PAestea
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`PATENT
`Docket No.: 4428-4001
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`particular implementation ofthe invention, one or more ofthe aspects may be used together in
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`various permutations and/or combinations, with the understanding that different permutations
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`and/or combinations may be better suited for particular applications or have more or less benefits
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`or advantages than others.
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`The underlying scenario commonto all these basic examplesis that there is a hierarchical
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`arrangementto the possible choices that canbeillustrated in a formof “tree” structure.
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`FIG.
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`|
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`is an example graph 100 representing a possible hierarchically arranged
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`transaction processing or decisional system suitable for use with the invention. The individual
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`boxes 102 - 120 are referred to as “nodes” and each represents a specific choice or option in the
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`hierarchy. For purposes described in more detail below, each nodeis arbitrarily uniquely
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`identified in some manner.
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`In the example of FIG. 1, the individual nodes 102 - 120 are
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`numbered | through 10 starting from the top node 102 in the hicrarchy.
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`Each “node”is associated with exactly one verbal description, for example in the case of
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`an airline system, a verbal description relating to some aspect of the reservation process. Each
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`such description contains “key” words that are deemed to be of importance and other words that
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`can be disregarded. For example, one node may have the associated verbal description “Would
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`you like to make a reservation?” In this description, there is only one “key” word —
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`“reservation” deemed important, so all of the other words in the description can be ignored.
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`A level in the hierarchy below that one may be used to obtain further narrowing
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`information, for example, using the verbal description “Is the reservation for a domestic or
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`international flight?” In this description, the terms “domestic” and “international” are “key”
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`words. Similarly, the word “flight” could be a “key” word, for example, for a system that
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`involves nol only airline travel but also rail and/or cruise travel or it could be an “ignored” or
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`stop wordfor a purely airline related system because it has minimal meaningin that context.
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`Again, the other words can be ignored as well.
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`The unique identification of each node allowsthe creation of a list of all the key words
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`and their associated nodes so that, if a key word is duplicated in two or more nodes,it need only
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`be listed once. For example, a hierarchical tree related to “pens” might have nodes for ball-point
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`pens, fine point pens, mediumpoint pens, fountain pens, felt-tip pens, quill pens, erasable pens,
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`etc. By using this approach, one could list the keyword“point” once, but associate it with each
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`of the nodes where that keyword appears by using the unique identifier for each node where the
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`term appears.
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`In this manner the keywords are obtained fromthe collection of available descriptions
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`found in the particular application in which the invention will be used.
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`In addition, each
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`particular node where the keyword appears is associated with the keyword. Thus, with respect to
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`the pen application above, the keyword “point” might appear in nodes 2, 3, 6, 7, 13 and 15,
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`Sunilarly, the keyword “erasable” might appear in nodes 3, 4, 5, 6 and 22. An index, as
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`described more fully below, associating these keywords with the nodes containing them is then
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`created, for example:
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`point: 2, 3, 6, 7, 13,15
`erasable: 3, 4, 5, 22
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`By making use of these associations the “tree” can be negotiated by allowing presentation
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`of relevant verbal descriptions for the nodes associated witha term, irrespective of wherein the
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`hierarchy they arc, thereby causing a “jump” to a particular node without necessarily traversing
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`the tree in the rigid hierarchical manner.
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`Various examples will now bepresented to illustrate certain concepts related to the
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`invention.
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`It should be understood that while these examples are presented in the context of
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`things and likely experiences of ordinary people, the same approach can be applied to other
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`forms of transaction processing including navigating throughhierarchically nested data files in a
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`computer system, pattern analysis or image processing,etc. the term “transaction” as used herein
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`relating to traversal througha hierarchy to a goal, not mathematical calculation perse.
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`Moreover, the specific formats used and presented in these examples are purely for
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`illustration purposes.
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`It should be understood that that other techniques for interrelating data,
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`suchas hashtables, direct or indirect indexing, etc. can be substituted in a straightforward
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`manner. Thus, for example, the relationship between the word and a node could be configured
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`such that the location of the word inalist as the “n-th” item could be used as an index into
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`another list containing the nodes correlated to the list. A similar approach could be used for the
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`thesaurus, the important aspect relative to the invention being the relationship amongcertain
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`words and the node(s) in which they occur and, where applicable, the relationship between
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`certain words and “synonyms” for those words, not the data structure or its form or format
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`whereby that information is kept or maintained,
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`Example1
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`Example 1 illustrates, in simplified form, how an index is used to jump among nodes
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`with reference to FIG. 2.
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`In this example, the hierarchical tree 200 represents a portion of a more
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`complex tree specifically involving possible decision relating to fruit and a decision between two
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`specific types offruits, an apple and an orange.
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`In prior art hierarchical trees, navigation ofthis graph 200 would necessarily involve
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`going through the “fruit” node 202 in orderto reach the “apple” 204 or “orange” 206 nodes. As
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`a result, assuming this simple tree was part of a larger tree for an on-line supermarketthat
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`prompted the user for what they wanted to purchase, the exchange would be both rigid and time
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`consuming. For example, in response to a prompt “What do you want to purchase?”if the
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`response was anything other than “fruit” traversal to the “fruit” node 202 could not occur, At the
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`point in the tree that would lead to the “fruit” node 202, neither apple nor orange would be an
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`acceptable response.
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`In accordance with the invention, assuming the only relevant keywordsfor that portion of
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`the tree were “fruit”, “apple” and “orange”, an inverted index would be created that includes an
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`association of“Fruit” with the top node 202, “Apple” with the bottom left node 204, and
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`“Orange” with the bottom right node 206. As shown above,that association can be created using
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`node identifiers, in this example, the node identifiers 1A01, 1A02 and 1A03 are arbitrarily
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`assigned and used. Thus, the information can be stored ina file, for example, as follows:
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`Fruit, LAO]
`Apple, 1A02
`Orange, 1A03
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`Accordingly, to navigate the system 200, whena responseto a verbal descriptionis
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`provided by a user, possible keywordsare identified in the response and used to search the index
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`and identify any node to which the response may be directed, irrespective of the hierarchy.
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`Thus, a user response of “an orange” to a verbal description located above the “fruit” node 202 in
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`the hierarchy, for example, ““What would you like to buy today?” would cause the system to
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`identify “orange” as a key word from the response, search the index, and directly identify node
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`| A03 (206) as the node whose verbal description should be presented next, thereby avoiding the
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`need to traverse intervening nodes, for example, through the “fruit” node (202) 1A01, atall.
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`This illustrates an example of a simple jump accordingto the invention,
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`Example 2
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`Having illustrated a simple “node jump” a more complex (andlikely) scenario can be
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`shown.
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`In this example, the Example | graph of FIG. 2 applies, but relevant portion of the index
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`is as follows:
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`Fruit, LAO1
`Apple, 1A02, 2F09
`Orange, 1A03
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`As aresult, there are two nodes relevant to the keyword “apple” one being the node 204
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`in the portion of the graph shown in FIG, 2 and one in the node uniquely identified as 2FO9
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`located somewhere else in the hierarchy (not shown).
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`In this example, a user response containing the keyword “apple” would identify nodes
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`with identifiers | A0Q2 and 2F09.
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`In this case, and unlike the prior art, the verbal descriptions
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`from both nodes would be presentedto the user, likely in alternative fashion. Thus, if the user
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`did not want an apple, they wanted apple cider, node 2FO9 might be more appropriate becauseit
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`is part ofthe “drinks” portion of the overall hierarchy.
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`Thus, presenting the user with the verbal description from both nodes would likely result
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`in a jump to the portion of the graph nearer to node 2F09 since it is closer to the user’s goal
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`thereby speeding up the process and avoiding potentially confusing or frustrating the user.
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`Example3
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`While the verbal descriptions associated with various nodes will generally be chosen to
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`accurately represent the node, in accordance with certain variants of the invention, it is possible
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`to create a situation where a user response takes them away from their ultimate desired goal.
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`Nevertheless, by using the teachings of the present invention, the user can often still be brought
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`to their goal quicker than possible with the prior art because the user need not rigidly trace
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`through the hierarchy. This is accomplished by virtue of the “grouping” aspect inherent in some
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`implementations of the invention.
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`This example illustrates the “grouping” aspect using a simplified graph 300 representing
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`a portion of an airline reservation system as shownin FIG. 3.
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`In particular, the graph of FIG.3 can be thought ofas part of a very simple interactive
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`voice response (“IVR”) system.
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`As described above, each node is uniquely identified, for example, by the numbers|
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`through 7 and the identified terms “Reservation”, “Domestic”, “International”, “Business Class”,
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`“Economy Class” are deemedthe relevant keywords. Note, there is no requirement for a the
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`“keyword”to be a single word, in some implementations, keywords could be single words,
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`phrases of two or more words, or even some other formof information like a specific data
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`pattern,
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`Again, an inverted index is created as described above associating those keywords with
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`the nodes, in this case:
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`Reservation, |
`Domestic, 2
`International, 3
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`Business Class, 4, 6
`Economy Class, 5, 7
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`Assuming that the top node is assigned the number1, its two child nodes (Domestic and
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`International) are assigned the numbers 2 and 3, and the grandchild nodes(i.e. at the lowest level
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`in the hierarchy) have been assigned numbers 4, 5, 6, and 7 taken from Ieft to right each node can
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`be uniquely located. Note that the last two entries in the inverted index are cach associated with
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`two nodes, 4 and 6 in the first case, and 5 and 7 in the second.
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`Using the above, the concept of grouping of nodes fromdifferent parts of the graph(i.e.
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`nodes that are not siblings or nodes that do not have a common parent) can be explained.
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`Presumethat the response to a verbal description presented as an initial query of “What
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`do you want to do?” was “Make a business class reservation.” In this case there are two
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`keywords present, “reservation” and “business class”.
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`Depending uponthe particular implementation, as noted previously, the verbal
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`descriptions associated with each identified node could be presented together or in sequence.
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`Alternatively, and as is the case here, a sect of rules can be established, for example, such that if
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`an identified node is a sub-node of another identified node, only the verbal description of the
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`sub-node(s) is provided because of inherent redundancy. Thus, since both “business class”
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`nodes 310, 314 are sub-nodesof the “reservation” node 302, the verbal description associated
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`with the “reservations” node can be suppressedif it can be determined that business class
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`necessarily implies reservations.
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`In this example, a search of the inverted index would identify nodes 4 and 6 (310, 314)
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`fromdifferent parts of the tree are associated with the keywordsin the query, and thus the
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`system, in presenting the verbal descriptions from each, in effect, alters the tree structure and
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`groups these nodesin the result. Thus, the combination of result nodes presented depends upon
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`the user query or response, not that predetermined by the graph structure itself,
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`Ofcourse, the goal would still not be reached because of the ambiguity caused by
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`“Business Class” being under both “Domestic” and “International”. However, that ambiguity
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`can be handled by suitable wording of the following verbal descriptions and whether they are
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`combinedor provided sequentially or by other nodes.
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`Example 4
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`A persistent and further drawback presentin the prior art is the inability to operate if any
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`termother than the specific allowed terms are provided. Thus, in an IVR of the priorart,
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`providing anything other than the recognized term(s) will likely result in meaningless repeat of
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`the same inquiry by the [VR or an error.
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`Advantageously, the teachings of the present invention allow for construction of a more
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`flexible systemthan available in the prior art. Specifically, we can incorporate a thesaurus to
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`accommodate synonyms for the keywords.
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`Example 4 illustrates the addition of a simple thesaurus as an aspect of a system so that a
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`synonym of a keyword may also be used by the system to jump to the desired nodesin the graph.
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`Example 4 is discussed with reference to a portion 400 of an interactive television program
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`listing system as shown in FIG. 4.
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`Such a system implementing the invention will allow a user to speak to or interact with a
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`device to look for programsofhis choice by time slot, genre, favorite actor or actress, etc.
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`Docket No.: 4428-4001
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`This example, as with the other examples above, use an inveried index, in this case one
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`where cach node 402, 404, 406 is uniquely identified by a string of six characters, the portion of
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`which corresponding to FIG. 4 is shown as follows.
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`Programs; acgyct
`Sitcoms; ifgnxh
`Films; vnymos
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`Since a common synonymfor “Films” is “Movies” a thesaurus can be created associating
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`the two. Depending upon the particular implementation, thesaurus terms to be equated to the
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`keywords can be taken from a standard thesaurus or can be customcreated for the particular
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`application,
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`In addition, the equating of terms can be done in any of a myriad ofdifferent ways,
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`the exact implementation details of which howeverre irrelevant to the invention, bul a few
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`representative examples of which howeverare contained herein for purposesof illustration.
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`In one example case, the equating can be done on a purely word basis. For example, a
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`file can be constructed such thal one or more single word synonyms are directly associated with
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`an index word, for example as follows:
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`Movies, Flicks—Films
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`Alternatively, the synonyms can be equated with the node identifier(s) corresponding to
`
`the index term, for example as follows:
`
`Movies, Flicks — vnymos
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`In the former case, the system wouldstill have to search the index after the thesaurus has
`
`provided the proper index term(s).
`
`In the latter case, the thesaurus provides a direct link to the
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`respective node(s) so that re-searching is not required.
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`In the system of Example 4, a user who providesthe input “Movies” would cause the
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`processing to occur as follows.
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`The system would search the inverted index of keywords and fail to locate “Movies” as a
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`keyword. Asa result, it would search the thesaurus and find that the word “Movies”is a
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`synonymthat can be correlated with a keyword. At this point, depending uponthe particular
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`thesaurus, it would either return to the inverted index and search using the synonym keyword
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`“Films” and return the result as the node 406 identified by “vnymos’”, or go directly to the node
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`406 identified by “vnymos” based upon the thesaurus entry.
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`Of course,it is possible (and likely) that in actual usage a synonym will be associated
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`with more than one keyword. For example, “Comedies” may be associated with both the
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`keywords “Sitcoms” and “‘Films’’, resulting in, for example, the following entry in a thesaurus:
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`Comedies—Sitcoms, Films
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`In this case, a search for “Comedies” would result in the system identifying that the
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`synonym was associated with nodes 404, 406 for both “Sitcoms” and “Films”, and it would
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`return both terms or node identifiers corresponding to the two keywordsas the result.
`
`Example 5
`
`Advantageously, the thesaurus concept can be extended furtherso that aninitially
`
`unknown word(i.c. a word that is neither a keyword nor a thesaurus word) can be learned by the
`
`system and added to a thesaurus for future use.
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`This example is described with reference to FIG. 5 which is a portion 500 of a larger
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`system graph as part of a very simple “geographic information system” found in some
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`automobiles, kiosks and elsewhere today. Such a system enables a user to, among other things,
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`identify and get information about different locations in an environment. For example,
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`information about particular types of restaurants in an area.
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`In this example, the inverted index for the portion 500 shownin FIG. 5 could look as
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`follows:
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`Restaurants, |
`Pizza, 2
`Burgers, 3
`Chinese, 4
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`A userissues the following query lo the system“‘fast food”in orderto find a quick meal.
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`The system’s search of both the index and thesaurus would result in the “term’’, in this
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`case a phrase, not being found in either.
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`In this case, il is an unknown phrase, and the system has
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`to learn the “meaning” of the term,
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`To do this, the systemfirst offers the verbal description from the top level node(s) 502 to
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`the user — in this example, just “Restaurants”, The user presumably provides a posilive response,
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`(Of course, in areal system, it is possible and likely there are more top level nodes than just one.
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`In that case, the user would be offered two or more of these nodes, and would haveto select
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`“Restaurants” to match his intended request.)
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`Continuing on, once the user has responded affirmatively, the system moves downthe
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`iree and offers the verbal description from each of the child nodes: “Pizza” (504), “Burgers”
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`(506), and “Chinese” (508). Presuming that the user picks “Pizza”, the transaction interaction
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`would look something like this:
`
`User: Fast food
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`System: Restaurants?
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`User: Yes
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`System: Pizza, Burgers, or Chinese?
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`User: Pizza
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`At this point, the system has “learned”for the time being that it can equate “fast food”
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`with “pizza” and can add “fast food” as a synonym to “pizza” in the thesaurus.
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`This user, who first used the unknown term “fast food”, had to trace a path downthe tree.
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`However, now the systemis able to associate “pizza” with “fast food” and create or add a
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`thesaurus entry to reflect this association, for example as follows:
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`Fast food — Pizza
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`Thus, the system has learned a meaning of the initially unknown term“fast food” and has
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`addedit to the thesaurus for future use.
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`As a result, a subsequent uses ofthe same term“fast food” will enable the system to jump
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`directly to the “pizza” node 504,
`
`Example 6
`
`This example illustrates how additional meanings for an existing thesaurus term or phrase
`
`can be leamed by the system for future use, whether the existing thesaurus term or phrase was an
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`original thesaurus term or one previously learned with continuing reference to FIG. 5.
`
`At this point, the inverted index is unchanged as:
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`Restaurants, |
`Pizza, 2
`Burgers, 3
`Chinese, 4
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`Additionally, presume the following entry now exists in the thesaurus.
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`Fast food — Pizza
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`Suppose a new user now issues the query “fast food” as above, but with “Burgers” rather
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`than “Pizza” in mind.
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`Based upon the thesaurus, the system would go directly to the “Pizza” node. However,
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`the user will reject “Pizza”, having “burgers” in mind. By rejecting the “Pizza” node 504
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`description, the user indicates that the “Pizza” node 504 is not of interest. The system is
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`therefore configured with a further set of rules, in this case one in which the system goes upin
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`the hierarchy to a higher node, the top node 502 in this portion of the example, and provides the
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`verbal descnptions for the other nodes 502, 504, 506, 508 so as to cause a tracing downthetree.
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`This can be illustrated by the following “dialog”:
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`User: Fast food
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`System: Pizza?
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`User: No
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`System: Restaurants?
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`User: Yes
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`System: Pizza, Burgers, or Chinese?
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`User: Burgers
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`This time, although this user has had to trace throughat least a portion ofthe path from a
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`higher-level node 502 ofthe tree 500, the system has learned yet another meaning for “‘fast
`
`food”.
`
`It now adds this meaning to the earlier entry in the thesaurus, for example as:
`
`Fast food — Pizza, Burgers
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`It has now learned two meaningsfor future use.
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`If a user were now to issue the query
`
`“Fast food”, the system would respond with the verbal descriptions from the nodes 504, 506
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`corresponding to both Pizza and Burgers.
`
`Thus, the system can keep learning new meanings of terms based on the intended
`
`meanings of users “deduced” fromthe interactions between users and the system.
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`Of course, the nature and extent to which the system will incorporate synonyms and/or
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`keywords in a continual learning process will not only depend upon its construction and rules,
`
`but also on the quality of the original thesaurus and the quality of the initial inverted index.
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`In
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`addition, where in the tree the system jumps if the user rejects the initial meaning(s) offered by
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`the system can be handled different ways in different implementations.
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`For example, the system can always jump to fixed ancestor(s) (either the top node or a
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`parent or some ancestor(s) at an intermediate point) or a fixed level (e.g. halfway from the top).
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`This approach has the advantage of being simple to implement, but it has the problem of
`
`inflexibility because it may be relatively efficient for certain graphs and associated verbal
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`descriptions, but not for all. For example, if two or more nodes’ verbal descriptions are offered
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`and rejected, the relevant node selected would have to be commonancestor(s) of the offered
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`nodes,
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`In other words, with reference to Example 6 which is part of a larger tree, going up to the
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`“Restaurants” node 502 would mean going to the parent ofthe “Pizza” node 504 rather than all
`
`the way to the top in the larger tree containing the portion 500 shown,
`
`A more flexible alternative uses the information recorded in the thesaurus to find every
`
`synonym for “pizza” in the thesaurus and collect all the other keywords associated with those
`
`synonyms. Then the system would search the inverted index to identify all the nodes associated
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`with these other associated keywords and identify the most common ancestor of all ofthose
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`nodes and go to it. By using the information in the thesaurus in this way the system makes use
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`of known properties of the one meaning of “fast food”, whichis “Pizza’’, to construct an
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`intelligent hypothesis about where the other meanings of “fast food” might lic in the graph. This
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`allows the user to reach another meaning with the least effort and allows the system thereby to
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`learn what the new meaning of “fast food” is more efficiently.
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`Example 7
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`Ofcourse, just as it may be desirable to create implementations to add meaningsto the
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`thesaurus, it may be equally or more desirable to cause an existing meaning for a thesaurus word
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`to be dropped, for example, dueto relative lack of use. This process is described with continuing
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`reference to FIG. 5 and the associated inverted index, particularly with respect to the thesaurus
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`entry resulting from the most recent example.
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`Fast food — Pizza, Burgers
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`In this example, presume that there have been several uses of the query “fast food” and
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`that the user(s) issuing these queries have almost always selected “Burgers” and almost never
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`“Pizza”,
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`In accordance with another implementation of the invention, the system is constructed to
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`track the frequency of use of a particular term in the thesaurus. Depending uponthe particular
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`implementation, the tracking can be donefor all entries in the thesaurus, for only those added as
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`part of the “learning” process, or for some specified combination thereof.
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`In addition, some specified criterion is used to determine when, and which terms,ifany,
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`should be removed from the thesaurus. Depending upon the particular implementation the
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`criterion can be based upon usagerelative to time, usage of a particular term relative to some
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`otherterm(s), term usage relative to overall thesaurus usage, or simply elimination of all added
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`terms not used sincethe last purge.
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`Thus, presuming that the system has kept track of the frequency of use ofdifferent
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`meanings of “fast food”, and that “Pizza” does not meet the criterion for a sufficiently high
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`frequency, the meaning “Pizza” can be dropped as a synonym for“Fast food” and the entry (after
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`purging) would look as follows:
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`Fast food — Burgers
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`Thus, a further enhanced implementation can be constructed so the system is dynamically
`updating the thesaurus, either adding meanings or dropping meanings for existing and/orinitially
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`unknown words.
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`Example 8
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`A further advantage to the invention is that, in some implementations,it can be
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`configured so that, when there are multiple relevant nodes to be presented, an associated ranking
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`can be used to determine the type, method or order of presentation. For example, the ranking can
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`be based upon the frequency of use of particular nodes, which is tracked in these
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`implementations, so that the most frequently selected or used nodes are presented first, more
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`prominently, or in a particular manner.
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`For example, this can be illustrated by continuing from Example 7, where the thesaurus
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`entry was as follows:
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`Fast food — Pizza, Burgers
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`Underthe assumption that the system has beentracking the frequency of usage of the “Pizza”
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`_ node and the “Burgers” node and each has been accessed anidentical number of times. When a
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`user enters the query “Fast food”’, as above, the system presents the user with both the “Pizza”
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`node 504 and the “Burgers” node 506, but becauseit tracks usage and the usage is the same,it
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`presents themin the order they arelisted, i.e. “Pizza” and then “Burgers”. However, at this
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`point, the user’s selection will cause one entry to have a greater frequency of usage relative to the
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`other entry, for example a selection of “Burgers” will make it have a higher frequency of usage
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`and, accordingly, a higher ranking for the next instance of use.
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`Thus, the next time the system will be presenting both the “Pizza” and “Burgers” nodes
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`to a user, the “Burgers” node 506 will have the higher frequency of usage and, accordingly, will
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`be presented first, or more prominently, or in some other specified manner becauseof its
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`ranking. Ifthe frequency reverses with use so that the “Pizza” node 504 outranks “Burgers”
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`node 506, then the “Pizza” node 504 will supplant the “Burgers” node 506.
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`A further variant of Example 8 allows the node rankings to be used to prune the nodes
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`themselves.
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`In this variant, a criterion can be specified, typically zero usage over a long
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`specified period of time, that is used to remove an entire node. This is advantageously made
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`possible because of the system's ability to “jump” among nodes. Thus, it may occur that a node
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`within the tree is never accessed, but a child node of that node is.
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`In some variants therefore,
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`when this state exists for a sufficiently long period of time, the systemis constructed to delete
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`that node.
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`It should be understood that, if handled properly, this process will not even affect the
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`“learning” process because, even if no user action ever directly causes the node ta be presented,
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`if the learning process causes the node to be presented the node’s access frequency will be non-
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`zero andit will not be “pruned”.
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`In addition, by tracking access frequency on a nodebasis, a qualitative evaluation of the
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`hierarchical system can be madeand visualized. This makes it possible to review the overall
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`hierarchy after some period of time and periodically optimize it based upon the result instead of
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`relying purely upon the dynamic optimization that inherently and naturally flows from use of the
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`teachings of the invention.
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`Having now described various component aspects of different variants implementing the
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`invention, by way of the above examples, it should be understood that the “jumps” can occur
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`from any node to any node,i.c. vertically and/or laterally and to another node thatis higher,
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`lower or on the same“level” as the node from which the jump is made. All mannerof vertical
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`and lateral jumps from multiple nodes to multiple nodes are possible.
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`In addition, it should be understoodthat in some applications (like documentretrieval
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`systems) the verbal description from the identified node may be the one issued whereas, in others
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`(like an [VR system), the verbal descriptions for the children of the identified nodes may be what
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`is presented. Nevertheless, in both cases, the process as described above by way of example will
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`be the same or directly analogous.
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`Having described the various aspects individually a more commercially suitable example,
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`employing a combination of the above examples, can now be presented with reference to FIG. 6
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`which illustrates a simplified example of an “interactive voice response unit” (TVR) hierarchy
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`600 that might be used in the airline industry. Of course, a real menu tree used in an TVR may
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`have any numberof nodes from several, up to a thousand, or more. For example, a tree with 4
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`branches from each node and which has 5 levels uniformly would have 1365 nodes. As shown
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`in FIG. 6, the tree 600 is a hierarchical tree and consists of the following nodes and branches:
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`Initial start (node a0) 602
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`domestic flight arrival information (node al) 604
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`domestic reservations (node a2) 606
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`international flight arrival information (node a3) 608
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`international reservations (node a4) 610
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`The node 604 identified by al is a service node with pre-recorded information.
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`The node 606 has two child node a 2, first/business class (node a5) and economy (node a6)
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`The node 608 identified by a3 is service node with pre-recorded information,
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`The node identified as a4 has three child nodes identificd as first class (node a7), business class
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`(node a8), and economy (nodea9).
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`The nodes 612, 614, 616, 618, 620 identificd as a5, a6, a7, a8, a9 are all service nodes(i,c.
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`terminal nodes) where a respective customerservice representative will interact with the caller.
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`Of course, areal system may also have