DETAILED ACTION
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 .
Claims 1-17 and 19-20 are pending, and claims 1, 7 and 8 are independent claims.
Claim Objections
Claims 19 and 20 are objected to because of the following informalities:
Claim 18 is missing and therefore claims 19 and 20 should be numbered 18 and 19.
Appropriate correction is required.
Claim Interpretations - 35 USC § 112(f)
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:
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;
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
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 limitations are found in claims 1-3 and 5-6. Such limitations in claim 1 include: “a first identification unit that receives, as input, utterance data including…a second identification unit that receives, as input, the utterance data and the utterance type of each of the utterances… a result classification unit that receives, as input, the output pair data of utterances, and, using a result classification model/rule for classifying…”; such limitation in claim 2 is: “The classification device according to claim 1, wherein the second identification unit identifies the first identification utterance as an inquiry utterance according to…”; such limitation in claim 3 is: “…wherein the first identification unit inputs the utterance data into the first identification model/rule, and based on output of the estimation result”; such limitation in claim 5 is: “…wherein the result classification unit inputs the pair data of utterances into…”; such limitation in claim 6 is: “…wherein the result classification unit inputs the pair data of utterances…”
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 § 112(b)
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.
Claims 9, 10, 12 - 15, 17 and 19 are 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 9 recites the phrase "the second identification unit" in line 1. There is insufficient antecedent basis for " the second identification unit" in the claim.
Claim 10 recites the phrase " the first identification unit " in line 5. There is insufficient antecedent basis for " the first identification unit " in the claim.
Claim 12 recites the phrase " the result classification unit" in line 4. There is insufficient antecedent basis for " the result classification unit" in the claim.
Claim 13 recites the phrase " the result classification unit" in line 4. There is insufficient antecedent basis for " the result classification unit" in the claim.
Claim 14 recites the phrase " the second identification unit" in line 1. There is insufficient antecedent basis for " the second identification unit " in the claim.
Claim 15 recites the phrase "the first identification unit" in line 5. There is insufficient antecedent basis for " the first identification unit " in the claim.
Claim 17 recites the phrase " the result classification unit " in line 4. There is insufficient antecedent basis for " the result classification unit" in the claim.
Claim 19 recites the phrase " the first identification unit" in line 4. There is insufficient antecedent basis for " the result classification unit" in the claim.
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 8 and 14-19 are drawn to a “program” per se as recited in the preamble and as such is non-statutory subject matter. Claims 8 and 14-19 are rejected under 35 U.S.C. 101 because the claims appear to be directed to a software embodiment and not to hardware embodiment, where a machine claim is directed towards a system, apparatus, or arrangement. The claim appears to be directed towards a software embodiment. Paragraph 00110 of the Published Specification describes the elements of the system being implemented as software alone actualizing the embodiments of the invention. The claimed limitations are capable of being performed as software as described in the above paragraphs, alone since no hardware component is being claimed. Software, alone, are not physical components and thus are not statutory since software do not define any structural and functional interrelationships between the computer programs and other claimed elements of a computer, which permit the computer' s program functionality to be realized. Hence, the stated functions comprise software and is thus not directed to a hardware embodiment. Data structures not claimed as embodied in computer readable media are descriptive material per se and are not statutory because they are not capable of causing functional change in the computer. See e.g., Warmerdam, 33 F.3d at 1361, 31, USPQ2d at 1760 (claim to a data structure per se held nonstatutory). Such claimed data structures do not define any structural and functional interrelationships between data and other claimed aspects of the invention, which permit the data structure' s functionality to be realized. In contrast, a claimed computer readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure' s functionality to be realized, and is thus statutory.
Claims 1-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 7 and 8 recite “a first identification unit that receives, as input, utterance data including an utterance of a first speaker and an utterance of a second speaker in a dialogue and, using a first identification model/rule for estimating an utterance type indicating a type of each of the utterances in the dialogue, identifies the respective utterance types of the utterances included in the utterance data; a second identification unit that receives, as input, the utterance data and the utterance type of each of the utterances, using a second identification model/rule preset according to the utterance types, identifies a first identification utterance indicating an inquiry and a second identification utterance in response to the first identification utterance in the utterance data, and outputs pair data of utterances indicating the first identification utterance and the second identification utterance; and a result classification unit that receives, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifies the response result of the dialogue included in the utterance data as the response result kind.” The limitation of claim 1 of “a first identification unit that receives, as input, utterance data …”, “identifies the respective utterance types …”, “a second identification unit that receives, as input, the utterance data and the utterance type of each of the utterances …”, “identifies a first identification utterance indicating an inquiry …”, “outputs pair data of utterances…”, and “classifies the response result of the dialogue” as drafted cover mental activity and data gathering. More specifically, for claim 1 a human can collect/receive samples of utterance data, identify the respective utterance types and classify the response result of the dialogue. For instance, estimating an utterance type mentally has been known to be a rapid, often subconscious, cognitive process involving pragmatic inferencing and Theory of Mind (ToM). This involves interpreting not just the literal words (locutionary act), but the intended purpose (illocutionary act) and its effect on the hearer (perlocutionary act). In Mentalizing (ToM), for instance, the brain uses its "Theory of Mind" network—specifically the medial prefrontal cortex (MPFC) and temporoparietal junction (TPJ)—to understand the speaker's intentions. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 1, 46 and 47 are rejected under 35 U.S.C. 101.
Claim 2, 9 and 14 recite “the second identification unit identifies the first identification utterance as an inquiry utterance according to the utterance type of the utterance of the first speaker, and identifies the second identification utterance according to the utterance type of the utterance of the second speaker after the identified first identification utterance.” The limitation of claims 2, 9 and 14 “the second identification unit identifies the first identification utterance as an inquiry utterance …, and identifies the second identification utterance …” as drafted cover mental activity. More specifically, for claims 2, 9 and 14, a human can mentally identify the first identification utterance as an inquiry utterance according to the utterance type of the utterance of the first speaker, and identify the second identification utterance according to the utterance type of the utterance. For instance, based on the available audio data collected/received using a simple audio data collection device such as audio data recorder, in an excel data sheet in a device or any generic computer, etc., one can easily mentally identify the first identification utterance as an inquiry or other utterance type. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 2, 9 and 14 are rejected under 35 U.S.C. 101.
Claim 3, 10 and 15 recite “a model in the first identification model/rule is trained to output, as the utterance type, an estimation result of a first utterance type indicating need hearing, a second utterance type indicating a question, or a third utterance type indicating an explanation or an answer, and wherein the first identification unit inputs the utterance data into the first identification model/rule, and based on output of the estimation result by the first identification model/rule, identifies whether each of the utterances belongs to the first utterance type, the second utterance type, or the third utterance type.” The limitation of claims 3, 10 and 15 of “a model … is trained to output, as the utterance type, an estimation result of a first utterance type indicating need hearing, a second utterance type indicating a question, or a third utterance type indicating an explanation or an answer, and wherein the first identification unit inputs the utterance data into the first identification model/rule,, and based on output of the estimation result by the first identification model/rule, identifies whether each of the utterances belongs to the first utterance type, the second utterance type, or the third utterance type” as drafted cover mental activity and data gathering. More specifically, for claims 3, 10 and 15 a human can collect/receive samples of utterance data and make a determination that an utterance type is a question, or an explanation or an answer and also a human brain can do the estimation result of a first utterance type to indicate hearing need. The additional element of trained “model” as in “a model in the first identification model/rule is trained to output” does not integrate the abstract idea into a practical application since the claim does not impose any meaningful limits on practicing the abstract idea. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 3, 10 and 15 are rejected under 35 U.S.C. 101.
Claim 4, 11 and 16 recite “identifying the first identification utterance is that the first identification utterance is one in which a speaker of the utterance is the first speaker, and that the utterance type of the utterance of the first speaker is the first utterance type, and in a rule for identifying the second identification utterance, a condition for a combination of a speaker, and the second utterance type and the third utterance type for each utterance in order of utterances is defined for the dialogue included in the utterance data.” The limitations of claims 4, 11 and 16 of “identifying the first identification utterance is that the first identification utterance is one in which a speaker of the utterance is the first speaker…”, “the utterance type of the utterance of the first speaker is the first utterance type …”, “identifying the second identification utterance …” as drafted cover mental activity and data gathering. More specifically, for claims 4, 11 and 16 a human can collect/receive samples of utterance data and a human brain can easily identify whether the first utterance belongs to the first speaker and make also the determination that the utterance type of the utterance of the first speaker is the first utterance type and similarly a human brain can easily make identification of the utterance, identifying the second identification utterance, along with the second utterance type and the third utterance type for each utterance in order of utterances for the dialogue included in the utterance data. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 4, 11 and 16 are rejected under 35 U.S.C. 101.
Claims 5, 12 and 17 recite “the result classification model/rule is trained to classify the response result kind as presence or absence of a need, and wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and, as for the dialogue included in the utterance data, classifies the response result kind as the presence or the absence of the need.” The limitation of claims 5, 12 and 17 of “the result classification model/rule is trained to classify the response result kind as presence or absence of a need”, “classifies the response result kind as the presence or the absence of the need …” as drafted cover mental activity and data gathering. More specifically, for claims 5, 12 and 17 a human can collect/receive utterance data and a human brain is very good at classifying the response result kinds as to whether a need is present or not. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 5, 12 and 17 are rejected under 35 U.S.C. 101.
Claims 6, 13 and 19 recite “the result classification model/rule is trained to perform classification to find out a degree of the need, and wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and classifies the response result of the dialogue as the response result kind to find out the degree of the need.” The limitation of claims 6, 13 and 19 of “the result classification model/rule is trained to perform classification to find out a degree of the need …”, “classifies the response result of the dialogue as the response result kind to find out the degree of the need” as drafted cover mental activity and data gathering. More specifically, for claims 6, 13 and 19 a human can mentally receive the utterance data as input and the human brain can also perform the task of classifying the response result kind as well as to find out and classify the degree or the extent of the need. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claims 6, 13 and 19 are rejected under 35 U.S.C. 101.
Claim 20 recites “wherein the response result of the dialogue is classified based on utterance concept in the dialogue.” The limitation of claim 20 of “the response result of the dialogue is classified based on utterance concept in the dialogue” as drafted cover mental activity and data gathering. More specifically, for claim 20 a human can mentally classify the response result of the dialogue based on utterance concept in the dialogue. The claimed invention is, therefore, directed to an abstract idea, a mental process and data gathering without significantly more and thus, claim 20 are rejected under 35 U.S.C. 101.
Thus, claims 1-17 and 19-20 as drafted cover a mental process and abstract idea of data gathering/retrieval and analysis/processing steps, and they are mental processes directed to an abstract idea of implementing mathematical formulae for data processing and data analysis using a conventional/generic (general-purpose) computer as well and thus, all the claims are directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, several claims recite additional element of “classification program” as per the independent claim 8 and dependent claims 14-17 and 19, “units” recited in claims 1-3, 5-6, and “computer” particularly recited in claims 7 and 8, and “model” recited in claims 1, 3-8, 10-13 and 15-19 (as filed Spec., paragraph 00110). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer as noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional general purpose computer implementation. Claims 1-17 and 19-20, are therefore not drawn to patent eligible subject matter as they are directed to an abstract idea without significantly more. Thus, the claimed invention is directed to an abstract idea and a mental process without significantly more and thus, claims 1-17 and 19-20 are rejected under 35 U.S.C. 101.
Dependent claims 1-17 and 19-20 are also directed toward an abstract idea and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Therefore, claims 1-17 and 19-20 do not contain patent eligible subject matter that has been identified by the courts.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-11, 14-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. Pat App No. EP 1489598 B1 (Huang) in view of Fukui et al. Pat App No. US 5918222 A1 (Fukui).
Regarding Claim 1, (Original) Huang discloses a classification device comprising:
a first identification unit that receives, as input, utterance data including an utterance of a first speaker and an utterance of a second speaker in a dialogue and, using a first identification model/rule for estimating an utterance type indicating a type of each of the utterances in the dialogue, identifies the respective utterance types of the utterances included in the utterance data (Huang,12th page, 3rd para, In the case that the utterance type consists of the question form "Q", an answer sentence associated with this utterance type is constituted by an affirmative form (A). As an example of an answer sentence created by this affirmative form (A), there is a sentence answering a question ed matter or the like. For example, when an uttered sentence is "Have you operated a slot machine?", an utterance type for this utterance sentence is the question form (Q). As an example of an answer sentence associated with this question form (Q), there is a sentence "I have operated a slot machine" (the affirmative form (A)); Huang, 9th page, 2nd para, As shown in Fig. 8, the conversation database 500 associates plural to pictitles (referred to as second morpheme information), which indicate morphemes consisting of one character, plural character strings, or a combination thereof, and replay sentences to a user with each other in advance, and stores the topic titles. In addition, plural answer types indicating types of the answer sentences are associated with the answer sentences; [i.e., “the conversation database” consists of multiple speakers, i.e., first, second, etc.]);
a second identification unit that receives, as input, the utterance data and the utterance type of each of the utterances, using a second identification model/rule preset according to the utterance types, identifies a first identification utterance indicating an inquiry and a second identification utterance in response to the first identification utterance in the utterance data, and outputs pair data of utterances indicating the first identification utterance and the second identification utterance (Huang, 12th page, 4th - 6th para, On the other hand, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal. The managing unit 310, to which the answer sentence signal has been inputted from the answer acquiring unit 350, outputs the inputted answer sentence signal to the output unit 600. The output unit 600 outputs the answer sentence acquired by the answer acquiring unit 350. As an example of this output unit 600, there is a speaker, a display, or the like. More specifically, the output unit 600, to which the answer sentence has been inputted from the managing unit 310, outputs an answer sentence, for example, "I like Sato, too" with speech on the basis of the inputted answer sentence; Huang, 13th page, 4th para, Subsequently, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350).
Huang does not specifically disclose a result classification unit that receives, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifies the response result of the dialogue included in the utterance data as the response result kind.
However, Fukui, in the same field of endeavor discloses a result classification unit that receives, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifies the response result of the dialogue included in the utterance data as the response result kind (Fukui, col 80, ln 61 – col 81, ln 7, When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which 'the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Fukui in the method of Huang because this would enable a speech medium input/output type, or speech recognition/understanding and speech synthesis that can be utilized to impart intention information and emotion information in understanding of the user's intention and emotions or speech synthesis in addition to the linguistic understanding of the contents of a speech input from a user (Fukui, col 53, ln 64 – col 54, ln 5).
Regarding Claim 2, Huang in view of Fukui disclose the classification device according to claim 1, wherein the second identification unit identifies the first identification utterance as an inquiry utterance according to the utterance type of the utterance of the first speaker, and identifies the second identification utterance according to the utterance type of the utterance of the second speaker after the identified first identification utterance (Huang, 12th page, 4th – 5th para, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal; Huang, 9th page, 2nd para, As shown in Fig. 8, the conversation database 500 associates plural to pictitles (referred to as second morpheme information), … plural answer types indicating types of the answer sentences are associated with the answer sentences; Huang, 16th page, 1st para, The answer acquiring unit 350 extracts answer sentence candidates corresponding to the received topic title from the conversation database 500, selects an answer sentence, which matches an input type received from the input type judging unit 440, in the extracted answer sentence candidates, and outputs the answer sentence to the managing unit 310; [i.e., example, “the answer sentence to the managing unit 310” as the response/reply/response to the first speaker or as “the second speaker”]).
Regarding Claim 3, Huang in view of Fukui disclose the classification device according to claim 1,
wherein a model in the first identification model/rule is trained to output, as the utterance type, an estimation result of a first utterance type indicating need hearing, a second utterance type indicating a question, or a third utterance type indicating an explanation or an answer (Huang, 9th page, 11th para – 10th page, 1st para, As shown in Fig. 11, in this embodiment, answer sentences are classified into types (answer types) such as declaration (D), time (T), location (L), and negation (N) in order to make an answer corresponding to a type of an uttered sentence of a user), and
wherein the first identification unit inputs the utterance data into the first identification model/rule, and based on output of the estimation result by the first identification model/rule, identifies whether each of the utterances belongs to the first utterance type, the second utterance type, or the third utterance type (Huang, 11th page, 9th para – 12th page, 2nd para, On the basis of the topic title searched by the topic searching unit 340, the answer acquiring unit 350 acquires an answer sentence associated with the topic title. In addition, on the basis of the topic title searched by the topic searching unit 340, the replay acquiring unit 350 collates answer types associated with the topic title with the utterance type judged by the input type judging unit 440. The answer acquiring unit 350, which has collated the answer types with the utterance type, searches an answer type, which matches the judged utterance type, out of the answer types. As shown in Fig. 12, for example, if the topic title searched by the topic searching unit 340 is the topic title 1-1 (Sato; *; like), the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA). "A" in "DA", "TA, and the like means an affirmative form. Therefore, when "A" is included in an utterance type and an answer type, this means that a certain matter is affirmed. In addition, since types such as "DQ" and "TQ" can also be included in the utterance type and the answer type. "Q" in "DQ", "TQ", and the like means a question about a certain matter).
Regarding Claim 4, Huang in view of Fukui disclose the classification device according to claim 3,
wherein, in a rule in the second identification model/rule (Huang, 11th page, 7th para, since the topic specifying information "Sato" is included in the inputted first morpheme information "Sato, like", the topic searching unit 340 collates topic titles 1-1, 1-2, ... associated with the topic specifying information "Sato" with the inputted first morpheme information "Sato, like". This topic searching unit 340 searches a topic title 1-1 (Sato; *; likes), which matches the inputted first morpheme information "Sato, like", out of the topic titles 1-1, 1-2, ... on the basis of a result of the collation. The topic searching unit 340 outputs the searched topic title 1-1 (Sato; *; like) to the answer acquiring unit 350 as a search result signal),
a rule for identifying the first identification utterance is that the first identification utterance is one in which a speaker of the utterance is the first speaker, and that the utterance type of the utterance of the first speaker is the first utterance type (Huang, 11th page, 9th para - 12th page, 1st para, the replay acquiring unit 350 collates answer types associated with the topic title with the utterance type judged by the input type judging unit 440. The answer acquiring unit 350, which has collated the answer types with the utterance type, searches an answer type, which matches the judged utterance type, out of the answer types…the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA); [i.e., “the topic title with the utterance type judged by the input type judging unit 440” as the input/”first speaker”]),
, and
in a rule for identifying the second identification utterance, a condition for a combination of a speaker, and the second utterance type and the third utterance type for each utterance in order of utterances is defined for the dialogue included in the utterance data (Huang, 13th page, 4th para, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350; [i.e., “the utterance type database 450… The input type judging unit 440… judges to which "type of an uttered sentence"” as “a speaker/speech type, and the (input) second utterance type and the (output) third utterance type ]).
Regarding Claim 7, Huang disclose a classification method for causing a computer to execute processing of:
receiving, as input, utterance data including an utterance of a first speaker and an utterance of a second speaker in a dialogue and, using a first identification model/rule for estimating an utterance type indicating a type of each of the utterances in the dialogue, identifying the respective utterance types of the utterances included in the utterance data (Huang,12th page, 3rd para, In the case that the utterance type consists of the question form "Q", an answer sentence associated with this utterance type is constituted by an affirmative form (A). As an example of an answer sentence created by this affirmative form (A), there is a sentence answering a question ed matter or the like. For example, when an uttered sentence is "Have you operated a slot machine?", an utterance type for this utterance sentence is the question form (Q). As an example of an answer sentence associated with this question form (Q), there is a sentence "I have operated a slot machine" (the affirmative form (A)); Huang, 9th page, 2nd para, As shown in Fig. 8, the conversation database 500 associates plural to pictitles (referred to as second morpheme information), which indicate morphemes consisting of one character, plural character strings, or a combination thereof, and replay sentences to a user with each other in advance, and stores the topic titles. In addition, plural answer types indicating types of the answer sentences are associated with the answer sentences; [i.e., “the conversation database” consists of multiple speakers, i.e., first, second, etc.]);
receiving, as input, the utterance data and the utterance type of each of the utterances, using a second identification model/rule preset according to the utterance types, identifying a first identification utterance indicating an inquiry and a second identification utterance in response to the first identification utterance in the utterance data, and outputting pair data of utterances indicating the first identification utterance and the second identification utterance (Huang, 12th page, 4th - 6th para, On the other hand, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal. The managing unit 310, to which the answer sentence signal has been inputted from the answer acquiring unit 350, outputs the inputted answer sentence signal to the output unit 600. The output unit 600 outputs the answer sentence acquired by the answer acquiring unit 350. As an example of this output unit 600, there is a speaker, a display, or the like. More specifically, the output unit 600, to which the answer sentence has been inputted from the managing unit 310, outputs an answer sentence, for example, "I like Sato, too" with speech on the basis of the inputted answer sentence; Huang, 13th page, 4th para, Subsequently, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350).
Huang does not specifically disclose receiving, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifying the response result of the dialogue included in the utterance data as the response result kind.
However, Fukui, in the same field of endeavor disclose receiving, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifying the response result of the dialogue included in the utterance data as the response result kind (Fukui, col 80, ln 61 – col 81, ln 7, When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which 'the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Fukui in the method of Huang because this would enable a speech medium input/output type, or speech recognition/understanding and speech synthesis that can be utilized to impart intention information and emotion information in understanding of the user's intention and emotions or speech synthesis in addition to the linguistic understanding of the contents of a speech input from a user (Fukui, col 53, ln 64 – col 54, ln 5).
Regarding Claim 8, Huang discloses a classification program for causing a computer to execute processing of:
receiving, as input, utterance data including an utterance of a first speaker and an utterance of a second speaker in a dialogue and, using a first identification model/rule for estimating an utterance type indicating a type of each of the utterances in the dialogue, identifying the respective utterance types of the utterances included in the utterance data (Huang,12th page, 3rd para, In the case that the utterance type consists of the question form "Q", an answer sentence associated with this utterance type is constituted by an affirmative form (A). As an example of an answer sentence created by this affirmative form (A), there is a sentence answering a question ed matter or the like. For example, when an uttered sentence is "Have you operated a slot machine?", an utterance type for this utterance sentence is the question form (Q). As an example of an answer sentence associated with this question form (Q), there is a sentence "I have operated a slot machine" (the affirmative form (A)); Huang, 9th page, 2nd para, As shown in Fig. 8, the conversation database 500 associates plural to pictitles (referred to as second morpheme information), which indicate morphemes consisting of one character, plural character strings, or a combination thereof, and replay sentences to a user with each other in advance, and stores the topic titles. In addition, plural answer types indicating types of the answer sentences are associated with the answer sentences; [i.e., “the conversation database” consists of multiple speakers, i.e., first, second, etc.]).
receiving, as input, the utterance data and the utterance type of each of the utterances, using a second identification model/rule preset according to the utterance types, identifying a first identification utterance indicating an inquiry and a second identification utterance in response to the first identification utterance in the utterance data, and outputting pair data of utterances indicating the first identification utterance and the second identification utterance (Huang, 12th page, 4th - 6th para, On the other hand, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal. The managing unit 310, to which the answer sentence signal has been inputted from the answer acquiring unit 350, outputs the inputted answer sentence signal to the output unit 600. The output unit 600 outputs the answer sentence acquired by the answer acquiring unit 350. As an example of this output unit 600, there is a speaker, a display, or the like. More specifically, the output unit 600, to which the answer sentence has been inputted from the managing unit 310, outputs an answer sentence, for example, "I like Sato, too" with speech on the basis of the inputted answer sentence; Huang, 13th page, 4th para, Subsequently, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350); and
Huang does not specifically disclose receiving, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifying the response result of the dialogue included in the utterance data as the response result kind.
However, Fukui, in the same field of endeavor discloses receiving, as input, the output pair data of utterances, and, using a result classification model/rule for classifying a response result of the dialogue as a response result kind, classifying the response result of the dialogue included in the utterance data as the response result kind (Fukui, col 80, ln 61 – col 81, ln 7, When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which 'the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Fukui in the method of Huang because this would enable a speech medium input/output type, or speech recognition/understanding and speech synthesis that can be utilized to impart intention information and emotion information in understanding of the user's intention and emotions or speech synthesis in addition to the linguistic understanding of the contents of a speech input from a user (Fukui, col 53, ln 64 – col 54, ln 5).
Regarding Claim 9, Huang in view of Fukui disclose the classification method according to claim 7, wherein the second identification unit identifies the first identification utterance as an inquiry utterance according to the utterance type of the utterance of the first speaker, and identifies the second identification utterance according to the utterance type of the utterance of the second speaker after the identified first identification utterance (Huang, 12th page, 4th – 5th para, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal; Huang, 16th page, 1st para, The answer acquiring unit 350 extracts answer sentence candidates corresponding to the received topic title from the conversation database 500, selects an answer sentence, which matches an input type received from the input type judging unit 440, in the extracted answer sentence candidates, and outputs the answer sentence to the managing unit 310; [i.e., example, “the answer sentence to the managing unit 310” as the response/reply/response to the first speaker or as “the second speaker”] ).
Regarding Claim 10, Huang in view of Fukui disclose the classification method according to claim 7,
wherein a model in the first identification model/rule is trained to output, as the utterance type, an estimation result of a first utterance type indicating need hearing, a second utterance type indicating a question, or a third utterance type indicating an explanation or an answer (Huang, 9th page, 11th para – 10th page, 1st para, As shown in Fig. 11, in this embodiment, answer sentences are classified into types (answer types) such as declaration (D), time (T), location (L), and negation (N) in order to make an answer corresponding to a type of an uttered sentence of a user), and
wherein the first identification unit inputs the utterance data into the first identification model/rule, and based on output of the estimation result by the first identification model/rule, identifies whether each of the utterances belongs to the first utterance type, the second utterance type, or the third utterance type (Huang, 11th page, 9th para – 12th page, 2nd para, On the basis of the topic title searched by the topic searching unit 340, the answer acquiring unit 350 acquires an answer sentence associated with the topic title. In addition, on the basis of the topic title searched by the topic searching unit 340, the replay acquiring unit 350 collates answer types associated with the topic title with the utterance type judged by the input type judging unit 440. The answer acquiring unit 350, which has collated the answer types with the utterance type, searches an answer type, which matches the judged utterance type, out of the answer types. As shown in Fig. 12, for example, if the topic title searched by the topic searching unit 340 is the topic title 1-1 (Sato; *; like), the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA). "A" in "DA", "TA, and the like means an affirmative form. Therefore, when "A" is included in an utterance type and an answer type, this means that a certain matter is affirmed. In addition, since types such as "DQ" and "TQ" can also be included in the utterance type and the answer type. "Q" in "DQ", "TQ", and the like means a question about a certain matter).
Regarding Claim 11, Huang in view of Fukui disclose the classification method according to claim 7,
wherein, in a rule in the second identification model/rule (Huang, 11th page, 7th para, since the topic specifying information "Sato" is included in the inputted first morpheme information "Sato, like", the topic searching unit 340 collates topic titles 1-1, 1-2, ... associated with the topic specifying information "Sato" with the inputted first morpheme information "Sato, like". This topic searching unit 340 searches a topic title 1-1 (Sato; *; likes), which matches the inputted first morpheme information "Sato, like", out of the topic titles 1-1, 1-2, ... on the basis of a result of the collation. The topic searching unit 340 outputs the searched topic title 1-1 (Sato; *; like) to the answer acquiring unit 350 as a search result signal),
a rule for identifying the first identification utterance is that the first identification utterance is one in which a speaker of the utterance is the first speaker, and that the utterance type of the utterance of the first speaker is the first utterance type (Huang, 11th page, 10th para - 12th page, 1st para, the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA); [i.e., “the topic title with the utterance type judged by the input type judging unit 440” as the input/”first speaker”]), and
in a rule for identifying the second identification utterance, a condition for a combination of a speaker, and the second utterance type and the third utterance type for each utterance in order of utterances is defined for the dialogue included in the utterance data (Huang, 13th page, 4th para, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350; [i.e., “the utterance type database 450… The input type judging unit 440… judges to which "type of an uttered sentence"” as “a speaker/speech type, and the (input) second utterance type and the (output) third utterance type]).
Regarding Claim 14, Huang in view of Fukui disclose the classification program according to claim 8, wherein the second identification unit identifies the first identification utterance as an inquiry utterance according to the utterance type of the utterance of the first speaker, and identifies the second identification utterance according to the utterance type of the utterance of the second speaker after the identified first identification utterance (Huang, 12th page, 4th – 5th para, in the case that an utterance type consists of the affirmative form (A), an answer sentence associated with this utterance type is constituted by the question form (Q). As an example of an answer sentence created in this question form (Q), there is a question sentence asking about an uttered content again, a question sentence finding out a specific matter, or the like. For example, when an uttered sentence is "It is my hobby to play a slot machine", an utterance type for this uttered sentence is the affirmative form (A). An example of an answer sentence associated with this affirmative form (A) is, for example, there is a sentence "Isn't it your hobby to play pachinko?" (the question sentence "Q" finding out a specific matter). The answer acquiring unit 350 outputs the acquired answer sentence to the managing unit 310 as an answer sentence signal; Huang, 9th page, 2nd para, As shown in Fig. 8, the conversation database 500 associates plural to pictitles (referred to as second morpheme information), … plural answer types indicating types of the answer sentences are associated with the answer sentences; Huang, 16th page, 1st para, The answer acquiring unit 350 extracts answer sentence candidates corresponding to the received topic title from the conversation database 500, selects an answer sentence, which matches an input type received from the input type judging unit 440, in the extracted answer sentence candidates, and outputs the answer sentence to the managing unit 310; [i.e., example, “the answer sentence to the managing unit 310” as the response/reply/response to the first speaker or as “the second speaker”]).
Regarding Claim 15, Huang in view of Fukui disclose the classification program according to claim 8,
wherein a model in the first identification model/rule is trained to output, as the utterance type, an estimation result of a first utterance type indicating need hearing, a second utterance type indicating a question, or a third utterance type indicating an explanation or an answer (Huang, 9th page, 11th para – 10th page, 1st para, As shown in Fig. 11, in this embodiment, answer sentences are classified into types (answer types) such as declaration (D), time (T), location (L), and negation (N) in order to make an answer corresponding to a type of an uttered sentence of a user), and
wherein the first identification unit inputs the utterance data into the first identification model/rule, and based on output of the estimation result by the first identification model/rule, identifies whether each of the utterances belongs to the first utterance type, the second utterance type, or the third utterance type (Huang, 11th page, 9th para – 12th page, 2nd para, On the basis of the topic title searched by the topic searching unit 340, the answer acquiring unit 350 acquires an answer sentence associated with the topic title. In addition, on the basis of the topic title searched by the topic searching unit 340, the replay acquiring unit 350 collates answer types associated with the topic title with the utterance type judged by the input type judging unit 440. The answer acquiring unit 350, which has collated the answer types with the utterance type, searches an answer type, which matches the judged utterance type, out of the answer types. As shown in Fig. 12, for example, if the topic title searched by the topic searching unit 340 is the topic title 1-1 (Sato; *; like), the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA). "A" in "DA", "TA, and the like means an affirmative form. Therefore, when "A" is included in an utterance type and an answer type, this means that a certain matter is affirmed. In addition, since types such as "DQ" and "TQ" can also be included in the utterance type and the answer type. "Q" in "DQ", "TQ", and the like means a question about a certain matter).
Regarding Claim 16, Huang in view of Fukui disclose the classification program according to claim 8,
wherein, in a rule in the second identification model/rule (Huang, 11th page, 7th para, since the topic specifying information "Sato" is included in the inputted first morpheme information "Sato, like", the topic searching unit 340 collates topic titles 1-1, 1-2, ... associated with the topic specifying information "Sato" with the inputted first morpheme information "Sato, like". This topic searching unit 340 searches a topic title 1-1 (Sato; *; likes), which matches the inputted first morpheme information "Sato, like", out of the topic titles 1-1, 1-2, ... on the basis of a result of the collation. The topic searching unit 340 outputs the searched topic title 1-1 (Sato; *; like) to the answer acquiring unit 350 as a search result signal),
a rule for identifying the first identification utterance is that the first identification utterance is one in which a speaker of the utterance is the first speaker, and that the utterance type of the utterance of the first speaker is the first utterance type (Huang, 11th page, 10th para - 12th page, 1st para, the answer acquiring unit 350 specifies an answer type (DA), which matches the "type of an uttered sentence" (e.g., DA) judged by the input type judging unit 440, out of the answer sentences 1-1 (DA, TA, etc.) associated with the topic title 1-1. On the basis of the specified replay type (DA), the answer acquiring unit 350, which has specified this answer type (DA), acquires the answer sentence 1-1 ("I like Sato, too") associated with the replay type (DA) on the basis of the specified answer type (DA); [i.e., “the topic title with the utterance type judged by the input type judging unit 440” as the input/”first speaker”]), and
in a rule for identifying the second identification utterance, a condition for a combination of a speaker, and the second utterance type and the third utterance type for each utterance in order of utterances is defined for the dialogue included in the utterance data (Huang, 13th page, 4th para, the input type judging unit 440 performs a step of judging a "type of an uttered sentence" on the basis of the morphemes constituting the sentence specified by the character string specifying unit 410 (step S1305). More specifically, the input type judging unit 440, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string and the dictionaries stored in the utterance type database 450 on the basis of the character string, and extracts an element relating to the dictionaries. The input type judging unit 440, which has extracted this element, judges to which "type of an uttered sentence" the extracted element belongs on the basis of the element. The input type judging unit 440 outputs the judged "type of an uttered sentence" (utterance type) to the answer acquiring unit 350; [i.e., “the utterance type database 450… The input type judging unit 440… judges to which "type of an uttered sentence"” as “a speaker/speech type, and the (input) second utterance type and the (output) third utterance type]).
Regarding Claim 20, Huang in view of Fukui disclose the classification device according to claim 1, wherein the response result of the dialogue is classified based on utterance concept in the dialogue (Huang, 8th page, 2nd para, This morpheme extracting unit 420 outputs the extracted morphemes to a topic specifying information searching unit 320 as first morpheme information. Note that the first morpheme information is not required to be structured. Here, "structuring" means classifying and arranging morphemes included in a character string on the basis of a part of speech or the like, for example, converting a character string, which is an uttered sentence, into data formed by arranging morphemes in a predetermined order such as "subject + object + predicative"; Huang, 13th page, 2nd para, Thereafter, the morpheme extracting unit 420 performs a step of, on the basis of the character string specified by the character string specifying unit 410, extracting morphemes constituting a minimum unit of the character string as first morpheme information (step S1304). More specifically, the morpheme extracting unit 420, to which the character string has been inputted from the character string specifying unit 410, collates the inputted character string with the morpheme group that is stored in the morpheme database 430 in advance. Note that, in this embodiment, the morpheme group is prepared as a morpheme dictionary in which, for each morpheme belonging to respective classifications of parts of speech, an entry word, a reading, a part of speech, a conjugated form, and the like for the morpheme are described).
Claims 5, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Fukui, and further in view of Watson et al. Pat App No. US 20140278388 A1 (Watson).
Regarding Claim 5, Huang in view of Fukui disclose the classification device according to claim 1
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and, as for the dialogue included in the utterance data, classifies the response result kind as the presence or the absence of the need (Fukui, col 80, ln 67 – col 81, ln 21, the progress of the pair of user's and agent's utterances is classified into an input state in which the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values; Fukui, col 39, ln 24-30, The text input by the user is classified, e.g, as an intention shown in FIG. 49. The input text is classified into "12 request" in accordance with the conjugation of a verb "tell". When the intention of the utterance is "desire", "request", or "proposal", a user demand is contained, so that the agent shifts to a demand understanding state in FIG. 47 and performs the following analysis).
Huang in view of Fukui do not specifically disclose wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need.
However, Watson, in the same field of endeavor discloses wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need (Watson, 0070-0071, a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Watson in the method of Huang in view of Fukui because this would enable the classifier to be trained based on training data and the metrics of the measurements would conform to the training data that may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof (Watson, para 0066).
Regarding Claim 12, Huang in view of Fukui disclose the classification method according to claim 7,
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and, as for the dialogue included in the utterance data, classifies the response result kind as the presence or the absence of the need (Fukui, col 80, ln 67 – col 81, ln 21, the progress of the pair of user's and agent's utterances is classified into an input state in which the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values; Fukui, col 39, ln 24-30, The text input by the user is classified, e.g, as an intention shown in FIG. 49. The input text is classified into "12 request" in accordance with the conjugation of a verb "tell". When the intention of the utterance is "desire", "request", or "proposal", a user demand is contained, so that the agent shifts to a demand understanding state in FIG. 47 and performs the following analysis).
Huang in view of Fukui do not specifically disclose wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need.
However, Watson, in the same field of endeavor discloses wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need (Watson, 0070-0071, a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Watson in the method of Huang in view of Fukui because this would enable the classifier to be trained based on training data and the metrics of the measurements would conform to the training data that may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof (Watson, para 0066).
Regarding Claim 17, Huang in view of Fukui disclose the classification program according to claim 8,
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and, as for the dialogue included in the utterance data, classifies the response result kind as the presence or the absence of the need (Fukui, col 80, ln 67 – col 81, ln 21, the progress of the pair of user's and agent's utterances is classified into an input state in which the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values; Fukui, col 39, ln 24-30, The text input by the user is classified, e.g, as an intention shown in FIG. 49. The input text is classified into "12 request" in accordance with the conjugation of a verb "tell". When the intention of the utterance is "desire", "request", or "proposal", a user demand is contained, so that the agent shifts to a demand understanding state in FIG. 47 and performs the following analysis).
Huang in view of Fukui do not specifically disclose wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need.
However, Watson, in the same field of endeavor discloses wherein a model in the result classification model/rule is trained to classify the response result kind as presence or absence of a need (Watson, 0070-0071, a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Watson in the method of Huang in view of Fukui because this would enable the classifier to be trained based on training data and the metrics of the measurements would conform to the training data that may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof (Watson, para 0066).
Claims 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Fukui, further in view of Watson, and further in view of Imoto et al. Pat App No. WO 2014156388 A1 (Imoto).
Regarding Claim 6, Huang in view of Fukui and Watson disclose the classification device according to claim 5.
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and classifies the response result of the dialogue as the response result kind to find out the degree of the need (Fukui, col 79, ln 38 – col 80, ln 21, Of all the emotional words having non-linguistic information such as "ah . . . ah" or "eh?" uttered by the user in the interactive operation with the agent, frequently used emotional words are formed into a dictionary such that the degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] are registered… When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values).
Furthermore, Watson teaches:
the model in the result classification model/rule is trained to perform classification (Watson, 0066-0071, the classifier may be trained based on training data. Metrics may be measurements that conform to the training data, and may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof…a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Huang in view of Fukui and Watson do not specifically disclose wherein the model in the result classification model/rule is trained to perform classification to find out a degree of the need.
However, Imoto, in the same field of endeavor, discloses wherein the model in the result classification model/rule is trained to perform classification to find out a degree of the need (Imoto, 8th page, 3rd para, For example, in the example shown in FIG. 3, the degree of necessity is determined in three stages, but the number of stages for classifying the degree of necessity is not limited to three, and may be smaller or larger. Also, for example, for each external information shown in FIG. 3 ("person approach", "person utterance", "specific gesture", "communication between devices", "with knowledge", "without knowledge") The score indicating the degree of necessity may be attached, and the degree-of-necessity determination unit 141 may calculate the degree of necessity by adding the scores attached to the plurality of detected external information ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Imoto in the method of Huang in view of Fukui and Watson because this would enable, for example, the necessity determination unit 141 determine the importance of the work that the user is performing based on the content information notified to the user: if the importance of the work is high, the user's work is prevented as much as possible and therefore, the necessity of notifying external information can be determined to be relatively low, and when the importance of work is low, the necessity to notify external information can be determined to be relatively high (Imoto, 11th page, 4th para).
Regarding Claim 13, Huang in view of Fukui and Watson disclose the classification method according to claim 7.
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and classifies the response result of the dialogue as the response result kind to find out the degree of the need (Fukui, col 79, ln 38 – col 80, ln 21, Of all the emotional words having non-linguistic information such as "ah . . . ah" or "eh?" uttered by the user in the interactive operation with the agent, frequently used emotional words are formed into a dictionary such that the degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] are registered… When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values).
Furthermore, Watson teaches:
the model in the result classification model/rule is trained to perform classification (Watson, 0066-0071, the classifier may be trained based on training data. Metrics may be measurements that conform to the training data, and may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof…a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Huang in view of Fukui and Watson do not specifically disclose wherein the model in the result classification model/rule is trained to perform classification to find out a degree of the need.
However, Imoto, in the same field of endeavor, discloses wherein the model in the result classification model/rule is to perform classification to find out a degree of the need (Imoto, 8th page, 3rd para, For example, in the example shown in FIG. 3, the degree of necessity is determined in three stages, but the number of stages for classifying the degree of necessity is not limited to three, and may be smaller or larger. Also, for example, for each external information shown in FIG. 3 ("person approach", "person utterance", "specific gesture", "communication between devices", "with knowledge", "without knowledge") The score indicating the degree of necessity may be attached, and the degree-of-necessity determination unit 141 may calculate the degree of necessity by adding the scores attached to the plurality of detected external information).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Imoto in the method of Huang in view of Fukui and Watson because this would enable, for example, the necessity determination unit 141 determine the importance of the work that the user is performing based on the content information notified to the user: if the importance of the work is high, the user's work is prevented as much as possible and therefore, the necessity of notifying external information can be determined to be relatively low, and when the importance of work is low, the necessity to notify external information can be determined to be relatively high (Imoto, 11th page, 4th para).
Regarding Claim 19, Huang in view of Fukui and Watson disclose the classification program according to claim 8.
Furthermore, Fukui teaches:
wherein the result classification unit inputs the pair data of utterances into the result classification model/rule, and classifies the response result of the dialogue as the response result kind to find out the degree of the need (Fukui, col 79, ln 38 – col 80, ln 21, Of all the emotional words having non-linguistic information such as "ah . . . ah" or "eh?" uttered by the user in the interactive operation with the agent, frequently used emotional words are formed into a dictionary such that the degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] are registered… When the emotions of a user are analyzed using a pair of user's and agent's utterances, a reflex emotion results from the display contents of the agent during the response sentence display. A certain emotion may often be caused by the manipulation sequence of the user himself/herself at the time of data input, or a certain impression may often result from the entire conversation. For this reason, the progress of the pair of user's and agent's utterances is classified into an input state in which'the user inputs a text sentence and waits for a response from the agent and a display state in which a response sentence from the agent is being displayed. A specific emotional word uttered by the user in one of the input and display states is analyzed to estimate the user emotion in the pair of user's and agent's utterances. The degrees of [composure-restlessness], [satisfaction-dissatisfaction], and [acceptance-rejection] of emotional words uttered in the input and display states are totalized to obtain an average value. The emotions shown in FIG. 161 are set in accordance with combinations of the values of the degrees obtained by subtracting the average value of the emotional words in the input state from the average value of the emotional words in the display state, and the respective emotions are assigned with numerical values such as [expectation: +1, anxiety: -1, composure: +2, consent: +2, restlessness: -2, confusion: -2, gratitude: +3, anger: -3]. Therefore, the emotions of the user in the pair of user's and agent's utterances can be estimated and expressed in numerical values).
Furthermore, Watson teaches:
the model in the result classification model/rule is trained to perform classification (Watson, 0066-0071, the classifier may be trained based on training data. Metrics may be measurements that conform to the training data, and may be based on patient sounds, patterns of patient sounds, speech commands, patterns of speech commands, any other suitable measurement related to the patient sound signal, or any combination thereof…a classifier output indicative of a patient need for medication may identify any suitable information relating to medication needs, in some embodiments, the classifier output …a classifier output indicative of a need for attention from a medical professional may identify any suitable information).
Huang in view of Fukui and Watson do not specifically disclose wherein the model in the result classification model/rule is trained to perform classification to find out a degree of the need.
However, Imoto, in the same field of endeavor, discloses wherein the model in the result classification model/rule is to perform classification to find out a degree of the need (Imoto, 8th page, 3rd para, For example, in the example shown in FIG. 3, the degree of necessity is determined in three stages, but the number of stages for classifying the degree of necessity is not limited to three, and may be smaller or larger. Also, for example, for each external information shown in FIG. 3 ("person approach", "person utterance", "specific gesture", "communication between devices", "with knowledge", "without knowledge") The score indicating the degree of necessity may be attached, and the degree-of-necessity determination unit 141 may calculate the degree of necessity by adding the scores attached to the plurality of detected external information).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Imoto in the method of Huang in view of Fukui and Watson because this would enable, for example, the necessity determination unit 141 determine the importance of the work that the user is performing based on the content information notified to the user: if the importance of the work is high, the user's work is prevented as much as possible and therefore, the necessity of notifying external information can be determined to be relatively low, and when the importance of work is low, the necessity to notify external information can be determined to be relatively high (Imoto, 11th page, 4th para).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUGETA T. DUGDA whose telephone number is (703)756-1106. The examiner can normally be reached Mon - Fri, 4:30am - 7:00pm.
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/MULUGETA TUJI DUGDA/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
02/20/2026