Creating a hierarchical tree of language models for a dialog system based on prompt and dialog context

Claim 3

The method of claim 1 , said merging step (c) further comprising: interpolating between said identified contextual models, said interpolation resulting in a combination of said identified contextual models.

Claim 4

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to a section of a document.

Claim 5

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to at least one user response received in a particular dialog state in the dialog based system.

Claim 6

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to at least one user response received at a particular location within a particular transaction within the dialog based system.

Claim 7

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to a syntax of a prompt in the dialog based system.

Claim 8

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to a particular and known dialog based system prompt.

Claim 9

The method of claim 1 , wherein at least one of said plurality of contextual models corresponds to a received electronic mail message.

Claim 12

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) measuring the distance between each of a plurality of contextual models using a distance metric, wherein at least one of said plurality of contextual models corresponds to at least a portion of a document or a user response within a dialog based system; (b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models; (c) merging said identified contextual models into a parent contextual model; (d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and (e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model.

Claim 13

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) measuring the distance between each of a plurality of contextual models using a distance metric, wherein at least one of said plurality of contextual models corresponds to at least a portion of a document or a user response within a dialog based system; (b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models; (c) merging said identified contextual models into a parent contextual model; (d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and (e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model.

Claim 14

The machine readable storage of claim 10 , said merging step (c) further comprising: interpolating between said identified contextual models, said interpolation resulting in a combination of said identified contextual models.

Claim 15

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to a section of a document.

Claim 16

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to at least one user response received in a particular dialog state in the dialog based system.

Claim 17

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to at least one user response received at a particular location within a particular transaction within the dialog based system.

Claim 18

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to a syntax of a prompt in the dialog based system.

Claim 19

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to a particular and known dialog based system prompt.

Claim 20

The machine readable storage of claim 10 , wherein at least one of said plurality of contextual models corresponds to a received electronic mail message.

Claim 21

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) measuring the distance between each of a plurality of contextual models using a distance metric wherein at least one of said plurality of contextual models corresponds to at least a portion of a document or a user response within a dialog based system; (b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models; (c) merging said identified contextual models into a parent contextual model by building a parent contextual model using data corresponding to said identified contextual models; (d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and (e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model.

Claim 22

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) measuring the distance between each of a plurality of contextual models using a distance metric wherein at least one of said plurality of contextual models corresponds to at least a portion of a document or a user response within a dialog based system; (b) identifying two of said plurality of contextual models, said identified contextual models being closer in distance than other ones of said plurality of contextual models; (c) merging said identified contextual models into a parent contextual model by building a parent contextual model using data corresponding to said identified contextual models; (d) repeating said steps (a), (b), and (c) until a hierarchy of said plurality of contextual models is created, said hierarchy having a root node; and (e) statistically smoothing said hierarchy of said plurality of contextual models resulting in a language model.

Claim 25

The method of claim 19 , wherein language models at the leaves of the tree are partitioned based upon dialog context.

Claim 26

The method of claim 20 , wherein the dialog context is determined by a transaction the dialog system is in when a user response is collected.

Claim 27

The method of claim 20 , wherein the dialog context is determined by a type of information for which the dialog system has asked when a user response is collected.

Claim 28

The method of claim 20 , wherein the dialog context is determined by a type of question the dialog system has asked when a user response is collected.

Claim 29

The method of claim 23 , wherein the dialog system has asked a yes or no question.

Claim 30

The method of claim 23 , wherein the dialog system has asked a multiple choice question.

Claim 31

The method of claim 23 , wherein the dialog system has asked a which question.

Claim 32

The method of claim 23 , wherein the dialog context is determined by a number of consecutive prompts given to a user for a current dialog context.

Claim 33

The method of claim 19 , wherein the hierarchical tree is constructed a priori from collected training data prior to the dialog system, within which the hierarchical tree is to be used, is deployed.

Claim 34

The method of claim 19 , further comprising: collecting data from users and adding the data to at least one of the non-overlapping partitions; creating additional leaf language models using the collected data; and rebuilding the hierarchical tree using the new leaves.

Claim 35

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) creating a language model at the leaves of a tree, wherein each leaf corresponds to a different non-overlapping partition of user responses to prompts in a dialog system; (b) identifying at least two closest child language models for leaves in the tree; (c) merging said identified language models into a parent language model by using data corresponding to each child language model; and (d) repeating steps (b) and (c) until the hierarchical tree is formed, whereby a root of the hierarchical tree is built using all available training data.

Claim 36

A machine readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform the steps of: (a) creating a language model at the leaves of a tree, wherein each leaf corresponds to a different non-overlapping partition of user responses to prompts in a dialog system; (b) identifying at least two closest child language models for leaves in the tree; (c) merging said identified language models into a parent language model by using data corresponding to each child language model; and (d) repeating steps (b) and (c) until the hierarchical tree is formed, whereby a root of the hierarchical tree is built using all available training data.

Claim 37

The machine readable storage of claim 30 , wherein language models at the leaves of the tree are partitioned based upon dialog context.

Claim 38

The machine readable storage of claim 31 , wherein the dialog context is determined by a transaction the dialog system is in when a user response is collected.

Claim 39

The machine readable storage of claim 31 , wherein the dialog context is determined by a type of information for which the dialog system has asked when a user response is collected.

Claim 40

The machine readable storage of claim 31 , wherein the dialog context is determined by a type of question the dialog system has asked when a user response is collected.

Claim 41

The machine readable storage of claim 34 , wherein the dialog system has asked a yes or no question.

Claim 42

The machine readable storage of claim 34 , wherein the dialog system has asked a multiple choice question.

Claim 43

The machine readable storage of claim 34 , wherein the dialog system has asked a which question.

Claim 44

The machine readable storage of claim 34 , wherein the dialog context is determined by a number of consecutive prompts given to a user for a current dialog context.

Claim 45

The machine readable storage of claim 30 , wherein the hierarchical tree is constructed a priori from collected training data prior to the dialog system, within which the hierarchical tree is to be used, is deployed.

Claim 46

The machine readable storage of claim 30 , further comprising: collecting data from users and adding the data to at least one of the non-overlapping partitions; creating additional leaf language models using the collected data; and rebuilding the hierarchical tree using the new leaves.