interest to a user
US5835905A (en) * 1997-04-09 1998-11-10 Xerox Corporation System for predicting documents relevant to focus documents by spreading activation through network representations of a linked collection of documents
US5943667A (en) * 1997-06-03 1999-08-24 International Business Machines Corporation Eliminating redundancy in generation of association rules for on-line mining
US6003029A (en) * 1997-08-22 1999-12-14 International Business Machines Corporation Automatic subspace clustering of high dimensional data for data mining applications
US5946683A (en) * 1997-11-25 1999-08-31 Lucent Technologies Inc. Technique for effectively instantiating attributes in association rules
US6202062B1 (en) * 1999-02-26 2001-03-13 Ac Properties B.V. System, method and article of manufacture for creating a filtered information summary based on multiple profiles of each single user

Cited By (143)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7921068B2 (en) 1998-05-01 2011-04-05 Health Discovery Corporation Data mining platform for knowledge discovery from heterogeneous data types and/or heterogeneous data sources
US8126825B2 (en) 1998-05-01 2012-02-28 Health Discovery Corporation Method for visualizing feature ranking of a subset of features for classifying data using a learning machine
US7542947B2 (en) 1998-05-01 2009-06-02 Health Discovery Corporation Data mining platform for bioinformatics and other knowledge discovery
US20080097938A1 (en) * 1998-05-01 2008-04-24 Isabelle Guyon Data mining platform for bioinformatics and other knowledge discovery
US20080097939A1 (en) * 1998-05-01 2008-04-24 Isabelle Guyon Data mining platform for bioinformatics and other knowledge discovery
US20110184896A1 (en) * 1998-05-01 2011-07-28 Health Discovery Corporation Method for visualizing feature ranking of a subset of features for classifying data using a learning machine
US20050080783A1 (en) * 2000-01-05 2005-04-14 Apple Computer, Inc. One Infinite Loop Universal interface for retrieval of information in a computer system
US8086604B2 (en) * 2000-01-05 2011-12-27 Apple Inc. Universal interface for retrieval of information in a computer system
US9246975B2 (en) 2000-03-17 2016-01-26 Facebook, Inc. State change alerts mechanism
US9203879B2 (en) 2000-03-17 2015-12-01 Facebook, Inc. Offline alerts mechanism
US9736209B2 (en) 2000-03-17 2017-08-15 Facebook, Inc. State change alerts mechanism
US6990498B2 (en) * 2001-06-15 2006-01-24 Sony Corporation Dynamic graphical index of website content
WO2002103954A2 (en) * 2001-06-15 2002-12-27 Biowulf Technologies, Llc Data mining platform for bioinformatics and other knowledge discovery
US7444308B2 (en) 2001-06-15 2008-10-28 Health Discovery Corporation Data mining platform for bioinformatics and other knowledge discovery
WO2002103954A3 (en) * 2001-06-15 2003-04-03 Biowulf Technologies Llc Data mining platform for bioinformatics and other knowledge discovery
US20020194151A1 (en) * 2001-06-15 2002-12-19 Fenton Nicholas W. Dynamic graphical index of website content
US20040215651A1 (en) * 2001-06-22 2004-10-28 Markowitz Victor M. Platform for management and mining of genomic data
US20040054662A1 (en) * 2002-09-16 2004-03-18 International Business Machines Corporation Automated research engine
US7076484B2 (en) * 2002-09-16 2006-07-11 International Business Machines Corporation Automated research engine
US9621376B2 (en) 2002-11-18 2017-04-11 Facebook, Inc. Dynamic location of a subordinate user
US9560000B2 (en) 2002-11-18 2017-01-31 Facebook, Inc. Reconfiguring an electronic message to effect an enhanced notification
US9515977B2 (en) 2002-11-18 2016-12-06 Facebook, Inc. Time based electronic message delivery
US9356890B2 (en) 2002-11-18 2016-05-31 Facebook, Inc. Enhanced buddy list using mobile device identifiers
US9319356B2 (en) 2002-11-18 2016-04-19 Facebook, Inc. Message delivery control settings
US9253136B2 (en) 2002-11-18 2016-02-02 Facebook, Inc. Electronic message delivery based on presence information
US9769104B2 (en) 2002-11-18 2017-09-19 Facebook, Inc. Methods and system for delivering multiple notifications
US9203647B2 (en) 2002-11-18 2015-12-01 Facebook, Inc. Dynamic online and geographic location of a user
US9571440B2 (en) 2002-11-18 2017-02-14 Facebook, Inc. Notification archive
US9774560B2 (en) 2002-11-18 2017-09-26 Facebook, Inc. People lists
US9852126B2 (en) 2002-11-18 2017-12-26 Facebook, Inc. Host-based intelligent results related to a character stream
US9894018B2 (en) 2002-11-18 2018-02-13 Facebook, Inc. Electronic messaging using reply telephone numbers
US9203794B2 (en) 2002-11-18 2015-12-01 Facebook, Inc. Systems and methods for reconfiguring electronic messages
US9171064B2 (en) 2002-11-18 2015-10-27 Facebook, Inc. Intelligent community based results related to a character stream
US9075868B2 (en) 2002-11-18 2015-07-07 Facebook, Inc. Intelligent results based on database queries
US9075867B2 (en) 2002-11-18 2015-07-07 Facebook, Inc. Intelligent results using an assistant
US9053173B2 (en) 2002-11-18 2015-06-09 Facebook, Inc. Intelligent results related to a portion of a search query
US9053174B2 (en) 2002-11-18 2015-06-09 Facebook, Inc. Intelligent vendor results related to a character stream
US9047364B2 (en) 2002-11-18 2015-06-02 Facebook, Inc. Intelligent client capability-based results related to a character stream
US8965964B1 (en) 2002-11-18 2015-02-24 Facebook, Inc. Managing forwarded electronic messages
US8954530B2 (en) 2002-11-18 2015-02-10 Facebook, Inc. Intelligent results related to a character stream
US8954534B2 (en) 2002-11-18 2015-02-10 Facebook, Inc. Host-based intelligent results related to a character stream
US8954531B2 (en) 2002-11-18 2015-02-10 Facebook, Inc. Intelligent messaging label results related to a character stream
US8819176B2 (en) 2002-11-18 2014-08-26 Facebook, Inc. Intelligent map results related to a character stream
US8775560B2 (en) 2002-11-18 2014-07-08 Facebook, Inc. Host-based intelligent results related to a character stream
US8701014B1 (en) 2002-11-18 2014-04-15 Facebook, Inc. Account linking
US8452849B2 (en) 2002-11-18 2013-05-28 Facebook, Inc. Host-based intelligent results related to a character stream
US8156193B1 (en) 2002-11-18 2012-04-10 Aol Inc. Enhanced buddy list using mobile device identifiers
US9571439B2 (en) 2002-11-18 2017-02-14 Facebook, Inc. Systems and methods for notification delivery
US9647872B2 (en) 2002-11-18 2017-05-09 Facebook, Inc. Dynamic identification of other users to an online user
US9667585B2 (en) 2002-11-18 2017-05-30 Facebook, Inc. Central people lists accessible by multiple applications
US8005919B2 (en) 2002-11-18 2011-08-23 Aol Inc. Host-based intelligent results related to a character stream
US8001199B2 (en) 2002-11-18 2011-08-16 Aol Inc. Reconfiguring an electronic message to effect an enhanced notification
US9729489B2 (en) 2002-11-18 2017-08-08 Facebook, Inc. Systems and methods for notification management and delivery
US20100077049A1 (en) * 2002-11-18 2010-03-25 Aol Llc Reconfiguring an Electronic Message to Effect an Enhanced Notification
US20070288648A1 (en) * 2002-11-18 2007-12-13 Lara Mehanna Host-based intelligent results related to a character stream
US9053175B2 (en) 2002-11-18 2015-06-09 Facebook, Inc. Intelligent results using a spelling correction agent
US9736255B2 (en) 2003-03-26 2017-08-15 Facebook, Inc. Methods of providing access to messages based on degrees of separation
US9516125B2 (en) 2003-03-26 2016-12-06 Facebook, Inc. Identifying and using identities deemed to be known to a user
US9531826B2 (en) 2003-03-26 2016-12-27 Facebook, Inc. Managing electronic messages based on inference scores
US8874672B2 (en) 2003-03-26 2014-10-28 Facebook, Inc. Identifying and using identities deemed to be known to a user
US20050038893A1 (en) * 2003-08-11 2005-02-17 Paul Graham Determining the relevance of offers
US8458033B2 (en) * 2003-08-11 2013-06-04 Dropbox, Inc. Determining the relevance of offers
US9070118B2 (en) 2003-09-05 2015-06-30 Facebook, Inc. Methods for capturing electronic messages based on capture rules relating to user actions regarding received electronic messages
US8577972B1 (en) 2003-09-05 2013-11-05 Facebook, Inc. Methods and systems for capturing and managing instant messages
US20050108207A1 (en) * 2003-11-17 2005-05-19 International Business Machines Corporation Personnel search enhancement for collaborative computing
US7647378B2 (en) 2003-11-17 2010-01-12 International Business Machines Corporation Personnel search enhancement for collaborative computing
US8583087B2 (en) 2004-07-09 2013-11-12 Nuance Communications, Inc. Disambiguating ambiguous characters
US7966003B2 (en) 2004-07-09 2011-06-21 Tegic Communications, Inc. Disambiguating ambiguous characters
US20060013487A1 (en) * 2004-07-09 2006-01-19 Longe Michael R Disambiguating ambiguous characters
US7580767B2 (en) 2004-07-10 2009-08-25 Kla-Tencor Corporation Methods of and apparatuses for maintenance, diagnosis, and optimization of processes
US20090292506A1 (en) * 2004-07-10 2009-11-26 Kla-Tencor Corporation Methods of and apparatuses for maintenance, diagnosis, and optimization of processes
US20060112079A1 (en) * 2004-11-23 2006-05-25 International Business Machines Corporation System and method for generating personalized web pages
US7987187B2 (en) * 2004-12-27 2011-07-26 Sap Aktiengesellschaft Quantity offsetting service
US20060156293A1 (en) * 2004-12-27 2006-07-13 Stephan Hetzer Quantity offsetting service
US20060229753A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Probabilistic modeling system for product design
US20060229769A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Control system and method
US20060229854A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Computer system architecture for probabilistic modeling
US20100250202A1 (en) * 2005-04-08 2010-09-30 Grichnik Anthony J Symmetric random scatter process for probabilistic modeling system for product design
US20060229852A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Zeta statistic process method and system
US7565333B2 (en) 2005-04-08 2009-07-21 Caterpillar Inc. Control system and method
US20060230097A1 (en) * 2005-04-08 2006-10-12 Caterpillar Inc. Process model monitoring method and system
US20090132216A1 (en) * 2005-04-08 2009-05-21 Caterpillar Inc. Asymmetric random scatter process for probabilistic modeling system for product design
US20080021681A1 (en) * 2005-04-08 2008-01-24 Caterpillar Inc. Process modeling and optimization method and system
US8209156B2 (en) 2005-04-08 2012-06-26 Caterpillar Inc. Asymmetric random scatter process for probabilistic modeling system for product design
US7877239B2 (en) 2005-04-08 2011-01-25 Caterpillar Inc Symmetric random scatter process for probabilistic modeling system for product design
US8364610B2 (en) 2005-04-08 2013-01-29 Caterpillar Inc. Process modeling and optimization method and system
US20060241911A1 (en) * 2005-04-20 2006-10-26 Leong Kian F Systems and methods for aggregating telephony and internet data
US20110208768A1 (en) * 2005-05-26 2011-08-25 Aol Inc. Sourcing terms into a search engine
US7962504B1 (en) 2005-05-26 2011-06-14 Aol Inc. Sourcing terms into a search engine
US9753972B2 (en) 2005-05-26 2017-09-05 Facebook, Inc. Searching based on user interest
US8874606B2 (en) 2005-05-26 2014-10-28 Facebook, Inc. Sourcing terms into a search engine
US8996560B2 (en) 2005-05-26 2015-03-31 Facebook, Inc. Search engine utilizing user navigated documents
US20070061144A1 (en) * 2005-08-30 2007-03-15 Caterpillar Inc. Batch statistics process model method and system
US20070179769A1 (en) * 2005-10-25 2007-08-02 Caterpillar Inc. Medical risk stratifying method and system
US7584166B2 (en) 2005-10-25 2009-09-01 Caterpillar Inc. Expert knowledge combination process based medical risk stratifying method and system
US7487134B2 (en) * 2005-10-25 2009-02-03 Caterpillar Inc. Medical risk stratifying method and system
US20070094048A1 (en) * 2005-10-25 2007-04-26 Caterpillar Inc. Expert knowledge combination process based medical risk stratifying method and system
US7499842B2 (en) 2005-11-18 2009-03-03 Caterpillar Inc. Process model based virtual sensor and method
US20070118487A1 (en) * 2005-11-18 2007-05-24 Caterpillar Inc. Product cost modeling method and system
US7505949B2 (en) 2006-01-31 2009-03-17 Caterpillar Inc. Process model error correction method and system
US20070203864A1 (en) * 2006-01-31 2007-08-30 Caterpillar Inc. Process model error correction method and system
US20070198491A1 (en) * 2006-02-10 2007-08-23 Hon Hai Precision Industry Co., Ltd. System and method for searching and filtering web pages
US20070203810A1 (en) * 2006-02-13 2007-08-30 Caterpillar Inc. Supply chain modeling method and system
US8190650B2 (en) 2006-05-02 2012-05-29 Microsoft Corporation Efficiently filtering using a web site
US20070260585A1 (en) * 2006-05-02 2007-11-08 Microsoft Corporation Efficiently filtering using a web site
US8478506B2 (en) 2006-09-29 2013-07-02 Caterpillar Inc. Virtual sensor based engine control system and method
US7483774B2 (en) 2006-12-21 2009-01-27 Caterpillar Inc. Method and system for intelligent maintenance
US20080154459A1 (en) * 2006-12-21 2008-06-26 Caterpillar Inc. Method and system for intelligent maintenance
US20080154811A1 (en) * 2006-12-21 2008-06-26 Caterpillar Inc. Method and system for verifying virtual sensors
US20080183449A1 (en) * 2007-01-31 2008-07-31 Caterpillar Inc. Machine parameter tuning method and system
US7787969B2 (en) 2007-06-15 2010-08-31 Caterpillar Inc Virtual sensor system and method
US20080312756A1 (en) * 2007-06-15 2008-12-18 Caterpillar Inc. Virtual sensor system and method
US7831416B2 (en) 2007-07-17 2010-11-09 Caterpillar Inc Probabilistic modeling system for product design
US20090024367A1 (en) * 2007-07-17 2009-01-22 Caterpillar Inc. Probabilistic modeling system for product design
US20090037153A1 (en) * 2007-07-30 2009-02-05 Caterpillar Inc. Product design optimization method and system
US7788070B2 (en) 2007-07-30 2010-08-31 Caterpillar Inc. Product design optimization method and system
US20090063087A1 (en) * 2007-08-31 2009-03-05 Caterpillar Inc. Virtual sensor based control system and method
US7542879B2 (en) 2007-08-31 2009-06-02 Caterpillar Inc. Virtual sensor based control system and method
US9122728B2 (en) 2007-09-20 2015-09-01 Hal Kravcik Internet data mining method and system
US20090216748A1 (en) * 2007-09-20 2009-08-27 Hal Kravcik Internet data mining method and system
US8600966B2 (en) 2007-09-20 2013-12-03 Hal Kravcik Internet data mining method and system
US20090119275A1 (en) * 2007-10-29 2009-05-07 International Business Machines Corporation Method of monitoring electronic media
US8010524B2 (en) 2007-10-29 2011-08-30 International Business Machines Corporation Method of monitoring electronic media
US7593804B2 (en) 2007-10-31 2009-09-22 Caterpillar Inc. Fixed-point virtual sensor control system and method
US20090112334A1 (en) * 2007-10-31 2009-04-30 Grichnik Anthony J Fixed-point virtual sensor control system and method
US8224468B2 (en) 2007-11-02 2012-07-17 Caterpillar Inc. Calibration certificate for virtual sensor network (VSN)
US20090119065A1 (en) * 2007-11-02 2009-05-07 Caterpillar Inc. Virtual sensor network (VSN) system and method
US8036764B2 (en) 2007-11-02 2011-10-11 Caterpillar Inc. Virtual sensor network (VSN) system and method
US8886583B2 (en) * 2008-04-10 2014-11-11 Ntt Docomo, Inc. Recommendation information evaluation apparatus using support vector machine with relative dissatisfactory feature vectors and satisfactory feature vectors
US20110131168A1 (en) * 2008-04-10 2011-06-02 Ntt Docomo, Inc. Recommendation information evaluation apparatus and recommendation information evaluation method
US8086640B2 (en) 2008-05-30 2011-12-27 Caterpillar Inc. System and method for improving data coverage in modeling systems
US20090293457A1 (en) * 2008-05-30 2009-12-03 Grichnik Anthony J System and method for controlling NOx reactant supply
US20090300052A1 (en) * 2008-05-30 2009-12-03 Caterpillar Inc. System and method for improving data coverage in modeling systems
US20100050025A1 (en) * 2008-08-20 2010-02-25 Caterpillar Inc. Virtual sensor network (VSN) based control system and method
US7917333B2 (en) 2008-08-20 2011-03-29 Caterpillar Inc. Virtual sensor network (VSN) based control system and method
US20110270788A1 (en) * 2010-04-30 2011-11-03 Moore Douglas A Neural Network For Clustering Input Data Based On A Gaussian Mixture Model
US8521671B2 (en) * 2010-04-30 2013-08-27 The Intellisis Corporation Neural network for clustering input data based on a Gaussian Mixture Model
US8793004B2 (en) 2011-06-15 2014-07-29 Caterpillar Inc. Virtual sensor system and method for generating output parameters
US9313046B2 (en) 2012-09-15 2016-04-12 Facebook, Inc. Presenting dynamic location of a user
CN102929952A (en) * 2012-10-08 2013-02-13 北京奇虎科技有限公司 Web page image display device and method
US9846885B1 (en) * 2014-04-30 2017-12-19 Intuit Inc. Method and system for comparing commercial entities based on purchase patterns
US10033669B2 (en) 2014-07-31 2018-07-24 Facebook, Inc. Managing electronic messages sent to reply telephone numbers
CN105488054A (en) * 2014-09-17 2016-04-13 阿里巴巴集团控股有限公司 Method and device for browsing image

Also Published As

Publication number Publication date Type
US6266668B1 (en) 2001-07-24 grant

Similar Documents

Publication Publication Date Title
Carmel et al. Personalized social search based on the user's social network
Middleton et al. Ontological user profiling in recommender systems
Craven et al. Learning to extract symbolic knowledge from the World Wide Web
US7117206B1 (en) Method for ranking hyperlinked pages using content and connectivity analysis
US8086605B2 (en) Search engine with augmented relevance ranking by community participation
US7062488B1 (en) Task/domain segmentation in applying feedback to command control
Menczer ARACHNID: Adaptive retrieval agents choosing heuristic neighborhoods for information discovery
Chen et al. Internet browsing and searching: User evaluations of category map and concept space techniques
Freitas A survey of evolutionary algorithms for data mining and knowledge discovery
US6463430B1 (en) Devices and methods for generating and managing a database
Micarelli et al. Anatomy and empirical evaluation of an adaptive web-based information filtering system
US7117208B2 (en) Enterprise web mining system and method
US6836773B2 (en) Enterprise web mining system and method
US20030115191A1 (en) Efficient and cost-effective content provider for customer relationship management (CRM) or other applications
Pirolli et al. Silk from a sow's ear: Extracting usable structures from the web
US6199067B1 (en) System and method for generating personalized user profiles and for utilizing the generated user profiles to perform adaptive internet searches
US20040215606A1 (en) Method and apparatus for machine learning a document relevance function
US20060242135A1 (en) System and method for personalized search
Hotho et al. Information retrieval in folksonomies: Search and ranking
US20030105589A1 (en) Media agent
US8005643B2 (en) System and method for measuring the quality of document sets
Xue et al. Optimizing web search using web click-through data
US20060248076A1 (en) Automatic expert identification, ranking and literature search based on authorship in large document collections
US20120101808A1 (en) Sentiment analysis from social media content
US20010039544A1 (en) Method for interactively creating an information database including preferred information elements, such as preferred authority, world

Legal Events

Date Code Title Description
AS Assignment

Owner name: DRYKEN TECHNOLOGIES, INC. A CORP. OF DE., TENNES

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE SERIAL NUMBER. FILED ON 08-06-01, RECORDED ON REEL 012092 FRAME 0136;ASSIGNORS:VANDERVELDT, INGRID V.;BLACK, CHRISTOPHER LEE;REEL/FRAME:012521/0649;SIGNING DATES FROM 19991123 TO 19991129