DETAILED ACTION
This office action is in response to submission of application on 04/19/2024.
Claims 1-20 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 12 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.
Claims 5 and 12 each recite the limitation "the conversational action classifier." There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the limitation will be interpreted as referring to the machine learning-based intent classifier introduced in the respective parent claims 1 and 8.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1: The claim is directed to a method, which falls within the statutory category of a process.
Step 2A Prong 1: The claim is directed to an abstract idea. Specifically, the claim recites:
assigning one or more labels to one or more utterances comprising the one or more conversation sequences (Abstract idea – mental process. Assigning labels to conversational utterances can practically be performed in the human mind or with the aid of pen and paper, for example, by viewing the utterances on a sheet of paper, mentally determining the type of each utterance, and labeling each utterance by hand with its associated type. The courts have recognized that claims can recite a mental process even if they are claimed as being performed on a computer. See MPEP 2106.04(a)(2)(III).)
determining if a confidence score for at least one of the one or more assigned labels is below a predetermined threshold; (Abstract idea – mental process. Determining if a confidence score is below a threshold can practically be performed in the human mind or with the aid of pen and paper, for example, by mentally comparing the confidence score to the threshold. See MPEP 2106.04(a)(2)(III).)
determining at least one variant operation of a plurality of variant operations to perform on the plurality of labeled conversation content using a natural conversation variator; and (Abstract idea – mental process. Determining a variant operation to perform on labeled conversation content can practically be performed in the human mind or with the aid of pen and paper, for example, by viewing the labeled conversation content on a sheet of paper and mentally determining variations that could be made to the conversation content without changing its meaning (e.g. paraphrasing). See MPEP 2106.04(a)(2)(III).)
performing the at least one operation of the plurality of variant operations on the plurality of labeled conversation content using the natural conversation variator to generate one or more variations of the plurality of labeled conversation content. (Abstract idea – mental process. Performing the variant operation on the labeled conversation content to generate variations can practically be performed in the human mind or with the aid of pen and paper, for example, by viewing the labeled conversation content on a sheet of paper and modifying it by hand in a manner that preserves its meaning. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2: The additional elements recited in the claim do not integrate the abstract idea into a practical application, individually or in combination. Specifically, the claim recites the additional elements:
A computer-implemented method for generating natural conversation content for training conversational artificial intelligence (AI) systems (Implementation of the method in a generic computing environment amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
receiving conversation content comprising one or more conversation sequences; (Receiving conversation sequence data amounts to adding insignificant extra-solution activity (necessary data gathering) to the judicial exception – see MPEP2106.05(g).)
using a machine learning-based intent classifier to produce a plurality of labeled conversation content; (Using a generic machine learning-based classifier to produce labeled data is standard in the field of machine learning, and thus amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claim recites the additional elements:
A computer-implemented method for generating natural conversation content for training conversational artificial intelligence (AI) systems (Implementation of the method in a generic computing environment amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
receiving conversation content comprising one or more conversation sequences; (Receiving conversation sequence data amounts to adding insignificant extra-solution activity (necessary data gathering) to the judicial exception – see MPEP2106.05(g). Further, the limitation is directed to receiving or transmitting data over a network, which the courts have found to be well-understood, routine, and conventional in the computer arts – see MPEP 2106.05(d).)
using a machine learning-based intent classifier to produce a plurality of labeled conversation content; (Using a generic machine learning-based classifier to produce labeled data is standard in the field of machine learning, and thus amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).)
Claims 2-20:
Claim 2 recites The method of claim 1, further comprising: responsive to determining that a confidence score for at least one of the one or more assigned labels is below the predetermined threshold value, applying one or more predefined rules to the one or more assigned labels with low confidence scores using a rule-based classifier. Applying predefined rules to low-confidence labels can practically be performed in the human mind or with the aid of pen and paper (i.e. mental process), for example, by viewing the utterances associated with the low-confidence labels on a sheet of paper and mentally applying “IF-THEN” rules to determine new classification labels. Therefore, the claim merges with the abstract idea recited in claim 1, and does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 3 recites The method of claim 1, wherein the assigning of the one or more labels to the one or more utterances comprising the one or more conversation sequences using the machine learning-based intent classifier is based on an utterance's respective generic conversational function. Assigning labels to conversational utterances based on generic conversational function can practically be performed in the human mind or with the aid of pen and paper (i.e. mental process), for example, by viewing the utterances within their conversational sequences on a sheet of paper and mentally determining their function within the conversation. Therefore, the claim merges with the abstract idea recited in claim 1, and does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 4 recites The method of claim 1, wherein the determining of the at least one variant operation of the plurality of variant operations to perform on the plurality of labeled conversation content is based on one or more conversation patterns in the plurality of labeled conversation content. Determining the variant operation based on conversation patterns in the labeled conversation content can practically be performed in the human mind or with the aid of pen and paper (i.e. mental process), for example, by viewing the labeled conversation content on a sheet of paper, mentally identifying conversation patterns in the data, and mentally selecting an appropriate variant operation. Therefore, the claim merges with the abstract idea recited in claim 1, and does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 5 recites The method of claim 1, wherein the conversational action classifier and the natural conversation variator are grounded in natural conversation framework. Performing conversational action classification and natural conversation variation based on natural conversation framework can practically be performed in the human mind or with the aid of pen and paper (i.e. mental process), for example, by mentally considering NCF’s sequence patterns when classifying and variating conversational utterances. Therefore, the claim merges with the abstract idea recited in claim 1, and does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 6 recites The method of claim 1, wherein the natural conversation variator incorporates a rule-based system including the plurality of variant operations. Variating conversational utterances using a rule-based system for variant operations can practically be performed in the human mind or with the aid of pen and paper (i.e. mental process), for example, by viewing the labeled conversational utterances on a sheet of paper, mentally identifying the presence of certain labels, and mentally applying rules that determine which variant operations can be performed based on the labels. Therefore, the claim merges with the abstract idea recited in claim 1, and does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 7 recites The method of claim 1, wherein the assigning of the one or more labels to the one or more utterances is performed using a trained machine learning model. Using a generic trained machine learning model to assign labels to data is standard in the field of machine learning, and thus amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea – see MPEP 2106.05(f). Therefore, the claim does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claims 8-14 are system claims containing substantially the same elements as method claims 1-7, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as claims 1-7, respectively, mutatis mutandis. The additional components of one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method are interpreted as a general-purpose computing environment and mere instructions to apply the judicial exception on the computer. Therefore, the claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claims 15-18 and 20 are product claims containing substantially the same elements as method claims 1-4 and 6, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as claims 1-4 and 6, respectively, mutatis mutandis. The additional components of one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor to cause the processor to perform a method are interpreted as a general-purpose computing environment and mere instructions to apply the judicial exception on the computer. Therefore, the claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim 19 recites The computer program product of claim 15, wherein the machine learning-based intent classifier and a rule-based classifier are incorporated into a conversational action classifier. Incorporating the machine learning-based classifier and a rule-based classifier into a conversational action classifier amounts to adding insignificant extra-solution activity to the judicial exception. Therefore, the claim does not recite additional elements that are sufficient to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
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, 3-4, 6-8, 10-11, 13-15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over
Raux et al. (hereinafter Raux), U.S. Patent US-10140977-B1 (filed 07/31/2018) in view of
Niu et al. (hereinafter Niu), “Automatically Learning Data Augmentation Policies for Dialogue Tasks” (published 09/27/2019).
Regarding Claim 1,
Raux teaches A computer-implemented method for generating natural conversation content for training conversational artificial intelligence (AI) systems, the method comprising: (Abstract: “Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating additional training data for a natural language understanding engine.”)
receiving conversation content comprising one or more conversation sequences; (Col. 7, line 62 – col. 8, line 1: “During operation of the system, the system obtains a conversational turn that was provided as input to the NLU engine… In particular, the conversational turn was received or generated by the system, i.e., from user speech, during a conversation between a user and the system.”)
assigning one or more labels to one or more utterances comprising the one or more conversation sequences using a machine learning-based intent classifier to produce a plurality of labeled conversation content; (Col. 5, lines 49-57: “Once the text input 112 has been generated, a natural language understanding (NLU) engine 120 converts the text input 112 into an annotation 122. Generally, the annotation 122 represents the text input 112 in a way that captures the semantics of the text input 112. The annotation 122 can represent the semantics of the text input 112 in any of a variety of ways. For example, the annotation 122 can be a frame that identifies (i) the intent of the text input…”)
determining if a confidence score for at least one of the one or more assigned labels is below a predetermined threshold; (Col. 9, lines 56-65: “The system can apply a set of one or more criteria when determining whether the annotation accurately characterized the conversational turn… For example, the set of criteria can include a criterion that is based on the confidence score generated by the NLU engine for the annotation. In particular, this criterion can be satisfied when the score is below a threshold value.”)
performing the at least one operation [of the plurality of variant operations] on the plurality of labeled conversation content using the natural conversation variator to generate one or more variations of the plurality of labeled conversation content. (Col. 8, lines 38-45: “The system obtains one or more paraphrases of the conversational turn (step 208). A paraphrase of the current conversational turn is a different conversational turn, i.e., a conversational turn that uses different words from the current conversational turn, but that should be interpreted the same way as the current conversational turn by the system in the context of the current conversation.” The system performs paraphrasing (i.e. a variant operation) on the labeled conversation content to generate one or more paraphrases (i.e. variations).)
Raux does not appear to explicitly disclose the remaining features of claim 1.
However, Niu teaches determining at least one variant operation of a plurality of variant operations to perform on the plurality of labeled conversation content using a natural conversation variator; and (Abstract: “In our work, we adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. We start with a pool of atomic operations that apply subtle semantic-preserving perturbations to the source inputs of a dialogue task…” Pg. 3, section 3, ‘Search Space’: “[O]ur pool of operations contains Random Swap, Stopword Dropout, Paraphrase, Grammar Errors, and Stammer, which cover a substantial enough search space of real-world noise in text… For generality, we do not distinguish in advance which operation alone is effective as an augmentation policy on the target dataset, but rather let the controller figure it out.” The controller (i.e. natural conversation variator) determines which semantic-preserving perturbation operation (i.e. variant operation) from a pool of operations to perform on the source inputs of a dialogue task (i.e. conversation content).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Raux and Niu. Raux teaches generating additional training data for a natural language processing engine by paraphrasing input conversational data. Niu teaches generating additional training data for natural language processing tasks by selectively applying operations from a pool of semantic-preserving perturbations to dialogue data. One of ordinary skill would have motivation to combine Raux and Niu because Niu’s approach “allow[s] the controller to learn more complex augmentation policies by searching over the space of the various combinations of these atomic operations,” and this enables the controller to “discover useful data augmentation policies, and achieve significant improvements over the previous state-of-the-art, including trained on manually-designed policies” (Niu, pg. 1, Abstract).
Regarding Claim 3, Raux and Niu teach The method of claim 1, as shown above.
Raux also teaches wherein the assigning of the one or more labels to the one or more utterances comprising the one or more conversation sequences using the machine learning-based intent classifier is based on an utterance's respective generic conversational function. (Col. 5, lines 56-67: “For example, the annotation 122 can be a frame that identifies (i) the intent of the text input 112 and (ii) values for one or more slots corresponding to the intent. Each slot corresponding to the intent is a slot whose value is required to be filled in order for the intent to be satisfied. For example, when the intent is to order a pizza from a restaurant menu, slots required to be filled include one or more slots for toppings for the pizza and a slot for the size of the pizza. As a specific example, for the conversational turn ‘I'd like a large pineapple pizza,’ the NLU engine 120 can generate an annotation 122 that selects a ‘request item’ intent from a set of intents for the current task…” Assigning the annotations (i.e. labels) to the text inputs (i.e. utterances) is based on the input’s intent, for example, to request an item (i.e. a generic conversational function).)
Regarding Claim 4, Raux and Niu teach The method of claim 1, as shown above.
Niu also teaches wherein the determining of the at least one variant operation of the plurality of variant operations to perform on the plurality of labeled conversation content is based on one or more conversation patterns in the plurality of labeled conversation content. (Pg. 2, Section 1: “Lastly, observing that some operations require the source inputs to have certain linguistic features (e.g., we cannot apply Stopword Dropout to inputs that do not contain stopwords), we also explore a controller that conditions on the source inputs of the target dataset, via a sequence-to-sequence (seq2seq) controller.” When determining which operations to apply, the controller conditions on the linguistic features (i.e. conversation patterns) of the source inputs (i.e. conversation content).)
Regarding Claim 6, Raux and Niu teach The method of claim 1, as shown above.
Niu also teaches wherein the natural conversation variator incorporates a rule-based system including the plurality of variant operations. (Pg. 2, Section 1: “Lastly, observing that some operations require the source inputs to have certain linguistic features (e.g., we cannot apply Stopword Dropout to inputs that do not contain stopwords), we also explore a controller that conditions on the source inputs of the target dataset, via a sequence-to-sequence (seq2seq) controller.” The controller (i.e. natural conversation variator) incorporates rules about when different variant operations can be applied based on linguistic features of the source inputs.)
Regarding Claim 7, Raux and Niu teach The method of claim 1, as shown above.
Raux also teaches wherein the assigning of the one or more labels to the one or more utterances is performed using a trained machine learning model. (Col. 6, lines 61-64: “As described, the NLU engine 120 generally includes one or more machine learning models, e.g., RNNs, and has been trained to generate accurate annotations by a training engine 180.”)
Claims 8, 10-11, and 13-14 are system claims containing substantially the same elements as method claims 1, 3-4, and 6-7, respectively. Raux and Niu teach the elements of claims 1, 3-4, and 6-7, as shown above.
Raux also teaches one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method (Examiner notes that this limitation is interpreted as implementation of the disclosed method in a generic computing environment. Col. 14, lines 32-45: “The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output… Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read only memory or a random access memory or both.”)
Claims 15, 17-18, and 20 are product claims containing substantially the same elements as method claims 1, 3-4, and 6, respectively. Raux and Niu teach the elements of claims 1, 3-4, and 6, as shown above.
Raux also teaches one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor to cause the processor to perform a method (Col. 13, lines 35-40: “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory storage medium for execution by, or to control the operation of, data processing apparatus.”)
Claims 2, 9, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Raux in view of Niu, and further in view of
Terry et al. (hereinafter Terry), U.S. Patent Application Publication US-20190180195-A1 (filed 12/03/2018).
Regarding Claim 2, Raux and Niu teach The method of claim 1, as shown above.
Raux and Niu do not appear to explicitly disclose the remaining features of claim 2.
However, Terry teaches further comprising: responsive to determining that a confidence score for at least one of the one or more assigned labels is below the predetermined threshold value, applying one or more predefined rules to the one or more assigned labels with low confidence scores using a rule-based classifier. (0148: “Returning to FIG. 14, after model deployment, a hard rule fallback process is performed (at 1470). This hard rule fallback process may be employed when the deployed model falls below a confidence threshold, but before a human intervention is required.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Raux, Niu, and Terry. Raux teaches generating additional training data for a natural language processing engine by paraphrasing input conversational data. Niu teaches generating additional training data for natural language processing tasks by selectively applying operations from a pool of semantic-preserving perturbations to dialogue data. Terry teaches a conversational intent classifier, including fallback to a hard rule classifier when confidence of a primary intent model falls below a threshold. One of ordinary skill would have motivation to combine Raux, Niu, and Terry because Raux teaches “determin[ing] that the annotation did not accurately characterize the conversational turn” (Raux, col. 9, lines 54-55), and according to Terry, “[i]n some cases, deficiency in the model may be adequately addressed utilizing more traditional hard rule processes, thereby enabling continued automated performance by the system” (Terry, 0148).
Claim 9 is a system claim containing substantially the same elements as method claim 2. Raux, Niu, and Terry teach the elements of claim 2, as shown above.
Claim 16 is a product claim containing substantially the same elements as method claim 2. Raux, Niu, and Terry teach the elements of claim 2, as shown above.
Regarding Claim 19, Raux and Niu teach The computer program product of claim 15, as shown above.
Raux and Niu do not appear to explicitly disclose the remaining features of claim 19.
However, Terry teaches wherein the machine learning-based intent classifier and a rule-based classifier are incorporated into a conversational action classifier. (0022: “A response is then received, and the intent of the response is determined using the intent model… The intent and entity information is then used by an action model to determine the appropriate action to be taken for the response.” 0148: “Returning to FIG. 14, after model deployment, a hard rule fallback process is performed (at 1470). This hard rule fallback process may be employed when the deployed model falls below a confidence threshold, but before a human intervention is required.” The intent model (i.e. machine learning-based intent classifier) and hard rule fallback process (i.e. rule-based classifier) are incorporated into an action model to determine the appropriate response (i.e. a conversational action classifier).)
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Raux in view of Niu, and further in view of
Moore, “A Natural Conversation Framework for Conversational UX Design” (published 09/04/2018).
Regarding Claim 5, Raux and Niu teach The method of claim 1, as shown above.
Raux and Niu do not appear to explicitly disclose the remaining features of claim 5.
However, Moore teaches wherein the conversational action classifier and the natural conversation variator are grounded in natural conversation framework. (Pg. 182, section 9.1: “In applying Conversation Analysis to user experience (UX) design, we have developed a Natural Conversation Framework (NCF) for the design of conversational user interaction and experience that is grounded in observational science… The NCF provides a library of generic conversational UX patterns…” Pg. 202, section 9.2: “[T]he patterns are abstract enough to apply to any natural language platform.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Raux, Niu, and Moore. Raux teaches generating additional training data for a natural language processing engine by paraphrasing input conversational data. Niu teaches generating additional training data for natural language processing tasks by selectively applying operations from a pool of semantic-preserving perturbations to dialogue data. Moore teaches Natural Conversation Framework, a library of generic conversational patterns that can be applied to the design of any natural language processing platform. One of ordinary skill would have motivation to combine Raux, Niu, and Moore because “[c]onversation is a distinctive form of natural-language use that involves particular methods for taking turns and ordering them into sequences, the persistence of context across turns and characteristic actions for managing the interaction itself… The mechanics of how humans take turns and sequentially organize conversations are formally studied in the social sciences, especially in the field of Conversation Analysis (CA). To leverage this literature of observational studies, we are applying the concepts and findings from CA to the design of conversational agents” (pg. 182, section 9.1).
Claim 12 is a system claim containing substantially the same elements as method claim 5. Raux, Niu, and Moore teach the elements of claim 5, as shown above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN M ROHD whose telephone number is (571)272-6445. The examiner can normally be reached Mon-Thurs 8:00-6:00 EST.
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/B.M.R./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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