CTNF 18/386,833 CTNF 94458 DETAILED ACTION 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 The information disclosure statement (IDS) submitted on 12/19/2023 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. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 According to the first part of the analysis, in the instant case, claims 1-10 are directed to a method, claim 11-19 are directed to a apparatus comprising at least a processor, and claim 20 is directed to a non-transitory computer-readable medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Claim 1 recites: Step 2A, Prong 1 “ selecting, by the device , an optimization criterion for the large language model-based troubleshooting agent based on the input request ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an optimization criterion. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “… select a particular large language model to process the input request based on the optimization criterion ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an LLM to process an input request. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “ receiving, at a device, an input request for a large language model-based troubleshooting agent for a network ” (insignificant extra-solution activity) “ selecting, by the device , an optimization criterion for the large language model-based troubleshooting agent based on the input request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ providing, by the device, the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ sending, by the device and to a user interface, an indication of a result of the particular large language model processing the input request ” (insignificant extra-solution activity) This judicial exception is not integrated into a practical application. Step 2B “ receiving, at a device, an input request for a large language model-based troubleshooting agent for a network ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); See MPEP 2106.05 (d) (II).) “ selecting, by the device , an optimization criterion for the large language model-based troubleshooting agent based on the input request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ providing, by the device, the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ sending, by the device and to a user interface, an indication of a result of the particular large language model processing the input request ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); See MPEP 2106.05 (d) (II).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 2 recites: Step 2A, Prong 1 “ wherein the optimization criterion indicates that the large language model-based troubleshooting agent should minimize a number of tokens sent by the large language model-based troubleshooting agent to the particular large language model ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can an optimization criterion to reduce the number of tokens sent to a LLM. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 3 recites: Step 2A, Prong 1 “ wherein the optimization criterion indicates that the large language model-based troubleshooting agent should select the particular large language model based on it having a highest degree of efficacy from among a set of available large language models ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select the LLM with the highest degree of efficacy. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 4 recites: Step 2A, Prong 1 “ wherein the optimization criterion indicates that the large language model-based troubleshooting agent should select the particular large language model based on it having a highest degree of processing speed from among a set of available large language models ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select the LLM with the fastest processing speed. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 5 recites: Step 2A, Prong 1 “ wherein the optimization criterion causes the large language model-based troubleshooting agent to generate one or more prompts for the particular large language model to satisfy the optimization criterion ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an optimization criterion to generate a prompt to satisfy the criterion. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 6 recites: Step 2A, Prong 1 “ wherein the optimization criterion limits a number of actions between the large language model-based troubleshooting agent and the particular large language model to process the input request ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an optimization criterion that limits the number of action between the LLM and agent. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 7 recites: Step 2A, Prong 1 “ wherein the optimization criterion indicates a degree of determinism that controls a level of randomness of the particular large language model ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an optimization criterion that controls a level of randomness. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 8 recites: Step 2A, Prong 1 Claim 8 recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “ wherein the input request indicates an issue in the network for the large language model-based troubleshooting agent to troubleshoot ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “ wherein the input request indicates an issue in the network for the large language model-based troubleshooting agent to troubleshoot ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 9 recites: Step 2A, Prong 1 “wherein the device selects the optimization criterion based on a criticality associated with the input request ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select an optimization criterion based on how important the input request is. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “ wherein the device selects the optimization criterion based on a criticality associated with the input request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “ wherein the device selects the optimization criterion based on a criticality associated with the input request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 10 recites: Step 2A, Prong 1 Claim 10 recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “ providing, by the device, performance metrics for the particular large language model for review by an administrator ” (insignificant extra-solution activity) This judicial exception is not integrated into a practical application. Step 2B “ wherein the input request indicates an issue in the network for the large language model-based troubleshooting agent to troubleshoot ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); See MPEP 2106.05 (d) (II).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 11 recites: Step 2A, Prong 1 See rejection of claim 1. Same rationale applies. Step 2A, Prong 2 & 2B The claim recites additional elements (“An apparatus, comprising: one or more network interfaces; a processor coupled to the one or more network interfaces and configured to execute one or more processes; and a memory configured to store a process that is executable by the processor”). (Mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 12 recites: See rejection of claim 2. Same rationale applies. Claim 13 recites: See rejection of claim 3. Same rationale applies. Claim 14 recites: See rejection of claim 4. Same rationale applies. Claim 15 recites: See rejection of claim 5. Same rationale applies. Claim 16 recites: See rejection of claim 6. Same rationale applies. Claim 17 recites: See rejection of claim 7. Same rationale applies. Claim 18 recites: See rejection of claim 8. Same rationale applies. Claim 19 recites: See rejection of claim 9. Same rationale applies. Claim 20 recites: Step 2A, Prong 1 See rejection of claim 1. Same rationale applies. Step 2A, Prong 2 & 2B The claim recites additional elements (“A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process”). (Mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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, 3-4, 8, 11, 13-14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US-20240311405-A1) in view of Simaria et al . (US-20250077551-A1) . Regarding Claim 1 , Kim (US 20240311405 A1) teaches a method comprising: receiving, at a device, an input request for a large language model-based agent for a network ( para [0037] “Further, the client device 110, the routing system 120, the generative system(s) 130, and/or the training system 140 can include one or more memories for storage of data and/or software applications, one or more processors for accessing data and executing the software applications, and/or other components that facilitate communication over one or more of the networks 199. ” P ara [0043]-[0045] discloses a selection engine (i.e., LLM based agent) that includes a ML model that receives a query request. para [0055] “In FIG. 2A, the client device 110 submits the request 201A. The routing system 120 receives the request 201A, generates request features 203A based on the request 201A, and processes the request features 203A using the ML model(s) 152 to generate ML output 204A.” ); selecting, by the device, an optimization criterion for the large language model- based agent based on the input request ( para [0043] “As one particular example, the selection engine 126 can process request feature(s) of a request to generate a first measure for LLM 150A, a second measure for LLM 150B, an nth measure for LLM 150N, and optionally additional measure(s) for additional LLM(s) and/or other generative models (indicated generally by the vertical ellipsis in FIG. 1). Each of the generated measures characterizes a corresponding probability of generating a correct response to the request using the corresponding generative model.” selection engine (i.e., large language-based agent) selects a measure (i.e., optimization criterion). ); providing, by the device, the optimization criterion to the large language model-based agent to cause the large language model-based agent to select a particular large language model to process the input request based on the optimization criterion ( para [0045] “In some implementations and/or for some requests, the selection engine 126 utilizes ML model(s) 152 (e.g., a trained neural network model) in selecting from among the candidate generative models 150. The ML model(s) 152 utilized by the selection engine 126 are more computationally efficient than at least some of the candidate generative models 150. In some of those implementations, the selection engine 126 processes at least the request feature(s) (determined by the request features engine 122) for a request, using the ML model(s) 152, to generate output that indicates, for each of the candidate generative models 150, a corresponding probability of generating a correct response.” ML output provided to selection engine (i.e., large language-based agent) to select a LLM. ); and sending, by the device and to a user interface, an indication of a result of the particular large language model processing the input request ( para [0034] “In various implementations, the client device 110 can include a rendering engine 112 that is configured to provide content (e.g., a natural language based response generated by an LLM) for audible and/or visual presentation to a user of the client device 110 using one or more user interface output devices.” para [0058] “Further, the one of the generative system(s) 130 generates a response 208A based on the LLM output 207A, and transmits the response 208A to the client device 110.” ). While Kim discloses a large language model-based agent, Kim does not explicitly disclose the agent is a troubleshooting agent. However, Simaria (US 20250077551 A1) teaches a large language model-based troubleshooting agent for a network ( para [0081] “In some implementations, a user of the network device may wish to utilize the large language model to request troubleshooting of the network device.” ) Kim and Simaria are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Kim with the troubleshooting LLM of Simaria. Doing so would allow for a user of the network device to utilize the large language model to configure the network device (Simaria para [0080] ). Regarding Claim 3 , Kim and Simaria teach the method as in claim 1. Kim further teaches wherein the optimization criterion indicates that the large language model-based troubleshooting agent should select the particular large language model based on it having a highest degree of efficacy from among a set of available large language models ( para [0008] “As one example, the request feature(s) can be processed to generate a first measure for the smaller LLM and a second measure for the second LLM, where the first measure characterizes a probability of generating a correct response (e.g., a response that is of high quality and/or high relevance) to the request using the smaller LLM, and where second measure characterizes a predicted probability of generating a correct response to the request using the larger LLM.” The probability of a correct response reflects the effectiveness (i.e., efficacy) of the model. ). Regarding Claim 4 , Kim and Simaria teach the method as in claim 1. Kim further teaches wherein the optimization criterion indicates that the large language model-based troubleshooting agent should select the particular large language model based on it having a highest degree of processing speed from among a set of available large language models ( para [0007] “Some of those implementations can, in selecting between at least the smaller LLM and the larger LLM for a given request, make the selection based on considering request feature(s) of the request, measured or expected current server load, and/or the respective computational efficiencies of the smaller LLM and the larger LLM.” Computation efficiency (i.e. speed). ). Regarding Claim 8 , Kim and Simaria teach the method as in claim 1. Simaria further teaches wherein the input request indicates an issue in the network for the large language model-based troubleshooting agent to troubleshoot ( para [0081] In some implementations, a user of the network device may wish to utilize the large language model to request troubleshooting of the network device. In such implementations, the network device may generate a request to troubleshoot the network device, and may provide, to the LLM system and via the API, the request to troubleshoot the network device. ). Kim and Simaria are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Kim with the troubleshooting LLM of Simaria. Doing so would allow for a user of the network device to utilize the large language model to configure the network device (Simaria para [0080] ). Regarding Claim 11 , Claim 11 is the apparatus corresponding to the method of claim 1. Claim 11 is substantially similar to claim 1 and is rejected on the same grounds. Regarding Claim 13 , Claim 13 is the apparatus corresponding to the method of claim 3. Claim 13 is substantially similar to claim 3 and is rejected on the same grounds. Regarding Claim 14 , Claim 14 is the apparatus corresponding to the method of claim 4. Claim 14 is substantially similar to claim 4 and is rejected on the same grounds. Regarding Claim 18 , Claim 18 is the apparatus corresponding to the method of claim 8. Claim 18 is substantially similar to claim 8 and is rejected on the same grounds. Regarding Claim 20 , Claim 20 is the apparatus corresponding to the method of claim 1. Claim 20 is substantially similar to claim 1 and is rejected on the same grounds . 07-21-aia AIA Claim s 2, 5, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kim/Simaria , as applied above, and further in view of Han et al. (US-20250103809-A1) . Regarding Claim 2 , Kim and Simaria teach the method as in claim 1. Kim and Simaria does not explicitly disclose wherein the optimization criterion indicates that the large language model-based troubleshooting agent should minimize a number of tokens sent by the large language model-based troubleshooting agent to the particular large language model. However, Han (US 20250103809 A1) teaches wherein the optimization criterion indicates that the large language model-based troubleshooting agent should minimize a number of tokens sent by the large language model-based troubleshooting agent to the particular large language model ( para [0028] “In some implementations, the communication service may opportunistically reduce the token size of a given prompt by selectively removing tokens from the prompt (for example, by omitting older and/or less relevant contextual information) or asynchronously compacting the prompt into fewer tokens (for example, using text compression or summarization techniques) before sending the prompt to the selected LLM.” ). Kim, Simaria, and Han are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the LLM of Kim and Simaria with the token window of Han. Doing so would allow for omitting older and/or less relevant contextual information before sending the prompt to the selected LLM (Han para [0028] ). Regarding Claim 5 , Kim and Simaria teach the method as in claim 1. Kim and Simaria do not explicitly disclose wherein the optimization criterion causes the large language model-based troubleshooting agent to generate one or more prompts for the particular large language model to satisfy the optimization criterion. However, Han (US 20250103809 A1) teaches wherein the optimization criterion causes the large language model-based troubleshooting agent to generate one or more prompts for the particular large language model to satisfy the optimization criterion ( para [0009] “In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, selecting the LLM includes operations, features, means, or instructions for selecting the LLM from the set of LLMs based on a response latency associated with the LLM, the token window size of the LLM, and the token size of the prompt.” ). Kim, Simaria, and Han are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the LLM of Kim and Simaria with the prompts of Han. Doing so would allow for omitting older and/or less relevant contextual information before sending the prompt to the selected LLM (Han para [0028] ). Regarding Claim 12 , Claim 12 is the apparatus corresponding to the method of claim 2. Claim 12 is substantially similar to claim 2 and is rejected on the same grounds. Regarding Claim 15 , Claim 15 is the apparatus corresponding to the method of claim 5. Claim 15 is substantially similar to claim 5 and is rejected on the same grounds . 07-21-aia AIA Claim s 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kim/Simaria , as applied above, and further in view of Blum et al. (US-20250068667-A1) . Regarding Claim 6 , Kim and Simaria teach the method as in claim 1. Kim and Simaria do not explicitly disclose wherein the optimization criterion limits a number of actions between the large language model-based troubleshooting agent and the particular large language model to process the input request. However, Blum (US 20250068667 A1) teaches wherein the optimization criterion limits a number of actions between the large language model-based troubleshooting agent and the particular large language model to process the input request ( para [0073] “Whilst the approach of injecting verification requests can be advantageous in circumstances where it is desirable to limit the number of API calls made to the LLM (e.g. to reduce network traffic or to avoid reaching limits instituted by the host of the LLM), in circumstances where such constraints do not apply, supplying two prompts may be preferable.” ). Kim, Simaria, and Blum are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the LLM of Kim and Simaria with the method of limiting API calls to the LLM of Blum. Doing so would allow for reducing network traffic or to avoid reaching limits instituted by the host of the LLM (Blum para [0073] ). Regarding Claim 16 , Claim 16 is the apparatus corresponding to the method of claim 6. Claim 16 is substantially similar to claim 6 and is rejected on the same grounds . 07-21-aia AIA Claim s 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kim/Simaria , as applied above, and further in view of Gupta et al. (US-20250131020-A1) . Regarding Claim 7 , Kim and Simaria teach the method as in claim 1. Kim and Simaria does not explicitly disclose wherein the optimization criterion indicates a degree of determinism that controls a level of randomness of the particular large language model. However, Gupta (US 20250131020 A1) teaches wherein the optimization criterion indicates a degree of determinism that controls a level of randomness of the particular large language model ( para [0087] “The computing system may analyze the user's prompt to identify key attributes that dictate content generation needs, communicate with a fine-tuned model repository, cross-referencing user data with available models, and select one or more Level of randomness adjustment LLMs or fine-tuned models based on the contextual parameters such as location (public, office, home, social), motion (stationary, moving), urgency (offline/background, online/real-time), and I/O device (mobile/handheld, stationary).” ). Kim, Simaria, and Gupta are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the LLM of Kim and Simaria with the level of randomness of Gupta. Doing so would allow for reducing latency and improving data privacy (Gupta para [0023] ). Regarding Claim 17 , Claim 17 is the apparatus corresponding to the method of claim 7. Claim 17 is substantially similar to claim 7 and is rejected on the same grounds . 07-21-aia AIA Claim s 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kim/Simaria , as applied above, and further in view of Kumar et al. (US-20250110618-A1) . Regarding Claim 9 , Kim and Simaria teach the method as in claim 1. Kim and Simaria do not explicitly disclose wherein the device selects the optimization criterion based on a criticality associated with the input request. However, Kumar (US 20250110618 A1) teaches wherein the device selects the optimization criterion based on a criticality associated with the input request ( para [0211] “For example, the centralized gateway (which can precondition prompts, generate prompts, modify prompts, postprocess generative output, handle recursive generative output in which a first generative output is used to produce a second generative output, and so on) can be configured to determine priority of different requests for generative output across multiple systems… In yet other cases, a centralized gateway can be used to manage or load balance between multiple different LLMs.” ). Kim, Simaria, and Kumar are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Kim and Simaria with the request priority determination of Kumar. Doing so would allow for system to manage or load balance between multiple different LLMs (Kumar para [0211] ). Regarding Claim 19 , Claim 18 is the apparatus corresponding to the method of claim 8. Claim 18 is substantially similar to claim 8 and is rejected on the same grounds . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kim/Simaria , as applied above, and further in view of Goche et al. (US-20240386213-A1) . Regarding Claim 10 , Kim and Simaria teach the method as in claim 1. Kim and Simaria do not explicitly disclose further comprising: providing, by the device, performance metrics for the particular large language model for review by an administrator. However, Ghoche (US 20240386213 A1) teaches further comprising: providing, by the device, performance metrics for the particular large language model for review by an administrator ( para [0147] “For example, the performance metrics may be used to generate a user interface (UI) to display customer support issue topics and associated performance metrics.” ). Kim, Simaria, and Ghoche are analogous because they are directed towards LLMs. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Kim and Simaria with the performance metrics of Ghoche. Doing so would allow for providing valuable intelligence to the user about trends in the performance metrics to provide actionable clues and suggest actions (Ghoche para [0147] ). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY K NGUYEN whose telephone number is (571)272-0217. The examiner can normally be reached Mon - Fri 7:00am-4:30pm. 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, Li B Zhen can be reached at 5712723768. 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. 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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. /HENRY NGUYEN/Examiner, Art Unit 2121 Application/Control Number: 18/386,833 Page 2 Art Unit: 2121 Application/Control Number: 18/386,833 Page 3 Art Unit: 2121 Application/Control Number: 18/386,833 Page 4 Art Unit: 2121 Application/Control Number: 18/386,833 Page 5 Art Unit: 2121 Application/Control Number: 18/386,833 Page 6 Art Unit: 2121 Application/Control Number: 18/386,833 Page 7 Art Unit: 2121 Application/Control Number: 18/386,833 Page 8 Art Unit: 2121 Application/Control Number: 18/386,833 Page 9 Art Unit: 2121 Application/Control Number: 18/386,833 Page 10 Art Unit: 2121 Application/Control Number: 18/386,833 Page 11 Art Unit: 2121 Application/Control Number: 18/386,833 Page 12 Art Unit: 2121 Application/Control Number: 18/386,833 Page 13 Art Unit: 2121 Application/Control Number: 18/386,833 Page 14 Art Unit: 2121 Application/Control Number: 18/386,833 Page 15 Art Unit: 2121 Application/Control Number: 18/386,833 Page 16 Art Unit: 2121 Application/Control Number: 18/386,833 Page 17 Art Unit: 2121 Application/Control Number: 18/386,833 Page 18 Art Unit: 2121 Application/Control Number: 18/386,833 Page 19 Art Unit: 2121 Application/Control Number: 18/386,833 Page 20 Art Unit: 2121 Application/Control Number: 18/386,833 Page 21 Art Unit: 2121 Application/Control Number: 18/386,833 Page 22 Art Unit: 2121 Application/Control Number: 18/386,833 Page 23 Art Unit: 2121 Application/Control Number: 18/386,833 Page 24 Art Unit: 2121
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