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 .
Claims 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/14/2025 and 9/23/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered and attached by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1 and 10 the limitations of “obtaining digital audio data associated with an audio signal from a user equipment of a user; determining, via a trained machine learning (ML) model including one or more neural networks, an indication associated with the digital audio data, wherein the indication is selected from any one or more of a prediction of an ending of one or more portions of the digital audio data, an intention of the digital audio data, a key term of the digital audio data, an action to take in view of the digital audio data, or context associated with the digital audio data; generating a response based upon the determined indication associated with the digital audio data; and transmitting the response to the user equipment of the user”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human hearing speech, determining key terms utilizing a set of rules or instructions in the mind, thinking of a response based on the key terms, and writing the response on paper using a pen or pencil. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Regarding claim 17 the limitations of “training, via a video dataset associated with graphical stickers, a video generation machine learning (ML) model employing one or more conditioning signals; finetuning the trained video generation ML model, wherein the finetuning includes applying feedback based upon quality of motion and content to an output of the video generation ML model; and analyzing, via the trained and finetuned video generation ML model, a text string associated with a first graphical sticker input to generate a second graphical sticker”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human understanding a set of rules or instructions on sticker generation and thinking of a sticker associated with read text. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of a system in claim 10, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using P0049-P0066 in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to hear speech, determine key terms utilizing a set of rules or instructions in the mind, think of a response based on the key terms, and write the response on paper using a pen or pencil amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the additional element of using generalized computer components to understand a set of rules or instructions on sticker generation and think of a sticker associated with text amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, the claim recites “determining a latency associated with generating the response, wherein the latency exceeds a predetermined threshold”, which reads on a human determining a time and threshold time to think of a response. No additional limitations are present.
With respect to claim 3, the claim recites “sending, to the user based upon the latency, a masking phrase or term prior transmitting the generated response”, which reads on a human writing a note if thinking of a response is taking longer than a threshold time. No additional limitations are present.
With respect to claim 4, the claim recites “predicting one or more responses to the predicted ending of the one or more portions of the digital audio data”, which reads on a human thinking of responses to predicted ending of heard speech. No additional limitations are present.
With respect to claim 5, the claim recites “comparing the one or more predicted responses to an entire content of the digital audio data, and selecting an optimal response from the one or more predicted responses, wherein the optimal response is the transmitted response”, which reads on a human selecting an optimal response after hearing all the speech. No additional limitations are present.
With respect to claim 6, the claim recites “wherein the predicted ending is based upon one or more of a word, context, pitch or tone associated with the user of the digital audio data”, which reads on a human predicting the ending to heard speech utilizing context of what was heard. No additional limitations are present.
With respect to claim 7, the claim recites “wherein the generated response is based upon a synthesis of two or more of the determined indications”, which reads on a human thinking of a response based on two or more key terms heard. No additional limitations are present.
With respect to claim 8, the claim recites “wherein each neural network of the ML model is trained on a phoneme”, which reads on a human determining a response from rules or instructions that work with phonemes. No additional limitations are present.
With respect to claim 9, the claim recites “wherein the user equipment includes any one or more of a smartphone, laptop, tablet, wearable device”, which reads on additional elements of generalized computer components. No additional limitations are present.
With respect to claim 18, the claim recites “wherein the first and second graphical stickers include any one or more of animated stickers, images, animations or videos”, which reads on a human thinking of an image. No additional limitations are present.
With respect to claim 19, the claim recites “wherein the video generation ML model employs image and text conditioning signals”, which reads on a human thinking of text and image. No additional limitations are present.
With respect to claim 20, the claim recites “wherein the second graphical sticker exhibits greater motion consistency than the first graphical sticker based upon a motion conditioning signal”, which reads on a human thinking of an image with better motion consistency. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-7, 9-11, and 13-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tang et al. (U.S. PG Pub No. 20230142836), hereinafter Tang.
Regarding claim 1 and 10 Tang teaches:
(Claim 1) A method comprising: (P0011, Systems and methods for utilizing query auto completion (QAC) in a voice search setting to reliably autocomplete an intermediate text (e.g., or transcription of a partial query) to obtain a predicted voice query before the user finishes speaking. This predictive capability helps improve overall system latency.)
(Claim 10) A system comprising: one or more processors; and at least one memory storing instructions, that when executed by the one or more processors, cause the system to: (P0011, Systems and methods for utilizing query auto completion (QAC) in a voice search setting to reliably autocomplete an intermediate text (e.g., or transcription of a partial query) to obtain a predicted voice query before the user finishes speaking. This predictive capability helps improve overall system latency.; P0082, The computing device 600 may include … CPU(s) may be standard programmable processors.; P0104, Computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner.)
obtaining digital audio data associated with an audio signal from a user equipment of a user; (P0019, The user input may be detected and used to generate capture data (e.g., audio data). The capture data may be sent to the user device.)
determining, via a trained machine learning (ML) model including one or more neural networks, an indication associated with the digital audio data, wherein the indication is selected from any one or more of a prediction of an ending of one or more portions of the digital audio data, an intention of the digital audio data, a key term of the digital audio data, an action to take in view of the digital audio data, or context associated with the digital audio data; (P0039, If the incoming voice query portion is not a final transcription, the method may, at step 230, predict one or more ranked transcriptions (e.g., top-K final transcriptions), where the ranked transcriptions may be ordered based on their relevance to the incoming voice query portion.; P0047, p(x(k)) may be modelled as a neural language model (NLM) rather than an observed distribution. By employing an NLM, unseen suffixes may be generated.)
generating a response based upon the determined indication associated with the digital audio data; and (P0039, The method may generate precomputed responses by processing predicted queries before they finish speaking at step 240 and/or step 250.)
transmitting the response to the user equipment of the user. (P0066, A response for the voice query may be output.; P0067, The output may be provided by a server to a user device, by a user device to an application, etc.)
Regarding claim 2 and 11 Tang teaches claim 1 and 10.
Tang further teaches:
determining a latency associated with generating the response, wherein the latency exceeds a predetermined threshold. (P0012, The downstream information retrieval, natural language processing, and action resolution components may take a total of 500-800 ms (e.g., without the disclosed techniques). This delay is far beyond the acceptable 100 ms threshold for an event to be instantaneous by humans.)
Regarding claim 4 and 13 Tang teaches claim 1 and 10.
Tang further teaches:
predicting one or more responses to the predicted ending of the one or more portions of the digital audio data. (P0039, If the incoming voice query portion is not a final transcription, the method may, at step 230, predict one or more ranked transcriptions (e.g., top-K final transcriptions), where the ranked transcriptions may be ordered based on their relevance to the incoming voice query portion. The method may then, at step 240, call the NLP System 130. At step 250, the method may cache NLP responses in NLP Cache for predicted queries.)
Regarding claim 5 and 14 Tang teaches claim 4 and 13.
Tang further teaches:
comparing the one or more predicted responses to an entire content of the digital audio data, and selecting an optimal response from the one or more predicted responses, wherein the optimal response is the transmitted response. (P0040, It follows that efficiency of returning a response to the final transcription may be greatly increased by comparing the final transcription to one of the predicted queries. Based on the comparison, one of the predicted queries may be selected based on one or more similarities to the final transcription. The precomputed response may be returned for the selected predicted query.)
Regarding claim 6 and 15 Tang teaches claim 1 and 10.
Tang further teaches:
wherein the predicted ending is based upon one or more of a word, context, pitch or tone associated with the user of the digital audio data. (P0028, Language models may be used to give context to words by analyzing the words proceeding and following the word in order to interpret different meanings the word may have. Language modeling and statistical analysis may be used to train a speech recognition system in order to improve recognition of incomplete voice queries or transcriptions.; P0039, If the incoming voice query portion is not a final transcription, the method may, at step 230, predict one or more ranked transcriptions (e.g., top-K final transcriptions), where the ranked transcriptions may be ordered based on their relevance to the incoming voice query portion.)
Regarding claim 7 and 16 Tang teaches claim 1 and 10.
Tang further teaches:
wherein the generated response is based upon a synthesis of two or more of the determined indications. (P0040, Based on the comparison, one of the predicted queries may be selected based on one or more similarities to the final transcription. The precomputed response may be returned for the selected predicted query.)
Regarding claim 9 Tang teaches claim 1.
Tang further teaches:
wherein the user equipment includes any one or more of a smartphone, laptop, tablet, wearable device. (P0017, The user device may comprise a computing device, smart device (e.g., smart glasses, smart watch, smart phone), a mobile device, a tablet.)
Claims 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Su et al. (U.S. PG Pub No. 20250166135), hereinafter Su.
Regarding claim 17 Su teaches:
A method comprising: (P0004, Controllable video generation system implemented as computer programs on one or more computers in one or more locations that generates a video conditioned on a text prompt and a control input.)
training, via a video dataset associated with graphical stickers, a video generation machine learning (ML) model employing one or more conditioning signals; (P0089, To train the joint encoder neural network and the video generation neural network, the training system obtains fine-tuning data, e.g., as an upload by a user of the training system or from a server, and then trains the joint encoder neural network and the video generation neural network to determine the trained values of at least some of the parameters of the neural networks based on optimizing an objective function using the fine-tuning data.; P0090, The fine-tuning data includes a plurality of training inputs. Each training input includes (i) a training text prompt which specifies one or more objects, (ii) one or more training images that each include a depiction of a respective one of the one or more objects.)
finetuning the trained video generation ML model, wherein the finetuning includes applying feedback based upon quality of motion and content to an output of the video generation ML model; and (P0089, To train the joint encoder neural network and the video generation neural network, the training system obtains fine-tuning data, e.g., as an upload by a user of the training system or from a server, and then trains the joint encoder neural network and the video generation neural network to determine the trained values of at least some of the parameters of the neural networks based on optimizing an objective function using the fine-tuning data.; P0090, The fine-tuning data includes a plurality of training inputs. Each training input includes (i) a training text prompt which specifies one or more objects, (ii) one or more training images that each include a depiction of a respective one of the one or more objects.)
analyzing, via the trained and finetuned video generation ML model, a text string associated with a first graphical sticker input to generate a second graphical sticker. (P0041, Having obtained the text prompt and the control input, the controllable video generation system generates the video using multiple neural networks based on the text prompt and the control input. The video includes a respective video frame at each of a plurality of time steps.)
Regarding claim 18 Su teaches claim 17.
Su further teaches:
wherein the first and second graphical stickers include any one or more of animated stickers, images, animations or videos. (P0041, Having obtained the text prompt and the control input, the controllable video generation system generates the video using multiple neural networks based on the text prompt and the control input. The video includes a respective video frame at each of a plurality of time steps.)
Regarding claim 19 Su teaches claim 17.
Su further teaches:
wherein the video generation ML model employs image and text conditioning signals. (P0041, Having obtained the text prompt and the control input, the controllable video generation system generates the video using multiple neural networks based on the text prompt and the control input. The video includes a respective video frame at each of a plurality of time steps.; P0044, Control input includes an image.)
Regarding claim 20 Su teaches claim 19.
Su further teaches:
wherein the second graphical sticker exhibits greater motion consistency than the first graphical sticker based upon a motion conditioning signal. (P0053, When the control input includes a sketch that characterizes a particular trajectory provided by the user, the video will depict a moving object that follows the particular trajectory characterized by the one or more user inputs.)
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 3 and 12 is rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Eide et al. (U.S. PG Pub No. 20080167874), hereinafter Eide.
Regarding claim 3 and 12 Tang teaches claim 2 and 11.
Tang does not specifically teach:
sending, to the user based upon the latency, a masking phrase or term prior transmitting the generated response.
Eide, however, teaches:
sending, to the user based upon the latency, a masking phrase or term prior transmitting the generated response. (P0025, In block 314, latency is determined by testing whether a response is ready after receiving a communication from a user in block 302. If a response is not ready, a filler generator selects a paralinguistic event or canned phrase from a database in block 316. In block 318, the random paralinguistic event or fixed phrase is conveyed to the user through a speech synthesis system.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to send a masking phrase based on latency. It would have been obvious to combine the references because generating an output can incur latency from ASR, NLU, and NLG that may lead to confusion or frustration by the user and it is desirable to send a masking phrase to mask latency in a more natural manner that does not confuse, frustrate or annoy the user. Eide P0004, P0006.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Yin et al. (U.S. PG Pub No. 20240242709), hereinafter Yin.
Regarding claim 8 Tang teaches claim 1.
Tang further teaches:
wherein each neural network of the ML model is trained on a phoneme. (P0047, Modelled as a neural language model (NLM).; P0050, g may provide a string representation of minimum trailing word deletions from the prefix and suffix words to add. For example, X′:=(“Show”, “Show Who”, “Show Hulu”), g(X′, 1)=“Show Hulu” and g(X′, 2)=“Show Who [<] Hulu.” A training set may be built from a corpus [Image Omitted] for each prefix-suffix pair. Based on this training, an efficient, state-of-the-art NLM may be fine-tuned over this training.)
Tang does not specifically teach:
wherein each neural network of the ML model is trained on a phoneme.
Yin, however, teaches:
wherein each neural network of the ML model is trained on a phoneme. (P0089, Training a language model by using the synonymous phoneme sequence group.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to train a neural network on a phoneme. It would have been obvious to combine the references because training a language model neural network using phoneme is a known technique that yields a predictable result of generating words from a sequence of phonemes by the language model. Yin P0328.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL WONSUK CHUNG whose telephone number is (571)272-1345. The examiner can normally be reached Monday - Friday (7am-4pm)[PT].
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/DANIEL W CHUNG/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
