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 .
The amendments were received on 12/12/2025. Claims 1-3, 5, 6, 8, 10-14, 16, 17, and 19-25 are pending where claims 1-3, 5, 6, 8, 10-14, 16, 17, and 19-23 were previously presented, claims 4, 7, 9, 15, and 18 were cancelled; and claims 24-25 are newly added.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/12/2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5, 6, 8, 10-14, 16, 17, and 19-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
With respect to claims 1, 12, and 20, the applicant amended the independent claims to incorporate limitations describing an essential component (semantic understanding and content summarization engine) where the amendments recite various essential and critical features of that are used in training that component; however, the underlying specification does not adequately describe the respective features or how that task is accomplished (i.e. no direction provided by the inventor nor is there the existence of working examples). Although the various models and algorithms might be individually known in the art (various computer programmers associated with AI training/development would be highly-skilled and know of the models/algorithms individually), the respective amendments recite utilizing all those techniques together to train an engine (model/program) where the specification only appears to mention the respective algorithms/models in paragraphs [0029] and [0031]; with each recitation discussing that the respective engine is being trained by some platform that makes use of those models/algorithms. In other words, there is no discussion illustrating how all the models are used together, what inputs the respective models have, and how the outputs of the models interrelate with each other. The breadth of the claim limitations listing the usage of all the models/algorithms together create a large number of possible combinations of the different models and algorithms together; which, in turn would require large amount of experimentation to determine how the respective models would work together and in what combination; not to mention what inputs the respective models would have or not have. Therefore, due to the lack of details in the disclosure, the respective independent claims are not enabled.
The respective dependent claims inherit the same deficiencies of the independent claims and are rejected for similar rationale as discussed above.
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 1-3, 5, 6, 8, 10-14, 16, 17, and 19-25 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.
With respect to claims 1, 12, and 20, the applicant amended the independent claims to incorporate new limitations describing an essential component (semantic understanding and content summarization engine) where the amendments recite various essential and critical features that are used to train/create the respective component; however, the claims recite the usage of a large list of algorithms/models with no details on how those models/algorithms are used together. As noted above in the 35 USC 112(a) rejections, the specification does not adequately describe/enable the respective features (i.e. no direction provided by the inventor nor is there the existence of working examples) and how they are utilized together. As such, the lack of disclosure and direction of the essential steps of creating the semantic understanding and content summarization engine as recited in the claim in view of the specification as well as no details on how the respective models/algorithms would work together, lead the claim limitations to not be particularly pointed out or distinctly claimed and are thus rejected for being indefinite.
The respective dependent claims inherit the same deficiencies of the independent claims and are rejected for similar rationale as discussed above.
Claims 8 and 19 depend upon claims 1 and 12 which were amended to recite “wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same”; which according to applicant’s specification at paragraphs 30 and 31 focuses on different response for different users. Although the specification indicates it as an example, applicant’s dependent claim 8 indicates that the responses are different from one user to another user thus causing contention as to what the metes and bounds of the respective limitation of the independent claims (additionally, applicant’s arguments indicate on page 13 that their claimed invention selects “identical rankings of responses for multiple different users…”; , i.e. if claim 8 selects/ranks responses differently for users then they aren’t identical rankings of responses for multiple different users). In other words, claims 8 and 19 appear to contradict the limitations of their respective parent claim and may not be possible if the rankings are meant to be identical as applicant alleges in their arguments. How can the system be designed to provide identical rankings of responses but actually provide different rankings of responses? If the response ranking is similar to applicant’s specification and trained on a user-by-user basis, then it appears that the independent claims limitation of responses being ranked the same for two different users just happens to turn out that way (i.e. intended result that two users happen to have the same output); however, if the interpretation is that the responses are not personalized (user by user basis) then all users with respective interaction would generate the same listing of responses. How can the system be able to both provide same results for some users and different results for other users for the same interactions? In other words, is the ranking independent of the user (responses are the same because of same context information) or the ranking is trained on a user by user basis (i.e. first user and second user happen to have similar models and first user and third user have different models). For purposes of compact prosecution, the Examiner is construing the claim limitations to refer to the context information and that the first and second user have the “same” or similar context information and the first and third user have substantially different context information.
The respective dependent claims inherit the same deficiencies of the independent claims and are rejected for similar rationale as discussed above.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5, 6, 8, 10, 12, 14, 16, 17, 19-21, 24, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Akkapeddi [US 2024/0184859], and Sontag et al [US 2012/0323828 A1].
With regard to claim 1, Hao teaches a computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor (see col 11, lines 30 through col 12, line 10; see col 12, line 47-67; the system can be implemented with computer components), cause the computing platform to:
receive chatbot interaction information indicating interactions of a user with a chatbot (see Figures 6A and 6B; see col 4, lines 60-65; the system can utilize a chatbot to receive interactions from a user);
identify context information associated with the user (see col 5, lines 1-12; the system can determine context associated with the user);
generate, by inputting the chatbot interaction information from the user’s interaction with the chatbot system to determine answers/responses for the user and be able to select a response from the plurality of responses where multiple machine learning algorithms can be used including regression, naïve Bayes, and SVM);
wherein selecting the first response comprises: ranking,
generate a plurality of image frames corresponding to the first response; arrange,
render a video output using the plurality of image frames and based on the first sequence, wherein the video output comprises a response to the chatbot interaction information; and send, to a user device of the user, the video output and one or more commands directing the user device to display the video output, wherein sending the one or more commands directing the user device to display the video output causes the user device to display the video output (see Figure 6B and col 9, line 60 through col 10, line 2; the system can create/render the video output/clip based on the selected/extracted frames and transmit the video to the respective client/user).
Hao does not appear to explicitly teach:
generate, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions,
wherein the semantic understanding and content summarization engine comprises … neural network, random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model;
ranking, based on the context information, the plurality of responses,
wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same;
arrange, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence for the first user, wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence.
Miller teaches generate, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions (see paragraphs [0030]-[0031], [0043]-[0044], and [0037] and [0073]-[0074]; the system can utilize various pieces of information including context data to determine responses to the user interactions);
wherein training the semantic understanding and content summarization engine comprises applying … neural network, see paragraph [0096]; the machine learning models can include neural networks).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao by including means to identify context of a user and leverage that context when making decisions on responses as taught by Miller in order to provide a more robust and adaptable system that can provide more relevant answers/responses to a user by using the context related to the user’s interactions to help determine what answers are associated with the context thereby preventing or reducing the chance of irrelevant answers/responses being presented to the user which in turn boosts the user’s confidence in the system to provide reliable answers.
Hao in view of Miller teach ranking, based on the context information, the plurality of responses (see Miller, paragraph [0043]; see Hao, col 8, lines 10-26; the system can perform ranking that makes use of the context information);
arrange, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence for the first user (see Hao, see col 8, lines 37-51 and col 9, lines 51-59; and col 8, lines 10-33; see Miller, [0037] and [0073]-[0074]; the system can utilize information from the user’s interaction with the chatbot system to determine answers/responses for the user and be able to select a response from the plurality of responses and then determine frames to extract and arrange into a sequence to form a video snippet/clip).
Hao in view of Miller teach ranking of responses but do not appear to explicitly teach:
wherein the semantic understanding and content summarization engine comprises … random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model.
wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same;
wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence.
Akkapeddi teaches wherein the semantic understanding and content summarization engine comprises … random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model (see paragraph [0032]; the system can make use of multiple different well-known and widely used machine learning algorithms).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller by using well-known and widely used machine learning algorithms as taught by Akkapeddi in order to leverage usage of widely used and known techniques to allow the for implementers of the system to have flexibility of usage of different techniques to achieve particular functionality desired by the designers of the system.
Hao in view of Miller and Akkapeddi teach ranking but do not appear to explicitly teach:
wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same;
wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence.
Sontag teaches wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same (see paragraph [0051]; the system can make use of a standard ranking function that is not personalized to the user thus allowing the same ranking of responses/answers/results no matter the user using the system).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller and Akkapeddi by utilizing non-personalized initial ranking means as taught by Sontag in order to allow all users to be able to receive the same quality or relevant results as answers to their query while still having means to personalize information at a later stage thus allowing consistent/standard results/responses to be returned while still being able to customize information to the user at a later stage to make the respective result(s) more meaningful or relevant to the user.
Hao in view of Miller, Akkapeddi, and Sontag teach wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence (see Sontag, paragraph [0052]; see Hao, see col 8, lines 31-51 and col 9, lines 51-64; see Miller, [0037] and [0073]-[0074]; the system allows for the selection of various frames from a selected result/response based on its relevancy to the user’s input where relevancy can be based on personalized relevancy associated with the user).
With regard to claim 3, Hao in view of Miller, Akkapeddi, and Sontag teach wherein the context information includes historical chatbot interactions for the user (see Miller, paragraphs [0036], [0059], and [0064]; the context can include past/historical interactions from the user).
With regard to claim 5, Hao in view of Miller, Akkapeddi, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train, using historical chatbot interaction information and historical context information, the semantic understanding and content summarization engine, wherein training the semantic understanding and content summarization engine configures the semantic understanding and content summarization engine to output a plurality of responses to chatbot requests (see Miller, paragraphs [0041], [0064]-[0065], [0073], and [0036]; the system can train the respective models for a user based on historical interactions of the user as well as the respective historical context information too).
With regard to claim 6, Hao in view of Miller, Akkapeddi, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: extract, from the chatbot interaction information, one or more keywords, wherein generating the plurality of responses is based on the one or more keywords (see Hao, col 8, lines 10-14; col 8, line 52 through col 9, line 4; see col 10, lines 34-53; the system can utilize the query input and extract words from the query interaction/input to be able to discern ).
With regard to claim 8, Hao in view of Miller, Akkapeddi, and Sontag teach wherein the ranking of the plurality of responses for the first user is different than a ranking of the plurality of responses for a third user (see Hao, col 2, lines 44-52; Miller, paragraphs [0029]-[0030]; Sontag, paragraphs [0051]-[0052]; first user and third user have different context information which thus affects the respective ranking, see 35 USC 112 rejection above for more detailed discussion on the claim construction).
With regard to claim 10, Hao in view of Miller, Akkapeddi, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train, using historical chatbot interaction information and historical context information, the intelligent frame estimation engine, wherein training the intelligent frame estimation engine configures the intelligent frame estimation engine to output frame sequences (see Hao, col 8, 26-61; see Miller, paragraphs [0041], [0064]-[0065], [0073], and [0036]; the system can train the respective models for a user based on historical interactions of the user as well as the respective historical context information too).
With regards to claims 12 and 20, these claims are substantially similar to claim 1 and are rejected for similar reasons as discussed above.
With regards to claims 14, 16, 17, 19, and 21, these claims are substantially similar to claims 3, 5, 6, 8, and 10 respectively and are rejected for similar reasons as discussed above.
With regard to claim 24, Hao in view of Miller, Akkapeddi, and Sontag teach wherein: a first portion of the plurality of responses is generated for the first user and a second portion of the plurality of responses is generated for the third user, the first portion of the plurality of responses is generated using a naive Bayesian model, and the second portion of the plurality of responses is generated using hierarchical clustering (see Hao, col 9, line 33-36; Akkapeddi, paragraph [0032]; the system can utilize a plurality of models including naïve Bayes and hierarchical clustering).
With regard to claim 25, Hao in view of Miller, Akkapeddi, and Sontag teach wherein: a first portion of the plurality of responses is generated for the first user and a second portion of the plurality of responses is generated for the third user, the first portion of the plurality of responses is generated using a principal component analysis model, and the second portion of the plurality of responses is generated using k-means clustering (see Akkapeddi, paragraph [0032]; the system can utilize a plurality of models including principal component analysis and k-means clustering).
Claims 1, 3, 5, 6, 8, 10, 12, 14, 16, 17, 19-21, 24, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Anglin et al [US 2020/0218940], and Sontag et al [US 2012/0323828 A1].
With regard to claim 1, Hao teaches a computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor (see col 11, lines 30 through col 12, line 10; see col 12, line 47-67; the system can be implemented with computer components), cause the computing platform to:
receive chatbot interaction information indicating interactions of a user with a chatbot (see Figures 6A and 6B; see col 4, lines 60-65; the system can utilize a chatbot to receive interactions from a user);
identify context information associated with the user (see col 5, lines 1-12; the system can determine context associated with the user);
generate, by inputting the chatbot interaction information to the user interactions (see col 4, line 65 through col 5, line 3; col 6, lines 16-35 and col 8, lines 10-33; col 9, lines 33-36; col 13, lines 29-35; the system can utilize information from the user’s interaction with the chatbot system to determine answers/responses for the user and be able to select a response from the plurality of responses where multiple machine learning algorithms can be used including regression, naïve Bayes, and SVM);
wherein selecting the first response comprises: ranking,
generate a plurality of image frames corresponding to the first response; arrange,
render a video output using the plurality of image frames and based on the first sequence, wherein the video output comprises a response to the chatbot interaction information; and send, to a user device of the user, the video output and one or more commands directing the user device to display the video output, wherein sending the one or more commands directing the user device to display the video output causes the user device to display the video output (see Figure 6B and col 9, line 60 through col 10, line 2; the system can create/render the video output/clip based on the selected/extracted frames and transmit the video to the respective client/user).
Hao does not appear to explicitly teach:
generate, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions,
wherein the semantic understanding and content summarization engine comprises … neural network, random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model;
ranking, based on the context information, the plurality of responses,
wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same;
arrange, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence for the first user, wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence.
Miller teaches generate, by inputting the chatbot interaction information and the context information into a semantic understanding and content summarization engine, a plurality of responses to the user interactions (see paragraphs [0030]-[0031], [0043]-[0044], and [0037] and [0073]-[0074]; the system can utilize various pieces of information including context data to determine responses to the user interactions);
wherein training the semantic understanding and content summarization engine comprises applying … neural network, see paragraph [0096]; the machine learning models can include neural networks).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao by including means to identify context of a user and leverage that context when making decisions on responses as taught by Miller in order to provide a more robust and adaptable system that can provide more relevant answers/responses to a user by using the context related to the user’s interactions to help determine what answers are associated with the context thereby preventing or reducing the chance of irrelevant answers/responses being presented to the user which in turn boosts the user’s confidence in the system to provide reliable answers.
Hao in view of Miller teach ranking, based on the context information, the plurality of responses (see Miller, paragraph [0043]; see Hao, col 8, lines 10-26; the system can perform ranking that makes use of the context information);
arrange, based on the context information and using an intelligent frame estimation engine, the plurality of image frames in a first sequence for the first user (see Hao, see col 8, lines 37-51 and col 9, lines 51-59; and col 8, lines 10-33; see Miller, [0037] and [0073]-[0074]; the system can utilize information from the user’s interaction with the chatbot system to determine answers/responses for the user and be able to select a response from the plurality of responses and then determine frames to extract and arrange into a sequence to form a video snippet/clip).
Hao in view of Miller teach ranking of responses but do not appear to explicitly teach:
wherein the semantic understanding and content summarization engine comprises … random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model.
wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same;
wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence.
Anglin teaches wherein the semantic understanding and content summarization engine comprises … random forest model, principal component analysis model, hierarchical clustering model, and K-means clustering model (see paragraph [0051]; the system can make use of multiple different well-known and widely used machine learning algorithms).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller by using well-known and widely used machine learning algorithms as taught by Anglin in order to leverage usage of widely used and known techniques to allow the for implementers of the system to have flexibility of usage of different techniques to achieve particular functionality desired by the designers of the system.
Sontag teaches wherein a ranking of the plurality of responses for a first user and a ranking of the plurality of responses for a second user are the same (see paragraph [0051]; the system can make use of a standard ranking function that is not personalized to the user thus allowing the same ranking of responses/answers/results no matter the user using the system).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller and Anglin by utilizing non-personalized initial ranking means as taught by Sontag in order to allow all users to be able to receive the same quality or relevant results as answers to their query while still having means to personalize information at a later stage thus allowing consistent/standard results/responses to be returned while still being able to customize information to the user at a later stage to make the respective result(s) more meaningful or relevant to the user.
Hao in view of Miller, Anglin, and Sontag teach wherein the plurality of image frames are arranged in a second sequence for the second user, wherein the second sequence is different than the first sequence (see Sontag, paragraph [0052]; see Hao, see col 8, lines 31-51 and col 9, lines 51-64; see Miller, [0037] and [0073]-[0074]; the system allows for the selection of various frames from a selected result/response based on its relevancy to the user’s input where relevancy can be based on personalized relevancy associated with the user).
With regard to claim 3, Hao in view of Miller, Anglin, and Sontag teach wherein the context information includes historical chatbot interactions for the user (see Miller, paragraphs [0036], [0059], and [0064]; the context can include past/historical interactions from the user).
With regard to claim 5, Hao in view of Miller, Anglin, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train, using historical chatbot interaction information and historical context information, the semantic understanding and content summarization engine, wherein training the semantic understanding and content summarization engine configures the semantic understanding and content summarization engine to output a plurality of responses to chatbot requests (see Miller, paragraphs [0041], [0064]-[0065], [0073], and [0036]; the system can train the respective models for a user based on historical interactions of the user as well as the respective historical context information too).
With regard to claim 6, Hao in view of Miller, Anglin, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: extract, from the chatbot interaction information, one or more keywords, wherein generating the plurality of responses is based on the one or more keywords (see Hao, col 8, lines 10-14; col 8, line 52 through col 9, line 4; see col 10, lines 34-53; the system can utilize the query input and extract words from the query interaction/input to be able to discern ).
With regard to claim 8, Hao in view of Miller, Anglin, and Sontag teach wherein the ranking of the plurality of responses for the first user is different than a ranking of the plurality of responses for a third user (see Hao, col 2, lines 44-52; Miller, paragraphs [0029]-[0030]; the system can utilize models that are specific to a particular user thus allowing each user to have their own specific model).
With regard to claim 10, Hao in view of Miller, Anglin, and Sontag teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train, using historical chatbot interaction information and historical context information, the intelligent frame estimation engine, wherein training the intelligent frame estimation engine configures the intelligent frame estimation engine to output frame sequences (see Hao, col 8, 26-61; see Miller, paragraphs [0041], [0064]-[0065], [0073], and [0036]; the system can train the respective models for a user based on historical interactions of the user as well as the respective historical context information too).
With regards to claims 12 and 20, these claims are substantially similar to claim 1 and are rejected for similar reasons as discussed above.
With regards to claims 14, 16, 17, 19, and 21, these claims are substantially similar to claims 3, 5, 6, 8, and 10 respectively and are rejected for similar reasons as discussed above.
With regard to claim 24, Hao in view of Miller, Anglin, and Sontag teach wherein: a first portion of the plurality of responses is generated for the first user and a second portion of the plurality of responses is generated for the third user, the first portion of the plurality of responses is generated using a naive Bayesian model, and the second portion of the plurality of responses is generated using hierarchical clustering (see Hao, col 9, line 33-36; Anglin, paragraph [0051]; the system can utilize a plurality of models including naïve Bayes and hierarchical clustering).
With regard to claim 25, Hao in view of Miller, Anglin, and Sontag teach wherein: a first portion of the plurality of responses is generated for the first user and a second portion of the plurality of responses is generated for the third user, the first portion of the plurality of responses is generated using a principal component analysis model, and the second portion of the plurality of responses is generated using k-means clustering (see Anglin, paragraph [0051]; the system can utilize a plurality of models including principal component analysis and k-means clustering).
Claims 2, 13, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Akkapeddi [US 2024/0184859], and Sontag et al [US 2012/0323828 A1] in further view of Croitoru et al [US 2024/0355119 A1], Bansal [US 11,295,583], Perdomo Ortiz et al [Us 2022/0147358 A1], and Ford et al [US 9,882,918].
With regard to claim 2, Hao in view of Miller, Akkapeddi, and Sontag teach all the claim limitations of claim 1 as discussed above.
Hao in view of Miller, Akkapeddi, and Sontag teach the usage of various videos as responses and data storage techniques (see Akkapeddi, paragraph [0023]) but do not appear to explicitly teach:
wherein: the chatbot interaction information includes a request from the user, and wherein the video output includes a tutorial providing a response to the request,
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator,
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator;
the context information is stored using a distributed ledger.
Croitoru teaches wherein: the video output includes a tutorial providing a response to the request (see paragraph [0054]; the videos being used to provide the video clip answers/responses can be a tutorial video).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Akkapeddi, and Sontag by including means to provide answers/responses associated with tutorial videos as taught by Croitoru in order to expand the flexibility of the system to be able handle particular types of content depicted in videos thereby helping users be able to ask specific questions to learn how to do some task that is associated with the respective tutorial video.
Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru teach wherein: the chatbot interaction information includes a request from the user, and wherein the video output includes a tutorial providing a response to the request (see Croitoru, paragraph [0054]; see Hao, see Figures 6A and 6B; see col 4, lines 60-65; the system can utilize a chatbot to receive interactions from a user and receive a video response that can be a tutorial video).
Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator,
the context information is stored using a distributed ledger.
Ford teaches the context information is stored using a distributed ledger (see col 5, lines 37-53 and col 10, lines 43-49; context information can be stored in a distributed ledger/blockchain).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru by utilizing well-known data structure for storing data about a user as taught by Ford in order to utilize a widely-known and used data structure that allows for confidence and integrity in the stored data since the data structure ensures that the data is immutable while also providing distributed and replicated copies of the data to help ensure data protection.
Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru and Ford do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator,
Perdomo Ortiz teaches inputting the input information into the quantum image generator (see paragraph [0036] and [0040]-[0041]; the system can receive input to generate images via quantum component).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru and Ford by utilizing quantum computers to assist in image generation functionality as taught by Perdomo Ortiz in order to allow the system to be able to utilize almost any video no matter its quality/resolution since the quantum image generator can take the lower-resolution videos/content and be able to provide higher resolution versions thus providing a greater experience for the user by not having to be subjected to highly relevant but low-quality/low-resolution video results.
Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru, Ford, and Perdomo Ortiz do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction.
Bansal teaches wherein generating the plurality of image frames corresponding to the first response comprises: generating,
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the quantum image generation process of Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru, Ford, and Perdomo Ortiz by utilizing quantum error correction as taught by Bansal in order to protect the converted quantum information from errors or other quantum noise when the standard videos are being converted.
Hao in view of Miller, Akkapeddi, and Sontag in further view of Croitoru, Ford, Perdomo Ortiz, and Bansal teach generating, based on the first response, input information for a quantum image generator (see Hao, col 8, lines 17-36; Bansal, col 5, lines 4-28; col 5, line 52 through col 6, line 31; the system can select a response and associated video frames that can then be converted into quantum images for additional processing).
With regard to claims 13 and 23, these claims are substantially similar to claim 2 and is rejected for similar reasons as discussed above.
Claims 2, 13, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Anglin et al [US 2020/0218940], and Sontag et al [US 2012/0323828 A1] in further view of Croitoru et al [US 2024/0355119 A1], Bansal [US 11,295,583], Perdomo Ortiz et al [Us 2022/0147358 A1], and Ford et al [US 9,882,918].
With regard to claim 2, Hao in view of Miller, Anglin, and Sontag teach all the claim limitations of claim 1 as discussed above.
Hao in view of Miller, Anglin, and Sontag teach the usage of various videos as responses and data storage techniques (see Anglin, abstract) but do not appear to explicitly teach:
wherein: the chatbot interaction information includes a request from the user, and wherein the video output includes a tutorial providing a response to the request,
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator,
the context information is stored using a distributed ledger.
Croitoru teaches wherein: the video output includes a tutorial providing a response to the request (see paragraph [0054]; the videos being used to provide the video clip answers/responses can be a tutorial video).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Anglin, and Sontag by including means to provide answers/responses associated with tutorial videos as taught by Croitoru in order to expand the flexibility of the system to be able handle particular types of content depicted in videos thereby helping users be able to ask specific questions to learn how to do some task that is associated with the respective tutorial video.
Hao in view of Miller, Anglin, and Sontag in further view of Croitoru teach wherein: the chatbot interaction information includes a request from the user, and wherein the video output includes a tutorial providing a response to the request (see Croitoru, paragraph [0054]; see Hao, see Figures 6A and 6B; see col 4, lines 60-65; the system can utilize a chatbot to receive interactions from a user and receive a video response that can be a tutorial video).
Hao in view of Miller, Anglin, and Sontag in further view of Croitoru do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator,
the context information is stored using a distributed ledger.
Ford teaches the context information is stored using a distributed ledger (see col 5, lines 37-53 and col 10, lines 43-49; context information can be stored in a distributed ledger/blockchain).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Anglin, and Sontag in further view of Croitoru by utilizing well-known data structure for storing data about a user as taught by Ford in order to utilize a widely-known and used data structure that allows for confidence and integrity in the stored data since the data structure ensures that the data is immutable while also providing distributed and replicated copies of the data to help ensure data protection.
Hao in view of Miller, Anglin, and Sontag in further view of Croitoru and Ford do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction,
and inputting the input information into the quantum image generator,
Perdomo Ortiz teaches inputting the input information into the quantum image generator (see paragraph [0036] and [0040]-[0041]; the system can receive input to generate images via quantum component).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversation service application of Hao in view of Miller, Anglin, and Sontag in further view of Croitoru and Ford by utilizing quantum computers to assist in image generation functionality as taught by Perdomo Ortiz in order to allow the system to be able to utilize almost any video no matter its quality/resolution since the quantum image generator can take the lower-resolution videos/content and be able to provide higher resolution versions thus providing a greater experience for the user by not having to be subjected to highly relevant but low-quality/low-resolution video results.
Hao in view of Miller, Anglin, and Sontag in further view of Croitoru, Ford, and Perdomo Ortiz do not appear to explicitly teach:
generating the plurality of image frames corresponding to the first response comprises:
generating, based on the first response, input information for a quantum image generator;
formatting the input information using quantum error correction.
Bansal teaches wherein generating the plurality of image frames corresponding to the first response comprises: generating,
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the quantum image generation process of Hao in view of Miller, Anglin, and Sontag in further view of Croitoru, Ford, and Perdomo Ortiz by utilizing quantum error correction as taught by Bansal in order to protect the converted quantum information from errors or other quantum noise when the standard videos are being converted.
Hao in view of Miller, Anglin, and Sontag in further view of Croitoru, Ford, Perdomo Ortiz, and Bansal teach generating, based on the first response, input information for a quantum image generator (see Hao, col 8, lines 17-36; Bansal, col 5, lines 4-28; col 5, line 52 through col 6, line 31; the system can select a response and associated video frames that can then be converted into quantum images for additional processing).
With regard to claims 13 and 23, these claims are substantially similar to claim 2 and is rejected for similar reasons as discussed above.
Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Anglin et al [US 2020/0218940], and Sontag et al [US 2012/0323828 A1] in further view of Khalil et al [US 2024/0292073 A1].
With regard to claim 11, Hao in view of Miller, Anglin, and Sontag teach all the claim limitations of claim 1 as discussed above.
Hao in view of Miller, Anglin, and Sontag teach do not appear to explicitly teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive feedback information on the video output; and update, based on the feedback information, the semantic understanding and content summarization engine and the intelligent frame estimation engine.
Khalil teaches wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive feedback information on the video output; and update, based on the feedback information, the semantic understanding and content summarization engine and the intelligent frame estimation engine (see paragraphs [0094] and [0096]; the user has means to provide feedback and have the respective models be updated accordingly).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversational response generation system of Hao in view of Miller, Anglin, and Sontag by allowing means for users to provide feedback to the presented responses/output as taught by Khalil in order to allow flexibility in the system from not having to used only trained models but to be able to adapt as time goes by to users’ preferences by being able to adjust/adapt the model based on feedback from the generated responses/output thus allowing future uses of the system to have a higher chance of providing high-quality and relevant responses.
With regard to claim 22, this claim is substantially similar to claim 11 and is rejected for similar reasons as discussed above.
Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al [US 12,235,897] in view of Miller et al [US 2019/0005021 A1], Akkapeddi [US 2024/0184859], and Sontag et al [US 2012/0323828 A1] in further view of Khalil et al [US 2024/0292073 A1]
With regard to claim 11, Hao in view of Miller, Akkapeddi, and Sontag teach all the claim limitations of claim 1 as discussed above.
Hao in view of Miller, Akkapeddi, and Sontag teach do not appear to explicitly teach wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive feedback information on the video output; and update, based on the feedback information, the semantic understanding and content summarization engine and the intelligent frame estimation engine.
Khalil teaches wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive feedback information on the video output; and update, based on the feedback information, the semantic understanding and content summarization engine and the intelligent frame estimation engine (see paragraphs [0094] and [0096]; the user has means to provide feedback and have the respective models be updated accordingly).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the conversational response generation system of Hao in view of Miller, Akkapeddi, and Sontag by allowing means for users to provide feedback to the presented responses/output as taught by Khalil in order to allow flexibility in the system from not having to used only trained models but to be able to adapt as time goes by to users’ preferences by being able to adjust/adapt the model based on feedback from the generated responses/output thus allowing future uses of the system to have a higher chance of providing high-quality and relevant responses.
With regard to claim 22, this claim is substantially similar to claim 11 and is rejected for similar reasons as discussed above.
Response to Arguments
Applicant's arguments (see the first paragraph on page 11 through second to last paragraph on page 12) have been fully considered but they are not persuasive. The applicant amended the claims to indicate that the semantic understanding and content summarization engine is trained using a wide variety of techniques and thus the respective 35 USC 112 rejections should be withdrawn. The Examiner respectfully disagrees. As indicated in the 35 USC 112 rejections, the respective rejection of the various techniques are recited in the specification with little detail as to how they are utilized together. As noted in the rejection, the various techniques are individually known but the usage of all the techniques together to train the semantic understanding and content summarization engine is not enabled for the reasons set forth in the 35 USC 112 rejections above. Therefore, applicant’s arguments are not persuasive.
Applicant’s arguments (see last paragraph on page 12 through the second to last paragraph on page 13) with respect to the rejection(s) of claim(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sontag. The applicant amended the claims to incorporate new limitations describing identical rankings of responses for multiple different users. As see in the 35 USC 103 rejections above, the combination of prior art references illustrate the usage of conventional or non-personalized ranking of results/responses thus allowing each user to receive the same rankings for the same input.
Applicant's arguments (see the last paragraph on page 13 through second to last paragraph on page 14) have been fully considered but they are not persuasive. The applicant argues that the combination with Akkapeddi would not be evident without the benefit of hindsight reasoning. The Examiner respectfully disagrees. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). As illustrated in the 35 USC 103 rejections, the Hao reference makes use of different techniques to find results/responses to the user that evaluates and determines relevancy of responses to the user input/query as well as relevancy of the image frames in order to choose the frames to form a clip/sequence. Hao makes mention of being able to use machine learning techniques with Miller illustrating that a machine learning model can include multiple different machine learning techniques. However, Miller’s indicates that other machine learning techniques are also considered via “and such”. Although a listing of any widely known and used machine learning technique could be provided, the Akkapeddi reference provided an example of the usage of multiple machine learning techniques together. Accordingly, one of ordinary skill in the art associated with machine learning dealing with a problem pertinent to machine learning, such as what models are available to be used to process data, would have known of such widely known and utilized techniques and their ability to be utilized together thus the respective combination of Akkapeddi is not based on improper hindsight reasoning since one of ordinary skill in the art of machine learning techniques would review any teaching that provides machine learning models to determine what models can be utilized together. As such, applicant’s arguments are not persuasive.
Applicant's arguments (see the last paragraph on page 14 through second paragraph on page 15) have been fully considered but they are not persuasive. The applicant argues that that the other independent claims and dependent claims for the reasons discussed above. The Examiner respectfully disagrees. As discussed above, the respective 35 USC 103 rejections still stand therefore the respective rejections for the other independent and dependent claims still stand too for similar reasons as discussed above.
Applicant's arguments (see the third paragraph on page 15 through last paragraph on page 16) have been fully considered but they are not persuasive. The applicant argues that that the rejections involving Anglin should be withdrawn for similar reasons as discussed above with regard to Akkapeddi, in particular the improper hindsight reasoning. As discussed above, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). As illustrated in the 35 USC 103 rejections, the Hao reference makes use of different techniques to find results/responses to the user that evaluates and determines relevancy of responses to the user input/query as well as relevancy of the image frames in order to choose the frames to form a clip/sequence. Hao makes mention of being able to use machine learning techniques with Miller illustrating that a machine learning model can include multiple different machine learning techniques. However, Miller’s indicates that other machine learning techniques are also considered via “and such”. Although a listing of any widely known and used machine learning technique could be provided, the Anglin reference provided an example of the usage of multiple machine learning techniques together. Accordingly, one of ordinary skill in the art associated with machine learning dealing with a problem pertinent to machine learning, such as what models are available to be used to process data, would have known of such widely known and utilized techniques and their ability to be utilized together thus the respective combination of Anglin is not based on improper hindsight reasoning since one of ordinary skill in the art of machine learning techniques would review any teaching that provides machine learning models to determine what models can be utilized together. As such, applicant’s arguments are not persuasive.
Applicant's arguments (see the first paragraph on page 17 through the last paragraph on page 19) have been fully considered but they are not persuasive. The applicant argues that (a) the amendments to claims 2 and 13 would result in a seven way combination of references that would not be evident to one of ordinary skill in the art without hindsight reasoning; and (b) the rejections for the other dependent claims be withdrawn for similar reasons as discussed above with independent claims.
With regard to argument (a) regarding the number of references and hindsight reasoning; in response to applicant's argument that the examiner has combined an excessive number of references, reliance on a large number of references in a rejection does not, without more, weigh against the obviousness of the claimed invention. See In re Gorman, 933 F.2d 982, 18 USPQ2d 1885 (Fed. Cir. 1991). As noted above, the combination of references complement each other and would have been obvious to one of ordinary skill in the art. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). As noted by applicant, although various references may be related to different problems and fields, the Examiner notes that it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, with respect to the Croitoru and Ford references, the Croitoru reference was being utilized to illustrate the content of the videos (i.e. the purpose of the video is a tutorial about some topic) while Ford illustrates that particular type of information (context information) can be stored in widely-known and used data structures. Therefore, as seen from the above discussion, the respective references are pertinent to the applicant’s problem of using tutorial videos (Croitoru) and storing context/metadata information in a distributed ledger (Ford). The applicant argues that the Croitoru and Ford reference rely on conventional computing architecture; however, as noted above, the respective teachings of Croitoru and Ford are not relied upon for the computing machine architecture but rather the meaning of the data (i.e. although the references teach video data, Croitoru illustrates that the video can be a tutorial video) with Ford illustrating storage of context/metadata information (meaning of the data/information) in a distributed ledger. Additionally, the Perdomo Ortiz reference modifies the video clip generation process of Hao (in view of combination of references) by utilizing a quantum component to generate images that can assist in generating higher resolution images/videos thus helping to improve the quality of the result by not returning low quality resolution video clips. Lastly, the Bansal reference discusses a process of including error correction for quantum converted images/video thus helping to protect the data by being fault tolerant and overcome any noise that was introduced when generating the higher resolution video clips. Therefore, applicant’s arguments are not persuasive and the respective rejections still stand.
With regard to argument (b), the Examiner notes that the respective rejections of the independent claims still stand; therefore, the respective rejections of the dependent claims still stand too.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC S SOMERS whose telephone number is (571)270-3567. The examiner can normally be reached M-F 11-8 EST.
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/MARC S SOMERS/Primary Examiner, Art Unit 2159 2/20/2026