Source: http://www.google.com/patents/US7908430?dq=6,128,731
Timestamp: 2017-04-27 09:18:58
Document Index: 284731869

Matched Legal Cases: ['§371', 'Application No. 2003', 'Application No. 2003', 'Application No. 01120429', 'Application No. 01120429', 'Application No. 01120429', 'Application No. 01127768', 'Application No. 01', 'Application No. 2003', 'Application No. 2']

Patent US7908430 - Associative memory - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA computer-implemented method of realizing an associative memory capable of storing a set of documents and retrieving one or more stored documents similar to an inputted query document, said method comprising: coding each document or a part of it through a corresponding feature vector consisting of a...http://www.google.com/patents/US7908430?utm_source=gb-gplus-sharePatent US7908430 - Associative memoryAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS7908430 B2Publication typeGrantApplication numberUS 12/191,774Publication dateMar 15, 2011Filing dateAug 14, 2008Priority dateAug 18, 2000Fee statusPaidAlso published asCA2419776A1, CA2419776C, CA2796392A1, CA2796392C, EP1182577A1, US6983345, US7433997, US8209481, US20040054666, US20060212431, US20090193022, US20120011131, WO2002015045A2, WO2002015045A3Publication number12191774, 191774, US 7908430 B2, US 7908430B2, US-B2-7908430, US7908430 B2, US7908430B2InventorsGannady Lapir, Harry UrbshatOriginal AssigneeBdgb Enterprise Software S.A.R.L.Export CitationBiBTeX, EndNote, RefManPatent Citations (68), Non-Patent Citations (82), Referenced by (12), Classifications (17), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetAssociative memory
This application is a continuation of Ser. No. 11/240,632 filed Oct. 3, 2005, which is a continuation of Ser. No. 10/362,027 filed Feb. 19, 2003, now U.S. Pat. No. 6,983,345, which is the National Stage filing under 35 §371 of International Application No. PCT/EP01/09577, filed Aug. 20, 2001. The entirety of all of the above-listed Applications are incorporated herein by reference.
Many biologists consider the question of memory as the brain's “Rosetta Stone”: a well defined riddle, which when answered opens the way to understanding other cognitive functions as well. Although modern experimental techniques like NMR (nuclear magnetic resonance) allow a direct imaging of brain activity, it is almost sure that the human memory is not strongly localized. The idea that when I recognize my grandmother a certain neuron in my brain becomes active—the so-called “grandmother neuron” hypothesis—has been given up long time ago.
Next, let us assume that there is a two-dimensional square lattice of 16 neurons, which should somehow “learn” to represent this high dimensional feature. This is the neuronal space.
If we throw the ball not very far away from the bottom of a hole, it will go there. The difficult problem in this simple idea is how to define the holes such that to the bottom corresponds to useful information, or a special combination of features which are defined by some examples given to the system. Then slightly perturbed variants of this pattern will all “relax” to the good pattern. Such automatic correction mechanisms are, of course, very useful in associating input patterns to some predefined “representant” or “canonical” pattern. The Hopfield model has a very is simple learning rule but is not particularly fast or scalable.
Before pattern recognition methods can be applied we have to express usual text into some typical features. In an embodiment of the present invention there are two types of features: one expressing orthographic and a second one so expressing meaning (semantic) similarity. The semantic similarity is enforced through a “semantic class table”, which expresses each word as a set of the concept classes it belongs to. In the present embodiment this is done by representing the text of a document through a coding which codes for the presence or absence for certain trigram in the document text. Assuming 32 possible character this leads to 32^3 possible trigrams requiring a bitstring of the length 32^3. Each trigram is assigned a certain number, and if the text document contains a certain trigram being numbered n then the n-th bit in the bitstring will be set. This results in a bitstring having 32^ bits which codes for the presence or absence of certain trigrams in the text.
As an example the term “extension” consists of the monograms “e”+“x”+“t”+“n”+“s”+“i”+“o”+“n”, the diagrams “ex”+“xt”+“te”+“en”+“ns”+“si”+“io”+“on”, etc.
Now the query process itself will be explained in somewhat more detail with reference to FIG. 1. The 1's of the query bitstring tell us which columns of the BAM should be investigated. Hence, the selection process is defined by a horizontal “register” of the same width as the BAM, in which the query is stored and whose set elements indicate a relevant column. The selected columns are shown in FIG. 1 below the bit attribute matrix. For the columns selected than there is carried out an adding with a bit-by-bit AND in sequential order for the selected columns. The result is stored In an additional vertical register shown on the right-hand of FIG. 1. It is easy to see that in each AND-step the number of selected rows will roughly decrease by the amount of zeros in one selected column. If the search is exact, we will keep in the and only those rows, which survived the AND-ing operations. This means that only those pages which have all, the features of the query will show up in the result column. Based on the result column the corresponding document(s) then can be retrieved.
In one embodiment the results are sent back in packages, so the user has from the beginning what to do (while the associative memory crunches on). Another parameter instructs the engine WHAT to send back. One option is only URLs+confidence, the other one is URL+relevant text+confidence. The answer will contain a list, whose length is yet another possible query parameter, of such items. Other options are the setting of the allowed retrieval error (how precise the query words should be found in the retrieved documents) and the inclusion of attributes and keywords. An attribute is a special word which has been so defined during the storing command. A typical article should have an AUTHOR, AUTHOR—AFFILIATION, TITLE, PUBLISHER DATUM, etc as possible attributes associated to it. The query with such attributes will be done only on the documents having such attributes and will do a fizzy search for the content of the attribute. Another possible attribute is the classification attribute as defined through any classification engine.
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