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 .
Pre-Grant publication 20240420588 published dated 12/19/2024.
Claims 1-14,16-21 pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/6/2026 has been entered.
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 1-14, 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is to a process, device and a computer readable medium and thus fall within one of the four statutory categories (Step 1: YES).
Claims 1 , 10 and 16 are directed to obtaining a signal sequence, determining, based on a learning object, that an error is detected at a target position of the signal sequence, detecting a target error pattern corresponding to the target position of the signal sequence, matching one of a plurality of predetermined error patterns associated with the target position , selecting, from a plurality of feedbacks corresponding to the plurality of predetermined error patterns and providing the target feedback. Applicant is characterizing the method of computer signal processing that allows links and service to be tailored for specific types of communications. Target position comprises of a video clip including action correction to a wrong action trajectory in the video signal sequence or a video frame corresponding to a wrong posture in the video signal sequence, wherein detecting the target error pattern comprises extracting feature information from the signals. The claims appears to be directed to an abstract idea with regard to collecting data via obtaining signal sequence, analyzing that data by determining error, detecting a target error pattern, and providing an output based on that analysis selecting a feedback and presenting it) and is therefore abstract as a mental process. Could also be characterized as a method of organizing human activity in terms of teaching human subjects since claiming employing a “deep-learning neural network” such use of a machine learning model in a particular technological environment is itself an abstract idea, following the CAFC’s decision on a Recentive Analytics case. These involve steps drawn to concept categorized as an actions that are receiving, observing, identifying, evaluating and judging of textual and visual inputs. A concept that are mental processes and by including generating feedback text revision and processing of information for analysis and use of machine language like organizing of certain human activities. Further the use of pattern recognition and selection by machine-learned model could also be categorized as a use mathematical calculations within some mathematical concepts under certain rules. They are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", "databases of digital content with predetermined storage of instructions for target positions and error patterns”,. Error is further detected comprising of processing signal sequence using a trained deep-learning neural network model executed on dedicated neural-network hardware selected from the group consisting of a graphics processing unit (GPU1) neural-processing unit (NPU) or tensor processing unit (TPU), detecting a target error pattern corresponding to the target position of the signal sequence.
The amended claims for detecting a target error pattern corresponding to the target position of the signal sequence extracting spatiotemporal feature information from multiple consecutive frames of the video signal sequence including the target position, wherein the spatiotemporal feature information comprises motion vectors representing changes in position between the consecutive frames, and comparing the extracted spatiotemporal feature information to reference feature information stored in association with a plurality of predetermined error patterns is identifying a matching error pattern. A disclosure of how to make and/or use a model would not be enabling were it not already Well known, Routine and Common. Furthermore, using training data to train a neural network etc. and then using that network to generate some output has been held by the CAFC to itself be an abstract idea such as in Recentive Analytics. 23-2437.OPINION.4-18-2025_2500790.pdf.
Similarly plurality of feedbacks comprise a plurality of video feedbacks related to action correction and similarity score presentation during performance of the action are determinations based on learning objects output user-interface because of various input field analysis that are merely use of generic computer functions of Machine or Artificial Language use and computer input/output parts known in art. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art of pronunciation error determination. Fig.8 of the instant specification depict learning object movements of error diagnosis and correction module for a hardware/ software in a standard environment with image panel implement the process claimed here. Application are in reference to figures of specification indicate that some sensor or some device or “machine learning” or “artificial intelligence” or “speech recognition”, etc. are generically used in addition to the abstract idea. They are disclosed in their specification in a manner that indicates that those features are well-known, routine, and conventional. As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
The dependent claims of the instant case, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. § 101 because the additional recited limitations defines first and second features, video clips , trajectory, plurality of feedbacks, pronunciations sequence These are merely an involvement of activities generally categorized as insignificant extra pre and post solution activity as that relates to an abstract idea of monitoring, collection, comparison, rule applications, filtering, outputting etc. They are like in precedential Electric Power Group LLC, v. Alstom Limited (hereinafter EPG) case where court found there that the claims are directed to collecting, monitoring of rule based filtered data and are abstract idea in itself . The recitations are improving the functioning of a computer itself that qualify this to be as significantly more (Step 2A: YES). They are based on generic computer processing of comparison, calculations and aggregation of information from components and peripherals such as from input devices, output interface and interactive network elements. There are sensors used. But all are generic ones operating under standard conditions The recitations are not improving the functioning of a computer itself that could qualify this to be as significantly more. (Step 2B: No).
Response to Arguments/Remarks
Applicant's arguments/amendments filed on February 6, 2026 have been considered.
New search and consideration on claim elements amended..
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims.
35USC101 rejection is maintained while 35USC103 withdrawn,
35 U.S.C. § 101 (pages 11-18 of argument/remarks 2/6/2026)
Examiner finds that the amended claim does not recite generic data processing or generic machine learning-it recites specific video processing operations (spatiotemporal feature extraction with motion vectors from consecutive frames) performed on specific hardware (dedicated but standard neural-network hardware GPU/NPU/TPU) to achieve specific technical results (real-time processing under input/analysis/output enabling designed board for feedback during action performance). These are meaningful technical output under specific known conditions.
But the specification of GPU, NPU, or TPU is a known neural-network hardware as specification could only reflect and designate a particular class of parallel-processing hardware architectures. This real-time video processing is not providing an a technological improvement for a machine to run fast, cheaper options, filtering advantages etc. and this also not explicitly recited in the claim.
35USC101 rejection is maintained.
35 U.S.C. § 103 (pages 18-23 of argument/remarks 2/6/2026)
Applicant indicated that the Examiner states prior art Arora teaches audio-based pronunciation training but acknowledges that Arora does not teach video-based action analysis or dedicated neural-network hardware (GPU/NPU/TPU), and cites Yoo paragraphs 0054, 0057, 0065, 0066, and 0097 as allegedly teaching these missing limitations. Applicant respectfully traverses this rejection because the cited combination fails to establish a prima facie case of obviousness, and the amendments to claim 1 add specific technical limitations that distinguish over the prior art.
The art Arora however generally discusses audio-based speech pronunciation training and operates entirely in the acoustic signal processing domain without explicit on video processing, action trajectories, posture analysis, motion vectors, spatiotemporal features, or any computer vision techniques. Arora's phonological features are specifically designed for acoustic analysis and have no applicability to visual action analysis. Furthermore, Arora employs a multi-stage architecture with separate neural networks-DNN 112 for phonological feature extraction (col. 5, lines 60-67), separate DNN 113 for phoneme probabilities (col. 6, lines 20-35), HMM 114 (col. 6, lines 36-45), and separate NN 116 for mispronunciation detection (col. 10, lines 41-60). This may be characterized as fundamentally different from the claimed integrated architecture where the same trained neural network model performs both feature extraction and pattern comparison. Arora is also not specifying GPU, NPU, or TPU hardware, is not teaching extracting spatiotemporal features or motion vectors from video for similarity scoring of video-based error patterns, to perform post-hoc analysis without real-time processing requirements.
The secondary art Yoo, on the other hand generally discusses text-based language learning through morphological and syntactic analysis of written sentences. Yoo's disclosure focuses on processing text input-paragraph 0056 describing users inputting encountered text, paragraph 0057 describing morphological analysis breaking down sentences into linguistic units, paragraph 0058 describes phrase-structure parsing, and paragraph 0074 describes pattern matching for grammar patterns.
Examiner tend to agree that the prior art combination and an art on record do not specify GPU, NPU, or TPU hardware, nor teach extracting spatiotemporal features or motion vectors from video for similarity scoring of video-based error patterns, and performs post-hoc analysis without real-time processing requirements. It appears that the claim include both spatial and temporal qualities for the speech analysis to be evaluated for desired results.
This necessitated an updated search and consideration. However 35 U.S.C. § 103 rejection is withdrawn.
Following traversals/Remark are retained as a summarized from prior comments so
as to address apriority varied interpretations. This is also answering proactively
some of the new questions that may arise because of current arguments:
Applicant's arguments/amendments filed on August 15, 2025 have been considered.
Amendments to claims 5,14 made overcome the 35USC112 rejection. It is withdrawn.
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims.
Some of examiner’s response may cite a different portions of an applied reference but do not go further and merely elaborates upon, what is taught in the previously cited portion of a reference. Thus those portion not constituting a new ground of rejection.
Claim Rejections - 35 U.S.C. § 101 (Arguments traversal samples)/101 rejection maintained
Applicant on pages 11,12 of argument remarks 8/15/2025 has indicated that the claimed invention is not been directed to an abstract idea without significantly more. Applicant has added limitations requiring a signal sequence wherein the target position comprises a video clip including action correction to a wrong action trajectory in the video signal sequence or a video frame corresponding to a wrong posture in the video signal sequence, wherein determining that the error is detected comprises processing the signal sequence using trained deep-learning neural network model. Applicant asserted that claims now recite a particular machine-specialized neural-network hardware-that is integrated into the claims. But examiners find that though this may not be routine activity in art for providing feedback on error determination and remains a part of managing personal behavior common in educational art. These are concepts partly performed in human mind involving observation and judgement with the use of machine language. 23-2437.OPINION.4-18-2025_2500790.pdf. There is no practical and unconventional improvement in computer-related technology by performing a known computer performance application. Hence the 35 U.S.C. 101 rejection is maintained.
Examiners would further like to note that the use of the machine language ML model could be construed to be an additional abstract idea and the use of the ML model be added to “practical application” and/or “significantly more” analyses. But still be not patent eligible subject matter. This is because their disclosure in their specification in regard to how to make and/or use of claimed “trained deep-learning neural network model executed on such hardware” is so limited to be enabling, since this technology is already well-known, routine, and conventional. And, as such, it cannot establish either a “practical application” and/or “significantly more” than their claimed abstract idea. In other words, invention not claiming some new form of “deep learning”, but discussing the employing off the shelf ML technology as part of abstract idea. The invention focus is to determine student errors or patterns to provide feedback.
Claim Rejections - 35 U.S.C. § 103 (Arguments traversal samples)/103 rejection maintained
Applicant on pages 13 and 14 indicates that the prior art Arora is silent regarding determining, based on a learning object, that an error is detected at a target position of the signal sequence, wherein the target position comprises a video clip including action correction to a wrong action trajectory in the video signal sequence or a video frame corresponding to a wrong posture in the video signal. Prior art Arora fails to include any discussion of detecting a target error pattern corresponding to the target position of the signal sequence and regarding in accordance with a determination that the target error pattern matches one of a plurality of predetermined error patterns associated with the target position.
Examiner agree that prior art Arora et al. generally discusses outputting derivations from a comparison of expected features of a signal as feedback to a user pronunciation input detecting, based on a learning object, a deviation or error is detected at a target position of the signal sequence without explicit discussion of video clip use to include target error pattern detection matching any predetermined values.
A secondary prior art Yoo has been cited at (¶0054-0057, 0065-0066) wherein error analyzers are generally deep neural networks hardware at least having a neural processing unit, NPU, based on Machine Language i.e. Machine Learning on a pattern-based reference library; In paragraph 0097 extracting features where in pen-text user-feedback system cited that reviewed and curated by the service's language-teaching experts extracts and enter useful updates to the library to include graphical processing units and tensor processing units options available in art to process signal sequences.
35USC103 rejection is maintained since the original claim language is not focusing on specifics of video processing of target position from plurality of feedbacks of error patterns.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230090625 A1 ZHANG; Ruiqing et al.
METHOD FOR CORRECTING TEXT, METHOD FOR GENERATING TEXT CORRECTION MODEL, DEVICE
US 20210319786 A1 KAIN A et al.
Method for detecting phoneme mispronunciation and facilitating phonological feature feedback based on speech representation of sampled speech waveform and expected linguistic content, involves determining expected phonological features values
GB 2458461 A YU, KAI
Computing system for facilitating learning of spoken language for foreign language learner, has feedback system for providing feedback to user using feedback data to facilitate user to achieve spoken language goal.
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.Z/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715