CTFR 18/652,575 CTFR 82155 DETAILED ACTION This is responsive to the amendment filed 17 March 2026. Claims 1-3, 6-12 and 15-20 are currently pending and considered below. 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. Response to Arguments Applicant’s arguments with respect to claims 1-3, 6-12 and 15-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 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 1, 7, 9-10 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Carbune et al. (US 2019/0156831) in view of Chang et al. (US 2024/0370736) and Marrone et al. (" Identifying Users' Emotional States through Keystroke Dynamics. " DeLTA 2022 (2022)) . Claim 1: Carbune discloses a computer-implemented method, the method comprising: receiving a typed user input that includes one or more words and that is typed at an input device (“ textual input 201A that is based on user interface input generated by one or more user interface input device(s) 102 ”, [0071], see also “ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard) ”, [0062]); determining a plurality typing events for the typed user input (“ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard), the user state information may be determined based on a typing speed of the typed input, applied pressure for one or more characters of typed input (e.g., as sensed by a touch screen implementing a virtual keyboard), a “delay time” for starting to provide the typed input (e.g., when the typed input is provided responsive to other content), and/or other sensed features of the typed input ”, [0062]); generating a temporal representation that represents typing speed across the typed user input based on the plurality of typing events ( one or more search parameters determined based on the user state information, note these could include typing speed, see [0062]); generating a combined representation that combines a representation of the typed user input ( parameters determined based on the textual input ) and the temporal representation (“ the reply content engine 126 causes the search system 130 to issue a search of search databases 154 based on one or more search parameters determined based on the textual input 201C and based on one or more search parameters determined based on the user state information 207C. In some of those implementations, one or more of the parameters may individually be based on both the textual input 201C and the user state information 207C ”, [0093]); processing the combined representation, using a machine learning model, to generate model output from which a response responsive to the typed user input is derived (“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ a typing speed of a user determined from sensor data associated with one of the user interface input device(s) may be provided by the user state engine 122 and applied by the reply content engine 126 as direct input to a neural network model or other machine learning model(s) that are utilized to determined one or more aspects of reply content to provide to a user ”, [0059]); and causing the response to be rendered via an output device in response to the typed user input (“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ The reply content engine 126 provides the reply content 209A for presentation, to one or more users engaged in the dialog, via the user interface input device(s) 104 of those user(s) ”, [0076]). Carbune does not explicitly disclose that the representation of the typed user input is an embedded representation and that the temporal representation is an encoding (e.g. embedding). In a similar machine learning system similarly processing mixed data comprising input text and second input, Chang discloses generating embeddings for the input text and the second input (“ Multi modal models comprising an encoder and decoder are described. The encoder projects inputs into embeddings, which are used to generate a multi modal prompt, which is provided to the decoder. The encoder input comprises context information. The multi modal prompt comprises mixed types of data. This mixed data is converted into embeddings and combined to form the multi modal prompt. For example, text may be converted to embeddings using a text encoder and images may be converted to embeddings using an image encoder ”, [0006]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of generating embeddings for Carbune’s typed user input and temporal representation in order to use a transformer based encoder/decoder LLM as the machine learning system because they are the state of the art providing the best results in AI (see Chang, [0003] and [0006]). Carbune in view of Chang does not explicitly disclose that the representation of typing speed across the typed user input includes variation in typing speed. In an analogous system similarly determining a user state (emotion) using typing speed, Marrone discloses representation of typing speed across typed user input which includes variation in typing speed (section 2.2, see for example “ standard variation of the dwell time of keys pressed in the selected time window ” and “ standard variation of the flight time of keys pressed in the selected time window ”). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of including variation in typing speed in Carbune’s representation of typing speed because using variations of features that affect typing speed such as dwell time (i.e. how long a physical key is held down before it is released) and flight time (i.e. the time between releasing one key and pressing the next)) over a certain interval results in better user state (emotion) recognition performance (see Marrone, section 2.2). Claim 7: Carbune in view of Chang and Marrone discloses the method of claim 1, wherein the machine learning model is trained to cause generation of more serious content when the temporal encoding indicates a high user state (e.g. jovial) in the typed user input, and to cause generation of less serious content when the temporal encoding indicates a lower user state (e.g. less jovial i.e. sad) in the typed user input (Carbune, “ assume textual input of “I'm bored” provided by a user to an automated assistant during a dialog that includes the user and the automated assistant. If user state information of the user indicates the user is jovial, textual reply content of “want to hear some jokes?” may be provided by the automated assistant as a reply to the textual input. If the user state information indicates the user is sad, textual reply content of “anything you want to talk about?” may instead be provided ”, [0006]). Carbune in view of Chang does not explicitly disclose the serious content as authoritative content and the user state as user confidence. However, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to have the serious content as authoritative content and the user state as user confidence because “ classes that are indicative of happy, sad, neutral, active, tired, stressed, and/or other user state(s) may be utilized ” (emphasis added, Carbune, [0057]) and “ the reply content engine 126 may generate reply content that conforms to that user state information ” (Carbune, [0116]). Claim 9: Carbune in view of Chang and Marrone discloses the method of claim 1, further comprising: receiving an additional typed user input that includes the same one or more words as the typed user input; determining one or more alternative typing events for the additional typed user input, wherein the one or more alternative typing events indicate one or more alternative temporal characteristics, of typing of the additional typed user input, that differ from the one or more temporal characteristics of the typed user input; generating an alternative combined representation that combines the embedded representation of the additional typed user input and an alternative temporal encoding that is determined based on the alternative typing events for the additional typed user input; processing the alternative combined representation, using the machine learning model, to generate alternative model output from which an alternative response responsive to the typed user input is derived; and causing the alternative response, to be rendered in response to the additional typed user input ([0106]-[0107] and related Figs 4A and 4B). Claim 10: Carbune discloses a computer-implemented method, the method comprising: receiving a user input that includes one or more words and that is provided via an input device (“ textual input 201A that is based on user interface input generated by one or more user interface input device(s) 102 ”, [0071], see also “ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard) ”, [0062]); determining a plurality input events for the user input (“ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard), the user state information may be determined based on a typing speed of the typed input, applied pressure for one or more characters of typed input (e.g., as sensed by a touch screen implementing a virtual keyboard), a “delay time” for starting to provide the typed input (e.g., when the typed input is provided responsive to other content), and/or other sensed features of the typed input ”, [0062]); generating a temporal representation that represents input speed across the user input based on the plurality of input events ( one or more search parameters determined based on the user state information, note these could include typing speed, see [0062]); generating a combined representation that combines a representation of the user input ( parameters determined based on the textual input ) and the temporal representation (“ the reply content engine 126 causes the search system 130 to issue a search of search databases 154 based on one or more search parameters determined based on the textual input 201C and based on one or more search parameters determined based on the user state information 207C. In some of those implementations, one or more of the parameters may individually be based on both the textual input 201C and the user state information 207C ”, [0093]); processing the combined representation, using a machine learning model, to generate model output from which a response responsive to the user input is derived (“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ a typing speed of a user determined from sensor data associated with one of the user interface input device(s) may be provided by the user state engine 122 and applied by the reply content engine 126 as direct input to a neural network model or other machine learning model(s) that are utilized to determined one or more aspects of reply content to provide to a user ”, [0059]); and causing the response to be rendered via an output device in response to the user input (“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ The reply content engine 126 provides the reply content 209A for presentation, to one or more users engaged in the dialog, via the user interface input device(s) 104 of those user(s) ”, [0076]). Carbune does not explicitly disclose that the representation of the user input is an embedded representation and that the temporal representation is an encoding (e.g. embedding). In a similar machine learning system similarly processing mixed data comprising input text and second input, Chang discloses generating embeddings for the input text and the second input (“ Multi modal models comprising an encoder and decoder are described. The encoder projects inputs into embeddings, which are used to generate a multi modal prompt, which is provided to the decoder. The encoder input comprises context information. The multi modal prompt comprises mixed types of data. This mixed data is converted into embeddings and combined to form the multi modal prompt. For example, text may be converted to embeddings using a text encoder and images may be converted to embeddings using an image encoder ”, [0006]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of generating embeddings for Carbune’s user input and temporal representation in order to use a transformer based encoder/decoder LLM as the machine learning system because they are the state of the art providing the best results in AI (see Chang, [0003] and [0006]). Carbune in view of Chang does not explicitly disclose that the representation of input speed across the user input includes variation in input speed. In an analogous system similarly determining a user state (emotion) using input (typing) speed, Marrone discloses representation of typing speed across typed user input which includes variation in typing speed (section 2.2, see for example “ standard variation of the dwell time of keys pressed in the selected time window ” and “ standard variation of the flight time of keys pressed in the selected time window ”). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of including variation in typing speed in Carbune’s representation of typing speed because using variations of features that affect typing speed such as dwell time (i.e. how long a physical key is held down before it is released) and flight time (i.e. the time between releasing one key and pressing the next)) over a certain interval results in better user state (emotion) recognition performance (see Marrone, section 2.2). Claim 15: Carbune in view of Chang and Marrone discloses the method of claim 10, wherein content of the response varies in dependence on the temporal encoding (Carbone, [0062]). Claim 16: Carbune in view of Chang and Marrone discloses the method of claim 15, wherein the machine learning model is trained to cause generation of more serious content when the temporal encoding indicates a high user state (e.g. jovial) in the typed user input, and to cause generation of less serious content when the temporal encoding indicates a lower user state (e.g. less jovial i.e. sad) in the typed user input (Carbune, “ assume textual input of “I'm bored” provided by a user to an automated assistant during a dialog that includes the user and the automated assistant. If user state information of the user indicates the user is jovial, textual reply content of “want to hear some jokes?” may be provided by the automated assistant as a reply to the textual input. If the user state information indicates the user is sad, textual reply content of “anything you want to talk about?” may instead be provided ”, [0006]). Carbune in view of Chang does not explicitly disclose the serious content as authoritative content and the user state as user confidence. However, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to have the serious content as authoritative content and the user state as user confidence because “ classes that are indicative of happy, sad, neutral, active, tired, stressed, and/or other user state(s) may be utilized ” (emphasis added, Carbune, [0057]) and “ the reply content engine 126 may generate reply content that conforms to that user state information ” (Carbune, [0116]). Claim 17: Carbune in view of Chang and Marrone discloses the method of claim 10, wherein the machine learning model includes a decoder (Chang, [0006]), and the model output is text-token specific (Carbune, [0005]). Claim 18: Carbune in view of Chang and Marrone discloses the method of claim 10, wherein the response includes a recommended action that varies in dependence on the temporal encoding (Carbune, [0006], see also [0062]). Claim 19: Carbune in view of Chang discloses the method of claim 10, wherein the user input is a spoken user input, or a touch user input (Carbune, [0046]) . 07-21-aia AIA Claim s 2-3 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Carbune et al. (US 2019/0156831) in view of Chang et al. (US 2024/0370736), Marrone et al. (" Identifying Users' Emotional States through Keystroke Dynamics. " DeLTA 2022 (2022)) and Stewart et al. (US 2016/0070465) . Claim 2: Carbune in view of Chang and Marrone discloses the method of claim 1, but does not explicitly disclose wherein determining a plurality of typing events associated with the typed user input comprises: determining a receiving time for each character in the one or more words at the input device. In a system similarly determining typing events associated with the typed user input, Stewart discloses determining a receiving time for each character in the one or more words at the input device (“ a user's keystroke timing patterns may refer to the detailed timing information that describes when a key was pressed and when it was released as a user is typing on a keyboard. Thus, the timing modification module 402, in certain embodiments, monitors the user's typing speed, rhythm, timing of a key press and/or release, or the like, in order to determine an appropriate threshold duration used by the timing module 302. For example, the timing modification module 402 may adjust the “flight time” threshold, i.e., the amount of time between a key up event for a key press and a key down event for a subsequent key press, in response to determining that the user is a fast or slow typist ”, [0064]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of determining a receiving time for each character in Carbune’s one or more words at the input device because that is a standard practice for determining typing speed (see, Stewart, [0064]). Claim 3: Carbune in view of Chang, Marrone and Stewart discloses the method of claim 2, wherein the temporal encoding is an inter-character temporal encoding that encodes time intervals between each pair of adjacent characters ( flight time ) in the typed user input (Stewart, [0064]). Claim 11: Carbune in view of Chang and Marrone discloses the method of claim 10, wherein the user input is a typed user input (Carbune, [0062]), but does not explicitly wherein determining a plurality of input events associated with the user input comprises: determining a receiving time for each character in the one or more words at the input device. In a system similarly determining typing events associated with the typed user input, Stewart discloses determining a receiving time for each character in the one or more words at the input device (“ a user's keystroke timing patterns may refer to the detailed timing information that describes when a key was pressed and when it was released as a user is typing on a keyboard. Thus, the timing modification module 402, in certain embodiments, monitors the user's typing speed, rhythm, timing of a key press and/or release, or the like, in order to determine an appropriate threshold duration used by the timing module 302. For example, the timing modification module 402 may adjust the “flight time” threshold, i.e., the amount of time between a key up event for a key press and a key down event for a subsequent key press, in response to determining that the user is a fast or slow typist ”, [0064]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of determining a receiving time for each character in Carbune’s one or more words at the input device because that is a standard practice for determining typing speed (see, Stewart, [0064]). Claim 12: Carbune in view of Chang, Marrone and Stewart discloses the method of claim 11, wherein the temporal encoding determined based on the input events is an inter-character temporal encoding that encodes time intervals between each two adjacent characters ( flight time ) in the typed user input (Stewart, [0064]) . 07-21-aia AIA Claim s 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Carbune et al. (US 2019/0156831) in view of Chang et al. (US 2024/0370736), Marrone et al. (" Identifying Users' Emotional States through Keystroke Dynamics. " DeLTA 2022 (2022)) and Guo et al. (US 2022/0309348) . Claim 6: Carbune in view of Chang and Marrone discloses the method of claim 1, but does not explicitly disclose wherein the combined representation further includes a positional encoding that encodes positions of the one or more words in the user input. In a system similarly generating a combined representation which combines embedded representations, Guo discloses wherein the combined representation further includes a positional encoding that encodes positions of the one or more words in the user input (“ word embedding all of the words in the input sequence to obtain corresponding word embeded [sic] vectors, then performing a position encoding, and correspondingly adding the word embeded [sic] vectors and position encoded vectors to obtain an input vector representation of the model ”, [0009]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of further including a positional encoding that encodes positions of the one or more words in the user input in Carbune’s combined representation because, in large language models (LLMs), understanding the order of words is important for capturing accurate context in language processing. Claim 20: Carbune discloses a system comprising one or more processors and memory storing instructions that, when executed, cause the one or more processors ([0025]) to: receive, via an input device, a user input that includes one or more words (“ textual input 201A that is based on user interface input generated by one or more user interface input device(s) 102 ”, [0071], see also “ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard) ”, [0062]); determine a plurality of input events associated with the user input (“ when the given user has provided typed input in a dialog (e.g., using a physical keyboard or virtual keyboard), the user state information may be determined based on a typing speed of the typed input, applied pressure for one or more characters of typed input (e.g., as sensed by a touch screen implementing a virtual keyboard), a “delay time” for starting to provide the typed input (e.g., when the typed input is provided responsive to other content), and/or other sensed features of the typed input ”, [0062]); map the user input to a representation of the user input (“ on one or more search parameters determined based on the textual input 201C ”, [0093]); generate a temporal representation that represents input speed across the user input based on the plurality of input events ( one or more search parameters determined based on the user state information, note these could include typing speed, see [0062]); combine the representation of the user input with a temporal encoding determined based on the input events associated with the user input ( one or more search parameters determined based on the user state information, note these could include typing speed, see [0062]) and the temporal representation, to generate a combined representation of the user input (“ the reply content engine 126 causes the search system 130 to issue a search of search databases 154 based on one or more search parameters determined based on the textual input 201C and based on one or more search parameters determined based on the user state information 207C. In some of those implementations, one or more of the parameters may individually be based on both the textual input 201C and the user state information 207C ”, [0093]); process the combined representation, using a machine learning model, to generate model output from which a response responsive to the user input is derived (“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ a typing speed of a user determined from sensor data associated with one of the user interface input device(s) may be provided by the user state engine 122 and applied by the reply content engine 126 as direct input to a neural network model or other machine learning model(s) that are utilized to determined one or more aspects of reply content to provide to a user ”, [0059]); and cause the response to be rendered via an output device, in response to the user input(“ The search system 130 returns responsive search result(s) and the reply content engine 126 selects one or more of the search results for including (in whole or in part) in reply content 209C ”, [0093], see also “ The reply content engine 126 provides the reply content 209A for presentation, to one or more users engaged in the dialog, via the user interface input device(s) 104 of those user(s) ”, [0076]). Carbune does not explicitly disclose that the representation of the typed user input is an embedded representation and the representation that is determined based on the typing events for the typed user input is an encoding (e.g. embedding). In a similar machine learning system similarly processing mixed data comprising input text and second input, Chang discloses generating embeddings for the input text and the second input (“ Multi modal models comprising an encoder and decoder are described. The encoder projects inputs into embeddings, which are used to generate a multi modal prompt, which is provided to the decoder. The encoder input comprises context information. The multi modal prompt comprises mixed types of data. This mixed data is converted into embeddings and combined to form the multi modal prompt. For example, text may be converted to embeddings using a text encoder and images may be converted to embeddings using an image encoder ”, [0006]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of generating embeddings for Carbune’s typed user input and typing events in order to use a transformer based encoder/decoder LLM as the machine learning system because they are the state of the art providing the best results in AI (see Chang, [0003] and [0006]). Carbune does not explicitly disclose that the representation of the typed user input is an embedded representation and the representation that is determined based on the typing events for the typed user input is an encoding (e.g. embedding). In a similar machine learning system similarly processing mixed data comprising input text and second input, Chang discloses generating embeddings for the input text and the second input (“ Multi modal models comprising an encoder and decoder are described. The encoder projects inputs into embeddings, which are used to generate a multi modal prompt, which is provided to the decoder. The encoder input comprises context information. The multi modal prompt comprises mixed types of data. This mixed data is converted into embeddings and combined to form the multi modal prompt. For example, text may be converted to embeddings using a text encoder and images may be converted to embeddings using an image encoder ”, [0006]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of generating embeddings for Carbune’s typed user input and typing events in order to use a transformer based encoder/decoder LLM as the machine learning system because they are the state of the art providing the best results in AI (see Chang, [0003] and [0006]). Carbune in view of Chang and Marrone does not explicitly disclose wherein the combined representation further includes a positional encoding that encodes positions of the one or more words in the user input. In a system similarly generating a combined representation which combines embedded representations, Guo discloses wherein the combined representation further includes a positional encoding that encodes positions of the one or more words in the user input (“ word embedding all of the words in the input sequence to obtain corresponding word embeded [sic] vectors, then performing a position encoding, and correspondingly adding the word embeded [sic] vectors and position encoded vectors to obtain an input vector representation of the model ”, [0009]). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of further including a positional encoding that encodes positions of the one or more words in the user input in Carbune’s combined representation because, in large language models (LLMs), understanding the order of words is important for capturing accurate context in language processing . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Carbune et al. (US 2019/0156831) in view of Chang et al. (US 2024/0370736), Marrone et al. (" Identifying Users' Emotional States through Keystroke Dynamics. " DeLTA 2022 (2022)) and Bui et al. (US 12,147,791) . Claim 8: Carbune in view of Chang and Marrone discloses the method of claim 1, wherein the machine learning model is a sequence-to-sequence model that includes a decoder with one or more attention layers ([0006]), but does not explicitly disclose wherein the model output includes a sequence of probability distributions over a vocabulary of tokens. In a system with a similar sequence-to-sequence machine learning model, Bui discloses wherein the model’s output includes a sequence of probability distributions over a vocabulary of tokens (“ the system output includes, for each position of a plurality of positions in a target text sequence, a probability distribution over target vocabulary for the position. In these implementations, the Transformer decoder neural network is configured to compute the probability distribution over the target vocabulary for each position ”, col. 2, lines 31-40). It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of Carbune’s model’s output including a sequence of probability distributions over a vocabulary of tokens because “ probability distribution over the target vocabulary for each position assigns a respective score to each candidate token in the set of candidate tokens, in which the respective score represents a likelihood the candidate token is the correct token that should be placed at the position in the target text sequence ” (Bui, col. 3, lines 59-64). Conclusion 07-40 AIA 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 SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST. 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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. /SAMUEL G NEWAY/Primary Examiner, Art Unit 2657 Application/Control Number: 18/652,575 Page 2 Art Unit: 2657 Application/Control Number: 18/652,575 Page 3 Art Unit: 2657 Application/Control Number: 18/652,575 Page 4 Art Unit: 2657 Application/Control Number: 18/652,575 Page 5 Art Unit: 2657 Application/Control Number: 18/652,575 Page 6 Art Unit: 2657 Application/Control Number: 18/652,575 Page 7 Art Unit: 2657 Application/Control Number: 18/652,575 Page 8 Art Unit: 2657 Application/Control Number: 18/652,575 Page 9 Art Unit: 2657 Application/Control Number: 18/652,575 Page 10 Art Unit: 2657 Application/Control Number: 18/652,575 Page 11 Art Unit: 2657 Application/Control Number: 18/652,575 Page 12 Art Unit: 2657 Application/Control Number: 18/652,575 Page 13 Art Unit: 2657 Application/Control Number: 18/652,575 Page 14 Art Unit: 2657 Application/Control Number: 18/652,575 Page 15 Art Unit: 2657 Application/Control Number: 18/652,575 Page 16 Art Unit: 2657 Application/Control Number: 18/652,575 Page 17 Art Unit: 2657 Application/Control Number: 18/652,575 Page 18 Art Unit: 2657 Application/Control Number: 18/652,575 Page 19 Art Unit: 2657 Application/Control Number: 18/652,575 Page 20 Art Unit: 2657 Application/Control Number: 18/652,575 Page 21 Art Unit: 2657 Application/Control Number: 18/652,575 Page 22 Art Unit: 2657
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