Source: http://www.google.se/patents/US8209481?hl=sv
Timestamp: 2013-05-26 03:37:32
Document Index: 617020173

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

Patent US8209481 - Associative memory - Google PatentS�k Bilder Kartor Play YouTube Nyheter Gmail Drive Mer » Avancerad patents�kning | Webbhistorik | Logga in Avancerad patents�kning PatentA system of retrieving documents comprising: coding a plurality of stored documents as a respective document feature vector; generating a query feature vector based on a query document; and performing one or more logical operations between the query feature vector and the document feature vector to obtain...http://www.google.se/patents/US8209481?utm_source=gb-gplus-sharePatent US8209481 - Associative memory PublikationsnummerUS8209481 B2Typ av kung�relseBeviljande Ans�kningsnummer13/024,086 Publiceringsdatum26 jun 2012 Registreringsdatum9 feb 2011 Prioritetsdatum18 aug 2000�ven publicerat somCA2419776A1CA2419776CCA2796392A1EP1182577A1US6983345US7433997US7908430US20040054666US20060212431US20090193022US20120011131WO2002015045A2WO2002015045A3 UppfinnareGannady LapirHarry Urbshat Ursprunglig innehavareBdgb Enterprise Software S.A.R.LSer Solutions, Inc.Bdgb Enterprise Software S.A.R.L. USA-klassificering711/108707/708707/728707/769 Internationell klassificeringG06F12/00G06F17/30 Kooperativ klassningG06F17/30982G06F17/30637G06F17/30979 Europeisk klassificeringG06F17/30T2FG06F17/30Z2PG06F17/30Z2P3H�nvisningarCitat fr�n patent (85)Citat fr�n andra k�llor (98)Externa l�nkarUSPTO �verl�telse av �gander�tt till patent som har registrerats av USPTO EspacenetAssociative memoryUS 8209481 B2 Sammanfattning A system of retrieving documents comprising: coding a plurality of stored documents as a respective document feature vector; generating a query feature vector based on a query document; and performing one or more logical operations between the query feature vector and the document feature vector to obtain respective similarity measures.
1. A system of retrieving documents comprising the steps of:
coding a plurality of stored documents as respective document feature vector;
generating a query feature vector based on a query document; and
performing one or more logical operations between the query feature vector and said document feature vector to obtain respective similarity measures.
2. The system of claim 1, further comprising a step of formulating a similarity score for respective ones of said stored documents, each similarity score representing how often a certain feature or a set of features defined by the logical 1's in the query vector occurs within a corresponding feature vector.
3. The system of claim 1, wherein, based on said similarity measures, a set of candidate documents is chosen for further inspection, said set of candidate documents being further evaluated with respect to their similarity to the query document, said evaluation resulting in one or more further similarity measures reflecting the similarity between query and candidate under one or more aspects.
4. The system of claim 1, further comprising a step selected from the set comprising:
returning results which lie above a certain similarity threshold, and/or retrieving a user definable number of documents which have the highest similarity.
5. The system of claim 1, further comprising a step of a user defining a desired degree of similarity to be:
a settable degree of similarity or a similarity threshold; and/or
6. The system of claim 1, wherein features coded by a feature vector for their presence or absence comprise one or more of the following:
7. The system of claim 1, wherein a matrix formed by the feature vector representing the documents to be searched is stored column-wise, and wherein said method further comprises:
8. The system of claim 1, wherein there is a plurality of result columns such that the result columns form a matrix, and wherein a row of the thus formed result column matrix represents a degree of similarity between the query and the stored document corresponding to said row.
9. The system of claim 1 further comprising the steps of:
10. The system of claim 1, wherein there is a plurality of result columns such that the result columns form a matrix, and wherein a row of the thus formed result column matrix represents a degree of similarity between the query and the stored document corresponding to said row.
11. A computer-implemented system of realizing an associative memory capable of storing a set of documents and retrieving one or more of said stored documents similar to an inputted query document, said method comprising:
coding each of said stored document or a part of it through a corresponding feature vector;
arranging said feature vector in a matrix; and
generating a query feature vector based on the query document.
12. The system of claim 11, wherein there is a plurality of result columns which code a similarity score which is based on the following:
13. The system of claim 11, wherein based on said similarity measure a set of candidate documents is chosen for further inspection, said set of candidate documents being further evaluated with respect to their similarity to the query documents being further evaluated with respect to their similarity measures reflecting the similarity between query and candidate under one or more aspects.
14. The system of claim 11, wherein results returned which lie above a certain similarity threshold, and/or
15. The system of claim 11, wherein a desired degree similarity can be defined by the user to be:
a settable degree of similarity or a similarity threshold; and or
16. The system of claim 11 wherein features coded by a feature vector for their presence or absence comprise one or more of the following;
17. The system of claim 11, wherein the matrix formed by the feature vector representing the documents to be searched is stored column-wise, and wherein said method further comprises:
18. The system of claim 11 further comprising the steps of:
CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 12/191,774, filed Aug. 14, 2008; which is a continuation of U.S. patent application Ser. No. 11/240,632 filed Oct. 3, 2005, now U.S. Pat. No. 7,433,997; which is a continuation of U.S. patent application 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; which claims priority to EP 00117850.8, filed Aug. 18, 2000. 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.
Another typical model is the Hopfield (CalTech) model for so autoassociative memory, developed in the early 80's; This model is based very strongly on physical analogies and on the concept of energy. Everybody knows that if you throw a ball into a hole, the ball will eventually settle down at the bottom of this hole. A physical system is said to be in its ground state if it approaches the state of minimal (potential) energy.
Another known approach of associative search is based on representing documents as bitstrings and searching for those documents where certain bits as defined in a query string are set Such a method is e.g. described in �Managing Gigabytes�, by Written et al (Morgan Kaufmann Publ, 1999), on pages 128 through 142.
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.
The classes themselves and their organizational scheme can largely depend on the definitions chosen, i.e. which type of classes have been compiled and how many of them. There may be classes which are formed very narrowly such that they contain only synonyms; and other ones which contain words which are similar or correlated on a much more abstract level, e.g. in the sense that they relate to the same general field such as economics, sports, science, etcetera. Such more abstract �concept classes� represent meaning of the text in a more abstract manner than individual words do. Based on this concept different hierarchical levels of such �concept classes� can be compiled, and they all may be taken into account�when forming the �final� feature vector coding for the features occurring in the text document. This means that the final feature vector may have one bit for each.
One may expect that this unit is small enough so that its content is more or less unique in its topic; only a single topic or idea is expressed in it A page is itself a collection of called fragments, whose length is a free parameter (selectable by the user) but lies around 32 bytes (letters). The fragments are usually containing one or more words. The method of representation-through bitstrings can be applied to any of these units, in the following we just for example choose as a unit a so-called page (about 1 kbyte). Each page is stored as a representation its features through a feature vector. For simplicity we here assume that in this embodiment the feature vector only codes for the presence or absence of trigrams. Thereby a matrix is formed, the so-called bit attribute matrix in which one row is a feature vector coding for the features of a corresponding page.
If we have a method to map a page of text into a characteristic set coding for the presence (absence) of a certain feature, then we can map the pages into the structure shown in Table I called the Bit-Attribute-Matrix (BAM). Note that each row here corresponds exactly to a page of text (in other embodiments this correspondence may be to another unit of text such as a certain number of words or characters) and there is an associated reference pointer (or URL) associated to each, allowing one to retrieve later the whole text.
The result columns again farm a result matrix, and one row of the result matrix consists of a sequence of bits which may represent a coding of the frequency of occurrence of logical ones in the rows of the matrix at the positions indicated through the query string. For the example of FIG. 1 this is schematically illustrated in FIG. 2. The result columns code for the number of occurrence of 1's as can be easily seen from FIG. 2.
Apart from these functions, there is some further functionality related to storing. The cache administrator can send a document suing to the engine required that it is stored. The same functionality is provided also for a whole set of documents.
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.
As already mentioned, a classification class can also be represented in the BAM as a feature, thereby refining the results obtained through the associative memory. In this manner a combination of a classification engine and associative memory can become extremely powerful. A classification can be incorporated into the BAM, this improves the associative retrieval, this can be used to improve the classification, e.g. by increasing the number of documents which belong to a certain class by assigning retrieved documents to a certain classification class, or by using the retrieved documents as a set of learning examples. The improved classification (either refined fn accuracy by re-learning or extended through additional classes or with expanded content in the clams through assigning) may be again (in whole or in part) be incorporated into the BAM (e.g. by adding further columns to the BAM), and so on. Using such an iterative process far a combination of a classification engine and an associative memory can then lead to a self-improving or self-learning system.
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