Source: http://www.google.com.tw/patents/US8078629
Timestamp: 2013-05-21 15:13:21
Document Index: 297032250

Matched Legal Cases: ['Application No. 200510085373', 'Application No. 200510085370', 'Application No. 200510085371', 'Application No. 200510085372', 'Application No. 2005203238', 'Application No. 2005203239', 'Application No. 2005203240', 'Application No. 2005203237', 'Application No. 06719537', 'Application No. 08799272', 'Application No. 08799272', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 200510085371', 'Application No. 200510085371', 'Application No. 200510085371', 'Application No. 200680007173', 'Application No. 05254644', 'Application No. 05254646', 'Application No. 05254647', 'Application No. 05254646', 'Application No. 2005', 'Application No. 2005', 'Application No. 20050068056', 'Application No. 2', 'Application No. 200510085371', 'Application No. 05254644', 'Application No. 05254647', 'Application No. 2005', 'Application No. 2005', 'Application No. 2005']

�M�Q US8078629 - Detecting spam documents in a phrase based information retrieval system - Google �M�Q�j�M �Ϥ� �a�� Play YouTube �s�D Gmail ���ݵw�� ��h »�i���M�Q�j�M | �������� | �n�J�i���M�Q�j�M�M�QAn information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. A spam document is identified based on the number of related...http://www.google.com.tw/patents/US8078629?utm_source=gb-gplus-share�M�Q US8078629 - Detecting spam documents in a phrase based information retrieval system���}��US8078629 B2�X���������v�ӽЮѽs��12/578,339�o�G���2011�~12��13���ӽФ��2009�~10��13�� �u���v���2004�~7��26����L���}�M�Q��CN1728142ACN1728142BEP1622053A1EP1622053B1US7580921US7603345US20060018551US20060294155US20110131223�o��HAnna Lynn Patterson��M�Q�v�HGoogle Inc. ���M�Q������707/754707/750707/737��ڱM�Q������G06FG06F17/30 �X�@����G06F17/30616 �ڬw������G06F17/30T1E�ѦҤ��m�M�Q�ޥ� (122)�D�M�Q�ޥ� (103)�Q�H�U�M�Q�ޥ� (1)�~���s�����M�Q�ӼЧ� ���M�Q�ӼЧ��M�Q����T�� �ڬw�M�Q��Detecting spam documents in a phrase based information retrieval systemUS 8078629 B2�K�n An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. A spam document is identified based on the number of related phrases included in a document.
identifying the document as a spam document where, for each of the first phrase, the second phrase, and the third phrase, the actual number of related phrases present in the document exceeds the expected number of related phrases based on a threshold. ����
CROSS REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 11/478,330, filed Jun. 28, 2006, entitled ��Detecting Spam Documents in a Phrase Based Information Retrieval System,�� now U.S. Pat. No. 7,603,345, issued on Oct. 13, 2009, which in turn is related to U.S. patent application Ser. No. 10/900,021, filed Jul. 26, 2004, entitled ��Phrase Identification in an Information Retrieval System,�� now U.S. Pat. No. 7,580,921, issued on Aug. 25, 2009, each of which is incorporated by reference herein in its entirety.
BACKGROUND OF THE INVENTION Information retrieval systems, generally called search engines, are now an essential tool for finding information in large scale, diverse, and growing corpuses such as the Internet. Generally, search engines create an index that relates documents (or ��pages��) to the individual words present in each document. A document is retrieved in response to a query containing a number of query terms, typically based on having some number of query terms present in the document. The retrieved documents are then ranked according to other statistical measures, such as frequency of occurrence of the query terms, host domain, link analysis, and the like. The retrieved documents are then presented to the user, typically in their ranked order, and without any further grouping or imposed hierarchy. In some cases, a selected portion of a text of a document is presented to provide the user with a glimpse of the document's content.
Direct ��Boolean�� matching of query terms has well known limitations, and in particular does not identify documents that do not have the query terms, but have related words. For example, in a typical Boolean system, a search on ��Australian Shepherds�� would not return documents about other herding dogs such as Border Collies that do not have the exact query terms. Rather, such a system is likely to also retrieve and highly rank documents that are about Australia (and have nothing to do with dogs), and documents about ��shepherds�� generally.
The problem here is that conventional systems index documents based on individual terms, rather than on concepts. Concepts are often expressed in phrases, such as ��Australian Shepherd,�� ��President of the United States,�� or ��Sundance Film Festival��. At best, some prior systems will index documents with respect to a predetermined and very limited set of ��known�� phrases, which are typically selected by a human operator. Indexing of phrases is typically avoided because of the perceived computational and memory requirements to identify all possible phrases of say three, four, or five or more words. For example, on the assumption that any five words could constitute a phrase, and a large corpus would have at least 200,000 unique terms, there would approximately 3.2��1026 possible phrases, clearly more than any existing system could store in memory or otherwise programmatically manipulate. A further problem is that phrases continually enter and leave the lexicon in terms of their usage, much more frequently than new individual words are invented. New phrases are always being generated, from sources such technology, arts, world events, and law. Other phrases will decline in usage over time.
Another problem that arises in existing information retrieval systems is the appearance of ��spam�� documents. Some spam pages are documents that have little if any meaningful content, but instead comprise collections of popular words and phrases, often hundreds or even thousands of them; these pages are sometime called ��keyword stuffing pages.�� Others include specific words and phrases known to be of interest to advertisers. These types of documents (often called ��honeypots��) are created to cause search engines to retrieve such documents for display along with paid advertisements. However, to the user searching for meaningful content, retrieval of such documents results in waste of time, and frustration.
SUMMARY OF THE INVENTION An information retrieval system and methodology uses phrases to index, search, rank, and describe documents in the document collection. The system is adapted to identify phrases that have sufficiently frequent and/or distinguished usage in the document collection to indicate that they are ��valid�� or ��good�� phrases. In this manner multiple word phrases, for example phrases of four, five, or more terms, can be identified. This avoids the problem of having to identify and index every possible phrases resulting from the all of the possible sequences of a given number of words.
The system is further adapted to identify phrases that are related to each other, based on a phrase's ability to predict the presence of other phrases in a document. More specifically, a prediction measure is used that relates the actual co-occurrence rate of two phrases to an expected co-occurrence rate of the two phrases. Information gain, as the ratio of actual co-occurrence rate to expected co-occurrence rate, is one such prediction measure. Two phrases are related where the prediction measure exceeds a predetermined threshold. In that case, the second phrase has significant information gain with respect to the first phrase. Semantically, related phrases will be those that are commonly used to discuss or describe a given topic or concept, such as ��President of the United States�� and ��White House.�� For a given phrase, the related phrases can be ordered according to their relevance or significance based on their respective prediction measures.
In the context of this application, ��documents�� are understood to be any type of media that can be indexed and retrieved by a search engine, including web documents, images, multimedia files, text documents, PDFs or other image formatted files, and so forth. A document may have one or more pages, partitions, segments or other components, as appropriate to its content and type. Equivalently a document may be referred to as a ��page,�� as commonly used to refer to documents on the Internet. No limitation as to the scope of the invention is implied by the use of the generic term ��documents.�� The search system 100 operates over a large corpus of documents, such as the Internet and World Wide Web, but can likewise be used in more limited collections, such as for the document collections of a library or private enterprises. In either context, it will be appreciated that the documents are typically distributed across many different computer systems and sites. Without loss of generality then, the documents generally, regardless of format or location (e.g., which website or database) will be collectively referred to as a corpus or document collection. Each document has an associated identifier that uniquely identifies the document; the identifier is preferably a URL, but other types of identifiers (e.g., document numbers) may be used as well. In this disclosure, the use of URLs to identify documents is assumed.
The phrase identification operation of the indexing system 110 identifies ��good�� and ��bad�� phrases in the document collection that are useful to indexing and searching documents. In one aspect, good phrases are phrases that tend to occur in more than certain percentage of documents in the document collection, and/or are indicated as having a distinguished appearance in such documents, such as delimited by markup tags or other morphological, format, or grammatical markers. Another aspect of good phrases is that they are predictive of other good phrases, and are not merely sequences of words that appear in the lexicon. For example, the phrase ��President of the United States�� is a phrase that predicts other phrases such as ��George Bush�� and ��Bill Clinton.�� However, other phrases are not predictive, such as ��fell down the stairs�� or ��top of the morning,�� ��out of the blue,�� since idioms and colloquisms like these tend to appear with many other different and unrelated phrases. Thus, the phrase identification phase determines which phrases are good phrases and which are bad (i.e., lacking in predictive power).
Traverse the words of the document with a phrase window length of n, where n is a desired maximum phrase length. The length of the window will typically be at least 2, and preferably 4 or 5 terms (words). Preferably phrases include all words in the phrase window, including what would otherwise be characterized as stop words, such as ��a��, ��the,�� and so forth. A phrase window may be terminated by an end of line, a paragraph return, a markup tag, or other indicia of a change in content or format.
FIG. 3 illustrates a portion of a document 300 during a traversal, showing the phrase window 302 starting at the word ��stock�� and extending 5 words to the right. The first word in the window 302 is candidate phrase i, and the each of the sequences i+1, i+2, i+3, 1+4, and i+5 is likewise a candidate phrase. Thus, in this example, the candidate phrases are: ��stock��, ��stock dogs��, ��stock dogs for��, ��stock dogs for the��, ��stock dogs for the Basque��, and ��stock dogs for the Basque shepherds��.
In each phrase window 302, each candidate phrase is checked in turn to determine if it is already present in the good phrase list 208 or the possible phrase list 206. If the candidate phrase is not present in either the good phrase list 208 or the possible phrase list 206, then the candidate has already been determined to be ��bad�� and is skipped.
M(p): Number of interesting instances of the possible phrase. An instance of a possible phrase is ��interesting�� where the possible phrase is distinguished from neighboring content in the document by grammatical or format markers, for example by being in boldface, or underline, or as anchor text in a hyperlink, or in quotation marks. These (and other) distinguishing appearances are indicated by various HTML markup language tags and grammatical markers. These statistics are maintained for a phrase when it is placed on the good phrase list 208.
In addition the various lists, a co-occurrence matrix 212 (G) for the good phrases is maintained. The matrix G has a dimension of m��m, where m is the number of good phrases. Each entry G(j, k) in the matrix represents a pair of good phrases (gj, gk). The co-occurrence matrix 212 logically (though not necessarily physically) maintains three separate counts for each pair (gj, gk) of good phrases with respect to a secondary window 304 that is centered at the current word i, and extends +/−h words. In one embodiment, such as illustrated in FIG. 3, the secondary window 304 is 30 words. The co-occurrence matrix 212 thus maintains:
Referring to the example of FIG. 3, assume that the ��stock dogs�� is on the good phrase list 208, as well as the phrases ��Australian Shepherd�� and ��Australian Shepard Club of America��. Both of these latter phrases appear within the secondary window 304 around the current phrase ��stock dogs��. However, the phrase ��Australian Shepherd Club of America�� appears as anchor text for a hyperlink (indicated by the underline) to website. Thus the raw co-occurrence count for the pair {��stock dogs��, ��Australian Shepherd��} is incremented, and the raw occurrence count and the disjunctive interesting count for {��stock dogs��, ��Australian Shepherd Club of America��} are both incremented because the latter appears as distinguished text.
It should be noted that the good phrase list 208 will naturally include individual words as phrases, in addition to multi-word phrases, as described above. This is because each the first word in the phrase window 302 is always a candidate phrase, and the appropriate instance counts will be accumulated. Thus, the indexing system 110 can automatically index both individual words (i.e., phrases with a single word) and multiple word phrases. The good phrase list 208 will also be considerably shorter than the theoretical maximum based on all possible combinations of m phrases. In typical embodiment, the good phrase list 208 will include about 6.5��105 phrases. A list of bad phrases is not necessary to store, as the system need only keep track of possible and good phrases.
As noted above, the co-occurrence matrix 212 is an m��m matrix of storing data associated with the good phrases. Each row j in the matrix represents a good phrase gj and each column k represented a good phrase gk. For each good phrase gj, an expected value E(gj) is computed. The expected value E is the percentage of documents in the collection expected to contain gj. This is computed, for example, as the ratio of the number of documents containing gj to the total number T of documents in the collection that have been crawled: P(j)/T.
The final step of this stage is to prune the good phrase list 208 to remove incomplete phrases. An incomplete phrase is a phrase that only predicts its phrase extensions, and which starts at the left most side of the phrase (i.e., the beginning of the phrase). The ��phrase extension�� of phrase p is a super-sequence that begins with phrase p. For example, the phrase ��President of�� predicts ��President of the United States��, ��President of Mexico��, ��President of AT&T��, etc. All of these latter phrases are phrase extensions of the phrase ��President of�� since they begin with ��President of�� and are super-sequences thereof.
Accordingly, each phrase gj remaining on the good phrase list 208 will predict some number of other phrases, based on the information gain threshold previously discussed. Now, for each phrase gj the indexing system 110 performs a string match with each of the phrases gk that is predicts. The string match tests whether each predicted phrase gk is a phrase extension of the phrase gj. If all of the predicted phrases gk are phrase extensions of phrase gj, then phrase gj is incomplete, and is removed from the good phrase list 208, and added to an incomplete phrase list 216. Thus, if there is at least one phrase gk that is not an extension of gj, then gj is complete, and maintained in the good phrase list 208. For example then, ��President of the United�� is an incomplete phrase because the only other phrase that it predicts is ��President of the United States�� which is an extension of the phrase.
The incomplete phrase list 216 itself is very useful during actual searching. When a search query is received, it can be compared against the incomplete phase list 216. If the query (or a portion thereof) matches an entry in the list, then the search system 120 can lookup the most likely phrase extensions of the incomplete phrase (the phrase extension having the highest information gain given the incomplete phrase), and suggest this phrase extension to the user, or automatically search on the phrase extension. For example, if the search query is ��President of the United,�� the search system 120 can automatically suggest to the user ��President of the United States�� as the search query.
This high threshold is used to identify the co-occurrences of good phrases that are well beyond the statistically expected rates. Statistically, it means that phrases gj and gk co-occur 100 times more than the expected co-occurrence rate. For example, given the phrase ��Monica Lewinsky�� in a document, the phrase ��Bill Clinton�� is a 100 times more likely to appear in the same document, then the phrase ��Bill Clinton�� is likely to appear on any randomly selected document. Another way of saying this is that the accuracy of the predication is 99.999% because the occurrence rate is 100:1.
For example, assume the good phrase ��Bill Clinton�� is related to the phrases ��President��, ��Monica Lewinsky��, because the information gain of each of these phrases with respect to ��Bill Clinton�� exceeds the Related Phrase threshold. Further assume that the phrase ��Monica Lewinsky�� is related to the phrase ��purse designer��. These phrases then form the set R. To determine the clusters, the indexing system 110 evaluates the information gain of each of these phrases to the others by determining their corresponding information gains. Thus, the indexing system 110 determines the information gain I(��President��, ��Monica Lewinsky��), I(��President��, ��purse designer��), and so forth, for all pairs in R. In this example, ��Bill Clinton,�� ��President��, and ��Monica Lewinsky�� form a one cluster, ��Bill Clinton,�� and ��President�� form a second cluster, and ��Monica Lewinsky�� and ��purse designer�� form a third cluster, and ��Monica Lewinsky��, ��Bill Clinton,�� and ��purse designer�� form a fourth cluster. This is because while ��Bill Clinton�� does not predict ��purse designer�� with sufficient information gain, ��Monica Lewinsky�� does predict both of these phrases.
To summarize then, after this process there will be identified for each good phrase gj, a set of related phrases R, which are sorted in order of information gain I(gj, gk) from highest to lowest. In addition, for each good phrase there will be a cluster bit vector, the value of which is a cluster number identifying the primary cluster of which the phrase gj is a member, and the orthogonality values (1 or 0 for each bit position) indicating which of the related phrases in R are in common clusters with Thus in the above example, ��Bill Clinton��, ��President��, and ��Monica Lewinsky�� are in cluster 14 based on the values of the bits in the row for phrase ��Bill Clinton��.
This approach provides a useful organization for clusters. First, rather than a strictly�Xand often arbitrarily�Xdefined hierarchy of topics and concepts, this approach recognizes that topics, as indicated by related phrases, form a complex graph of relationships, where some phrases are related to many other phrases, and some phrases have a more limited scope, and where the relationships can be mutual (each phrase predicts the other phrase) or one-directional (one phrase predicts the other, but not vice versa). The result is that clusters can be characterized ��local�� to each good phrase, and some clusters will then overlap by having one or more common related phrases.
The above process provides a very robust way of identifying significant phrases that appear in the document collection, and beneficially, the way these related phrases are used together in natural ��clusters�� in actual practice. As a result, this data-driven clustering of related phrases avoids the biases that are inherent in any manually directed ��editorial�� selection of related terms and concepts, as is common in many systems.
For each good phrase gi (example g1 ��President�� and g4 ��President of ATT��) post the document identifier (e.g., the URL) to the posting list for the good phrase gi in the index 150. This update identifies that the good phrase gi appears in this specific document.
In one embodiment, the related phrase information is a related phase bit vector. This bit vector may be characterized as a ��bi-bit�� vector, in that for each related phrase gk there are two bit positions, gk-1, gk-2. The first bit position stores a flag indicating whether the related phrase gk is present in the document d (i.e., the count for gk in document d is greater than 0). The second bit position stores a flag that indicates whether a related phrase gl of gk is also present in document d. The related phrases gl of a related phrase gk of a phrase gj are herein called the ��secondary related phrases of gj��. The counts and bit positions correspond to the canonical order of the phrases in R (sorted in order of decreasing information gain). This sort order has the effect of making the related phrase gk that is most highly predicted by gj associated with the most significant bit of the related phrase bit vector, and the related phrase gl that is least predicted by gj associated with the least significant bit.
Given that the highest scoring documents for a given phrase are now at the beginning of the posting list, the posting list 214 is partitioned 508 between the primary index 150 and the secondary index 152. The posting list entries for up to the first K documents remain stored on the primary server 150, while the posting list entries for the remaining n>K documents are stored in the secondary index 152, and deleted from the end of the posting list 214 in the primary index 150. In one embodiment K is set to 32,768 (32 k), but a higher or lower value of K may be used. A phrase that has its posting list partitioned between the primary and the secondary index is called a ��common�� phrase, whereas a phrase that is not partitioned is called a ��rare�� phrase. The portion of a posting list stored in the primary index 150 is referred to as the primary posting list, and contains the primary entries, and portion of a posting list stored in the secondary index 152 is referred to as the secondary posting list and contains the secondary entries. The secondary entries for a given posting list 214 are assigned to a secondary server according to another hash function of the phrase number, e.g., phrase number MOD M2. The secondary server ID is stored in the posting list on the primary server, to allow the search system 120 to readily access the appropriate secondary server as needed. For each phrase posting list stored on one of the secondary servers, the secondary entries are stored physically in order of their document numbers, from lowest document number to highest (in contrast to the relevance ordering in the primary index 150). Preferably, no relevance information is stored in the secondary entries, so that the entries contain a minimal amount of data, such as the document number, and document locator (e.g., URL). The ranking and partitioning steps may be performed sequentially for each phrase; alternatively all (or a number of) phrases can first be ranked, and then partitioned; the algorithm design is merely a design choice and the above variations are considered equivalents. The ranking and partitioning steps are conducted during each indexing pass over a set of documents, so that any phrases that are updated with new documents during an indexing pass are re-ranked and re-partitioned. Other optimizations and operations are also possible.
In this embodiment, the document identifier encodes the identity of the document with respect to a date interval. The first time a document is crawled by the indexing system 110, the document identifier is stored as a hash of the document URL and the date stamp of the document, for example, MD5 (URL, first date). Associated with the particular instance of the document is date range field, which comprises a range of dates for which the document instance is deemed to valid. The date range can be specified as a date pair comprising a first date on which the document is deemed valid (the indexing date) and a last date on which the document is deemed valid (e.g., 11-01-04; 12-15-04). Alternatively, the date range can be specified as a first date, and a number indicating a number of days following the first date (e.g., 11-01-04, 45). A date can be specified in any useful format, including date strings or day numbers. During the period in which the document is the currently valid document, the second value is a status flag or token (including a NULL value), indicating this state; this is called the current interval. For example, (11-01-04, ��open��) indicates that the document is currently valid. This indicates that the document will satisfy search that includes a date limitation after the first date. Regardless of the particular implementation, the first date for a given date interval may be referred to as the ��open date��, and the last date for a given interval may be referred to as the ��closed date��.
During subsequent indexing passes by the indexing system 110, the indexing system 110 determines whether the document has changed. If there is no change in the document, then the indexing system 110 takes no further action with respect to document. If there has been a change in the document (thus a new instance or version of the document), then the indexing system 110 re-indexes the document. Upon re-indexing, the indexing system 110 closes the current interval, by changing the open status flag to the current date minus one day. For example, if the indexing system 110 indexes the document on Dec. 16, 2004 and determines that the document has changed, then current interval is closed as follows: (11-01-04, 12-15-04), and a new current interval is created, e.g., (12-16-04, ��open��). The indexing system 110 maintains each of the date ranges for the document, along with corresponding indexed relevance data (e.g., phrases, relevance statistics, document inlinks, and so forth) for the date range. Thus, each date range and set of relevance data is associated with a particular instance or version of the document. For each of date interval for a given document, the indexing system maintains a unique document identifier, e.g., MD5 (URL, first date), so as to be able to retrieve the appropriate cached document instance. In an embodiment using the primary and secondary indexes, when an indexing pass is completed, the posting lists 214 in the primary index are rescored, re-ranked, and repartitioned.
For example, assume the search query is ��Hillary Rodham Clinton Bill on the Senate Floor��. The search system 120 would identify the following candidate phrases, ��Hillary Rodham Clinton Bill on,�� ��Hillary Rodham Clinton Bill,�� and ��Hillary Rodham Clinton��. The first two are discarded, and the last one is kept as a valid query phrase. Next the search system 120 would identify ��Bill on the Senate Floor��, and the subsphrases ��Bill on the Senate��, ��Bill on the��, ��Bill on��, ��Bill��, and would select ��Bill�� as a valid query phrase Qp. Finally, the search system 120 would parse ��on the senate floor�� and identify ��Senate Floor�� as a valid query phrase.
Next, the search system 120 adjusts the valid phrases Qp for capitalization. When parsing the query, the search system 120 identifies potential capitalizations in each valid phrase. This may be done using a table of known capitalizations, such as ��united states�� being capitalized as ��United States��, or by using a grammar based capitalization algorithm. This produces a set of properly capitalized query phrases.
The search system 120 then makes a second pass through the capitalized phrases, and selects only those phrases are leftmost and capitalized where both a phrase and its subphrase is present in the set. For example, a search on ��president of the united states�� will be capitalized as ��President of the United States��.
The search system 120 provides a ranking stage 604 in which the documents in the search results are ranked, using the relevance information and document attributes, along with the phrase information in each document's related phrase bit vector, and the cluster bit vector for the query phrases. This approach ranks documents according to the phrases that are contained in the document, or informally ��body hits.��
The product value here is a score of how topical anchor phrase Q is to document D. This score is here called the ��inbound score component.�� This product effectively weights the current document D's related bit vector by the related bit vectors of anchor phrases in the referencing document R. If the referencing documents R themselves are related to the query phrase Q (and thus, have a higher valued related phrase bit vector), then this increases the significance of the current document D score. The body hit score and the anchor hit score are then combined to create the document score, as described above.
From the index 150 then, all of the (referencing document, referenced document) pairs are extracted for the anchor phrases Q. These pairs are then sorted by their associated (outbound score component, inbound score component) values. Depending on the implementation, either of these components can be the primary sort key, and the other can be the secondary sort key. The sorted results are then presented to the user. Sorting the documents on the outbound score component makes documents that have many related phrases to the query as anchor hits, rank most highly, thus representing these documents as ��expert�� documents. Sorting on the inbound document scores makes documents that are frequently referenced by the anchor terms the most highly ranked.
The search system 120 can use the date range information in several ways during the search and ranking operations. First, the search system 120 can use the date range as an explicit search delimiter. For example, a query may include terms or phrases and a date, such as ��United States Patent and Trademark Office Dec. 4, 2004��. The search system 120 can identify the date term, and then select documents that have the desired phrase and which are indexed for a date range that includes the date term in the query. From the selected documents, the search system 120 can then obtain a relevance score for each document using the indexed relevance data associated with the date range. In this manner, an older or previous instance of a document may be retrieved instead of the current instance where it is more relevant to the search query. This is particularly useful for documents and pages that change frequently, such as the home pages of news sites and other sites containing frequently changing information.
In addition, the terms used to describe various quantities, data values, and computations are understood to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as ��processing�� or ��computing�� or ��calculating�� or ��determining�� or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
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