CTNF 18/887,848 CTNF 92080 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-fti AIA The present application is being examined under the pre-AIA first to invent provisions. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 2-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7-9, 14-15, and 18-19 of U.S. Patent No. 12,118,148. Although the claims at issue are not identical, they are not patentably distinct from each other as outlined in the table below. Instant Application Patent 12,118,148 Claim 2 : Claim 1 : A method comprising: A method comprising: detecting a trigger word by a microphone of an EMG communication device; and detecting a trigger word by a microphone of an EMG communication device; transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode in response to detecting the trigger word. transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode; Claim 3 : Claim 1 : The method of claim 2, comprising: in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user; and in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user; applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features. applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features; Claim 4 : Claim 1 : The method of claim 3, comprising: generating at least one of a visual or audible output based on the one or more speech features; and generating at least one of a visual or audible output based on the one or more speech features; and causing the at least one of the visual or audible output to be processed by a messaging application to engage a feature of the messaging application. causing the at least one the visual or audible output to be processed by a messaging application to engage a feature of the messaging application. Claim 5 : Claim 2 : The method of claim 2, further comprising synthesizing one or more speech features to generate audible output, wherein a feature of a messaging application comprises a voice message transmission feature in which the audible output is transmitted from the messaging application of a user to another messaging application of another user. The method of claim 1, further comprising synthesizing the one or more speech features to generate the audible output, wherein the feature of the messaging application comprises a voice message transmission feature in which the audible output is transmitted from the messaging application of the user to another messaging application of another user. Claim 6 : Claim 3 : The method of claim 2, further comprising processing one or more speech features by a neural network to generate audible output. The method of claim 2, further comprising processing the one or more speech features by a neural network to generate the audible output. Claim 7 : Claim 4 : The method of claim 6, wherein the neural network comprises a WaveNet network. The method of claim 3, wherein the neural network comprises a WaveNet network. Claim 8 : Claim 5 : The method of claim 2, further comprising processing one or more speech features by a neural network classifier to associate the one or more speech features with one or more words or phonemes. The method of claim 1, further comprising processing the one or more speech features by a neural network classifier to associate the one or more speech features with one or more words or phonemes, Claim 9 : Claim 7 : The method of claim 2, wherein the EMG communication device comprises a wearable collar device, the wearable collar device comprising one or more EMG electrodes, a microphone, and a communication device, the EMG communication device being in communication with a mobile device that implements a messaging application. The method of claim 1, wherein the EMG communication device comprises a wearable collar device, the wearable collar device comprising the one or more EMG electrodes, a microphone, and a communication device, the EMG communication device being in communication with a mobile device that implements the messaging application. Claim 10 : Claim 8 : The method of claim 2, further comprising training the machine learning technique by performing training operations comprising: The method of claim 1, further comprising training the machine learning technique by performing training operations comprising: computing a deviation between an estimated set of speech features and a first set of ground truth speech features associated with a first set of subthreshold muscle activation signals; and computing a deviation between an estimated set of speech features and a first set of ground truth speech features associated with a first set of subthreshold muscle activation signals; and updating one or more parameters of the machine learning technique based on the computed deviation. updating one or more parameters of the machine learning technique based on the computed deviation. Claim 11 : Claim 9 : The method of claim 2, further comprising training the machine learning technique by performing operations comprising: The method of claim 1, further comprising training the machine learning technique by performing operations comprising: receiving training data comprising a first set of subthreshold muscle activation signals and a first set of ground truth speech features associated with the first set of subthreshold muscle activation signals; receiving training data comprising a first set of subthreshold muscle activation signals and a first set of ground truth speech features associated with the first set of subthreshold muscle activation signals; processing the first set of subthreshold muscle activation signals by the machine learning technique to generate an estimated set of speech features; processing the first set of subthreshold muscle activation signals by the machine learning technique to generate an estimated set of speech features; computing a deviation between the estimated set of speech features and the first set of ground truth speech features associated with the first set of subthreshold muscle activation signals; and computing a deviation between the estimated set of speech features and the first set of ground truth speech features associated with the first set of subthreshold muscle activation signals; and updating one or more parameters of the machine learning technique based on the computed deviation. updating one or more parameters of the machine learning technique based on the computed deviation. Claim 12 : Claim 14 : The method of claim 2, wherein the covert calibration mode performs operations comprising: The method of claim 13, wherein the covert calibration mode performs operations comprising: generating an instruction to a user to perform inner speech corresponding to a stimulus, the stimulus comprising a word or phrase; generating an instruction to the user to perform inner speech corresponding to a stimulus, the stimulus comprising a word or phrase; detecting a second set of subthreshold muscle activation signals by one or more EMG electrodes in response to generating the instruction; detecting a second set of subthreshold muscle activation signals by the one or more EMG electrodes in response to generating the instruction; processing the second set of subthreshold muscle activation signals by the machine learning technique to generate a second set of estimated speech features; processing the second set of subthreshold muscle activation signals by the machine learning technique to generate a second set of estimated speech features; comparing the second set of estimated speech features with a second set of training speech features corresponding to the word or phrase; and comparing the second set of estimated speech features with a second set of training speech features corresponding to the word or phrase; and computing a deviation between the second set of estimated speech features with the second set of training speech features. computing a deviation between the second set of estimated speech features with the second set of training speech features. Claim 13 : Claim 15 : The method of claim 12, further comprising training a neural network to map a first set of subthreshold muscle activation signals to the second set of subthreshold muscle activation signals. The method of claim 14, further comprising training a neural network to map the first set of subthreshold muscle activation signals to the second set of subthreshold muscle activation signals. Claim 14 : Claim 18 : A system comprising: A system comprising: one or more processors configured to perform operations comprising: at least one processor of the EMG communication device configured to perform operations comprising: detecting a trigger word by a microphone of an EMG communication device; and detecting a trigger word by a microphone of the EMG communication device; transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode in response to detecting the trigger word. transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode; Claim 15 : Claim 19 : A non-transitory computer-readable medium encoded with computer-readable instructions that, when executed by one or more processors, configure the one or more processors to perform operations comprising: A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: detecting a trigger word by a microphone of an EMG communication device; and detecting a trigger word by a microphone of an EMG communication device; transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode in response to detecting the trigger word. transitioning the EMG communication device between an overt calibration mode to perform overt calibration of a machine learning technique and a covert calibration mode; Claim 16 : Claim 1 : The non-transitory computer-readable medium of claim 15, the operations comprising: in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user; and in response to detecting the trigger word, detecting, by one or more EMG electrodes of the EMG communication device, subthreshold muscle activation signals of one or more muscles associated with speech production, the subthreshold muscle activation signals being generated in response to inner speech of a user; applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features. applying the machine learning technique to the subthreshold muscle activation signals to estimate one or more speech features corresponding to the subthreshold muscle activation signals, the machine learning technique being trained to establish a relationship between a plurality of training subthreshold muscle activation signals and ground truth speech features; Claim 17 : Claim 1 : The non-transitory computer-readable medium of claim 16, comprising: generating at least one of a visual or audible output based on the one or more speech features; and generating at least one of a visual or audible output based on the one or more speech features; and causing the at least one of the visual or audible output to be processed by a messaging application to engage a feature of the messaging application. causing the at least one the visual or audible output to be processed by a messaging application to engage a feature of the messaging application. Claim 18 : Claim 2 : The non-transitory computer-readable medium of claim 15, the operations comprising synthesizing one or more speech features to generate audible output, wherein a feature of a messaging application comprises a voice message transmission feature in which the audible output is transmitted from the messaging application of a user to another messaging application of another user. The method of claim 1, further comprising synthesizing the one or more speech features to generate the audible output, wherein the feature of the messaging application comprises a voice message transmission feature in which the audible output is transmitted from the messaging application of the user to another messaging application of another user. Claim 19 : Claim 3 : The non-transitory computer-readable medium of claim 15, the operations comprising processing one or more speech features by a neural network to generate audible output. The method of claim 2, further comprising processing the one or more speech features by a neural network to generate the audible output. Claim 20 : Claim 4 : The non-transitory computer-readable medium of claim 19, wherein the neural network comprises a WaveNet network. The method of claim 3, wherein the neural network comprises a WaveNet network. Claim 21 : Claim 5 : The non-transitory computer-readable medium of claim 15, further comprising processing one or more speech features by a neural network classifier to associate the one or more speech features with one or more words or phonemes. The method of claim 1, further comprising processing the one or more speech features by a neural network classifier to associate the one or more speech features with one or more words or phonemes, Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 2, 5-8, 10-11, 14-15, and 18-21 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. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 2-13 are directed to a method, which is a process; claim 14 is directed to a system, which is a machine or an article of manufacture; and claims 15-21 are directed to a computer-readable medium, which is a machine or an article of manufacture. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites detecting a word and transitioning modes, which are mental processes. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional limitations of processor, computer readable medium, and EMG device are generic computing components, and do not integrate the abstract idea into a practical application. Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself. Regarding claims 2 and 14-15 , detecting a word and transitioning modes are mental processes, which is an abstract idea. For example, a person could hear a trigger word being spoken, and decide to switch the modes. Additional limitations of processor, computer readable medium, and EMG device are generic computing components, and do not integrate the abstract idea into a practical application or constitute significantly more. Regarding claims 5 and 18 , synthesizing speech features is a mental process, which is an abstract idea, while transmitting outputs is mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more. Regarding claims 6, 8, 19, and 21 , processing using a neural network is a series of mathematical operations, while generating audible output is a mental process while using a generic computing component of a neural network, both of which are abstract ideas without integration into a practical application and without significantly more. Regarding claims 7 and 20 , the limitations are further clarifications of the above abstract ideas. Regarding claim 10 , the training, computing, and updating are mathematical calculations, which is an abstract idea without integration into a practical application and without significantly more. Regarding claim 11 , the training, processing, computing, and updating are mathematical calculations, which is an abstract idea. Receiving the training data is mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more. Regarding claims 3-4, 9, 12-13, and 16-17 , the specific physical elements being claimed would not be considered generic components, and thus integrate the abstract ideas into a practical application. The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp. , 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc. , 758 F.3d 1344 (Fed. Cir. 2014), Benson , 409 U.S. at 63. See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618). Allowable Subject Matter Claims 2, 5-8, 10-11, 14-15, and 18-21 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and the Double Patenting rejection, set forth in this Office action. Claims 3-4, 9, 12-13, and 16-17 would be allowable if rewritten to overcome the Double Patenting rejection, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: The closest prior art of Schultz teaches training using audible and silent EMG signals. However, Schultz does not teach transitioning between the calibration modes, nor does Schultz teach the transitioning being in response to a trigger word received at the EMG device. Hence, none of the cited prior art, either alone or in combination thereof, teaches the combination of limitations found in the claims . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2016/0314781 A1 Fig. 6, para [0112] teaches training using audible and silently uttered speech . Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN S BLANKENAGEL whose telephone number is (571)270-0685. The examiner can normally be reached 8:00am-5:30pm. 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, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2658 Application/Control Number: 18/887,848 Page 2 Art Unit: 2658 Application/Control Number: 18/887,848 Page 3 Art Unit: 2658 Application/Control Number: 18/887,848 Page 4 Art Unit: 2658 Application/Control Number: 18/887,848 Page 5 Art Unit: 2658 Application/Control Number: 18/887,848 Page 6 Art Unit: 2658 Application/Control Number: 18/887,848 Page 7 Art Unit: 2658 Application/Control Number: 18/887,848 Page 8 Art Unit: 2658 Application/Control Number: 18/887,848 Page 9 Art Unit: 2658 Application/Control Number: 18/887,848 Page 10 Art Unit: 2658 Application/Control Number: 18/887,848 Page 11 Art Unit: 2658 Application/Control Number: 18/887,848 Page 12 Art Unit: 2658 Application/Control Number: 18/887,848 Page 13 Art Unit: 2658 Application/Control Number: 18/887,848 Page 14 Art Unit: 2658 Application/Control Number: 18/887,848 Page 15 Art Unit: 2658 Application/Control Number: 18/887,848 Page 16 Art Unit: 2658 Application/Control Number: 18/887,848 Page 17 Art Unit: 2658 Application/Control Number: 18/887,848 Page 18 Art Unit: 2658 Application/Control Number: 18/887,848 Page 19 Art Unit: 2658 Application/Control Number: 18/887,848 Page 20 Art Unit: 2658 Application/Control Number: 18/887,848 Page 21 Art Unit: 2658 Application/Control Number: 18/887,848 Page 22 Art Unit: 2658 Application/Control Number: 18/887,848 Page 23 Art Unit: 2658 Application/Control Number: 18/887,848 Page 24 Art Unit: 2658 Application/Control Number: 18/887,848 Page 25 Art Unit: 2658 Application/Control Number: 18/887,848 Page 26 Art Unit: 2658 Application/Control Number: 18/887,848 Page 27 Art Unit: 2658 Application/Control Number: 18/887,848 Page 28 Art Unit: 2658 Application/Control Number: 18/887,848 Page 29 Art Unit: 2658
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