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 .
Introduction
This office action is in response to communications filed 02/07/2024. Claims 1-20 are pending and likewise have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/08/2025 and 02/07/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement filed 03/18/2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because NPL reference 2 is a foreign patent not NPL. It should be included in the foreign patent section of the IDS. A full version of what examiner believes to be the same foreign patent publication was accessed and used in the office action, and provided with this office action. The IDS has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Objections
Claim 18 objected to because of the following informalities:
On line 3 of Claim 18, “model based at least the user feedback” seems to be missing the word “on” to make grammatical sense. The duplicate claim 5 does have the word “on” at that point in the claim.
Appropriate correction is required.
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.
Claim 15 is 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 15 recites the limitation "the prompt" in lines 3 and 4. There is insufficient antecedent basis for this limitation in the claim. No “a prompt” has been established before this point in the claim. Claim 15 is dependent on Claim 12 which involves generating a tag, not a prompt. For this reason, Examiner believes Claim 15 may have been intended to be dependent on Claim 14, which does establish “a prompt”. For the purposes of examiner Claim 15 will be treated as depending on Claim 14.
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)(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.
Claim(s) 1-4, 7, 16-17 and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Samsung Electronics Co., Ltd (IN 201811030218), hereinafter Samsung.
Regarding Claim 1:
Samsung teaches a method implemented using one or more processors, the method comprising(Pg 7, Ln 12-15, system of facilitating a user to create a multimedia content….processor):
receiving a user query, the user query being received via a user interface of a client device(Pg 7, Ln 12-20, user device….display having an user interface….configured for: receiving at least one user input request to create a multimedia content);
generating, using a generative model, a generative response that is responsive to the user query and that includes at least media content to be rendered and one or more selectable elements to be rendered with respect to media content, wherein each of the one or more selectable elements are selectable and correspond to a respective type of user feedback that evaluates the media content with respect to the user query(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area. Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
causing the media content to be rendered at the user interface(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area);
causing the one or more selectable elements to be rendered at the user interface and with respect to the media content(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
receiving user input that selects one of the one or more selectable elements(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
determining, based on the received user input that selects the one of the one or more selectable elements, user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
and training or fine-tuning the generative model based on at least the user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-20, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection and their respective associated objection selection….implementation of derivative of reinforcement learning techniques. The reinforcement learning procedure is to learn in terms of rewards and penalties over suggestion based on user action).
Regarding Claim 2:
Samsung teaches the method of claim 1, wherein the media content is an image or a video(Pg 24, Ln 18-23, The multimedia content type include at least one of the 2d image, video).
Regarding Claim 3:
Samsung teaches the method of claim 1, wherein the media content is rendered as part of a multi-modal response that is generated as the generative response and responsive to the user query, and wherein the multi-modal response further includes textual content(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area. Pg 19, Ln 23-Pg 20, Ln 2, Decoder could be other than convolutional decoder which can output the other form of representation than multimedia content e.g. texts, symbols, etc).
Regarding Claim 4:
Samsung teaches the method of claim 1, wherein the one or more selectable elements includes a first selectable element that corresponds to positive user feedback(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.).
Regarding Claim 7:
Samsung teaches the method of claim 1, wherein the one or more selectable elements includes a second selectable element that corresponds to negative user feedback(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions).
Regarding Claim 16:
Samsung teaches a system including one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations of(Pg 7, Ln 12-15, system of facilitating a user to create a multimedia content….processor):
receiving a user query, the user query being received via a user interface of a client device(Pg 7, Ln 12-20, user device….display having an user interface….configured for: receiving at least one user input request to create a multimedia content);
generating, using a generative model, a generative response that is responsive to the user query and that includes at least media content to be rendered and one or more selectable elements to be rendered with respect to media content, wherein each of the one or more selectable elements are selectable and correspond to a respective type of user feedback that evaluates the media content with respect to the user query(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area. Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
causing the media content to be rendered at the user interface(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area);
causing the one or more selectable elements to be rendered at the user interface and with respect to the media content(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
receiving user input that selects one of the one or more selectable elements(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
determining, based on the received user input that selects the one of the one or more selectable elements, user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
and training or fine-tuning the generative model based on the user query and the user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-20, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection and their respective associated objection selection….implementation of derivative of reinforcement learning techniques. The reinforcement learning procedure is to learn in terms of rewards and penalties over suggestion based on user action).
Regarding Claim 17:
Claim 17 contains similar limitations as Claim 4 and is therefore rejected for the same reasons.
Regarding Claim 19:
Claim 19 contains similar limitations as Claim 7 and is therefore rejected for the same reasons.
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.
Claim(s) 5-6, 14-15, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samsung as applied to claim 1 above, and further in view of Gupta et al. (US 20250131020 A1).
Regarding Claim 5:
Samsung teaches the method of claim 4, wherein the user input selects the first selectable element that corresponds to the positive user feedback(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.),
Samsung does not teach and wherein training or fine-tuning the generative model based on at least the user feedback that evaluates the media content with respect to the user query comprises: generating a training instance to: include an input prompt that includes the user query as a training instance input, and include at least a response having a tag or prompt, that was utilized to obtain the media content, as training instance output.
In the same field of user feedback reinforcement learning, Gupta teaches and wherein training or fine-tuning the generative model based on at least the user feedback that evaluates the media content with respect to the user query comprises: generating a training instance to: include an input prompt that includes the user query as a training instance input, and include at least a response having a tag or prompt, that was utilized to obtain the media content, as training instance output(Para [0030], Ln 12-26, the training process may include causing the neural network to process a task for which an expected/desired output is known, comparing the activations generated by the neural network to the expected/desired output, and determining the values of the weights in each layer based on the comparison results. After the training process is complete, the neural network may begin “inference” to process a new task with the determined weights. Para [0134], Ln 1-6, The collected feedback can then be used to adjust parameters, weights, or even training data of the LXM to enhance its performance. Para [0139], Ln 1-5, processor may generate an enhanced prompt based on the user prompt…..and the selected LXM. Para [0140], Ln 1-12, processor may determine the most relevant LXM based on the user's initial query, create the enhanced prompt based on the user profile and contextual information, and forward this enhanced prompt to the LXM to receive a more tailored and precise response. Para [0023], Ln 1-14, provide feedback configured to enable finetuning of a selected LXM based on an observed user response to output received from the selected LXM. Para [0061], Ln 1-7, explicit feedback from users; use the results of the analysis to update…..LXM parameters, the enhanced prompt, and/or the feedback that is sent to the LXM system. Para [0065], Ln 5-12, capture explicit feedback from the user (e.g., likes, dislikes, comments). Para [0114], Ln 10-20, For users who consistently give positive feedback on specific response formats, the at least one processor could refine the enhanced prompt or the LXM to prioritize such formats in future interactions).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Samsung with the user feedback fine-tuning system of Gupta, as it can help improve the model’s performance(Para [0134], Ln 1-6).
Regarding Claim 6:
The combination of Samsung and Gupta teaches the method of claim 5, but does not teach further comprising training or fine-tuning the generative model using the generated training instance, wherein the training or fine-tuning comprises: processing the training instance input corresponding to the input prompt that includes the user query, using the generative model, to generate a predicted model output that includes at least a predicted tag or predicted prompt, comparing the predicted model output with the training instance output; and training or fine-tuning the generative model based on comparing the predicted model output with the training instance output.
In the same field of user feedback reinforcement learning, Gupta teaches further comprising training or fine-tuning the generative model using the generated training instance, wherein the training or fine-tuning comprises: processing the training instance input corresponding to the input prompt that includes the user query, using the generative model, to generate a predicted model output that includes at least a predicted tag or predicted prompt, comparing the predicted model output with the training instance output(Para [0030], Ln 12-26, the training process may include causing the neural network to process a task for which an expected/desired output is known, comparing the activations generated by the neural network to the expected/desired output, and determining the values of the weights in each layer based on the comparison results. After the training process is complete, the neural network may begin “inference” to process a new task with the determined weights. Para [0134], Ln 1-6, The collected feedback can then be used to adjust parameters, weights, or even training data of the LXM to enhance its performance. Para [0139], Ln 1-5, processor may generate an enhanced prompt based on the user prompt…..and the selected LXM. Para [0140], Ln 1-12, processor may determine the most relevant LXM based on the user's initial query, create the enhanced prompt based on the user profile and contextual information, and forward this enhanced prompt to the LXM to receive a more tailored and precise response. Para [0061], Ln 1-7, explicit feedback from users; use the results of the analysis to update…..LXM parameters, the enhanced prompt, and/or the feedback that is sent to the LXM system. Para [0114], Ln 10-20, For users who consistently give positive feedback on specific response formats, the at least one processor could refine the enhanced prompt or the LXM to prioritize such formats in future interactions);
and training or fine-tuning the generative model based on comparing the predicted model output with the training instance output(Para [0030], Ln 12-26, the training process may include causing the neural network to process a task for which an expected/desired output is known, comparing the activations generated by the neural network to the expected/desired output, and determining the values of the weights in each layer based on the comparison results).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Samsung and Gupta with the user feedback fine-tuning system of Gupta, as it can help improve the model’s performance(Para [0134], Ln 1-6).
Regarding Claim 14:
Samsung teaches the method of claim 1, but does not teach wherein the media content is generative media content that is obtained based on a prompt generated using the generative model.
In the same field of user feedback reinforcement learning, Gupta teaches wherein the media content is generative media content that is obtained based on a prompt generated using the generative model(Para [0139], Ln 1-5, processor may generate an enhanced prompt based on the user prompt…..and the selected LXM. Para [0140], Ln 1-12, processor may determine the most relevant LXM based on the user's initial query, create the enhanced prompt based on the user profile and contextual information, and forward this enhanced prompt to the LXM to receive a more tailored and precise response. Para [0023], Ln 1-14, provide feedback configured to enable finetuning of a selected LXM based on an observed user response to output received from the selected LXM. Para [0061], Ln 1-10, generated prompts to receive LXM-generated content that is tailored to the current user profile; and present the tailored LXM-generated content to the user).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Samsung with the user feedback fine-tuning system of Gupta, as it can help improve the model’s performance(Para [0134], Ln 1-6).
Regarding Claim 15:
The combination of Samsung and Gupta teaches the method of claim 12(being treated as Claim 14, see 112b antecedent basis rejection above), but does not teach further comprising: submitting, to an additional generative model that is in addition to the generative model, the prompt; and obtaining, based on submitting the prompt to the additional generative model, the media content.
In the same field of user feedback reinforcement learning, Gupta teaches further comprising: submitting, to an additional generative model that is in addition to the generative model, the prompt; and obtaining, based on submitting the prompt to the additional generative model, the media content(Para [0139], Ln 1-5, processor may generate an enhanced prompt based on the user prompt…..and the selected LXM. Para [0140], Ln 1-12, processor may determine the most relevant LXM based on the user's initial query, create the enhanced prompt based on the user profile and contextual information, and forward this enhanced prompt to the LXM to receive a more tailored and precise response. Para [0023], Ln 1-14, provide feedback configured to enable finetuning of a selected LXM based on an observed user response to output received from the selected LXM. Para [0061], Ln 1-10, generated prompts to receive LXM-generated content that is tailored to the current user profile; and present the tailored LXM-generated content to the user).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Samsung and Gupta with the user feedback fine-tuning system of Gupta, as it can help improve the model’s performance(Para [0134], Ln 1-6).
Regarding Claim 18:
Claim 18 contains similar limitations as Claim 5 and is therefore rejected for the same reasons.
Regarding Claim 20:
Samsung teaches receiving a user query, the user query being received via a user interface of a client device(Pg 7, Ln 12-20, user device….display having an user interface….configured for: receiving at least one user input request to create a multimedia content);
generating, using a generative model, a generative response that is responsive to the user query and that includes at least media content to be rendered and one or more selectable elements to be rendered with respect to media content, wherein each of the one or more selectable elements are selectable and correspond to a respective type of user feedback that evaluates the media content with respect to the user query(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area. Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
causing the media content to be rendered at the user interface(Pg 7, Ln 15-20, generating an intermediate multimedia content using generative AI model corresponding to the received user input request in a 20 main display area);
causing the one or more selectable elements to be rendered at the user interface and with respect to the media content(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions.);
receiving user input that selects one of the one or more selectable elements(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
determining, based on the received user input that selects the one of the one or more selectable elements, user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-15, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection);
and training or fine-tuning the generative model based on the user query and the user feedback that evaluates the media content with respect to the user query(Pg 22, Ln 9-20, Feedback Reinforcement AI Module 226 takes input from user 10 action in terms of selection, explicit rejection and even neutral towards main suggestions and focus based suggestions. And then uses these as feedback to further refine the process of key object selection and their respective associated objection selection….implementation of derivative of reinforcement learning techniques. The reinforcement learning procedure is to learn in terms of rewards and penalties over suggestion based on user action).
Samsung does not specifically teach a non-transitory storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations of:
In the same field of user feedback reinforcement learning, Gupta teaches a non-transitory storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations of(Para [0011], Ln 1-12, aspects may include a non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a at least one processor to perform various operations):
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Samsung with the generic computer components of Gupta, as it provides an environment for the system to be realized(Para [0011], Ln 1-12).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samsung as applied to claim 1 above, and further in view of Wei et al. (US 20190220889 A1).
Regarding Claim 8:
Samsung teaches the method of claim 7, wherein the user input selects the second selectable element that correspond to the negative user feedback and wherein determining the user feedback that evaluates the media content with respect to the user query comprises:(Pg 20, Ln 3-15, render the associated objects to user as main suggestions…. provide various action options to the user during creativity work e.g. selection of one or more suggestion, explicit rejection of one or more suggestions, neutral towards given suggestions),
Samsung does not specifically teach causing a feedback window to be rendered at the user interface, wherein the feedback window is rendered in response to receiving the user input that selects the second selectable element, and wherein the feedback window includes one or more selectable options each displaying a corresponding description that classifies the negative user feedback; receiving additional user input that selects one of the one or more selectable options; and determining a classification of the negative user feedback based on the additional user input.
In the same field of user feedback, Wei teaches causing a feedback window to be rendered at the user interface, wherein the feedback window is rendered in response to receiving the user input that selects the second selectable element, and wherein the feedback window includes one or more selectable options each displaying a corresponding description that classifies the negative user feedback(Para [0141], Ln 1-13, information is presented on a terminal side, and a closing entrance of the advertisement information may be provided. When a user clicks the closing entrance, the advertisement is blocked or closed, and a negative feedback reason selection interface is presented to the user. The interface includes a plurality of negative feedback reason options, then, the user may select a corresponding negative feedback reason from the interface, and then optionally a feedback success interface is presented, to prompt the user that feedback succeeds. Also See Fig 2b. Para [0118], Ln 1-7, users never click advertisement, and each time the advertisement emerges, the users click negative feedbacks(user initially has option to dismiss or select));
receiving additional user input that selects one of the one or more selectable options; and determining a classification of the negative user feedback based on the additional user input(Para [0141], Ln 1-13, information is presented on a terminal side, and a closing entrance of the advertisement information may be provided. When a user clicks the closing entrance, the advertisement is blocked or closed, and a negative feedback reason selection interface is presented to the user. The interface includes a plurality of negative feedback reason options, then, the user may select a corresponding negative feedback reason from the interface, and then optionally a feedback success interface is presented, to prompt the user that feedback succeeds. Also See Fig 2b).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Samsung with the feedback interface of Wei, as it can improve the appropriateness of the content provided to the user(Para [0058], Ln 1-14).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samsung as applied to claim 1 above, and further in view of Sharma et al. (US 20240095455 A1).
Regarding Claim 11:
Samsung teaches the method of claim 7, but does not teach wherein the feedback window further includes an input field to receive customized user feedback.
In the same field of user feedback reinforcement learning, Sharma teaches wherein the feedback window further includes an input field to receive customized user feedback(Para [0055], Ln 1-21, fine-tunes the natural language model based on feedback received from the user device……feedback may be an acknowledgement that the answer is correct. In another instance, the feedback may be an indication that there may be a more accurate answer in another document. In another instance, the feedback provided by the clinician may indicate that the answer is incorrect. Notably, the feedback may be in the form of a text (e.g., a series of words or sentences, or data), numbers, formulas, and/or chemical compositions, entered by the user operating user device).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Samsung with the custom user feedback of Sharma, as it can help improve the quality of the model output(Para [0055], Ln 1-21).
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Samsung as applied to claim 1 above, and further in view of Clarke et al. (US 11520785 B2).
Regarding Claim 12:
Samsung teaches the method of claim 1, but does not teach wherein the media content is non-generative media content that is obtained based on a tag generated using the generative model.
In the same field of natural language processing Clarke teaches wherein the media content is non-generative media content that is obtained based on a tag generated using the generative model(Abstract, Ln 1-17, computing system causes a query remediation interface to be presented to a user that entered the user search query. In some embodiments, the interface includes: a classification of the user search quer…. receives, from the user via the query remediation interface, input for altering the database query and determines an updated database query based on the input. The computing system may access the database system using the updated database query. Col 7, Ln 55 – Col 8, Ln 5, purpose of tagging module 362 is to tag or recognize terms in the search query 122. There are at least two possible bases for tagging terms that are contemplated. First, terms that match a vocabulary for the user (or some set of users that user is included in) may be tagged by tagging module 362. Second, query terms that match with a portion of a database schema may also be tagged…..then assigns tags to terms of a user search query based on these tagging parameters. Col 9, Ln 38-41, tagging module is a named entity recognition (NER) model that receives search phrases and assigns a label to one or more words in the search phrases).
It would have been obvious for one skilled in the art, at the effective time of filling to modify Samsung with the query tagging system of Clarke, as it can help improve the relevance of results provided to the user (Col 6, Ln 20-33).
Regarding Claim 13:
The combination of Samsung and Clark teaches the method of claim 12, but does not teach further comprising: generating, based on the tag, a query for the media content; and obtaining, based on submitting the query for the media content to a media content search system, the media content.
In the same field of natural language processing Clarke teaches further comprising: generating, based on the tag, a query for the media content; and obtaining, based on submitting the query for the media content to a media content search system, the media content(Abstract, Ln 1-17, computing system causes a query remediation interface to be presented to a user that entered the user search query. In some embodiments, the interface includes: a classification of the user search quer…. receives, from the user via the query remediation interface, input for altering the database query and determines an updated database query based on the input. The computing system may access the database system using the updated database query. Col 7, Ln 55 – Col 8, Ln 5, purpose of tagging module 362 is to tag or recognize terms in the search query 122. There are at least two possible bases for tagging terms that are contemplated. First, terms that match a vocabulary for the user (or some set of users that user is included in) may be tagged by tagging module 362. Second, query terms that match with a portion of a database schema may also be tagged…..then assigns tags to terms of a user search query based on these tagging parameters. Col 9, Ln 38-41, tagging module is a named entity recognition (NER) model that receives search phrases and assigns a label to one or more words in the search phrases).
It would have been obvious for one skilled in the art, at the effective time of filling to modify the combination of Samsung and Clarke with the query tagging system of Clarke, as it can help improve the relevance of results provided to the user (Col 6, Ln 20-33).
Allowable Subject Matter
Claims 9 and 10 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 9:
The combination of Samsung and Wei teaches the method of claim 8, but does not teach wherein training or fine-tuning the generative model based on the user query and the user feedback that evaluates the media content with respect to the user query comprises: in response to determining that the classification of the negative user feedback is a first classification, reinforcing the generative model to not output a tag utilized to obtain the media content in response to the user query, and in response to determining that the classification of the negative user feedback is a second classification different from the first classification, rewriting an input prompt that is processed as input using the generative model; and training or fine-tuning the generative model using the rewritten input prompt.
Clarke et al. (US 11520785 B2) does teach generating a tag in order to search for a content item, but Clarke does not teach the rest of the limitations described above.
Myron et al. (US 20250218057 A1) does teach training a model based on user feedback where there are more than one negative feedback classifications, but it does not teach the rest of the limitations above.
The prior art of record alone or in combination does not teach the recited limitations.
Claim 10 depends on Claim 9 and therefore also contains allowable subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Teng et al. (US 20250131190 A1)
Enhanced prompt generation and model fine-tuning.
Meeks et al. (US 20240265204 A1)
Fine-tuning media generation model using user feedback selections of media.
Bosnjakovic et al. (US 11875240 B1)
Fine-tuning generative model using user feedback.
Bhatt et al. (US 20230004988 A1)
Fine-tuning generative model using user feedback.
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/ALEXANDER G MARLOW/ Assistant Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658