DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims in application 18/745,086 filed on 11/6/2025.
The instant application claims benefit to provisional application #63/509,626 with a priority date of 6/22/2023.
The Pre-Grant publication # 20240428704 is published on 12/26/2024.
Claims 1-8, 10-21 are pending.
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 1-22 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 (claim 1--9) and a computer readable Media (10-20) and thus fall within one of the four statutory categories (Step 1: YES).
Claims 1 , 10 and 16 are directed to computing device for generating lesson content evaluating lip-reading skills and modifying the lesson content based on responses to visual speech recognition prompts. A user profile and a video source is obtained depicting a subject speaking. Evaluating and comparison to modify lesson content is a part of mental process. The activities, as claimed, also include adding the training instance to a content database that includes a set of training instances and corresponding audio output and/or text subtitles. Furthermore selecting from the content database; generating a set of lesson content for the user that includes the subset of training instances and a set of evaluation prompts, providing, to a user device, the set of lesson content; displaying of subset of training instances and set of evaluation prompts on the user device; receiving, from the user device, a set of responses to the set of evaluation prompts; and updating any portion of the set of lesson content based on the derived score for each of the set of responses; area working on information processing akin to organizing of certain human activities. The profile and a present learning level that includes down to word-level video portions from the selected subset of training instances stored in the content database, wherein each word-level video portion in the set of lesson content includes the video content, text subtitles; generating a set of evaluation prompts that, based on the learning goal and the lesson content, comprise: a video segment and the corresponding subtitles to test lip reading skills requesting the user to submit the text subtitle corresponding to a presented video or a text string to test tailored/silent speech skills, requesting the user to submit a video of the user speaking a presented evaluation text string; The determining of the speech content of the video source, by a visual speech recognition (VSR) model, and deriving, via a VSR-based evaluation model, a score for each set of responses by comparing the responses provided by the user with the predictions of the VSR model to the same set of evaluation prompts could be categorized as a use of mathematical calculations within some mathematical concepts They all are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claims found to be not including 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 string of video with text strings”, are merely use of generic computer functions and computer parts. That is simply a use of visual speech recognition (VSR) model to determine speech content block from text input and determining from storage only a corresponding filtered session for evaluation. There is no improvement of machine or 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. Paragraphs 0004, 0005 of the instant specification background indicates that visual speech recognition (VSR) or automated lip-reading aims to decode content of speech from a soundless video using various artificial intelligence (AI) techniques. In many cases, a computing node or series of interconnected computing nodes can utilize one or more sets of training data to train models such as a VSR model to implement a VSR system capable of decoding content of speech in a soundless video. Furthermore the Source data for training a VSR model can include multiple recorded and/or synthetically generated sources of content (e.g., soundless videos) with corresponding audio speech and/or text subtitles for each source of content. Hence the model is generically used in the art. They are disclosed in their specification in a manner that indicates that those features are well-known, routine, and conventional (WRC). They are not dealing with actual improvements. 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 (WRC), 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 2-9,11-15, 17-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective function as discussed above. In other words, these claims merely apply an abstract idea to a programmable processor or computer and do not improve the performance of the process or computer itself or provide a technical solution to a problem in a technical field. Claims 2-9,17-20 include specify any of: users with a hearing-impairment, users with a speech-impairment, learning goal identification, deriving attributes of each word, prompt evaluation description, video of a subject, score derivation threshold criterion, animation instances and
series of points providing a visual representation of facial features. Claims 11-15 specifies user profile, learning goals, processing standard steps of VSR model, reproduction of text strings and prompt types. Claims 20-22 identifies some training instances. They do not effect a transformation of a particular article to a different state or thing, the underlying computing elements remain the same. Instead, the additional features merely amount to an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art. Mere instructions to apply an exception using the generic computer components cannot provide an inventive concept. Therefore, for these reasons, it appears that claims 2-9,11-15, 17-22 are not patent-eligible under 35 USC 101.
Response to Arguments/Remarks
Applicant's arguments/amendments filed on August 14, 2020 have been considered.
Upon further consideration, a new ground(s) of rejection is made as necessitated by amendments changing the scope of the claims.
35USC101
Applicant on Pages 10-13 of argument/remark on 11/6/2025 asserted that the claims are eligible under step2A Prong1,2. However examiners find that applicant’s claims are directed to abstract ideas in terms of teaching a human being how to improve his/her lip-reading skills by providing him/her customized learning content. This may be characterized as both a mental process and/or a method of organizing human activity. No technology is improved as a result of Applicant’s invention, at best there is a possible improvement (although not required by the claims) to human performance as a result of the allegedly improved lip-reading training, which is not patent eligible under the Mayo test. See, e.g., the Trading Technologies decision by the CAFC that I have cited to you repeatedly.
A VSR model and an NLP model both are disclosed in such a cursory manner that indicates they are both well-known, routine, and conventional (WRC) and, thereby, not “significantly more”. The VSR model is disclosed in their PGPUB as being an off the shelf neural network model Paragraph 7747 This one paragraph of disclosure in regard to how to make and/or use the VSR model would not be enabling were it not already WRC. Furthermore, using training data to train a neural network and then using that network to generate some output has been held by the CAFC to itself be an abstract idea in Recentive Analytics. 23-2437.OPINION.4-18-2025_2500790.pdf.
A lack of disclosure for a VSR model indicates that the NLP model must be WRC, otherwise it would not be enabled by this limited disclosure. As such, it cannot add “significantly more” to their abstract idea. In other words, there is no claimed or disclosed improvements to the NLP (or VSR) models. There are numerous CAFC decisions in regard to other similar software techniques that were held to not add “significantly more”. See, e.g., the decision in Cxloyalty in regard to using off-the-shelf GUIs, APIs, and databased as not adding “significantly more”. 20-1307.opinion.2-8-2021_1729377.pdf See also, e.g., the CAFC’s decision in Enco Systems in regard to training and then employing an off-the-shelf text-to-speech program as not being “significantly more”. 20-1995.opinion.3-8-2021_1744098.pdf.
35USC101 rejection is maintained.
35USC102
Applicant on Pages 13 of argument/remark on 11/6/2025 asserted that the computing system is
automatically generating updated lesson content comprising new down to word-level video portions and a set of corresponding new evaluation prompts based on the derived score for each of the set of responses to the previously presented evaluation prompts. This is beneficial because the method automatically creates new educational content and evaluation exercises based on learning performance, eliminating the need for manual curriculum development while providing individualized instruction through technologically- generated video materials and adaptive prompts.
The cited reference, Stewart, generally describes a detection system that assesses whether a person viewed by a computer-based system is a live person or not. It further elaborated if a "liveness" check processes a lip-reading video provided by a user and determining whether the video is performed by a live user or a virtual user. So it is a kind of authenticating users (pass/fail determination), which fundamentally differs from the claimed method because of artificial intelligence use. The art fails to teach or suggest automatically updating educational content provided to the user. 35USC102 rejection is withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230031536 A1 to Lee; Jong Hwa et al.
System for correcting predictions of lip-reading by user talking in video for speech recognition applications, has processors for predicting words from mouth movement of user, correcting correction candidate words from predicted words, and predicting sentences from predicted words
US 20220020288 A1 to NABER; Emily K. et al.
AUTOMATED SYSTEMS AND METHODS FOR PROCESSING COMMUNICATION PROFICIENCY DATA.
US 20140343945 A1 to Benhaim; Eric et al.
METHOD OF VISUAL VOICE RECOGNITION BY FOLLOWING-UP THE LOCAL DEFORMATIONS OF A SET OF POINTS OF INTEREST OF THE SPEAKER'S MOUTH
US 3192321 A to NASSIMBENE ERNIE G
Electronic lip reader
US 20220020288 A1 to MEHRYAR S et al.
Method for enabling improved proficiency of speech, involves generating report to user resulting from applying previously-trained machine learning model based on score, where report is configured to enable user to improve speech proficiency
US 11386900 B2 to Shillingford; Brendan et al.
Visual speech recognition VSR by phoneme prediction
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SADARUZ ZAMAN whose telephone number is (571)270-3137. Stewart teaches the examiner can normally be reached M-F 9am to 5pm CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. Stewart teaches the fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.Z/Examiner, Art Unit 3715
February 7, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715