Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an electronic device configured to…” and “a computing device including at least one processor configured to…” in claim 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 11-12, 15-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is directed to a method, which is a statutory category of invention. (Step 1: YES).
Claim 1 recites a computerized method comprising:
generating at least one dialogue graph which models at least one conditional relationship for a dialogue dataset (a “dialogue graph” is an abstract representation of dialogue which models relationships between various types of utterances comprising nodes corresponding to a plurality of dialogue acts and edges representing various information on relationships between the plurality of dialogue acts, paragraph [163] of Applicant’s specification; the nodes and edges of a dialogue graphs can be specified by a human using a pen and paper, see Fig. 7, S10; the act of generating a dialogue graph, therefore, encompasses mental observations, evaluations, judgements and opinions because a human could define a dialogue graph using a pen and paper);
receiving a user dialogue input (receiving dialogue input, without further limitation, encompasses a human observing spoken or written dialogue and therefore encompasses mental observations);
sampling a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model (the recited “pre-trained dialogue model” is addressed below as an additional element; otherwise the act of “sampling” a plurality of dialogue act groups could be performed by a human simply selecting, at random or otherwise, a set of dialogue act groups from a larger observed set of dialogue act groups; this therefore encompasses mental observations, evaluations, judgements, and opinions);
adjusting the plurality of dialogue act groups based on the dialogue graph (“adjusting” a plurality of dialogue act groups, without further limitation, could be performed by a human mentally sorting a plurality of dialogue act groups based on the observed dialogue graph; this therefore encompasses mental observations, evaluations, judgements, and opinions); and
selecting one dialogue act group satisfying a predetermined condition among the plurality of dialogue act groups (selecting a dialogue act group, without further limitation, could be performed by a human mentally selecting a dialogue act group that satisfied an observed predetermined condition; this therefore encompasses mental observations, evaluations, judgements, and opinions).
As identified above, claim 1 recites steps that fall within the mental processes grouping of abstract ideas because they cover concepts performed in the human mind. Claim 1 therefore recites an abstract idea. (Step 2A, Prong One: YES).
Claim 1 recites two additional elements. The first is the recitation in the preamble that the method is “computerized”. The second is “using a pre-trained dialogue model” to sample a plurality of dialogue act groups for responding to the user dialogue input.
Regarding the first element, merely indicating that the method is “computerized” in the preamble of the claim amounts to no more than mere instructions to apply the exception using a generic computer.
Regarding the second element, the claim does not provide any meaningful limitations to the recited pre-trained dialogue model. The pre-trained dialogue model operates as a “black box” that merely provides the desired output (the sampled plurality of dialogue act groups) without any details as to how the model achieves this result. The pre-trained dialogue model is therefor used to generally apply the abstract idea without placing any limits on how the pre-trained dialogue model functions. This type of limitation merely confines the use of an abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims.
Additionally, when considering the claim as a whole, the claim cannot be considered to integrate the recited judicial exception into a practical application, because the result of performing the method is merely the selection of a dialogue act group. As described in Applicant’s specification, the practical application of the invention is to either 1) provide a more reliable response dialogue act to a user (paragraph [63] of Applicant’s specification) and/or 2) provide a more convenient task processing experience to the user (paragraph [64] of Applicant’s specification). Claim 1 does not provide any response to the user or execute any tasks requested by the user. Therefore, even when considering the claim as a whole, the claim does not integrate the judicial exception into a practical application. (Step 2A, Prong Two: NO).
Claim 1 is therefore directed to a judicial exception (Step 2A: YES).
As noted above, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component and/or mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Even when considered in combination, the additional elements do not provide an inventive concept. (Step 2B: NO).
Claim 1 is therefore ineligible.
Claim 2 further defines the conditional relationships. These relationships could be specified mentally with pen and paper. The analysis above for claim 1 applies equally to claim 2.
Claim 3 requires selecting the one dialogue act group which best satisfies the predetermined condition among the plurality of dialogue act groups. A human could mentally determine which group best satisfied a predetermined condition using observations, evaluations, judgements, and opinions. The analysis above for claim 1 applies equally to claim 3.
Claims 4-6 describe further steps for adjusting and selecting the dialogue act groups according to the predetermined conditions that all encompass a human mentally performing the acts using observations, evaluations, judgements, and opinions. The analysis above for claim 1 applies equally to claims 4-6.
Claim 9 recites generating the at least one dialogue graph by using a dialogue graph generation model learned based on a plurality of dialogue acts so that an expected value of a case where one of the dialogue acts in a dialogue context satisfies the first conditional relationship (the should relationship) is maximized to model the first conditional relationship (the should relationship) for the dialogue dataset. This is simply a textual description of the mathematical formula disclosed by Equation 1 in paragraphs [173-175] of the specification. Mathematical formulas or equations written in text format encompass mathematical concepts which are abstract ideas. Claim 9 therefore recites an additional abstract idea. Claim 9 recites the additional element of the dialogue graph generation model. However, the claim merely indicates that the model is generically “learned” by maximizing the expected value of the desired result using a mathematical loss function. The claim also does not recite any of the input variables used to determine the expected value, but merely instructs to maximize the expected value of the desired outcome (i.e. the expected value of a case where the dialogue act satisfies the first conditional relationship). Thus, these limitations merely recite the idea of a solution or outcome, without details of how the exception is applied to model the first conditional relationship. See MPEP 2106.05(f). Claim 9 therefore does not integrate the judicial exception into a practical application or provide an inventive concept.
Claim 10 recites generating the at least one dialogue graph by using a dialogue graph generation model learned based on a plurality of dialogue acts so that an expected value of a case where one of the dialogue acts in a dialogue context satisfies the second conditional relationship (the can relationship) and does not satisfy the third conditional relationship (the should-not relationship) is maximized to model the second conditional relationship and the third conditional relationship for the dialogue dataset. This is simply a textual description of the mathematical formula disclosed by Equation 2 in paragraphs [179-180] of the specification. Mathematical formulas or equations written in text format encompass mathematical concepts which are abstract ideas. Claim 10 therefore recites an additional abstract idea. Claim 10 recites the additional element of the dialogue graph generation model. However, the claim merely indicates that the model is generically “learned” by maximizing the expected value of the desired result using a mathematical loss function. The claim also does not recite any of the input variables used to determine the expected value, but merely instructs to maximize the expected value of the desired outcome (i.e. the expected value of a case where the dialogue act satisfies the second conditional relationship and does not satisfy the third conditional relationship). Thus, these limitations merely recite the idea of a solution or outcome, without details of how the exception is applied to model the second and third conditional relationships. See MPEP 2106.05(f). Claim 10 therefore does not integrate the judicial exception into a practical application or provide an inventive concept.
Claim 11 requires further generating a second dialogue graph. Claim 12 requires selecting among the first dialogue graph and the second dialogue graph. Each of these steps encompass mental steps for the same reasons as generating and selecting the first dialogue graph. The analysis above for claim 1 applies equally to claims 11 and 12.
Claim 15 is directed to a system comprising a memory and a processor configured to perform the method recited in claim 1. The additional elements of the memory and processor are merely generic computer components, recited at a high level of generality. As noted in MPEP 2106.04(a)(2), both product and process claims may recite a mental process. The result of the operations performed by the system of claim 15 is the selecting of one dialogue act group, as was discussed above with respect to claim 1. Therefore, for similar reasons as claim 1, claim 15 does not integrate the recited judicial exception into a practical application or provide an inventive concept.
Claims 16 and 18 are directed to a system comprising an electronic device configured to receive a user dialogue input and a computing device including at least one processor configured to perform the same method as recited in claim 1. The operations performed by the computing device including at least one processor do not integrate the recited judicial exception into a practical application or provide an inventive concept for the same reasons as discussed with respect to claim 15. The additional element of the electronic device configured to receive a user dialogue input amounts to a mere data gathering step and is insignificant extra solution activity. The claim generically covers any means to receive dialogue input. Receiving dialogue input in the form of text, voice, gesture, or touch is well-understood, routine, and conventional activity in the field (see prior art rejections below). Claims 16 and 18 therefore do not provide an inventive concept and are ineligible for similar reasons as claims 1 and 15.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites adjusting of the plurality of dialogue act groups comprises “removing a dialogue act not satisfying the third conditional relationship (the should-not relationship) from each of the plurality of dialogue act groups”.
This language is repeated in the specification at paragraphs [25].
Paragraph [188] of the specification describes removing one dialogue act “which does not satisfy the second conditional relationship (can relationship) and the third conditional relationship (should-not relationship)”. It is ambiguous from this description whether this is intended to mean removing a dialogue act that does not satisfy the second conditional relationship OR the third conditional relationship (i.e. IF(!CAN)->Remove, IF(!SHOULDNOT)->Remove), or if is intended to mean only removing a dialogue act that does not satisfy the second conditional relationship AND the third conditional relationship (i.e. IF(!CAN&&!SHOULDNOT)->Remove).
Finally, paragraphs [261]-[264] describe with reference to Fig. 10 a dialogue graph that includes A as a dialogue act which satisfies the third conditional relationship (should-not relationship), wherein A is removed from the first and second dialogue act groups in order to satisfy the conditional relationship.
One of ordinary skill in the art would likely understand that removing a dialogue act that satisfied the third conditional relationship (should-not relationship) would be more useful for selecting a dialogue act than removing a dialogue act that did not satisfy the third conditional relationship (should-not relationship). However, claim 5 expressly requires “removing a dialogue act not satisfying the third conditional relationship (the should-not relationship) from each of the plurality of dialogue act groups” (emphasis added), which would remove dialogue acts that did not have a should-not relationship.
For the purposes of examination, it is assumed that claim 5 is intended to recite “removing a dialogue act
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 and 11-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wen et al. (U.S. Patent No. 11,430,446, hereinafter “Wen”).
In regard to claim 1, Wen discloses a computerized method (see Fig. 10) comprising:
generating at least one dialogue graph which models at least one conditional relationship for a dialogue dataset (a dialogue policy module comprises a state machine represented by a graph created to model dialogue flows and expected node transitions, column 7, lines 61-66 and column 8, lines 51-56; the dialogue graph further comprising conditional rules specifying the conditions required to transition from a first node to a second node in the dialogue graph, column 5, lines 3-23);
receiving a user dialogue input (user speech or text is received as a dialogue input, column 44, lines 30-50);
sampling a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model (after the user’s intent is extracted from the dialogue input, a set of pre-defined dialogue act types are used to generate the system dialogue acts according to the dialogue policy, column 7, lines 11-32);
adjusting the plurality of dialogue act groups based on the dialogue graph (dialogue state information is updated by the conditional global rule actions and node functions of the dialogue policy module to determine a dialogue act, column 46, line 66 to column 7, line 10); and
selecting one dialogue act group satisfying a predetermined condition among the plurality of dialogue act groups (a final response dialogue act which satisfies the conditional global rule actions is selected, column 46, line 66 to column 47, line 20; see also column 13, lines 1-20 discussing the transitions between nodes in the state machine graph and conditional global rules).
In regard to claim 2, Wen discloses the at least one conditional relationship includes one or more of a first conditional relationship (a should relationship) indicating what utterance should occur for one utterance in a dialogue flow (each node is associated with a local transition to a second node that defines an expected dialogue pathway, the transitions comprising conditional functions, column 9, lines 1-36), a second conditional relationship (a can relationship) indicating what utterance can occur for one utterance in the dialogue flow (global rules comprise conditional functions that can be triggered at any point in the conversation, column 10, lines 26-53), and a third conditional relationship (a should-not relationship) indicating what utterance should not occur for one utterance in the dialogue flow (a “skip_nodes” routine defines nodes where the global rules should not be triggered, column 10, line 59 to column 11, line 4).
In regard to claim 3, Wen discloses the selecting of the one dialogue act group comprises selecting the one dialogue act group which best satisfies the predetermined condition among the plurality of dialogue act groups (global rules are assessed according to their priorities to determine the rule that best satisfies the condition associated with the rule, column 13, lines 21-33; see also description of conditions at column 10, lines 26-53).
In regard to claim 4, Wen discloses each of the plurality of dialogue act groups includes at least one dialogue act for a dialogue input (each of the set of pre-defined dialogue act types are associated with a dialogue act to respond to a dialogue input, column 47, lines 21-36).
In regard to claim 5, Wen discloses the adjusting of the plurality of dialogue act groups comprises adding a dialogue act satisfying the first conditional relationship (the should relationship) to each of the plurality of dialogue act groups, removing a dialogue act not satisfying the second conditional relationship (the can relationship) from each of the plurality of dialogue act groups, and removing a dialogue act not satisfying the third conditional relationship (the should-not relationship) from each of the plurality of dialogue act groups (see Fig. 5, when determining the next dialogue act node, in step S803 the global rules are assessed (can relationship), and if no global rules conditions are met, the dialogue acts associated with the global rules are discarded; if the current node corresponds to the “skip_nodes” condition (should not relationship), the global rules and associated dialogue acts are discarded; otherwise, the dialogue act specified by the current node (should relationship) is added as a next dialogue act, column 17, lines 13-51).
In regard to claim 6, Wen discloses the selecting of the one dialogue act group comprises selecting a dialogue act group having a largest number of dialogue acts satisfying the at least one conditional relationship among the plurality of dialogue act groups (preconditions associated with a node are assessed such that a dialogue act satisfying the most preconditions is selected, column 17, line 58 to column 18, line 13).
In regard to claim 7, Wen discloses providing one dialogue act included in the selected one dialogue act group as the response dialogue act responding to the user dialogue input (the selected dialogue act gives a response, column 47, lines 33-36).
In regard to claim 8, Wen discloses the providing of the one dialogue act comprises providing a dialogue act having a highest relevance to the user dialogue input as the response dialogue act responding to the user dialogue input among one or more dialogue acts included in the selected one dialogue act group (the response dialogue act is selected as most relevant to the user’s dialogue input, column 4, line 65 to column 7, line 23 and column 7, lines 54-60).
In regard to claim 11, Wen discloses the generating of the at least one dialogue graph comprises generating a first dialogue graph based on a first type of dialogue dataset, and generating a second dialogue graph based on a second type of dialogue dataset (the dialogue graphs comprise separate dialogue pathways defined according to the intended use cases and domains, column 8, lines 35-50 and column 44, lines 11-22).
In regard to claim 12, Wen discloses determining a type of the user dialogue input (slots defined by the domain of the user input are determined, column 44, lines 57-65); and
selecting a dialogue graph corresponding to the type of the user dialogue input among the first dialogue graph and the second dialogue graph (one of a first or second dialogue pathway is selected, column 8, lines 35-50),
wherein the adjusting of the plurality of dialogue act groups comprises adjusting the plurality of dialogue act groups based on the selected dialogue graph (dialogue state information is updated by the conditional global rule actions and node functions of the dialogue policy module to determine a dialogue act, column 46, line 66 to column 7, line 10).
In regard to claim 13, Wen discloses determining a context of task-oriented dialogues by analyzing the task-oriented dialogues including the user dialogue input and the selected one dialogue act group (a dialogue context comprising information about the user dialogue input and dialogue state information is determined, column 11, lines 14-41);
determining a type of a task requested by a user based on the context of the task-oriented dialogues (a user intent is determined based on the current user dialogue input and dialogue state information, column 46, line 66 to column 47, line 10); and
performing the task of which type is determined (the system dialogue act responsive to the user dialogue input is executed, column 47, lines 11-36).
In regard to claim 14, Wen discloses the determining of the context of the task-oriented dialogues comprises determining the context of the task-oriented dialogues by analyzing one or more of a correlation between a plurality of dialogue acts included in the task-oriented dialogues, intention and purpose of the task-oriented dialogues based on a plurality of keywords which are extracted from the task-oriented dialogues (the dialogue state information comprises slots associated with keywords which are analyzed to determine an intent associated with the dialogue state information, column 44, line 47 to column 45, line 14 and column 46, lines 30-42).
In regard to claim 15, Wen discloses a system (Fig. 1, 100) comprising:
at least one memory configured to store instructions that are executable (working memory 111); and
at least one processor configured to execute one or more of the instructions to perform operations (processor 105) comprising:
generating a dialogue graph which models at least one conditional relationship for a dialogue dataset (a dialogue policy module comprises a state machine represented by a graph created to model dialogue flows and expected node transitions, column 7, lines 61-66 and column 8, lines 51-56; the dialogue graph further comprising conditional rules specifying the conditions required to transition from a first node to a second node in the dialogue graph, column 5, lines 3-23);
receiving a user dialogue input (user speech or text is received as a dialogue input, column 44, lines 30-50);
sampling a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model (after the user’s intent is extracted from the dialogue input, a set of pre-defined dialogue act types are used to generate the system dialogue acts according to the dialogue policy, column 7, lines 11-32);
adjusting the plurality of dialogue act groups based on the dialogue graph (dialogue state information is updated by the conditional global rule actions and node functions of the dialogue policy module to determine a dialogue act, column 46, line 66 to column 7, line 10); and
selecting one dialogue act group satisfying a predetermined condition among the plurality of dialogue act groups (a final response dialogue act which satisfies the conditional global rule actions is selected, column 46, line 66 to column 47, line 20; see also column 13, lines 1-20 discussing the transitions between nodes in the state machine graph and conditional global rules).
In regard to claim 16, Wen discloses a system (Fig. 1, 100), comprising:
an electronic device configured to receive a user dialogue input (system 100 receives an input signal from another device, column 6, line 62 to column 7, line 5; the input comprising user speech or text is received as a dialogue input, column 44, lines 30-50); and
a computing device including at least one processor (processor 105) configured to generate a dialogue graph which models at least one conditional relationship for a dialogue dataset (a dialogue policy module comprises a state machine represented by a graph created to model dialogue flows and expected node transitions, column 7, lines 61-66 and column 8, lines 51-56; the dialogue graph further comprising conditional rules specifying the conditions required to transition from a first node to a second node in the dialogue graph, column 5, lines 3-23),
sample a plurality of dialogue act groups for responding to the user dialogue input by using a pre-trained dialogue model (after the user’s intent is extracted from the dialogue input, a set of pre-defined dialogue act types are used to generate the system dialogue acts according to the dialogue policy, column 7, lines 11-32),
adjust the plurality of dialogue act groups based on the dialogue graph (dialogue state information is updated by the conditional global rule actions and node functions of the dialogue policy module to determine a dialogue act, column 46, line 66 to column 7, line 10), and
select one dialogue act group satisfying a predetermined condition among the plurality of dialogue act groups (a final response dialogue act which satisfies the conditional global rule actions is selected, column 46, line 66 to column 47, line 20; see also column 13, lines 1-20 discussing the transitions between nodes in the state machine graph and conditional global rules).
In regard to claim 17, Wen discloses the at least one processor is configured to determine a type of a task requested by a user based on the user dialogue input and the selected one dialogue act group (a user intent is determined based on the current user dialogue input and dialogue state information, column 46, line 66 to column 47, line 10), and
perform the task of which type is determined (the system dialogue act responsive to the user dialogue input is executed, column 47, lines 11-36).
In regard to claim 18, Wen discloses the electronic device is configured to receive the user dialogue input in form of one or more of text, voice, gesture, or touch (speech or text, column 7, lines 21-24).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Johnston et al., Hou et al., Chatterjee et al., Ramnani et al., Huang et al., Lee et al., Venkatapathy et al., and Vibbert et al. disclose additional methods for modeling conditional relationships in task-oriented dialogues.
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BLA 6/11/26
/BRIAN L ALBERTALLI/Primary Examiner, Art Unit 2656
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