CTNF 18/828,519 CTNF 99230 DETAILED ACTION This office action is in response to Applicant’s submission filed on 9/09/2024. Claims 1-20 are pending in the application. As such, claims 1-20 have been examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement 06-52 The information disclosure statement (IDS) was submitted on 9/09/2024 and 2/27/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 20 is drawn to a computer readable medium having stored thereon a computer program, where the computer readable medium can be transitory, i.e., is not explicitly limited as disclosed as only being non-transitory computer readable media; therefore, fail(s) to fall within a statutory category of invention. Applicant should note that adding "non-transitory" to the claim to limit a claimed computer readable medium to being statutory would be acceptable. A claim directed to a computer readable medium having stored thereon a computer program is non-statutory, where the computer readable medium can be a signal, a carrier wave, or a data structure, per se, which are non-statutory as noted, infra. A claim directed to a signal, a carrier wave, or a data structure, per se, is non-statutory because it is not: A process, or A machine, or A manufacture, or A composition of matter. Claims 1-3, 8-13, and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claim 1, the claim recites “(a) obtaining a text input including a keyword,” “(b) obtaining a voice signal corresponding to an utterance of a user,” “(c) obtaining a probability value for the keyword,” “(d) obtaining a threshold value for the keyword,” and “(e) determining whether the keyword is included in the obtained voice signal based on the probability value and the threshold value.” Limitations (a) – (e) recite mental processes that may be practically performed in the mind using pen and paper. For example, limitation (a) can be done by someone determining a text input with a specific keyword. Limitation (b) can be done by someone receiving an utterance from a user. Limitation (c) can be done by someone determining a probability value for a keyword. Limitation (d) can be done by someone determining a threshold value for a keyword. Limitation (e) can be done by someone using a probability value and threshold value to determine if a keyword is in a voice signal. Under its broadest reasonable interpretation when read in light of the specification, the actions of “obtaining” and “determining” encompass mental processes practically performed in the human mind by evaluation and judgement using pen and paper. Accordingly, the claim recites an abstract idea (Step 2A, Prong One). The judicial exception is not integrated into a practical application. In particular, the claim recites additional elements of “(f) using a keyword adaptive detection model” and “(g) using a threshold determining model.” Limitations (a) - (e) are recited as being performed by a computer. In limitations (a) - (e), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. Further, the limitations (f) - (g) provide nothing more than mere instructions to implement an abstract idea on a generic computer. The different models recited in limitations (f) – (g) are used to perform limitations (c) – (d), respectively, without placing any limits on how the models function. Rather, these models only recite the outcomes and do not include any details on how the outcomes are accomplished. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to an abstract idea (Step 2A: YES). The claim does not include additional elements that are sufficient to amount to more than the judicial exception. As discussed above, the recitation of a computer to perform limitations (a) – (e) amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept (Step 2B). Regarding claim 11, the claim is rejected with similar analysis to claim 1. Similarly, dependent claims 2-3, 8-10, 12-13, and 18-19 include additional steps that are considered abstract ideas because they fail to provide meaningful significance that goes beyond generally linking the use of an abstract idea to a particular technological environment and using the computer to perform an abstract idea. Claims 2 and 12 recite training of different models using generic data. Claims 3 and 13 recite training of different models using generic inputs. Claims 8 and 18 read on someone dividing up a speech signal and determining a probability that each of the divided units contains the keyword. Claims 9 and 19 read on someone determining a threshold value without sing voice information of a user. Claim 10 reads on someone providing a text or sound after determining that a keyword is in a voice signal. Dependent claims 4-7 and 14-17 are not rejected under 35 U.S.C. 101 because they recite specific training of the keyword adaptive detection model using a stored voice signal from a user in order to improve keyword accuracy and update threshold values. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1, 8-9, 11, and 18-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Tian et al. (US 20220262352 A1) . Regarding claim 1, Tian discloses: A method for voice recognition, the method comprising: obtaining a text input including a keyword ([0024] A given keyword may be utilized to generate an utterance text containing a given keyword ); obtaining a voice signal corresponding to an utterance of a user ([0027] Speech signals 105 may be a section of continuous audio signals to be spotted to see whether it contains a given keyword. The audio signal may be obtained by means of audio acquisition or by means of TTS ); obtaining a probability value for the keyword ([0031] Decoder 130 determines an optimal decoding path containing the given keyword according to a probability vector provided by the acoustic model 120 at each frame, and outputs probabilities of the phonemes corresponding to each frame on the optimal decoding path to the confidence calculation module 140 ), by using a keyword adaptive detection model ([0032] Confidence calculation module 140 obtains the probabilities of phonemes corresponding to each frame on the optimal decoding path including the given keyword, and calculates a confidence value of the given keyword accordingly ); obtaining a threshold value for the keyword by using a threshold determining model ([0033] The confidence threshold determination module 150 compares the confidence value received from the confidence calculation module 140 with a confidence threshold to determine the spotting result 155 ); and determining whether the keyword is included in the obtained voice signal based on the probability value and the threshold value ([0033] If the received confidence value is greater than the confidence threshold, the given keyword is determined to be spotted, otherwise the given keyword is determined to be not spotted ). Regarding claim 8, Tian discloses: the method of claim 1, wherein the obtaining of the probability value comprises: dividing the obtained voice signal into a plurality of units ([0036] pre-weighted speech signal is divided into frames. For example, a specified number of sampling points are defined as a frame of speech signal ) comprising at least one syllable ([0045] S denotes the number of phonemes output to each frame by the acoustic model, such as a total number of phonemes included by the language used by speech signal input to the acoustic model. A phoneme can be a syllable. ); and by sequentially inputting the plurality of units to the keyword adaptive detection model ([0030] The acoustic model 120 classifies acoustic features of each input frame, corresponds the acoustic features of each frame to all possible phonemes, calculates a probability of each phoneme, and at last provides a probability vector containing the probabilities of each frame on all phonemes to the decoder 130. In one case, a sum of all elements in the probability vector may be 100%. The number of the elements in the probability vector depends on a modeling unit of the acoustic model and the number of phonemes included in a language used by the input speech signals ), obtaining the probability value with respect to whether the keyword is included in each of the divided units ([0031-0032] Decoder 130 determines an optimal decoding path containing the given keyword according to a probability vector provided by the acoustic model 120 at each frame, and outputs probabilities of the phonemes corresponding to each frame on the optimal decoding path to the confidence calculation module 140… Confidence calculation module 140 obtains the probabilities of phonemes corresponding to each frame on the optimal decoding path including the given keyword, and calculates a confidence value of the given keyword accordingly ). Regarding claim 9, Tian discloses: the method of claim 1, wherein the threshold determining model is configured to obtain the threshold value ([0102] Confidence threshold determination module 150 can determine the spotting result by comparing the above confidence value with the confidence threshold. As mentioned in FIG. 1, the confidence threshold corresponding to the given keyword may be adjusted to satisfy the performance indexes of the keyword spotting system. The performance indexes of the keyword spotting system may include, but are not limited to, a metric of correct accept and a metric of false accept for the given keyword. The metric of correct accept may be reflected by the spotting accuracy, which may be calculated by dividing the number of given keyword utterances correctly spotted by the total number of given keyword utterances ) without voice information of the user ([0104] the dataset including the given keyword may include for example at least a portion of the set of speech signals synthesized at 530 of FIG. 5. The synthesized speech does not use voice information of the user. ). Regarding claim 11, Tian discloses: an electronic device for voice recognition, the electronic device comprising: at least one processor including processing circuitry; and memory comprising one or more storage media storing at least one instruction that, when executed by the at least one processor individually or collectively, cause the electronic device to… ([0135] a processor, any portion of a processor, or any combination of processors presented in the present disclosure may be implemented with a microprocessor, microcontroller, digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a state machine, gated logic, discrete hardware circuits, and other suitable processing components configured to perform the various functions described throughout the present disclosure ). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly. Regarding claim 18, it recites similar limitations as claim 8 and therefore is rejected similarly. Regarding claim 19, it recites similar limitations as claim 9 and therefore is rejected similarly. Regarding claim 20, Tian discloses: a computer-readable recording medium having recorded thereon a program for performing a method… ([0132] The embodiments of the present disclosure may be embodied in a non-transitory computer-readable medium. The non-transitory computer-readable medium may comprise instructions that, when executed, cause one or more processors to perform any operations for optimizing the keyword spotting system ). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Zhu et al. (US 20220238100 A1; hereinafter referred to as Zhu) . Regarding claim 2, Tian discloses: the method of claim 1, wherein the keyword adaptive detection model is trained by using a voice training data set… ([0108] an acoustic model in the keyword spotting system may be optimized with at least a part of speech signals in the set of speech signals and utterance texts in the set of utterance texts corresponding to the at least a part of speech signals ). Tian does not explicitly, but Zhu teaches: and the threshold determining model is trained by using a text training data set corresponding to the voice training data set ([0057, 0102] the preset degree threshold may be predetermined based on a voice keyword retrieval training performed in advance… the deep learning-based voice data processing method and device provided in this application can convert the voice data to be detected into target text data based on the voice recognition model so that the keyword text corresponding to the predetermined target voice keyword can be converted. Then, the data is matched with the target text data to determine whether the voice data to be detected includes the target voice keyword based on the matching result ). Tian and Zhu are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian to combine the teachings of Zhu because doing so would improve keyword detection by using target text data corresponding to voice data to improve accuracy of matching voice data to a keyword (Zhu [0026] since the voice recognition model is obtained by deep learning based on the obtained training set composed of a plurality of voice recognition data samples, high precision target text data can be obtained, thereby improving the accuracy of voice data matching, and thereby solving the problem of low accuracy of keyword detection based on the traditional voice processing technology ). Regarding claim 12, it recites similar limitations as claim 2 and therefore is rejected similarly . 07-22-aia AIA Claim s 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tian , as applied to claim s 1, 8-9, 11, and 18-20 above, and further in view of Wang et al. (US 20200286465 A1; hereinafter referred to as Wang) and Zhu . Regarding claim 3, Tian discloses: the method of claim 1. Tian does not explicitly, but Wang teaches: wherein the keyword adaptive detection model comprises an artificial intelligence model trained to output the probability value based on an input of the obtained voice signal… ([0042] The first classification model is a neural network model that is trained in advance. Acoustic characteristics of the first speech segments may be inputted into the first classification model. Then, classification processing is performed on the first speech segments based on the acoustic characteristics of the first speech segments by using the first classification model, to obtain the first probabilities that are in a one-to-one correspondence with the first speech segments. The first probabilities corresponding to the first speech segments may include probabilities that the first speech segments respectively correspond to the pre-determined word segmentation units of the pre-determined keyword ). Tian and Wang are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian to combine the teachings of Wang because doing so would allow for the use of an AI model, such as a neural network, to be trained using speech signals and classification models to recognize keywords, resulting an improved efficiency and accuracy in keyword detection. (Wang [0079] recognition accuracy can be improved by using dual determination. On the other hand, for a to-be-recognized speech signal in which the pre-determined keyword does not exist, the recognition process ends prematurely, and the second classification model does not need to be used for performing the classification processing. Therefore, an actually meaningless operation is avoided, and system performance can be effectively optimized .) The combination of Tian and Wang does not explicitly, but Zhu teaches: and the threshold determining model comprises an artificial intelligence model ([0076] the language model may be a neural network model, such as a neural network language model based on a recurrent neural network ) trained to output the threshold value ([0057] the preset degree threshold may be predetermined based on a voice keyword retrieval training performed in advance. The training to process can be the deep learning-based voice data processing method provided in the embodiments of this application to adjust the correlation threshold according to the real result and the result obtained based on the method, so that the real result and the result obtained based on the method are adjusted ) based on an input of the text input ([0102] the deep learning-based voice data processing method and device provided in this application can convert the voice data to be detected into target text data based on the voice recognition model so that the keyword text corresponding to the predetermined target voice keyword can be converted. Then, the data is matched with the target text data to determine whether the voice data to be detected includes the target voice keyword based on the matching result ). Tian, Wang, and Zhu are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian and Wang to combine the teachings of Zhu because doing so would improve keyword detection by using target text data corresponding to voice data to improve accuracy of matching voice data to a keyword and adjusting the threshold to do so (Zhu [0026] since the voice recognition model is obtained by deep learning based on the obtained training set composed of a plurality of voice recognition data samples, high precision target text data can be obtained, thereby improving the accuracy of voice data matching, and thereby solving the problem of low accuracy of keyword detection based on the traditional voice processing technology ). Regarding claim 13, it recites similar limitations as claim 3 and therefore is rejected similarly . 07-21-aia AIA Claim s 4-6, 10, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Barros et al. (US 20230019737 A1; hereinafter referred to as Barros) . Regarding claim 4, Tian discloses: the method of claim 1. Tian does not explicitly, but Barros teaches: storing the voice signal, as a stored voice signal, based on a determination that the keyword is included in the obtained voice signal ([0026] if the user does grant permission to generate the response, the interaction to grant permission may be interpreted by the system as a positive hotword example. The system may then store the positive hotword example to function as a future training example for a model (e.g., a machine learning model) being used by the hotword detector to predict the probability that a hotword exists in audio data input to the hotword detector ); and training the keyword adaptive detection model by using the stored voice signal ([0026] the user's interaction may label the hotword example as a positive hotword example to generate supervised hotword training examples for the hotword detector (e.g., to improve or updated the functionality of the hotword detector) ). Tian and Barros are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian to combine the teachings of Barros because doing so would allow for valid user voice inputs to be stored and used as training data for a keyword detection model, resulting in improved accuracy for future keyword detections (Barros [0026] the user's interaction may label the hotword example as a positive hotword example to generate supervised hotword training examples for the hotword detector (e.g., to improve or updated the functionality of the hotword detector). In some examples, these positive hotword examples do not function as hotword training examples for the hotword detector in the broad sense, but rather function to train the hotword detector to perform more accurate soft acceptance (i.e., function as soft acceptance training examples) ). Regarding claim 5, the combination of Tian and Barros teaches: the method of claim 4. Barros further teaches: further comprising, based on the training of the keyword adaptive detection model, updating the threshold value based on a probability value corresponding to the stored voice signal ([0045] each occurrence becomes a training example to update the hotword detection model. In some configurations, training the hotword detection model with the training examples adjusts one or more of the first confidence threshold 214 or the second confidence threshold 216. By adjusting these thresholds 214, 216, the soft acceptor 200 may be configured to limit or to reduce the range of confidence scores 212 that correspond to the soft accept outcome category ). Regarding claim 6, the combination of Tian and Barros teaches: the method of claim 4. Barros further teaches: further comprising, based on the training of the keyword adaptive detection model, training the threshold determining model by using the stored voice signal ([0045] soft acceptor 200 may collect occurrences when it designates a soft hotword event 202, but the speech processing system 150 fails to identify an action 148 corresponding to the soft hotword event 202. The collected occurrences may then be provided to the soft acceptor 200 as feedback. For example, each occurrence becomes a training example to update the hotword detection model. In some configurations, training the hotword detection model with the training examples adjusts one or more of the first confidence threshold 214 or the second confidence threshold 216 ). Regarding claim 10, Tian discloses: the method of claim 1. Tian does not explicitly, but Barros teaches: further comprising providing at least one of an image, a text, or a sound based on the determination that the keyword is included in the obtained voice signal ([0038] The difference between a full accept and a soft accept is that the device 110 is typically permitted to perform an action 148 specified by a query 22 associated with a fully accepted hotword (e.g., automatically perform the action) whereas, with a soft accept, the device 110 cannot perform the action 148 specified by a query 22 associated with the soft accept until the soft acceptor 200 generates a notification 204 that prompts the user 10 associated with the device 110 to provide an affirmative input indication ). Tian and Barros are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian and Barros to further combine the teachings of Barros because doing so would allow for a user to receive a type of notification to confirm the intention of a keyword detection, leading to improved resource efficiency when a keyword is incorrectly detected (Barros [0023] with a soft accept, the user may not have spoken the hotword, “Hey Google” very clearly. This lack of clarity may result in the hotword detector indicating that an audio segment corresponding to “Hey Google” has a hotword confidence corresponding to the soft accept outcome category. With a soft accept outcome category, the hotword detector also commits the audio of “play my summer 2021 playlist” to full speech processing. In this soft accept example, instead of generating (e.g., automatically generating) the response of playing the “summer 2021 playlist,” the system responds with a limited response (or soft answer). For instance, the limited response is a response that notifies the user via the voice-enabled device of the system's interpreted response for the utterance ). Regarding claim 14, it recites similar limitations as claim 4 and therefore is rejected similarly. Regarding claim 15, it recites similar limitations as claim 5 and therefore is rejected similarly. Regarding claim 16, it recites similar limitations as claim 6 and therefore is rejected similarly . 07-22-aia AIA Claim s 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tian in view of Barros , as applied to claim s 4-6, 10, and 14-16 above, and further in view of Yun et al. (US 20150302847 A1; hereinafter referred to as Yun) . Regarding claim 7, the combination of Tian and Barros teaches: the method of claim 4. The combination of Tian and Barros does not explicitly, but Yun teaches: determining a similarity between the stored voice signal ([0027] The electronic device 100 may store a set of predetermined keywords and/or another set of user-defined keywords. As used herein, the term “predetermined keyword” may refer to a keyword that has been predetermined for activating a function or an application in the electronic device 100 ) and the obtained voice signal ([0040] the term “matching score” may refer to a value indicating the degree of similarity between an input sound and any keywords (e.g., a user-defined keyword or a predetermined keyword) ); and determining, based on the similarity, whether the obtained voice signal corresponds to a registered user ([0046] the electronic device 100 may detect the user-defined keyword based on a keyword model for the user-defined keyword and calculate a matching score of the input sound. When the matching score is higher than a keyword detection threshold, the electronic device 100 may determine the input sound as including the user-defined keyword. The electronic device 100 may then perform a user verification process on the input sound based on a user verification model and calculate a user verification score of the input sound… electronic device 100 may perform a keyword detection process and a user verification process based on one model for detecting the user-defined keyword and verifying a speech of a registered user ). Tian, Barros, and Yun are considered analogous in the field of speech processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tian and Barros to combine the teachings of Yun because doing so would allow for the use of specific user acoustic features to recognize the speech of a specific user in addition to detecting a specific keyword, resulting in better user control for a keyword detection model (Yun [0045] A keyword model for a user-defined keyword may incorporate user-specific acoustic features that may be used in recognizing the user. In one embodiment, the electronic device 100 may be configured with a function or an application for user verification using such user-specific acoustic features included in a keyword model for a user-defined keyword. Additionally, the electronic device 100 may include a user verification model that is used to verify a speech of a user, in addition to a keyword model for a user-defined keyword ). Regarding claim 17, it recites similar limitations as claim 7 and therefore is rejected similarly . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Ye et al. (US 20210304769 A1) – discloses a keyword spotting model trained using text-to-speech data to overcome data sparsity and train a s system for detecting a specific customizable keyword. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nathan Tengbumroong whose telephone number is (703)756-1725. The examiner can normally be reached Monday - Friday, 11:30 am - 8:00 pm EST. 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, Hai Phan can be reached at 571-272-6338. 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. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NATHAN TENGBUMROONG/Examiner, Art Unit 2654 /HAI PHAN/Supervisory Patent Examiner, Art Unit 2654 Application/Control Number: 18/828,519 Page 2 Art Unit: 2654 Application/Control Number: 18/828,519 Page 3 Art Unit: 2654 Application/Control Number: 18/828,519 Page 4 Art Unit: 2654 Application/Control Number: 18/828,519 Page 5 Art Unit: 2654 Application/Control Number: 18/828,519 Page 6 Art Unit: 2654 Application/Control Number: 18/828,519 Page 7 Art Unit: 2654 Application/Control Number: 18/828,519 Page 8 Art Unit: 2654 Application/Control Number: 18/828,519 Page 9 Art Unit: 2654 Application/Control Number: 18/828,519 Page 10 Art Unit: 2654 Application/Control Number: 18/828,519 Page 11 Art Unit: 2654 Application/Control Number: 18/828,519 Page 12 Art Unit: 2654 Application/Control Number: 18/828,519 Page 13 Art Unit: 2654 Application/Control Number: 18/828,519 Page 14 Art Unit: 2654 Application/Control Number: 18/828,519 Page 15 Art Unit: 2654 Application/Control Number: 18/828,519 Page 16 Art Unit: 2654 Application/Control Number: 18/828,519 Page 17 Art Unit: 2654 Application/Control Number: 18/828,519 Page 18 Art Unit: 2654
