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 .
Response to Arguments
Applicant’s arguments, see pages 9 - 12, filed 01/29/26, with respect to claims 1 – 5, 7 -14, 16 – 20 have been fully considered and are persuasive. The rejection of claims 1 – 5, 7 -14, 16 – 20 under 35 U.S.C 101 has been withdrawn.
Applicant respectfully submits that this feature integrates the claims into a practical application, because by virtue of the applying of the utterance to an AI model that is trained using historical utterance data that is augmented by using a keyboard perturbation technique that relates to randomly replacing characters within words with neighboring characters on a keyboard, an accuracy of a virtual voice assistant in responding to user queries is improved, and smoother conversations between users and virtual voice assistants are realized (Amendment, pages 9 – 12).
Applicant's arguments filed 01/29/26 have been fully considered but they are not persuasive.
Applicant argues that the prior art of record does not teach the AI model is trained by using historical utterance data that is augmented by using a keyboard perturbation technique that relates to randomly replacing characters within words with neighboring characters on a keyboard (Amendment, pages 12 – 15).
The examiner disagrees, since Xu et al. disclose “Language parser 214 may also tokenize the linguistic units of the utterance 202 (e.g., to convert each word into a separate token)… the training subsystem can generate duplicates or augmented copies of training data that correspond to an unresolvable class (e.g., out-of-scope utterances). Data augmentation techniques can include back-translating an utterance in the training dataset, synonym replacement of one or more tokens of the utterance, random insertion of tokens into the utterance, swapping between two tokens of the utterance, and random deletion of one or more tokens of the utterance…User interface input devices may include, for example, a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices (paragraphs 107, 204, 273).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 5, 7 -14, 16 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu et al. (US PAP 2022/0171946).
As per claims 1, 10, and 19, Xu et al. teach a method for using a virtual assistant to respond to a request of a user, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor, an utterance from the user (“the digital assistant determines if the user input utterance explicitly identifies a skill bot using its invocation name”; paragraphs 102, 119);
analyzing, by the at least one processor, the received utterance in order to make an initial determination of an intent of the user and a confidence level that relates to the initial determination of the intent (“the digital assistant evaluates the received user input utterance and computes confidence scores for the system intents and the skill bots associated with the digital assistant. The score computed for a skill bot or system intent represents how likely the user input is representative of a task that the skill bot is configured to perform or is representative of a system intent.”; paragraphs 83 – 87, 102, 119);
when the confidence level is less than a predetermined threshold, applying, to the received utterance by the at least one processor, an artificial intelligence (AI) model that is configured to assign the received utterance to at least one domain from among a predetermined plurality of domains (“the skill bot may decide that it cannot handle the input (e.g., because the confidence scores of every bot intent configured for the skill bot are below a certain threshold). In this situation, the skill bot may refer the input back to the master bot for handling (e.g., intent analysis using the intent classifier of the master bot), or the skill bot may prompt the user for clarification.”; paragraphs 54, 128);
outputting, by the at least one processor based on the at least one domain to which the received utterance is assigned, information that prompts the user to provide additional input that relates to the intent of the user (“the skill bot may decide that it cannot handle the input (e.g., because the confidence scores of every bot intent configured for the skill bot are below a certain threshold). In this situation, the skill bot may refer the input back to the master bot for handling (e.g., intent analysis using the intent classifier of the master bot), or the skill bot may prompt the user for clarification…Each intent is given an intent identifier or intent name. For example, for a skill bot trained for a bank, the intents specified for the skill bot may include “CheckBalance,” “TransferMoney,” “DepositCheck,” and the like.”; paragraphs 54, 83 – 87, 128);
receiving, by the at least one processor from the user, the additional input; and
secondarily determining, by the at least one processor based on the additional input, the intent of the user (“The bot system may also prompt the end user for additional input parameters or request other additional information… the skill bot designer may also provide one or more example utterances that are representative of and illustrate the intent. These example utterances are meant to represent utterances that a user may input to the skill bot for that intent. For example, for the CheckBalance intent, example utterances may include “What's my savings account balance?”, “How much is in my checking account?”, “How much money do I have in my account,” and the like. Accordingly, various permutations of typical user utterances may be specified as example utterances for an intent.”; paragraphs 48, 54, 83 – 87, 128);
wherein the AI model is trained by using historical utterance data that is augmented by using a keyboard perturbation technique that relates to randomly replacing characters within words with neighboring characters on a keyboard (“Language parser 214 may also tokenize the linguistic units of the utterance 202 (e.g., to convert each word into a separate token)… the training subsystem can generate duplicates or augmented copies of training data that correspond to an unresolvable class (e.g., out-of-scope utterances). Data augmentation techniques can include back-translating an utterance in the training dataset, synonym replacement of one or more tokens of the utterance, random insertion of tokens into the utterance, swapping between two tokens of the utterance, and random deletion of one or more tokens of the utterance…User interface input devices may include, for example, a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices”; paragraphs 107, 204, 273).
As per claims 2, 11, and 20, Xu et al. further disclose the outputting of the information comprises displaying, to the user, a respective predetermined list of items that corresponds to possible intentions associated with the at least one domain to which the received utterance is assigned (Digital assistant 106 may end the conversation by outputting information to the user indicating that the pizza has been ordered…output an intent inferred for the utterance by the machine-learning model… include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices 1302, 1304, 1306, and 1308.”; paragraphs 61, 83 – 87, 243).
As per claims 3, 12, Xu et al. further disclose the predetermined plurality of domains includes a first domain group that relates to products associated with a financial institution and a second domain group that relates to activity associated with the financial institution (“For example, for a skill bot trained for a bank, the intents specified for the skill bot may include “CheckBalance,” “TransferMoney,” “DepositCheck,” and the like…For example, for a banking skill, an AccountType entity may be defined by the skill bot designer that enables various banking transactions by checking the user input for keywords like checking, savings, and credit cards, etc.”; paragraphs 83 – 87).
As per claims 4, 13, Xu et al. further disclose the first domain group includes a first domain that relates to Zelle®, a second domain that relates to a bill payment product, a third domain that relates to a deposit making product, a fourth domain that relates to a card, and a fifth domain that relates to a wire transfer product (“For example, for a skill bot trained for a bank, the intents specified for the skill bot may include “CheckBalance,” “TransferMoney,” “DepositCheck,” and the like…For example, for a banking skill, an AccountType entity may be defined by the skill bot designer that enables various banking transactions by checking the user input for keywords like checking, savings, and credit cards, etc.”; paragraphs 83 – 87).
As per claims 5, 14, Xu et al. further disclose the second domain group includes a sixth domain that relates to a money transfer activity, a seventh domain that relates to a payee management activity, and an eighth domain that relates to a transaction tracking activity (paragraphs 47, 83 – 87).
As per claims 7, 16, Xu et al. further disclose the AI model is trained by using the historical utterance data that is further augmented by using a swapping character perturbation technique that relates to randomly swapping characters within a word while maintaining word length (“the training subsystem can generate duplicates or augmented copies of training data that correspond to an unresolvable class (e.g., out-of-scope utterances). Data augmentation techniques can include back-translating an utterance in the training dataset, synonym replacement of one or more tokens of the utterance, random insertion of tokens into the utterance, swapping between two tokens of the utterance, and random deletion of one or more tokens of the utterance.”; paragraphs 201 – 204).
As per claims 8, 17, Xu et al. further disclose the AI model is trained by using the historical utterance data that is further augmented by using a back-translation process that relates to translating a respective utterance from English to at least one from among French and German and then translating the translated respective utterance back to English (“digital assistant 106 is also capable of handling utterances in languages other than English. Digital assistant 106 may provide subsystems (e.g., components implementing NLU functionality) that are configured for performing processing for different languages… Data augmentation techniques can include back-translating an utterance in the training dataset, synonym replacement of one or more tokens of the utterance, random insertion of tokens into the utterance, swapping between two tokens of the utterance, and random deletion of one or more tokens of the utterance.”; paragraphs 64, 204).
As per claims 9, 18, Xu et al. further disclose the AI model is trained by using the historical utterance data that is further augmented by using a paraphrasing process that relates to generating, for a respective utterance, at least one additional example utterance that is different from the respective utterance while maintaining an original intent that is associated with the respective utterance (“the training subsystem performs batch balancing of the training dataset to generate an augmented training data set that includes augmented copies of out-of-scope utterances. This step can be optional based on the amount of out-of-scope utterances available in the training dataset. For example, the training subsystem can generate duplicates or augmented copies of training data that correspond to an unresolvable class (e.g., out-of-scope utterances).”; paragraphs 204 – 210, 210).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD SAINT-CYR whose telephone number is (571)272-4247. The examiner can normally be reached Monday- Friday.
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/LEONARD SAINT-CYR/Primary Examiner, Art Unit 2658
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