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 .
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 claims are directed toward an abstract idea without significantly more.
Claims 1-9 are directed toward a method, claims 17-20 are directed toward a method, and claims 10-16 are directed toward a computer readable medium with instructions to implement the method of claims 1-7.
Claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 1 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 1 recites the steps of “… receiving an unclassified utterance; processing the unclassified utterance to produce a politeness score”; “analyzing the unclassified utterance to produce a key linguistic terms count”; “making a first determination that the politeness score exceeds a politeness score threshold”; “making a second determination, based on the first determination, that the key linguistic terms count exceeds a key linguistic terms count threshold”; and “classifying, based on the second determination, the unclassified utterance as a polite utterance.” These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper or by using a general purpose computer) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “general purpose computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content. See paragraph 0026 of the instant application, which clearly states that “…Any client device (102A-102N) is illustrated and described in additional detail with respect to FIG. 1B, below. Examples of any client device (102A-102N) include, but are not limited to, a desktop computer, a laptop computer, a tablet computer, a smartphone, a smartwatch, and any other computing device similar to the exemplary computing system illustrated and described below with respect to FIG. 6.”
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 1 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 1 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 1 is therefore rejected under 35 U.S.C. § 101. Dependent claims 2-9 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.
Claims 10-16 are directed toward a non-transitory computer readable medium with instructions to implement the method of claims 1-7, and are similar in scope and content, and therefore rejected under similar rationale.
Claim 17, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claims 17 is directed toward a “method”, and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 17 recites the steps of “… for out-of-distribution data generalization, the method comprising: “selecting, of a polite dialog service, a polite dialog service module comprising module weights”; “creating a new polite dialog service module comprising new module weights”; “processing a first portion of a module input-target sample using the polite dialog service module to produce a module prediction value”; “processing a second portion of the module input-target sample using the new polite dialog service module to produce a new module prediction value”; “computing a de-biasing loss from the module prediction value, the new module prediction value, and a third portion of the module input-target sample”; “making a determination that the de-biasing loss falls below a de-biasing loss threshold”; and “deeming, based on the determination, the polite dialog service module as generalized for out-of-distribution data.” These limitations collectively recite the collection and evaluation of information, including language evaluation and processing using machine learning models. As characterized by the USPTO guidance and case law, such activities fall within the abstract-idea groupings of mental processes (e.g. observations, evaluations, and judgments that could be performed in the human mind or with pen and paper or using a general purpose computer) and organizing /transmitting information. Reference can be made to latest patent eligibility guidelines. Accordingly, claim 17 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “general purpose computer.” The use of a generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content. See paragraph 0026 of the instant application, which clearly states that “…Any client device (102A-102N) is illustrated and described in additional detail with respect to FIG. 1B, below. Examples of any client device (102A-102N) include, but are not limited to, a desktop computer, a laptop computer, a tablet computer, a smartphone, a smartwatch, and any other computing device similar to the exemplary computing system illustrated and described below with respect to FIG. 6.”
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “computer,” “device”(s) performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 17 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 17 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 17 is therefore rejected under 35 U.S.C. § 101. Dependent claims 18-20 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.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a1) as being anticipated by Vasylyev (US 2024/0412720 A1).
As per claims 1 and 10, Vasylyev teaches a method/non-transitory computer readable medium with instructions to implement said method, for utterance classification (0041-0042), the method comprising:
receiving an unclassified utterance (0011 - receive input);
processing the unclassified utterance to produce a politeness score (0107, 0111, 0208, 0233, 0450 – “dominant voice” detected by system);
analyzing the unclassified utterance to produce a key linguistic terms count (0200, 0146);
making a first determination that the politeness score exceeds a politeness score threshold (0108, 0331) ;
making a second determination, based on the first determination, that the key linguistic terms count exceeds a key linguistic terms count threshold (0200, 0146); and
classifying, based on the second determination, the unclassified utterance as a polite utterance (0146, 0316).
As per claims 2 and 11, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 1 and 10, wherein the unclassified utterance is processed using a politeness learning model comprising an ensemble of transformer models (0108, 0331, abstract, 0033-0034).
As per claims 3 and 12, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 3 and 10, wherein the unclassified utterance is analyzed using part-of-speech (POS) tagging (0602, 0734).
As per claims 4 and 13, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 3 and 12, wherein the unclassified utterance comprises a set of words, and wherein the key linguistic terms count reflects a cardinality of a subset of the set of words belonging to at least one grammatical category associated with politeness (0062-0063, 0149 – checking for appropriateness).
As per claims 5 and 14, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 4 and 13, wherein the at least one grammatical category comprises adjectives and pronouns (0062-0063, 0149).
As per claims 6 and 15, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 1 and 10, the method further comprising: prior to receiving the unclassified utterance: accessing a corpus of impolite utterances comprising impolite utterance samples; accessing a corpus of polite utterances comprising polite utterance samples; and optimizing, through training of, the politeness learning model using the impolite utterance samples and the polite utterance samples (0108, 0331, abstract, 0033-0034, 0043, 0149, 0063, 0071).
As per claims 7 and 16, Vasylyev teaches the method/non-transitory computer readable medium with instructions to implement said method of claims 6 and 15, wherein the politeness score quantifies a similarity of the unclassified utterance to the corpus of polite utterances (0108, 0331, abstract, 0033-0034, 0043, 0149, 0063, 0071).
As per claim 8, Vasylyev teaches the method of claim 1, the method further comprising: after classifying the unclassified utterance: receiving a second unclassified utterance; processing the second unclassified utterance to produce a second politeness score; analyzing the second unclassified utterance to produce a second key linguistic terms count; making a third determination that the second politeness score exceeds the politeness score threshold; making a fourth determination, based on the third determination, that the second key linguistic terms count equals or falls below the key linguistic terms count threshold; and classifying, based on the fourth determination, the second unclassified utterance as an impolite utterance (0108, 0331, abstract, 0033-0034, 0043, 0149, 0063, 0071).
As per claim 10, Vasylyev teaches the method of claim 1, the method further comprising: after classifying the unclassified utterance: receiving a second unclassified utterance; processing the second unclassified utterance to produce a second politeness score; making a third determination that the second politeness score equals or falls below the politeness score threshold; and classifying, based on the third determination, the second unclassified utterance as an impolite utterance ( 0108, 0331, abstract, 0033-0034, 0043, 0149, 0063, 0071).
As per claim 17, Vasylyev teaches a method of claim method for out-of-distribution data generalization, the method comprising: selecting, of a polite dialog service, a polite dialog service module comprising module weights (0172-0173, 0080, 0107-0108); creating a new polite dialog service module comprising new module weights (0080, 0200-0201); processing a first portion of a module input-target sample using the polite dialog service module to produce a module prediction value (0149, 0033-0034); processing a second portion of the module input-target sample using the new polite dialog service module to produce a new module prediction value (0080, 0200-0201, 0134, 0149, 0033-0034); computing a de-biasing loss from the module prediction value, the new module prediction value, and a third portion of the module input-target sample (0080, 0200-0201, 0134, 0149, 0033-0034); making a determination that the de-biasing loss falls below a de-biasing loss threshold (0131); and deeming, based on the determination, the polite dialog service module as generalized for out-of-distribution data (0450).
As per claim 18, Vasylyev teaches the method of claim 17, wherein the first portion of the module input-target sample comprises a set of input values accepted by the polite dialog service module, and wherein the set of input values pertain to an existing knowledge domain supported by the polite dialog service (0102, 0146-0147, 0173).
As per claim 19, Vasylyev teaches the method of claim 18, wherein the second portion of the module input-target sample comprises a second set of input values accepted by the new polite dialog service module, and wherein the second set of input values pertain to a new knowledge domain yet to be supported by the polite dialog service (0102, 0146-0147, 0173).
As per claim 20, Vasylyev teaches the method of claim 19, wherein the third portion of the module input-target sample comprises a target value that commonly corresponds to the first and second sets of input values, and wherein the target value pertains to the existing and new knowledge domains (0102, 0146-0147, 0173, 0175, 0284-0285, 0299, 0212).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
The following is the closest prior art of record that is applicable.
Liu et al., (US 2025/0200293 A1) teach techniques for using a model to generate a response to a user input, where the response is associated with a personality determined to be relevant to the user input, are described. The system receives a user input and context data associated with the user input. Using the user input data and/or the context data, the system determines a personality (e.g., including a personality type and/or personality characteristics) relevant to the user input. The system generates a prompt instructing a model to generate a response to the user input that corresponds to the personality. The model processes the prompt to generate a response to the user input that corresponds to the personality. In some embodiments, the model generates a request for another component of the system to generate information responsive to the user input. The model may transform the responsive information into the personality-associated response.
Vaughn et al., (US 11,704,493 B2) teach pairing a user response and associated context with a neural network associated with a virtual assistant computer during a dynamic text conversation with an end user. The virtual assistant computer receives a detected user generated text input; determines context of the detected user generated text input; compares the context of the detected user generated text input by comparing a confidence score representing context of the user generated input to a classification associated with each of a plurality of existing nodes of a neural network. For confidence scores below a threshold relative to the classification associated with each of the existing nodes of the neural network, the virtual assistant computer creates a new node within the neural network and assigns the context of the user generated text to the new node.
Yin et al., (US 2020/0104364 A1) teach a method that includes crawling a network for raw data. Emotion metrics are refined for the raw data. Labels for the raw data using refined emotion metrics are received. Factor analysis is performed for labeled data to obtain emotional tone factors. Adjusted labeled data are received based on the emotional tone factors. Words are analyzed using a tone model using the emotional tone factors and integrating the adjusted labeled data. Representative words for each emotional tone factor are provided based on using the tone model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658
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