DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA and is in response to communications filed on 9/04/2026 in which claims 1-7, 9-17, 19-22 are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/04/2026 has been entered.
Priority
Acknowledgment is made of Provisional Application 63/712,795.
Considerations under - 35 USC § 101
Claims are NOT directed to an abstract idea because at least the following is true:
Independent claims 1, 11 and 20 contain claims which amount to an inventive concept as found in pages 1 and 2 of the specification and represented in the claim limitations, “first prompt classifier and a second prompt classifier in parallel at inference time”, and “causing, by the device and responsive to a determination that the feedback is required, a solicitation for the feedback to be provided to the user”.
Dependent claims also recite significantly more than a judicial exception due at least to their dependency on their respective independent claims.
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 of this title, 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, 6, 11, 16, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al. US 20250053876 A1 (hereinafter referred to as “Upadhyay”) in view of Sun et al. US 12488184 B1 (hereinafter referred to as “Sun”).
As per claim 1, Upadhyay teaches:
A method, comprising:
classifying, by a device, a prompt from a user to a language model by a first prompt classifier and a second prompt classifier in parallel at inference time by the language model (Upadhyay, [0030] – A new model can be added to the system by simply adding a new scoring head in parallel with the other scoring heads, wherein the new scoring head can be trained independently of the other scoring heads (e.g., agnostic to the weights of the other scoring heads) and can be executed in parallel with the other scoring heads during inference (e.g., performance score prediction));
evaluating, by the device, an accuracy of the classification of the prompt by the first prompt classifier based on whether there is agreement between the classification of the prompt by the first prompt classifier and the classification of the prompt by the second prompt classifier (Upadhyay, [0116] – The SPM can be refined (e.g., the SPM layer weights can be refined or updated) based on comparison (e.g., loss) between the predicted performance score for each candidate model and the respective target performance score (e.g., using backpropagation));
Upadhyay doesn’t explicitly teach a determination of accuracy to determine if a solicitation of feedback is required, however, Sun teaches:
determining, by the device and based on the accuracy, whether feedback from the user on the classification of the prompt by the first prompt classifier is required (Sun, column 9, lines 32-41 – The system 100 may employ one or more feedback determining techniques to determine whether the user was satisfied with the system response); and
causing, by the device and responsive to a determination that the feedback is required, a solicitation for the feedback to be provided to the user (Sun, column 9, lines 50-60 – After outputting the system response, the system may cause a device to output the synthesized speech “did I answer your question,” “did I respond correctly,” or a similar natural language output requesting feedback from the user).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Upadhyay’s invention in view of Sun in order to include user feedback; this is advantageous because the system may determine whether an interaction was satisfactory or unsatisfactory based on explicit and/or implicit feedback corresponding to the system response (Sun, column 9, lines 50-60).
As per claim 6, Upadhyay in view of Sun teaches:
The method of claim 1, wherein the first prompt classifier includes a model trained with a loss function that penalizes when a predicted class from the first prompt classifier is different from a ground truth class (Sun, column 14, lines 50-67 – The outputs of the cross attention component 530 rep′.sub.p (where 1≤p≤m(k+1)) is then inputted to the classifier 540 to predict the importance score logits g.sub.p. Cross-entropy may be used to calculate the loss between each entity's predicted logits g.sub.p and its true label l.sub.p, and the final loss may be defined as the average of these losses. Column 28, lines 16-25 – Training a machine learning model requires establishing a “ground truth” for the training examples).
Claims 11 and 16 are directed to an apparatus performing steps recited in claims 1 and 6 with substantially the same limitations. Therefore, the rejections made to claims 1 and 6 are applied to claims 11 and 16.
Claim 20 is directed to a non-transitory, computer-readable medium performing steps recited in claim 1 with substantially the same limitations. Therefore, the rejection made to claim 1 is applied to claim 20.
As per claim 21, Upadhyay in view of Sun teaches:
The method as in claim 1, wherein:
the first prompt classifier comprises a model trained with a first loss function that penalizes a first predicted class from the first prompt classifier when the first predicted class differs from a ground-truth class (Upadhyay, [0109] – When a user metric indicates that a result was poor quality, the reward model can generate a performance score from the prompt-response pair and penalize it based on the user metric. [0116] – The training prompts, and optionally additional information (e.g., context, prompter identifier, etc.) can be used as the SPM training inputs, and the corresponding target performance score can be used as the training targets); and
the second prompt classifier comprises a model trained with a second loss function that penalizes
(i) agreement between a second predicted class from the second prompt classifier and the first predicted class when the first predicted class differs from the ground-truth class (Upadhyay, [0116] – The SPM can be refined (e.g., the SPM layer weights can be refined or updated) based on comparison (e.g., loss) between the predicted performance score for each candidate model and the respective target performance score (e.g., using backpropagation)), and
(ii) disagreement between the second predicted class and the first predicted class when the first predicted class corresponds to the ground-truth class (Upadhyay, [0105] – Determining a performance score for each prompt-response pair S110 functions to determine a target performance score (e.g., a ground-truth performance score) for each prompt-response pair).
Claims 2-4, 9, 12-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay in view of Sun and further in view of Podogorny et al. US 11093951 B1 (hereinafter referred to as “Podogorny”).
As per claim 2, Upadhyay as modified doesn’t go into detail about quality of user feedback, however, Podgorny teaches:
The method of claim 1, further comprising:
performing quality control checks on feedback received from the user in response to the solicitation (Podgorny, column 15, lines 54-61 – Receives structured and/or unstructured feedback from one or more content generating/trusted users, wherein the feedback identifies categories, and/or topics, and/or classifications, that are relevant to the self-help content that is responsive to the query).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Upadhyay’s invention as modified in view of Podgorny in order to include a quality check on user feedback; this is advantageous because the system can then find relevant helpful content as feedback (Sun, column 9, lines 50-60).
As per claim 3, Upadhyay as modified with Podgorny teaches:
The method of claim 1, further comprising:
adapting the solicitation for the feedback based on attributes of prior feedback received from the user (Podgorny, column 4, lines 32-40 – The customer self-help system then provides the relevant self-help content from the identified relevant customer self-help systems to the user. Next paragraph: structured and/or unstructured feedback is then solicited and received from users of the customer self-help systems. In one embodiment, the feedback is used to verify or augment/improve topics, and/or classifications, associated with self-help content and customer self-help systems).
As per claim 4, Upadhyay as modified with Podgorny teaches:
The method of claim 1, further comprising:
providing incentives to the user to provide the feedback based on a reliability of prior feedback received from the user (Podgorny, column 4, lines 41-47 – The feedback is then used to generate one or more additional self-help relationship models to replace or update the initial self-help relationship model, and/or to update/improve labeled training data sets, wherein improvement of the model is interpreted as providing an incentive).
As per claim 9, Upadhyay as modified with Podgorny teaches:
The method of claim 1, further comprising:
intercepting feedback received from the user in response to the solicitation without intervention by the language model (Podgorny, column 4, lines 32-40 –Structured and/or unstructured feedback is then solicited and received from users of the customer self-help systems. In one embodiment, the feedback is used to verify or augment/improve topics, and/or classifications, associated with self-help content and customer self-help systems, wherein no intervention from a language model is mentioned).
Claims 12-14 and 19 are directed to an apparatus performing steps recited in claims 2-4 and 9 with substantially the same limitations. Therefore, the rejections made to claims 2-4 and 9 are applied to claims 12-14 and 19.
Claims 5, 8, 10, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay in view of Sun and further in view of Tonnetti et al. US 20200043485 A1 (hereinafter referred to as “Tonnetti”).
As per claim 5, Upadhyay as modified doesn’t teach feedback volume determination, however, Tonnetti teaches:
The method of claim 1, further comprising:
determining whether feedback from the user on the classification of the prompt by the first prompt classifier is required additionally based on a volume of previous feedback requests to the user (Tonnetti, [0014] – Improves the quality of responses from an automatic dialogue system by dynamically adjusting response thresholds. More particularly, the automatic dialogue system may dynamically determine response threshold values in response to user feedback).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Upadhyay’s invention as modified in view of Tonnetti in order to dynamically adjust response thresholds; this is advantageous because the system can adjust the response threshold values to provide a better user experience as the amount of user-interaction with the system increases (Tonnetti, [0014]).
As per claim 8, Upadhyay as modified with Tonnetti teaches:
The method of claim 1, further comprising:
determining the accuracy of the classification of the prompt by the first prompt classifier based on whether there is agreement between the classification of the prompt by the first prompt classifier and the classification of the prompt by the second prompt classifier (Tonnetti, [0020] – The system may subsequently receive implicit or explicit user feedback that the response indicates a misclassification of the input).
As per claim 10, Upadhyay as modified with Tonnetti teaches:
The method of claim 1, wherein the classification of the prompt by the first prompt classifier identifies features of the prompt indicative of one or more of a task requested in the prompt, a constraint applicable to completing the task, or data needed to complete the task (Tonnetti, [0045] – The automatic classification system 108 of FIG. 1 may return a user intent 110 of “GET_WEATHER_REGION” with a confidence rating 114 of 0.23 based on determined intent 110, wherein the classification determining the intent of the prompt is interpreted as determining a constraint of the prompt).
Claims 15 and 18 are directed to an apparatus performing steps recited in claims 5 and 8 with substantially the same limitations. Therefore, the rejections made to claims 5 and 8 are applied to claims 15 and 18.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay in view of Sun and further in view of Tonnetti et al. US 20200043485 A1 (hereinafter referred to as “Tonnetti”).
As per claim 7, Upadhyay as modified with Sun doesn’t explicitly teach different loss functions, however, Gafni teaches:
The method of claim 6, wherein the second prompt classifier includes a model trained with a different loss function that promotes disagreement between a predicted class from the second prompt classifier and a predicted class from the first prompt classifier when it is different from the ground truth class (Gafni, [0031] – Which may predict image tokens. During the training, the transformer 250 may compare the predicted image tokens with the ground-truth image tokens 245 based on loss functions).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Upadhyay’s invention as modified in view of Gafni in order to include a different loss function; this is advantageous because the transformer may update itself based on the comparison thus saving time (Gafni, [0031]).
Claim 17 is directed to an apparatus performing steps recited in claim 7 with substantially the same limitations. Therefore, the rejection made to claim 7 is applied to claim 17.
Allowable Subject Matter
Claim 22 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
As per claim 22, the prior art of record doesn’t adequately teach or suggest the following limitations. Although the prior art of record teaches feedback from users, the prior art doesn’t teach determining whether the feedback from a user is required is based on the reliability of the feedback from the user. This would involve determining reliability based on a scoring metric of user feedback from a history of user feedback.
The method as in claim 1, further comprising determining a reliability of feedback from the user based on prior feedback received from the user for a prompt having a known classification, wherein determining whether feedback from the user on the classification of the prompt by the first prompt classifier is required is further based on the reliability of the feedback from the user.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are generally moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The primary reference, Kawasaki has been entirely replaced with the newly found reference, Upadhyay which teaches parallel models during inference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bai et al. 12 Apr 2022, https://arxiv.org/pdf/2204.05862, “Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback”, pgs. 4-14
Foltz et al. US 20190258903 A1 automated custom training of a scoring model which includes receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model (Abstract).
White et al., 2013, “A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT”.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J. Ellis whose telephone number is (571)270-3443. The examiner can normally be reached on Monday-Friday 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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September 28, 2026
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2153
